diff --git a/.devops/openvino.Dockerfile b/.devops/openvino.Dockerfile new file mode 100644 index 0000000000..e22ef16c7f --- /dev/null +++ b/.devops/openvino.Dockerfile @@ -0,0 +1,138 @@ +ARG OPENVINO_VERSION_MAJOR=2026.0 +ARG OPENVINO_VERSION_FULL=2026.0.0.20965.c6d6a13a886 +ARG UBUNTU_VERSION=24.04 + +# Optional proxy build arguments - empty by default +ARG http_proxy= +ARG https_proxy= + +## Build Image +FROM ubuntu:${UBUNTU_VERSION} AS build + +# Pass proxy args to build stage +ARG http_proxy +ARG https_proxy + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + ca-certificates \ + gnupg \ + wget \ + git \ + cmake \ + ninja-build \ + build-essential \ + libtbb12 \ + libssl-dev \ + ocl-icd-opencl-dev \ + opencl-headers \ + opencl-clhpp-headers \ + intel-opencl-icd && \ + rm -rf /var/lib/apt/lists/* + +# Install OpenVINO for Ubuntu 24.04 +ARG OPENVINO_VERSION_MAJOR +ARG OPENVINO_VERSION_FULL +RUN mkdir -p /opt/intel && \ + wget https://storage.openvinotoolkit.org/repositories/openvino/packages/${OPENVINO_VERSION_MAJOR}/linux/openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64.tgz && \ + tar -xf openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64.tgz && \ + mv openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64 /opt/intel/openvino_${OPENVINO_VERSION_MAJOR} && \ + cd /opt/intel/openvino_${OPENVINO_VERSION_MAJOR} && \ + echo "Y" | ./install_dependencies/install_openvino_dependencies.sh && \ + cd - && \ + ln -s /opt/intel/openvino_${OPENVINO_VERSION_MAJOR} /opt/intel/openvino + +ENV OpenVINO_DIR=/opt/intel/openvino + +WORKDIR /app + +COPY . . + +# Build Stage +RUN bash -c "source ${OpenVINO_DIR}/setupvars.sh && \ + cmake -B build/ReleaseOV -G Ninja \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_OPENVINO=ON && \ + cmake --build build/ReleaseOV -j$(nproc)" + +# Copy all necessary libraries +RUN mkdir -p /app/lib && \ + find build/ReleaseOV -name '*.so*' -exec cp {} /app/lib \; && \ + find ${OpenVINO_DIR}/runtime/lib/intel64 -name '*.so*' -exec cp -P {} /app/lib \; 2>/dev/null || \ + find ${OpenVINO_DIR}/lib/intel64 -name '*.so*' -exec cp -P {} /app/lib \; + +# Create runtime directories and copy binaries +RUN mkdir -p /app/full \ + && cp build/ReleaseOV/bin/* /app/full/ \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base Runtime Image +FROM ubuntu:${UBUNTU_VERSION} AS base + +# Pass proxy args to runtime stage +ARG http_proxy +ARG https_proxy + +RUN apt-get update \ + && apt-get install -y libgomp1 libtbb12 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app/ + +### Full (all binaries) +FROM base AS full + +ARG http_proxy +ARG https_proxy + +COPY --from=build /app/full /app/ + +WORKDIR /app + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + git \ + python3 \ + python3-venv \ + python3-pip && \ + python3 -m venv /ov-venv && \ + /ov-venv/bin/pip install --no-cache-dir --upgrade pip setuptools wheel && \ + /ov-venv/bin/pip install --no-cache-dir -r requirements.txt && \ + apt-get autoremove -y && \ + apt-get clean && \ + rm -rf /tmp/* /var/tmp/* && \ + find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete && \ + find /var/cache -type f -delete + +ENTRYPOINT ["/bin/bash", "-c", "source /ov-venv/bin/activate && exec /app/tools.sh \"$@\"", "--"] + + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app/ + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app/ + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile index 5d6c87ed6b..3112ec85ef 100644 --- a/.devops/vulkan.Dockerfile +++ b/.devops/vulkan.Dockerfile @@ -53,10 +53,11 @@ RUN apt-get update \ && apt-get install -y \ build-essential \ git \ - python3 \ - python3-dev \ + python3.13 \ + python3.13-dev \ python3-pip \ python3-wheel \ + && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.13 100 \ && pip install --break-system-packages --upgrade setuptools \ && pip install --break-system-packages -r requirements.txt \ && apt autoremove -y \ diff --git a/.github/actions/linux-setup-openvino/action.yml b/.github/actions/linux-setup-openvino/action.yml new file mode 100644 index 0000000000..46a659a827 --- /dev/null +++ b/.github/actions/linux-setup-openvino/action.yml @@ -0,0 +1,25 @@ +name: "Linux - Setup OpenVINO Toolkit" +description: "Setup OpenVINO Toolkit for Linux" +inputs: + path: + description: "Installation path" + required: true + version_major: + description: "OpenVINO major version (e.g., 2025.3)" + required: true + version_full: + description: "OpenVINO full version (e.g., 2025.3.0.19807.44526285f24)" + required: true + +runs: + using: "composite" + steps: + - name: Setup OpenVINO Toolkit + id: setup + uses: ./.github/actions/unarchive-tar + with: + url: https://storage.openvinotoolkit.org/repositories/openvino/packages/${{ inputs.version_major }}/linux/openvino_toolkit_ubuntu24_${{ inputs.version_full }}_x86_64.tgz + path: ${{ inputs.path }} + type: z + strip: 1 + diff --git a/.github/actions/windows-setup-rocm/action.yml b/.github/actions/windows-setup-rocm/action.yml index b83e6e295b..fd9f8e5a41 100644 --- a/.github/actions/windows-setup-rocm/action.yml +++ b/.github/actions/windows-setup-rocm/action.yml @@ -11,5 +11,5 @@ runs: - name: Setup ROCm uses: ./.github/actions/install-exe with: - url: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ inputs.version }}-WinSvr2022-For-HIP.exe + url: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ inputs.version }}-Win11-For-HIP.exe args: -install diff --git a/.github/workflows/build-3rd-party.yml b/.github/workflows/build-3rd-party.yml new file mode 100644 index 0000000000..642d978644 --- /dev/null +++ b/.github/workflows/build-3rd-party.yml @@ -0,0 +1,57 @@ +name: CI (3rd-party) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build-3rd-party.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + ubuntu-24-llguidance: + runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libssl-dev + + - name: Build + id: cmake_build + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_LLGUIDANCE=ON + cmake --build build --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + diff --git a/.github/workflows/build-android.yml b/.github/workflows/build-android.yml new file mode 100644 index 0000000000..cd9d99ffab --- /dev/null +++ b/.github/workflows/build-android.yml @@ -0,0 +1,140 @@ +name: CI (android) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build-android.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp' + ] + + pull_request: + types: [opened, synchronize, reopened] + paths: [ + '.github/workflows/build-android.yml', + 'examples/llama.android/**' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + android: + runs-on: ubuntu-latest + + steps: + - name: Clone + uses: actions/checkout@v6 + + # Disabled due to size (400MB) and always 0 cache hits + # - name: ccache + # uses: ggml-org/ccache-action@v1.2.16 + # with: + # key: android-build + # evict-old-files: 1d + + - name: Set up JDK + uses: actions/setup-java@v5 + with: + java-version: 17 + distribution: zulu + + - name: Setup Android SDK + uses: android-actions/setup-android@v3 + with: + log-accepted-android-sdk-licenses: false + + - name: Build + run: | + cd examples/llama.android + ./gradlew build --no-daemon + + android-ndk: + runs-on: ubuntu-latest + + env: + OPENCL_VERSION: 2025.07.22 + + strategy: + matrix: + include: + - build: 'arm64-cpu' + defines: '-D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -D GGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm -G Ninja -D LLAMA_OPENSSL=OFF -D GGML_OPENMP=OFF' + - build: 'arm64-snapdragon' + defines: '--preset arm64-android-snapdragon-release' + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Install OpenCL Headers and Libs + id: install_opencl + if: ${{ matrix.build == 'arm64-snapdragon' }} + run: | + mkdir opencl + curl -L -o opencl/clhpp.tar.gz https://github.com/KhronosGroup/OpenCL-CLHPP/archive/refs/tags/v${OPENCL_VERSION}.tar.gz + curl -L -o opencl/headers.tar.gz https://github.com/KhronosGroup/OpenCL-Headers/archive/refs/tags/v${OPENCL_VERSION}.tar.gz + curl -L -o opencl/icd-loader.tar.gz https://github.com/KhronosGroup/OpenCL-ICD-Loader/archive/refs/tags/v${OPENCL_VERSION}.tar.gz + tar -xaf opencl/headers.tar.gz -C opencl + tar -xaf opencl/clhpp.tar.gz -C opencl + tar -xaf opencl/icd-loader.tar.gz -C opencl + sudo cp -r opencl/OpenCL-Headers-${OPENCL_VERSION}/CL ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include + sudo cp -r opencl/OpenCL-CLHPP-${OPENCL_VERSION}/include/CL/* ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include/CL + cd opencl/OpenCL-ICD-Loader-${OPENCL_VERSION} + cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -DOPENCL_ICD_LOADER_HEADERS_DIR=${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=31 -DANDROID_STL=c++_shared + cmake --build build + sudo cp build/libOpenCL.so ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android + rm -rf opencl + + - name: Install Hexagon SDK + id: install_hexsdk + if: ${{ matrix.build == 'arm64-snapdragon' }} + env: + HEXSDK_VER: 6.4.0.2 + HEXTLS_VER: 19.0.04 + run: | + curl -L -o hex-sdk.tar.gz https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v$HEXSDK_VER/hexagon-sdk-v$HEXSDK_VER-amd64-lnx.tar.xz + mkdir hex-sdk + tar -xaf hex-sdk.tar.gz -C hex-sdk + ls -l hex-sdk + sudo mv hex-sdk /opt/hexagon + echo "HEXAGON_SDK_ROOT=/opt/hexagon/$HEXSDK_VER" >> "$GITHUB_ENV" + echo "HEXAGON_TOOLS_ROOT=/opt/hexagon/$HEXSDK_VER/tools/HEXAGON_Tools/$HEXTLS_VER" >> "$GITHUB_ENV" + echo "DEFAULT_HLOS_ARCH=64" >> "$GITHUB_ENV" + echo "DEFAULT_TOOLS_VARIANT=toolv19" >> "$GITHUB_ENV" + echo "DEFAULT_NO_QURT_INC=0" >> "$GITHUB_ENV" + echo "DEFAULT_DSP_ARCH=v73" >> "$GITHUB_ENV" + + - name: Update CMake presets + id: update_presets + if: ${{ matrix.build == 'arm64-snapdragon' }} + run: | + cp docs/backend/snapdragon/CMakeUserPresets.json . + + - name: Build + id: ndk_build + run: | + cmake ${{ matrix.defines }} -B build + cmake --build build + cmake --install build --prefix pkg-adb/llama.cpp + + - name: Test + id: cmake_test + run: | + echo "FIXME: test on devices" diff --git a/.github/workflows/build-apple.yml b/.github/workflows/build-apple.yml new file mode 100644 index 0000000000..51f0ef2302 --- /dev/null +++ b/.github/workflows/build-apple.yml @@ -0,0 +1,214 @@ +name: CI (apple) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build-apple.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.swift', + '**/*.m', + '**/*.metal' + ] + + pull_request: + types: [opened, synchronize, reopened] + paths: [ + '.github/workflows/build-apple.yml', + 'ggml/src/ggml-metal/**' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + macOS-latest-ios: + runs-on: macos-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.16 + with: + key: macOS-latest-ios + evict-old-files: 1d + save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_COMMON=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=iOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + + macos-latest-ios-xcode: + runs-on: macos-latest + + steps: + - name: Checkout code + uses: actions/checkout@v6 + + - name: Setup Xcode + uses: ggml-org/setup-xcode@v1 + with: + xcode-version: latest-stable + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_OPENSSL=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=iOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + + - name: xcodebuild for swift package + id: xcodebuild + run: | + ./build-xcframework.sh + + - name: Upload xcframework artifact + uses: actions/upload-artifact@v6 + with: + name: llama-xcframework + path: build-apple/llama.xcframework/ + retention-days: 1 + + - name: Build Xcode project + run: | + xcodebuild -downloadPlatform iOS + xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build + + macOS-latest-tvos: + runs-on: macos-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.16 + with: + key: macOS-latest-tvos + evict-old-files: 1d + save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_COMMON=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=tvOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + + macOS-latest-visionos: + runs-on: macos-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_COMMON=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=visionOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=1.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + + macOS-latest-swift: + runs-on: macos-latest + needs: macos-latest-ios-xcode + + strategy: + matrix: + destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS'] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.16 + with: + key: macOS-latest-swift + evict-old-files: 1d + save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + + - name: Download xcframework artifact + uses: actions/download-artifact@v7 + with: + name: llama-xcframework + path: build-apple/llama.xcframework/ + + - name: Build llama.cpp with CMake + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_OPENSSL=OFF \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TOOLS=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) diff --git a/.github/workflows/build-cache.yml b/.github/workflows/build-cache.yml index 3de0be9fad..bc0a92c7fc 100644 --- a/.github/workflows/build-cache.yml +++ b/.github/workflows/build-cache.yml @@ -37,12 +37,39 @@ jobs: path: ./vulkan_sdk version: ${{ env.VULKAN_SDK_VERSION }} - ubuntu-24-spacemit-cache: + #ubuntu-24-spacemit-cache: + # runs-on: ubuntu-24.04 + + # env: + # # Make sure this is in sync with build-linux-cross.yml + # SPACEMIT_IME_TOOLCHAIN_VERSION: "1.1.2" + + # steps: + # - name: Clone + # id: checkout + # uses: actions/checkout@v6 + + # - name: Setup Cache + # uses: actions/cache@v5 + # id: cache-toolchain + # with: + # path: ./spacemit_toolchain + # key: spacemit-ime-toolchain-v${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}-${{ runner.os }} + + # - name: Setup SpacemiT Toolchain + # if: steps.cache-toolchain.outputs.cache-hit != 'true' + # uses: ./.github/actions/linux-setup-spacemit + # with: + # path: ./spacemit_toolchain + # version: ${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }} + + ubuntu-24-openvino-cache: runs-on: ubuntu-24.04 env: - # Make sure this is in sync with build-linux-cross.yml - SPACEMIT_IME_TOOLCHAIN_VERSION: "1.1.2" + # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile + OPENVINO_VERSION_MAJOR: "2026.0" + OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886" steps: - name: Clone @@ -51,24 +78,25 @@ jobs: - name: Setup Cache uses: actions/cache@v5 - id: cache-toolchain + id: cache-openvino with: - path: ./spacemit_toolchain - key: spacemit-ime-toolchain-v${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}-${{ runner.os }} + path: ./openvino_toolkit + key: openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }} - - name: Setup SpacemiT Toolchain - if: steps.cache-toolchain.outputs.cache-hit != 'true' - uses: ./.github/actions/linux-setup-spacemit + - name: Setup OpenVINO Toolkit + if: steps.cache-openvino.outputs.cache-hit != 'true' + uses: ./.github/actions/linux-setup-openvino with: - path: ./spacemit_toolchain - version: ${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }} + path: ./openvino_toolkit + version_major: ${{ env.OPENVINO_VERSION_MAJOR }} + version_full: ${{ env.OPENVINO_VERSION_FULL }} windows-2022-rocm-cache: runs-on: windows-2022 env: # Make sure this is in sync with build.yml - HIPSDK_INSTALLER_VERSION: "25.Q3" + HIPSDK_INSTALLER_VERSION: "26.Q1" steps: - name: Clone diff --git a/.github/workflows/build-cann.yml b/.github/workflows/build-cann.yml new file mode 100644 index 0000000000..de641ca148 --- /dev/null +++ b/.github/workflows/build-cann.yml @@ -0,0 +1,102 @@ +name: CI (cann) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build-cann.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp' + ] + + pull_request: + types: [opened, synchronize, reopened] + paths: [ + '.github/workflows/build-cann.yml', + 'ggml/src/ggml-cann/**' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + openEuler-latest-cann: + defaults: + run: + shell: bash -el {0} + strategy: + matrix: + arch: [x86, aarch64] + chip_type: ['910b', '310p'] + build: ['Release'] + use_acl_graph: ['on', 'off'] + exclude: + # 310P does not support USE_ACL_GRAPH=on + - chip_type: '310p' + use_acl_graph: 'on' + runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} + steps: + - name: Checkout + uses: actions/checkout@v6 + with: + fetch-depth: 0 + + - name: Free up disk space + uses: ggml-org/free-disk-space@v1.3.1 + with: + tool-cache: true + + - name: Set container image + id: cann-image + run: | + image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}" + echo "image=${image}" >> "${GITHUB_OUTPUT}" + + - name: Pull container image + run: docker pull "${{ steps.cann-image.outputs.image }}" + + - name: Build + env: + BUILD_TYPE: ${{ matrix.build }} + SOC_TYPE: ascend${{ matrix.chip_type }} + USE_ACL_GRAPH: ${{ matrix.use_acl_graph }} + run: | + HOST_UID=$(id -u) + HOST_GID=$(id -g) + + docker run --rm \ + -v "${PWD}:/workspace" \ + -w /workspace \ + -e SOC_TYPE=${SOC_TYPE} \ + -e BUILD_TYPE=${BUILD_TYPE} \ + -e USE_ACL_GRAPH=${USE_ACL_GRAPH} \ + "${{ steps.cann-image.outputs.image }}" \ + bash -lc ' + set -e + yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake openssl-devel + yum clean all && rm -rf /var/cache/yum + git config --global --add safe.directory "/workspace" + export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DGGML_CANN=on \ + -DSOC_TYPE=${SOC_TYPE} \ + -DUSE_ACL_GRAPH=${USE_ACL_GRAPH} + cmake --build build -j $(nproc) + + chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build + ' diff --git a/.github/workflows/build-cmake-pkg.yml b/.github/workflows/build-cmake-pkg.yml index 259efa43c8..84cf8ddf48 100644 --- a/.github/workflows/build-cmake-pkg.yml +++ b/.github/workflows/build-cmake-pkg.yml @@ -5,7 +5,7 @@ on: jobs: linux: - runs-on: ubuntu-24.04 + runs-on: ubuntu-slim steps: - uses: actions/checkout@v6 with: @@ -14,7 +14,7 @@ jobs: - name: Install dependencies run: | sudo apt update - sudo apt install -y build-essential tcl + sudo apt install -y build-essential tcl cmake - name: Build run: | diff --git a/.github/workflows/build-linux-cross.yml b/.github/workflows/build-cross.yml similarity index 93% rename from .github/workflows/build-linux-cross.yml rename to .github/workflows/build-cross.yml index 8b6ebaf4a3..74508129ac 100644 --- a/.github/workflows/build-linux-cross.yml +++ b/.github/workflows/build-cross.yml @@ -1,7 +1,24 @@ -name: Build on Linux using cross-compiler +name: CI (cross) on: + # only manual triggers due to low-importance of the workflows + # TODO: for regular runs, provision dedicated self-hosted runners workflow_dispatch: - workflow_call: + push: + branches: + - master + paths: [ + '.github/workflows/build-cross.yml', + 'ggml/src/spacemit/*', + 'ggml/src/arch/loongarch/*' + ] + # run once every week + schedule: + - cron: '0 0 * * 0' + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + jobs: # ubuntu-24-riscv64-cpu-cross: @@ -142,7 +159,7 @@ jobs: # cmake --build build --config Release -j $(nproc) debian-13-loongarch64-cpu-cross: - runs-on: ubuntu-24.04 + runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} container: debian@sha256:653dfb9f86c3782e8369d5f7d29bb8faba1f4bff9025db46e807fa4c22903671 steps: @@ -197,7 +214,7 @@ jobs: cmake --build build --config Release -j $(nproc) debian-13-loongarch64-vulkan-cross: - runs-on: ubuntu-24.04 + runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} container: debian@sha256:653dfb9f86c3782e8369d5f7d29bb8faba1f4bff9025db46e807fa4c22903671 steps: @@ -264,15 +281,15 @@ jobs: steps: - uses: actions/checkout@v6 - - name: Use SpacemiT Toolchain Cache - uses: actions/cache@v5 - id: cache-toolchain - with: - path: ./spacemit_toolchain - key: spacemit-ime-toolchain-v${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}-${{ runner.os }} + #- name: Use SpacemiT Toolchain Cache + # uses: actions/cache@v5 + # id: cache-toolchain + # with: + # path: ./spacemit_toolchain + # key: spacemit-ime-toolchain-v${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}-${{ runner.os }} - name: Setup SpacemiT Toolchain - if: steps.cache-toolchain.outputs.cache-hit != 'true' + #if: steps.cache-toolchain.outputs.cache-hit != 'true' uses: ./.github/actions/linux-setup-spacemit with: path: ./spacemit_toolchain diff --git a/.github/workflows/build-msys.yml b/.github/workflows/build-msys.yml new file mode 100644 index 0000000000..431d9b6a53 --- /dev/null +++ b/.github/workflows/build-msys.yml @@ -0,0 +1,72 @@ +name: CI (msys) + +on: + # only manual triggers due to low-importance of the workflows + # TODO: for regular runs, provision dedicated self-hosted runners + workflow_dispatch: + # run once every week + schedule: + - cron: '0 0 * * 0' + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + windows-msys2: + runs-on: windows-2025 + + strategy: + fail-fast: false + matrix: + include: + - { sys: UCRT64, env: ucrt-x86_64, build: Release } + - { sys: CLANG64, env: clang-x86_64, build: Release } + + steps: + - name: Clone + uses: actions/checkout@v6 + + #- name: ccache + # uses: ggml-org/ccache-action@v1.2.16 + # with: + # key: windows-msys2 + # variant: ccache + # evict-old-files: 1d + # save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + + - name: Setup ${{ matrix.sys }} + uses: msys2/setup-msys2@v2 + with: + update: true + msystem: ${{matrix.sys}} + install: >- + base-devel + git + mingw-w64-${{matrix.env}}-toolchain + mingw-w64-${{matrix.env}}-cmake + mingw-w64-${{matrix.env}}-openblas + + - name: Build using CMake + shell: msys2 {0} + run: | + cmake -B build + cmake --build build --config ${{ matrix.build }} -j $(nproc) + + - name: Clean after building using CMake + shell: msys2 {0} + run: | + rm -rf build + + - name: Build using CMake w/ OpenBLAS + shell: msys2 {0} + run: | + cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS + cmake --build build --config ${{ matrix.build }} -j $(nproc) diff --git a/.github/workflows/build-riscv.yml b/.github/workflows/build-riscv.yml new file mode 100644 index 0000000000..36a3a1155a --- /dev/null +++ b/.github/workflows/build-riscv.yml @@ -0,0 +1,136 @@ +name: CI (riscv) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build-riscv.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp' + ] + + pull_request: + types: [opened, synchronize, reopened] + paths: [ + '.github/workflows/build-riscv.yml', + 'ggml/src/ggml-cpu/arch/riscv/**' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + ubuntu-riscv64-native-sanitizer: + runs-on: RISCV64 + + continue-on-error: true + + strategy: + matrix: + sanitizer: [ADDRESS, THREAD, UNDEFINED] + build_type: [Debug] + + steps: + - name: Install dependencies + run: | + sudo apt-get update + + # Install necessary packages + sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential wget ccache git-lfs + + # Set gcc-14 and g++-14 as the default compilers + sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100 + sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-14 100 + sudo ln -sf /usr/bin/gcc-14 /usr/bin/gcc + sudo ln -sf /usr/bin/g++-14 /usr/bin/g++ + + # Install Rust stable version + rustup install stable + rustup default stable + + git lfs install + + - name: GCC version check + run: | + gcc --version + g++ --version + + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Setup ccache + run: | + # Unique cache directory per matrix combination + export CCACHE_DIR="$HOME/.ccache/sanitizer-${{ matrix.sanitizer }}-${{ matrix.build_type }}" + mkdir -p "$CCACHE_DIR" + + # Configure ccache + ccache --set-config=max_size=5G + ccache --set-config=compression=true + ccache --set-config=compression_level=6 + ccache --set-config=cache_dir="$CCACHE_DIR" + ccache --set-config=sloppiness=file_macro,time_macros,include_file_mtime,include_file_ctime + ccache --set-config=hash_dir=false + + # Export for subsequent steps + echo "CCACHE_DIR=$CCACHE_DIR" >> $GITHUB_ENV + echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV + + - name: Build + id: cmake_build + if: ${{ matrix.sanitizer != 'THREAD' }} + run: | + cmake -B build \ + -DLLAMA_OPENSSL=OFF \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DGGML_OPENMP=ON \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ + -DLLAMA_BUILD_TESTS=OFF \ + -DCMAKE_C_COMPILER_LAUNCHER=ccache \ + -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ + -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 + + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) + + - name: Build (no OpenMP) + id: cmake_build_no_openmp + if: ${{ matrix.sanitizer == 'THREAD' }} + run: | + cmake -B build \ + -DLLAMA_OPENSSL=OFF \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DGGML_OPENMP=OFF \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ + -DLLAMA_BUILD_TESTS=OFF \ + -DCMAKE_C_COMPILER_LAUNCHER=ccache \ + -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ + -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 + + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 diff --git a/.github/workflows/build-sanitize.yml b/.github/workflows/build-sanitize.yml new file mode 100644 index 0000000000..0b17857504 --- /dev/null +++ b/.github/workflows/build-sanitize.yml @@ -0,0 +1,87 @@ +name: CI (sanitize) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build-sanitize.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + ubuntu-latest-sanitizer: + runs-on: ubuntu-latest + + continue-on-error: true + + strategy: + matrix: + sanitizer: [ADDRESS, THREAD, UNDEFINED] + build_type: [Debug] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.16 + with: + key: ubuntu-latest-sanitizer-${{ matrix.sanitizer }} + evict-old-files: 1d + save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libssl-dev + + - name: Build + id: cmake_build + if: ${{ matrix.sanitizer != 'THREAD' }} + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} + + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) + + - name: Build (no OpenMP) + id: cmake_build_no_openmp + if: ${{ matrix.sanitizer == 'THREAD' }} + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DGGML_OPENMP=OFF + + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 diff --git a/.github/workflows/build-self-hosted.yml b/.github/workflows/build-self-hosted.yml new file mode 100644 index 0000000000..7c7710fe45 --- /dev/null +++ b/.github/workflows/build-self-hosted.yml @@ -0,0 +1,242 @@ +name: CI (self-hosted) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.cu', + '**/*.cuh', + '**/*.swift', + '**/*.m', + '**/*.metal', + '**/*.comp', + '**/*.glsl', + '**/*.wgsl' + ] + + pull_request: + types: [opened, synchronize, reopened] + paths: [ + '.github/workflows/build-self-hosted.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.cu', + '**/*.cuh', + '**/*.swift', + '**/*.m', + '**/*.metal', + '**/*.comp', + '**/*.glsl', + '**/*.wgsl' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + ggml-ci-nvidia-cuda: + runs-on: [self-hosted, Linux, NVIDIA] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Test + id: ggml-ci + run: | + nvidia-smi + GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp + + ggml-ci-nvidia-vulkan-cm: + runs-on: [self-hosted, Linux, NVIDIA] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Test + id: ggml-ci + run: | + vulkaninfo --summary + GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp + + ggml-ci-nvidia-vulkan-cm2: + runs-on: [self-hosted, Linux, NVIDIA, COOPMAT2] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Test + id: ggml-ci + run: | + vulkaninfo --summary + GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp + + ggml-ci-cpu-amx: + runs-on: [self-hosted, Linux, CPU, AMX] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Test + id: ggml-ci + run: | + bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp + + # ggml-ci-amd-vulkan: + # runs-on: [self-hosted, Linux, AMD] + + # steps: + # - name: Clone + # id: checkout + # uses: actions/checkout@v6 + + # - name: Test + # id: ggml-ci + # run: | + # vulkaninfo --summary + # GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp + + # ggml-ci-amd-rocm: + # runs-on: [self-hosted, Linux, AMD] + + # steps: + # - name: Clone + # id: checkout + # uses: actions/checkout@v6 + + # - name: Test + # id: ggml-ci + # run: | + # amd-smi static + # GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp + + ggml-ci-mac-metal: + runs-on: [self-hosted, macOS, ARM64] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Test + id: ggml-ci + run: | + GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp + + ggml-ci-mac-webgpu: + runs-on: [self-hosted, macOS, ARM64] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Dawn Dependency + id: dawn-depends + run: | + DAWN_VERSION="v2.0.0" + DAWN_OWNER="reeselevine" + DAWN_REPO="dawn" + DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release" + echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip" + curl -L -o artifact.zip \ + "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip" + mkdir dawn + unzip artifact.zip + tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1 + + - name: Test + id: ggml-ci + run: | + GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \ + bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp + + ggml-ci-mac-vulkan: + runs-on: [self-hosted, macOS, ARM64] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Test + id: ggml-ci + run: | + vulkaninfo --summary + GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp + + ggml-ci-linux-intel-vulkan: + runs-on: [self-hosted, Linux, Intel] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + with: + persist-credentials: false + + - name: Test + id: ggml-ci + run: | + vulkaninfo --summary + GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp + + ggml-ci-intel-openvino-gpu-low-perf: + runs-on: [self-hosted, Linux, Intel, OpenVINO] + + env: + # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile + OPENVINO_VERSION_MAJOR: "2026.0" + OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886" + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: Setup OpenVINO Toolkit + uses: ./.github/actions/linux-setup-openvino + with: + path: ./openvino_toolkit + version_major: ${{ env.OPENVINO_VERSION_MAJOR }} + version_full: ${{ env.OPENVINO_VERSION_FULL }} + + - name: Install OpenVINO dependencies + run: | + cd ./openvino_toolkit + chmod +x ./install_dependencies/install_openvino_dependencies.sh + echo "Y" | sudo -E ./install_dependencies/install_openvino_dependencies.sh + + - name: Test + id: ggml-ci + run: | + source ./openvino_toolkit/setupvars.sh + GG_BUILD_OPENVINO=1 GGML_OPENVINO_DEVICE=GPU GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt diff --git a/.github/workflows/build-vulkan.yml b/.github/workflows/build-vulkan.yml new file mode 100644 index 0000000000..b25ec51471 --- /dev/null +++ b/.github/workflows/build-vulkan.yml @@ -0,0 +1,96 @@ +name: CI (vulkan) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build-vulkan.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.comp', + '**/*.glsl' + ] + + pull_request: + types: [opened, synchronize, reopened] + paths: [ + '.github/workflows/build-vulkan.yml', + 'ggml/src/ggml-vulkan/**' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + +jobs: + ubuntu-24-vulkan-llvmpipe: + runs-on: ubuntu-24.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.16 + with: + key: ubuntu-24-vulkan-llvmpipe + evict-old-files: 1d + save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + + - name: Dependencies + id: depends + run: | + sudo add-apt-repository -y ppa:kisak/kisak-mesa + sudo apt-get update -y + sudo apt-get install -y build-essential mesa-vulkan-drivers libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libssl-dev + + - name: Get latest Vulkan SDK version + id: vulkan_sdk_version + run: | + echo "VULKAN_SDK_VERSION=$(curl https://vulkan.lunarg.com/sdk/latest/linux.txt)" >> "$GITHUB_ENV" + + - name: Use Vulkan SDK Cache + uses: actions/cache@v5 + id: cache-sdk + with: + path: ./vulkan_sdk + key: vulkan-sdk-${{ env.VULKAN_SDK_VERSION }}-${{ runner.os }} + + - name: Setup Vulkan SDK + if: steps.cache-sdk.outputs.cache-hit != 'true' + uses: ./.github/actions/linux-setup-vulkan-llvmpipe + with: + path: ./vulkan_sdk + version: ${{ env.VULKAN_SDK_VERSION }} + + - name: Build + id: cmake_build + run: | + source ./vulkan_sdk/setup-env.sh + cmake -B build \ + -DGGML_VULKAN=ON + cmake --build build --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + export GGML_VK_VISIBLE_DEVICES=0 + export GGML_VK_DISABLE_F16=1 + export GGML_VK_DISABLE_COOPMAT=1 + # This is using llvmpipe and runs slower than other backends + ctest -L main --verbose --timeout 4800 diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 6c7ab71143..fef08d4c00 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -7,7 +7,6 @@ on: - master paths: [ '.github/workflows/build.yml', - '.github/workflows/build-linux-cross.yml', '.github/workflows/build-cmake-pkg.yml', '**/CMakeLists.txt', '**/.cmake', @@ -29,7 +28,6 @@ on: types: [opened, synchronize, reopened] paths: [ '.github/workflows/build.yml', - '.github/workflows/build-linux-cross.yml', '.github/workflows/build-cmake-pkg.yml', '**/CMakeLists.txt', '**/.cmake', @@ -59,7 +57,10 @@ env: LLAMA_LOG_TIMESTAMPS: 1 jobs: - macOS-latest-cmake-arm64: + build-cmake-pkg: + uses: ./.github/workflows/build-cmake-pkg.yml + + macOS-latest-arm64: runs-on: macos-latest steps: @@ -70,7 +71,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: macOS-latest-cmake-arm64 + key: macOS-latest-arm64 evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -93,9 +94,9 @@ jobs: id: cmake_test run: | cd build - ctest -L main --verbose --timeout 900 + ctest -L main -E "test-llama-archs" --verbose --timeout 900 - macOS-latest-cmake-x64: + macOS-latest-x64: runs-on: macos-15-intel steps: @@ -106,7 +107,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: macOS-latest-cmake-x64 + key: macOS-latest-x64 evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -131,7 +132,7 @@ jobs: cd build ctest -L main --verbose --timeout 900 - macOS-latest-cmake-arm64-webgpu: + macOS-latest-arm64-webgpu: runs-on: macos-latest steps: @@ -142,7 +143,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: macOS-latest-cmake-arm64-webgpu + key: macOS-latest-arm64-webgpu evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -173,7 +174,7 @@ jobs: cd build ctest -L main --verbose --timeout 900 - ubuntu-cpu-cmake: + ubuntu-cpu: strategy: matrix: include: @@ -196,7 +197,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-cpu-cmake-${{ matrix.build }} + key: ubuntu-cpu-${{ matrix.build }} evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -258,94 +259,7 @@ jobs: wget https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K-be.gguf ./bin/llama-completion -m stories260K-be.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 - ubuntu-latest-cmake-sanitizer: - runs-on: ubuntu-latest - - continue-on-error: true - - strategy: - matrix: - sanitizer: [ADDRESS, THREAD, UNDEFINED] - build_type: [Debug] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: ccache - uses: ggml-org/ccache-action@v1.2.16 - with: - key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }} - evict-old-files: 1d - save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential libssl-dev - - - name: Build - id: cmake_build - if: ${{ matrix.sanitizer != 'THREAD' }} - run: | - cmake -B build \ - -DLLAMA_FATAL_WARNINGS=ON \ - -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ - -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \ - -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} - - cmake --build build --config ${{ matrix.build_type }} -j $(nproc) - - - name: Build (no OpenMP) - id: cmake_build_no_openmp - if: ${{ matrix.sanitizer == 'THREAD' }} - run: | - cmake -B build \ - -DLLAMA_FATAL_WARNINGS=ON \ - -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ - -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \ - -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ - -DGGML_OPENMP=OFF - - cmake --build build --config ${{ matrix.build_type }} -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 - - ubuntu-latest-llguidance: - runs-on: ubuntu-latest - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential libssl-dev - - - name: Build - id: cmake_build - run: | - cmake -B build \ - -DLLAMA_FATAL_WARNINGS=ON \ - -DLLAMA_LLGUIDANCE=ON - cmake --build build --config Release -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 - - ubuntu-latest-cmake-rpc: + ubuntu-latest-rpc: runs-on: ubuntu-latest continue-on-error: true @@ -355,12 +269,6 @@ jobs: id: checkout uses: actions/checkout@v6 - # - name: ccache - # uses: ggml-org/ccache-action@v1.2.16 - # with: - # key: ubuntu-latest-cmake-rpc - # evict-old-files: 1d - - name: Dependencies id: depends run: | @@ -380,21 +288,14 @@ jobs: cd build ctest -L main --verbose - ubuntu-24-cmake-vulkan-deb: - runs-on: ubuntu-24.04 + ubuntu-24-vulkan: + runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} steps: - name: Clone id: checkout uses: actions/checkout@v6 - - name: ccache - uses: ggml-org/ccache-action@v1.2.16 - with: - key: ubuntu-24-cmake-vulkan-deb - evict-old-files: 1d - save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - - name: Dependencies id: depends run: | @@ -414,7 +315,7 @@ jobs: run: | cmake --build build -j $(nproc) - ubuntu-24-cmake-vulkan: + ubuntu-24-webgpu: runs-on: ubuntu-24.04 steps: @@ -425,65 +326,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-24-cmake-vulkan - evict-old-files: 1d - save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - - - name: Dependencies - id: depends - run: | - sudo add-apt-repository -y ppa:kisak/kisak-mesa - sudo apt-get update -y - sudo apt-get install -y build-essential mesa-vulkan-drivers libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libssl-dev - - - name: Get latest Vulkan SDK version - id: vulkan_sdk_version - run: | - echo "VULKAN_SDK_VERSION=$(curl https://vulkan.lunarg.com/sdk/latest/linux.txt)" >> "$GITHUB_ENV" - - - name: Use Vulkan SDK Cache - uses: actions/cache@v5 - id: cache-sdk - with: - path: ./vulkan_sdk - key: vulkan-sdk-${{ env.VULKAN_SDK_VERSION }}-${{ runner.os }} - - - name: Setup Vulkan SDK - if: steps.cache-sdk.outputs.cache-hit != 'true' - uses: ./.github/actions/linux-setup-vulkan - with: - path: ./vulkan_sdk - version: ${{ env.VULKAN_SDK_VERSION }} - - - name: Build - id: cmake_build - run: | - source ./vulkan_sdk/setup-env.sh - cmake -B build \ - -DGGML_VULKAN=ON - cmake --build build --config Release -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - export GGML_VK_VISIBLE_DEVICES=0 - export GGML_VK_DISABLE_F16=1 - # This is using llvmpipe and runs slower than other backends - ctest -L main --verbose --timeout 4800 - - ubuntu-24-cmake-webgpu: - runs-on: ubuntu-24.04 - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: ccache - uses: ggml-org/ccache-action@v1.2.16 - with: - key: ubuntu-24-cmake-webgpu + key: ubuntu-24-webgpu evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -541,23 +384,16 @@ jobs: run: | cd build # This is using llvmpipe and runs slower than other backends - ctest -L main --verbose --timeout 3600 + ctest -L main --verbose --timeout 900 - ubuntu-24-wasm-webgpu: - runs-on: ubuntu-24.04 + ubuntu-24-webgpu-wasm: + runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} steps: - name: Clone id: checkout uses: actions/checkout@v6 - - name: ccache - uses: ggml-org/ccache-action@v1.2.16 - with: - key: ubuntu-latest-wasm-webgpu - evict-old-files: 1d - save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - - name: Install Emscripten run: | git clone https://github.com/emscripten-core/emsdk.git @@ -584,7 +420,7 @@ jobs: cmake --build build-wasm --target test-backend-ops -j $(nproc) - ubuntu-22-cmake-hip: + ubuntu-22-hip: runs-on: ubuntu-22.04 container: rocm/dev-ubuntu-22.04:6.1.2 @@ -602,7 +438,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-22-cmake-hip + key: ubuntu-22-hip evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -615,7 +451,7 @@ jobs: -DGGML_HIP=ON cmake --build build --config Release -j $(nproc) - ubuntu-22-cmake-musa: + ubuntu-22-musa: runs-on: ubuntu-22.04 container: mthreads/musa:rc4.3.0-devel-ubuntu22.04-amd64 @@ -633,7 +469,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-22-cmake-musa + key: ubuntu-22-musa evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -644,7 +480,7 @@ jobs: -DGGML_MUSA=ON cmake --build build --config Release -j $(nproc) - ubuntu-22-cmake-sycl: + ubuntu-22-sycl: runs-on: ubuntu-22.04 continue-on-error: true @@ -679,7 +515,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-22-cmake-sycl + key: ubuntu-22-sycl evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -693,7 +529,7 @@ jobs: -DCMAKE_CXX_COMPILER=icpx cmake --build build --config Release -j $(nproc) - ubuntu-22-cmake-sycl-fp16: + ubuntu-22-sycl-fp16: runs-on: ubuntu-22.04 continue-on-error: true @@ -728,7 +564,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-22-cmake-sycl-fp16 + key: ubuntu-22-sycl-fp16 evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -743,194 +579,87 @@ jobs: -DGGML_SYCL_F16=ON cmake --build build --config Release -j $(nproc) - build-linux-cross: - uses: ./.github/workflows/build-linux-cross.yml + ubuntu-24-openvino: + name: ubuntu-24-openvino-${{ matrix.openvino_device }} + strategy: + matrix: + include: + - variant: cpu + runner: '"ubuntu-24.04"' + openvino_device: "CPU" + - variant: gpu + runner: '["self-hosted","Linux","X64","Intel"]' + openvino_device: "GPU" - build-cmake-pkg: - uses: ./.github/workflows/build-cmake-pkg.yml + runs-on: ${{ fromJSON(matrix.runner) }} - macOS-latest-cmake-ios: - runs-on: macos-latest + env: + # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile + OPENVINO_VERSION_MAJOR: "2026.0" + OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886" - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 - - name: ccache - uses: ggml-org/ccache-action@v1.2.16 - with: - key: macOS-latest-cmake-ios - evict-old-files: 1d - save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + - name: ccache + if: runner.environment == 'github-hosted' + uses: ggml-org/ccache-action@v1.2.16 + with: + key: ubuntu-24-openvino-${{ matrix.variant }}-no-preset-v1 + evict-old-files: 1d + save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - - name: Build - id: cmake_build - run: | - sysctl -a - cmake -B build -G Xcode \ - -DGGML_METAL_USE_BF16=ON \ - -DGGML_METAL_EMBED_LIBRARY=ON \ - -DLLAMA_BUILD_COMMON=OFF \ - -DLLAMA_BUILD_EXAMPLES=OFF \ - -DLLAMA_BUILD_TOOLS=OFF \ - -DLLAMA_BUILD_TESTS=OFF \ - -DLLAMA_BUILD_SERVER=OFF \ - -DCMAKE_SYSTEM_NAME=iOS \ - -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ - -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml - cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install -y build-essential libssl-dev libtbb12 cmake ninja-build python3-pip + sudo apt-get install -y ocl-icd-opencl-dev opencl-headers opencl-clhpp-headers intel-opencl-icd - macOS-latest-cmake-tvos: - runs-on: macos-latest + - name: Use OpenVINO Toolkit Cache + if: runner.environment == 'github-hosted' + uses: actions/cache@v5 + id: cache-openvino + with: + path: ./openvino_toolkit + key: openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }} - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 + - name: Setup OpenVINO Toolkit + if: steps.cache-openvino.outputs.cache-hit != 'true' + uses: ./.github/actions/linux-setup-openvino + with: + path: ./openvino_toolkit + version_major: ${{ env.OPENVINO_VERSION_MAJOR }} + version_full: ${{ env.OPENVINO_VERSION_FULL }} - - name: ccache - uses: ggml-org/ccache-action@v1.2.16 - with: - key: macOS-latest-cmake-tvos - evict-old-files: 1d - save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + - name: Install OpenVINO dependencies + run: | + cd ./openvino_toolkit + chmod +x ./install_dependencies/install_openvino_dependencies.sh + echo "Y" | sudo -E ./install_dependencies/install_openvino_dependencies.sh - - name: Build - id: cmake_build - run: | - sysctl -a - cmake -B build -G Xcode \ - -DGGML_METAL_USE_BF16=ON \ - -DGGML_METAL_EMBED_LIBRARY=ON \ - -DLLAMA_BUILD_COMMON=OFF \ - -DLLAMA_BUILD_EXAMPLES=OFF \ - -DLLAMA_BUILD_TOOLS=OFF \ - -DLLAMA_BUILD_TESTS=OFF \ - -DLLAMA_BUILD_SERVER=OFF \ - -DCMAKE_SYSTEM_NAME=tvOS \ - -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ - -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml - cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + - name: Build + id: cmake_build + run: | + source ./openvino_toolkit/setupvars.sh + cmake -B build/ReleaseOV -G Ninja \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_OPENVINO=ON + cmake --build build/ReleaseOV --config Release -j $(nproc) - macOS-latest-cmake-visionos: - runs-on: macos-latest + - name: Test + id: cmake_test + # TODO: fix and re-enable the `test-llama-archs` test below + run: | + cd ${{ github.workspace }} + if [ "${{ matrix.openvino_device }}" = "GPU" ]; then + export GGML_OPENVINO_DEVICE=GPU + fi + ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs" --verbose --timeout 2000 - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Build - id: cmake_build - run: | - sysctl -a - cmake -B build -G Xcode \ - -DGGML_METAL_USE_BF16=ON \ - -DGGML_METAL_EMBED_LIBRARY=ON \ - -DLLAMA_BUILD_COMMON=OFF \ - -DLLAMA_BUILD_EXAMPLES=OFF \ - -DLLAMA_BUILD_TOOLS=OFF \ - -DLLAMA_BUILD_TESTS=OFF \ - -DLLAMA_BUILD_SERVER=OFF \ - -DCMAKE_SYSTEM_NAME=visionOS \ - -DCMAKE_OSX_DEPLOYMENT_TARGET=1.0 \ - -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml - cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO - - macOS-latest-swift: - runs-on: macos-latest - needs: ios-xcode-build - - strategy: - matrix: - destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS'] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: ccache - uses: ggml-org/ccache-action@v1.2.16 - with: - key: macOS-latest-swift - evict-old-files: 1d - save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - - - name: Download xcframework artifact - uses: actions/download-artifact@v7 - with: - name: llama-xcframework - path: build-apple/llama.xcframework/ - - - name: Build llama.cpp with CMake - id: cmake_build - run: | - sysctl -a - cmake -B build -G Xcode \ - -DGGML_METAL_USE_BF16=ON \ - -DGGML_METAL_EMBED_LIBRARY=ON \ - -DLLAMA_OPENSSL=OFF \ - -DLLAMA_BUILD_EXAMPLES=OFF \ - -DLLAMA_BUILD_TOOLS=OFF \ - -DLLAMA_BUILD_TESTS=OFF \ - -DLLAMA_BUILD_SERVER=OFF \ - -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" - cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) - - windows-msys2: - runs-on: windows-2025 - - strategy: - fail-fast: false - matrix: - include: - - { sys: UCRT64, env: ucrt-x86_64, build: Release } - - { sys: CLANG64, env: clang-x86_64, build: Release } - - steps: - - name: Clone - uses: actions/checkout@v6 - - - name: ccache - uses: ggml-org/ccache-action@v1.2.16 - with: - key: windows-msys2 - variant: ccache - evict-old-files: 1d - save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - - - name: Setup ${{ matrix.sys }} - uses: msys2/setup-msys2@v2 - with: - update: true - msystem: ${{matrix.sys}} - install: >- - base-devel - git - mingw-w64-${{matrix.env}}-toolchain - mingw-w64-${{matrix.env}}-cmake - mingw-w64-${{matrix.env}}-openblas - - - name: Build using CMake - shell: msys2 {0} - run: | - cmake -B build - cmake --build build --config ${{ matrix.build }} -j $(nproc) - - - name: Clean after building using CMake - shell: msys2 {0} - run: | - rm -rf build - - - name: Build using CMake w/ OpenBLAS - shell: msys2 {0} - run: | - cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS - cmake --build build --config ${{ matrix.build }} -j $(nproc) - - windows-latest-cmake: + windows-latest: runs-on: windows-2025 env: @@ -965,7 +694,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: windows-latest-cmake-${{ matrix.build }} + key: windows-latest-${{ matrix.build }} variant: ccache evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -1052,7 +781,7 @@ jobs: # $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1 # & $sde -future -- ctest -L main -C Release --verbose --timeout 900 - ubuntu-latest-cmake-cuda: + ubuntu-latest-cuda: runs-on: ubuntu-latest container: nvidia/cuda:12.6.2-devel-ubuntu24.04 @@ -1071,7 +800,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-latest-cmake-cuda + key: ubuntu-latest-cuda evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -1088,7 +817,7 @@ jobs: -DGGML_CUDA_CUB_3DOT2=ON cmake --build build - windows-2022-cmake-cuda: + windows-2022-cuda: runs-on: windows-2022 strategy: @@ -1137,7 +866,7 @@ jobs: cmake --build build --config Release -j %NINJA_JOBS% -t ggml cmake --build build --config Release - windows-latest-cmake-sycl: + windows-latest-sycl: runs-on: windows-2022 defaults: @@ -1156,7 +885,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: windows-latest-cmake-sycl + key: windows-latest-sycl variant: ccache evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} @@ -1171,14 +900,12 @@ jobs: id: cmake_build run: examples/sycl/win-build-sycl.bat - windows-latest-cmake-hip: + windows-latest-hip: runs-on: windows-2022 env: - # The ROCm version must correspond to the version used in the HIP SDK. - ROCM_VERSION: "6.4.2" # Make sure this is in sync with build-cache.yml - HIPSDK_INSTALLER_VERSION: "25.Q3" + HIPSDK_INSTALLER_VERSION: "26.Q1" steps: - name: Clone @@ -1188,7 +915,7 @@ jobs: - name: Grab rocWMMA package id: grab_rocwmma run: | - curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/${{ env.ROCM_VERSION }}/pool/main/r/rocwmma-dev/rocwmma-dev_1.7.0.60402-120~24.04_amd64.deb" + curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70200-43~24.04_amd64.deb" 7z x rocwmma.deb 7z x data.tar @@ -1231,7 +958,7 @@ jobs: cmake -G "Unix Makefiles" -B build -S . ` -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" ` -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" ` - -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-${{ env.ROCM_VERSION }}/include/" ` + -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.2.0/include/" ` -DCMAKE_BUILD_TYPE=Release ` -DLLAMA_BUILD_BORINGSSL=ON ` -DROCM_DIR="${env:HIP_PATH}" ` @@ -1240,223 +967,95 @@ jobs: -DGGML_RPC=ON cmake --build build -j ${env:NUMBER_OF_PROCESSORS} - ios-xcode-build: - runs-on: macos-latest + ubuntu-cpu-riscv64-native: + runs-on: RISCV64 steps: - - name: Checkout code - uses: actions/checkout@v6 - - - name: Setup Xcode - uses: maxim-lobanov/setup-xcode@v1 - with: - xcode-version: latest-stable - - - name: Build - id: cmake_build + - name: Install dependencies run: | - sysctl -a - cmake -B build -G Xcode \ - -DGGML_METAL_USE_BF16=ON \ - -DGGML_METAL_EMBED_LIBRARY=ON \ - -DLLAMA_OPENSSL=OFF \ - -DLLAMA_BUILD_EXAMPLES=OFF \ - -DLLAMA_BUILD_TOOLS=OFF \ - -DLLAMA_BUILD_TESTS=OFF \ - -DLLAMA_BUILD_SERVER=OFF \ - -DCMAKE_SYSTEM_NAME=iOS \ - -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ - -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml - cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + sudo apt-get update - - name: xcodebuild for swift package - id: xcodebuild - run: | - ./build-xcframework.sh + # Install necessary packages + sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential libssl-dev wget ccache git-lfs - - name: Upload xcframework artifact - uses: actions/upload-artifact@v6 - with: - name: llama-xcframework - path: build-apple/llama.xcframework/ - retention-days: 1 + # Set gcc-14 and g++-14 as the default compilers + sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100 + sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-14 100 + sudo ln -sf /usr/bin/gcc-14 /usr/bin/gcc + sudo ln -sf /usr/bin/g++-14 /usr/bin/g++ - - name: Build Xcode project - run: | - xcodebuild -downloadPlatform iOS - xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build + # Install Rust stable version + rustup install stable + rustup default stable - android-build: - runs-on: ubuntu-latest + git lfs install - steps: - - name: Clone - uses: actions/checkout@v6 - - # Disabled due to size (400MB) and always 0 cache hits - # - name: ccache - # uses: ggml-org/ccache-action@v1.2.16 - # with: - # key: android-build - # evict-old-files: 1d - - - name: Set up JDK - uses: actions/setup-java@v5 - with: - java-version: 17 - distribution: zulu - - - name: Setup Android SDK - uses: android-actions/setup-android@v3 - with: - log-accepted-android-sdk-licenses: false - - - name: Build - run: | - cd examples/llama.android - ./gradlew build --no-daemon - - android-ndk-build: - runs-on: ubuntu-latest - - env: - OPENCL_VERSION: 2025.07.22 - - strategy: - matrix: - include: - - build: 'arm64-cpu' - defines: '-D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -D GGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm -G Ninja -D LLAMA_OPENSSL=OFF -D GGML_OPENMP=OFF' - - build: 'arm64-snapdragon' - defines: '--preset arm64-android-snapdragon-release' - - steps: - name: Clone id: checkout uses: actions/checkout@v6 - - name: Install OpenCL Headers and Libs - id: install_opencl - if: ${{ matrix.build == 'arm64-snapdragon' }} + - name: Check environment run: | - mkdir opencl - curl -L -o opencl/clhpp.tar.gz https://github.com/KhronosGroup/OpenCL-CLHPP/archive/refs/tags/v${OPENCL_VERSION}.tar.gz - curl -L -o opencl/headers.tar.gz https://github.com/KhronosGroup/OpenCL-Headers/archive/refs/tags/v${OPENCL_VERSION}.tar.gz - curl -L -o opencl/icd-loader.tar.gz https://github.com/KhronosGroup/OpenCL-ICD-Loader/archive/refs/tags/v${OPENCL_VERSION}.tar.gz - tar -xaf opencl/headers.tar.gz -C opencl - tar -xaf opencl/clhpp.tar.gz -C opencl - tar -xaf opencl/icd-loader.tar.gz -C opencl - sudo cp -r opencl/OpenCL-Headers-${OPENCL_VERSION}/CL ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include - sudo cp -r opencl/OpenCL-CLHPP-${OPENCL_VERSION}/include/CL/* ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include/CL - cd opencl/OpenCL-ICD-Loader-${OPENCL_VERSION} - cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -DOPENCL_ICD_LOADER_HEADERS_DIR=${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=31 -DANDROID_STL=c++_shared - cmake --build build - sudo cp build/libOpenCL.so ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android - rm -rf opencl + uname -a + gcc --version + g++ --version + ldd --version + cmake --version + rustc --version - - name: Install Hexagon SDK - id: install_hexsdk - if: ${{ matrix.build == 'arm64-snapdragon' }} - env: - HEXSDK_VER: 6.4.0.2 - HEXTLS_VER: 19.0.04 + - name: Setup ccache run: | - curl -L -o hex-sdk.tar.gz https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v$HEXSDK_VER/hexagon-sdk-v$HEXSDK_VER-amd64-lnx.tar.xz - mkdir hex-sdk - tar -xaf hex-sdk.tar.gz -C hex-sdk - ls -l hex-sdk - sudo mv hex-sdk /opt/hexagon - echo "HEXAGON_SDK_ROOT=/opt/hexagon/$HEXSDK_VER" >> "$GITHUB_ENV" - echo "HEXAGON_TOOLS_ROOT=/opt/hexagon/$HEXSDK_VER/tools/HEXAGON_Tools/$HEXTLS_VER" >> "$GITHUB_ENV" - echo "DEFAULT_HLOS_ARCH=64" >> "$GITHUB_ENV" - echo "DEFAULT_TOOLS_VARIANT=toolv19" >> "$GITHUB_ENV" - echo "DEFAULT_NO_QURT_INC=0" >> "$GITHUB_ENV" - echo "DEFAULT_DSP_ARCH=v73" >> "$GITHUB_ENV" + # Set unique cache directory for this job + export CCACHE_DIR="$HOME/.ccache/cpu-cmake-rv64-native" + mkdir -p "$CCACHE_DIR" - - name: Update CMake presets - id: update_presets - if: ${{ matrix.build == 'arm64-snapdragon' }} - run: | - cp docs/backend/snapdragon/CMakeUserPresets.json . + # Configure ccache for optimal performance + ccache --set-config=max_size=5G + ccache --set-config=compression=true + ccache --set-config=compression_level=6 + ccache --set-config=cache_dir="$CCACHE_DIR" + + # Enable more aggressive caching + ccache --set-config=sloppiness=file_macro,time_macros,include_file_mtime,include_file_ctime + ccache --set-config=hash_dir=false + + # Export for subsequent steps + echo "CCACHE_DIR=$CCACHE_DIR" >> $GITHUB_ENV + echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV - name: Build - id: ndk_build + id: cmake_build run: | - cmake ${{ matrix.defines }} -B build - cmake --build build - cmake --install build --prefix pkg-adb/llama.cpp + cmake -B build \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_OPENMP=OFF \ + -DLLAMA_BUILD_EXAMPLES=ON \ + -DLLAMA_BUILD_TOOLS=ON \ + -DLLAMA_BUILD_TESTS=ON \ + -DCMAKE_C_COMPILER_LAUNCHER=ccache \ + -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \ + -DGGML_RPC=ON \ + -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ + -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 + + cmake --build build --config Release -j $(nproc) - name: Test id: cmake_test run: | - echo "FIXME: test on devices" + cd build + ctest -L main --verbose --timeout 900 - openEuler-latest-cmake-cann: - defaults: - run: - shell: bash -el {0} - strategy: - matrix: - arch: [x86, aarch64] - chip_type: ['910b', '310p'] - build: ['Release'] - use_acl_graph: ['on', 'off'] - exclude: - # 310P does not support USE_ACL_GRAPH=on - - chip_type: '310p' - use_acl_graph: 'on' - runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} - steps: - - name: Checkout - uses: actions/checkout@v6 - with: - fetch-depth: 0 - - - name: Free up disk space - uses: ggml-org/free-disk-space@v1.3.1 - with: - tool-cache: true - - - name: Set container image - id: cann-image + - name: Test llama2c conversion + id: llama2c_test run: | - image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}" - echo "image=${image}" >> "${GITHUB_OUTPUT}" - - - name: Pull container image - run: docker pull "${{ steps.cann-image.outputs.image }}" - - - name: Build - env: - BUILD_TYPE: ${{ matrix.build }} - SOC_TYPE: ascend${{ matrix.chip_type }} - USE_ACL_GRAPH: ${{ matrix.use_acl_graph }} - run: | - HOST_UID=$(id -u) - HOST_GID=$(id -g) - - docker run --rm \ - -v "${PWD}:/workspace" \ - -w /workspace \ - -e SOC_TYPE=${SOC_TYPE} \ - -e BUILD_TYPE=${BUILD_TYPE} \ - -e USE_ACL_GRAPH=${USE_ACL_GRAPH} \ - "${{ steps.cann-image.outputs.image }}" \ - bash -lc ' - set -e - yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake openssl-devel - yum clean all && rm -rf /var/cache/yum - git config --global --add safe.directory "/workspace" - export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} - cmake -S . -B build \ - -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ - -DGGML_CANN=on \ - -DSOC_TYPE=${SOC_TYPE} \ - -DUSE_ACL_GRAPH=${USE_ACL_GRAPH} - cmake --build build -j $(nproc) - - chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build - ' + cd build + echo "Fetch tokenizer" + wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/tok512.bin + echo "Fetch llama2c model" + wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin + ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf + ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 # TODO: simplify the following workflows using a matrix # TODO: run lighter CI on PRs and the full CI only on master (if needed) @@ -1590,144 +1189,6 @@ jobs: run: | LLAMA_ARG_THREADS=$(nproc) GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt - ggml-ci-x64-nvidia-cuda: - runs-on: [self-hosted, Linux, X64, NVIDIA] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Test - id: ggml-ci - run: | - nvidia-smi - GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp - - ggml-ci-x64-nvidia-vulkan-cm: - runs-on: [self-hosted, Linux, X64, NVIDIA] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Test - id: ggml-ci - run: | - vulkaninfo --summary - GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp - - ggml-ci-x64-nvidia-vulkan-cm2: - runs-on: [self-hosted, Linux, X64, NVIDIA, COOPMAT2] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Test - id: ggml-ci - run: | - vulkaninfo --summary - GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp - - ggml-ci-x64-cpu-amx: - runs-on: [self-hosted, Linux, X64, CPU, AMX] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Test - id: ggml-ci - run: | - bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp - - # ggml-ci-x64-amd-vulkan: - # runs-on: [self-hosted, Linux, X64, AMD] - - # steps: - # - name: Clone - # id: checkout - # uses: actions/checkout@v6 - - # - name: Test - # id: ggml-ci - # run: | - # vulkaninfo --summary - # GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp - - # ggml-ci-x64-amd-rocm: - # runs-on: [self-hosted, Linux, X64, AMD] - - # steps: - # - name: Clone - # id: checkout - # uses: actions/checkout@v6 - - # - name: Test - # id: ggml-ci - # run: | - # amd-smi static - # GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp - - ggml-ci-mac-metal: - runs-on: [self-hosted, macOS, ARM64] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Test - id: ggml-ci - run: | - GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp - - ggml-ci-mac-webgpu: - runs-on: [self-hosted, macOS, ARM64] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Dawn Dependency - id: dawn-depends - run: | - DAWN_VERSION="v2.0.0" - DAWN_OWNER="reeselevine" - DAWN_REPO="dawn" - DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release" - echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip" - curl -L -o artifact.zip \ - "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip" - mkdir dawn - unzip artifact.zip - tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1 - - - name: Test - id: ggml-ci - run: | - GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \ - bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp - - ggml-ci-mac-vulkan: - runs-on: [self-hosted, macOS, ARM64] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Test - id: ggml-ci - run: | - vulkaninfo --summary - GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp - ggml-ci-arm64-cpu-kleidiai: runs-on: ubuntu-22.04-arm @@ -1754,342 +1215,7 @@ jobs: run: | GG_BUILD_KLEIDIAI=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt - ubuntu-cpu-cmake-riscv64-native: - runs-on: RISCV64 - - steps: - - name: Install dependencies - run: | - sudo apt-get update - - # Install necessary packages - sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential libssl-dev wget ccache git-lfs - - # Set gcc-14 and g++-14 as the default compilers - sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100 - sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-14 100 - sudo ln -sf /usr/bin/gcc-14 /usr/bin/gcc - sudo ln -sf /usr/bin/g++-14 /usr/bin/g++ - - # Install Rust stable version - rustup install stable - rustup default stable - - git lfs install - - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Check environment - run: | - uname -a - gcc --version - g++ --version - ldd --version - cmake --version - rustc --version - - - name: Setup ccache - run: | - # Set unique cache directory for this job - export CCACHE_DIR="$HOME/.ccache/cpu-cmake-rv64-native" - mkdir -p "$CCACHE_DIR" - - # Configure ccache for optimal performance - ccache --set-config=max_size=5G - ccache --set-config=compression=true - ccache --set-config=compression_level=6 - ccache --set-config=cache_dir="$CCACHE_DIR" - - # Enable more aggressive caching - ccache --set-config=sloppiness=file_macro,time_macros,include_file_mtime,include_file_ctime - ccache --set-config=hash_dir=false - - # Export for subsequent steps - echo "CCACHE_DIR=$CCACHE_DIR" >> $GITHUB_ENV - echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV - - - name: Build - id: cmake_build - run: | - cmake -B build \ - -DCMAKE_BUILD_TYPE=Release \ - -DGGML_OPENMP=OFF \ - -DLLAMA_BUILD_EXAMPLES=ON \ - -DLLAMA_BUILD_TOOLS=ON \ - -DLLAMA_BUILD_TESTS=ON \ - -DCMAKE_C_COMPILER_LAUNCHER=ccache \ - -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \ - -DGGML_RPC=ON \ - -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ - -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 - - cmake --build build --config Release -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 - - - name: Test llama2c conversion - id: llama2c_test - run: | - cd build - echo "Fetch tokenizer" - wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/tok512.bin - echo "Fetch llama2c model" - wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin - ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf - ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 - - ubuntu-cmake-sanitizer-riscv64-native: - runs-on: RISCV64 - - continue-on-error: true - - strategy: - matrix: - sanitizer: [ADDRESS, THREAD, UNDEFINED] - build_type: [Debug] - - steps: - - name: Install dependencies - run: | - sudo apt-get update - - # Install necessary packages - sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential wget ccache git-lfs - - # Set gcc-14 and g++-14 as the default compilers - sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100 - sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-14 100 - sudo ln -sf /usr/bin/gcc-14 /usr/bin/gcc - sudo ln -sf /usr/bin/g++-14 /usr/bin/g++ - - # Install Rust stable version - rustup install stable - rustup default stable - - git lfs install - - - name: GCC version check - run: | - gcc --version - g++ --version - - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Setup ccache - run: | - # Unique cache directory per matrix combination - export CCACHE_DIR="$HOME/.ccache/sanitizer-${{ matrix.sanitizer }}-${{ matrix.build_type }}" - mkdir -p "$CCACHE_DIR" - - # Configure ccache - ccache --set-config=max_size=5G - ccache --set-config=compression=true - ccache --set-config=compression_level=6 - ccache --set-config=cache_dir="$CCACHE_DIR" - ccache --set-config=sloppiness=file_macro,time_macros,include_file_mtime,include_file_ctime - ccache --set-config=hash_dir=false - - # Export for subsequent steps - echo "CCACHE_DIR=$CCACHE_DIR" >> $GITHUB_ENV - echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV - - - name: Build - id: cmake_build - if: ${{ matrix.sanitizer != 'THREAD' }} - run: | - cmake -B build \ - -DLLAMA_OPENSSL=OFF \ - -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ - -DGGML_OPENMP=ON \ - -DLLAMA_BUILD_EXAMPLES=ON \ - -DLLAMA_BUILD_TOOLS=ON \ - -DLLAMA_BUILD_TESTS=OFF \ - -DCMAKE_C_COMPILER_LAUNCHER=ccache \ - -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \ - -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ - -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ - -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 - - cmake --build build --config ${{ matrix.build_type }} -j $(nproc) - - - name: Build (no OpenMP) - id: cmake_build_no_openmp - if: ${{ matrix.sanitizer == 'THREAD' }} - run: | - cmake -B build \ - -DLLAMA_OPENSSL=OFF \ - -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ - -DGGML_OPENMP=OFF \ - -DLLAMA_BUILD_EXAMPLES=ON \ - -DLLAMA_BUILD_TOOLS=ON \ - -DLLAMA_BUILD_TESTS=OFF \ - -DCMAKE_C_COMPILER_LAUNCHER=ccache \ - -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \ - -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ - -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ - -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 - - cmake --build build --config ${{ matrix.build_type }} -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 - - - ubuntu-llguidance-riscv64-native: - runs-on: RISCV64 - steps: - - name: Install dependencies - run: | - sudo apt-get update - - # Install necessary packages - sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential wget ccache git-lfs - - # Set gcc-14 and g++-14 as the default compilers - sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100 - sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-14 100 - sudo ln -sf /usr/bin/gcc-14 /usr/bin/gcc - sudo ln -sf /usr/bin/g++-14 /usr/bin/g++ - - # Install Rust stable version - rustup install stable - rustup default stable - - git lfs install - - - name: GCC version check - run: | - gcc --version - g++ --version - - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Setup ccache - run: | - export CCACHE_DIR="$HOME/.ccache/llguidance-riscv64" - mkdir -p "$CCACHE_DIR" - - ccache --set-config=max_size=5G - ccache --set-config=compression=true - ccache --set-config=compression_level=6 - ccache --set-config=cache_dir="$CCACHE_DIR" - ccache --set-config=sloppiness=file_macro,time_macros,include_file_mtime,include_file_ctime - ccache --set-config=hash_dir=false - - echo "CCACHE_DIR=$CCACHE_DIR" >> $GITHUB_ENV - echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV - - - name: Build - id: cmake_build - run: | - cmake -B build \ - -DLLAMA_OPENSSL=OFF \ - -DCMAKE_BUILD_TYPE=Release \ - -DGGML_OPENMP=OFF \ - -DLLAMA_BUILD_EXAMPLES=ON \ - -DLLAMA_BUILD_TOOLS=ON \ - -DLLAMA_BUILD_TESTS=OFF \ - -DCMAKE_C_COMPILER_LAUNCHER=ccache \ - -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \ - -DLLAMA_LLGUIDANCE=ON \ - -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ - -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 - - cmake --build build --config Release -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 - - - ubuntu-cmake-rpc-riscv64-native: - runs-on: RISCV64 - - continue-on-error: true - - steps: - - name: Install dependencies - run: | - sudo apt-get update - - # Install necessary packages - sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential libssl-dev wget ccache git-lfs - - # Set gcc-14 and g++-14 as the default compilers - sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100 - sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-14 100 - sudo ln -sf /usr/bin/gcc-14 /usr/bin/gcc - sudo ln -sf /usr/bin/g++-14 /usr/bin/g++ - - # Install Rust stable version - rustup install stable - rustup default stable - - git lfs install - - - name: GCC version check - run: | - gcc --version - g++ --version - - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: Setup ccache - run: | - export CCACHE_DIR="$HOME/.ccache/rpc-riscv64" - mkdir -p "$CCACHE_DIR" - - ccache --set-config=max_size=5G - ccache --set-config=compression=true - ccache --set-config=compression_level=6 - ccache --set-config=cache_dir="$CCACHE_DIR" - ccache --set-config=sloppiness=file_macro,time_macros,include_file_mtime,include_file_ctime - ccache --set-config=hash_dir=false - - echo "CCACHE_DIR=$CCACHE_DIR" >> $GITHUB_ENV - echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV - - - name: Build - id: cmake_build - run: | - cmake -B build \ - -DCMAKE_BUILD_TYPE=Release \ - -DGGML_OPENMP=OFF \ - -DLLAMA_BUILD_EXAMPLES=ON \ - -DLLAMA_BUILD_TOOLS=ON \ - -DLLAMA_BUILD_TESTS=ON \ - -DCMAKE_C_COMPILER_LAUNCHER=ccache \ - -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \ - -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \ - -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \ - -DGGML_RPC=ON - - cmake --build build --config Release -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose - - ggml-ci-arm64-graviton4-kleidiai: + ggml-ci-arm64-cpu-kleidiai-graviton4: runs-on: ah-ubuntu_22_04-c8g_8x steps: @@ -2126,7 +1252,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ggml-ci-arm64-graviton4-kleidiai + key: ggml-ci-arm64-cpu-kleidiai-graviton4 evict-old-files: 1d save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 8062177ba5..9b0a3f8a70 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -47,6 +47,7 @@ jobs: - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" } - { tag: "s390x", dockerfile: ".devops/s390x.Dockerfile", platforms: "linux/s390x", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04-s390x" } - { tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true, runs_on: "ubuntu-22.04" } + - { tag: "openvino", dockerfile: ".devops/openvino.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" } steps: - name: Check out the repo uses: actions/checkout@v6 diff --git a/.github/workflows/gguf-publish.yml b/.github/workflows/gguf-publish.yml index 0e95766459..5bdab0f157 100644 --- a/.github/workflows/gguf-publish.yml +++ b/.github/workflows/gguf-publish.yml @@ -21,7 +21,7 @@ on: jobs: deploy: - runs-on: ubuntu-slim + runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml index 8d1dd7a7d5..e21b3b6568 100644 --- a/.github/workflows/python-lint.yml +++ b/.github/workflows/python-lint.yml @@ -4,10 +4,16 @@ on: push: branches: - master - paths: ['.github/workflows/python-lint.yml', '**/*.py'] + paths: [ + '.github/workflows/python-lint.yml', + '**/*.py' + ] pull_request: types: [opened, synchronize, reopened] - paths: ['.github/workflows/python-lint.yml', '**/*.py'] + paths: [ + '.github/workflows/python-lint.yml', + '**/*.py' + ] concurrency: group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 860acc6b1b..b0f2714ffd 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -10,7 +10,22 @@ on: push: branches: - master - paths: ['.github/workflows/release.yml', '**/CMakeLists.txt', '**/.cmake', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp'] + paths: [ + '.github/workflows/release.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.cu', + '**/*.cuh', + '**/*.swift', + '**/*.m', + '**/*.metal', + '**/*.comp', + '**/*.glsl' + ] concurrency: group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} @@ -34,7 +49,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: macOS-latest-cmake-arm64 + key: macOS-latest-arm64 evict-old-files: 1d - name: Build @@ -81,7 +96,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: macOS-latest-cmake-x64 + key: macOS-latest-x64 evict-old-files: 1d - name: Build @@ -140,7 +155,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-cpu-cmake-${{ matrix.build }} + key: ubuntu-cpu-${{ matrix.build }} evict-old-files: 1d - name: Dependencies @@ -191,7 +206,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-22-cmake-vulkan + key: ubuntu-22-vulkan evict-old-files: 1d - name: Dependencies @@ -231,6 +246,86 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz name: llama-bin-ubuntu-vulkan-x64.tar.gz + ubuntu-24-openvino: + runs-on: ubuntu-24.04 + + outputs: + openvino_version: ${{ steps.openvino_version.outputs.value }} + + env: + # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile + OPENVINO_VERSION_MAJOR: "2026.0" + OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886" + + steps: + - name: Set OpenVINO version output + id: openvino_version + run: echo "value=${{ env.OPENVINO_VERSION_MAJOR }}" >> $GITHUB_OUTPUT + + - name: Clone + id: checkout + uses: actions/checkout@v6 + with: + fetch-depth: 0 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.16 + with: + key: ubuntu-24-openvino-release-no-preset-v1 + evict-old-files: 1d + + - name: Dependencies + run: | + sudo apt-get update + sudo apt-get install -y build-essential libssl-dev libtbb12 cmake ninja-build python3-pip + sudo apt install ocl-icd-opencl-dev opencl-headers opencl-clhpp-headers intel-opencl-icd + + - name: Use OpenVINO Toolkit Cache + uses: actions/cache@v5 + id: cache-openvino + with: + path: ./openvino_toolkit + key: openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }} + + - name: Setup OpenVINO Toolkit + if: steps.cache-openvino.outputs.cache-hit != 'true' + uses: ./.github/actions/linux-setup-openvino + with: + path: ./openvino_toolkit + version_major: ${{ env.OPENVINO_VERSION_MAJOR }} + version_full: ${{ env.OPENVINO_VERSION_FULL }} + + - name: Install OpenVINO dependencies + run: | + cd ./openvino_toolkit + chmod +x ./install_dependencies/install_openvino_dependencies.sh + echo "Y" | sudo -E ./install_dependencies/install_openvino_dependencies.sh + + - name: Build + id: cmake_build + run: | + source ./openvino_toolkit/setupvars.sh + cmake -B build/ReleaseOV -G Ninja \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_OPENVINO=ON + cmake --build build/ReleaseOV --config Release -j $(nproc) + + - name: Determine tag name + id: tag + uses: ./.github/actions/get-tag-name + + - name: Pack artifacts + id: pack_artifacts + run: | + cp LICENSE ./build/ReleaseOV/bin/ + tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/ReleaseOV/bin . + + - name: Upload artifacts + uses: actions/upload-artifact@v6 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz + name: llama-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz + windows-cpu: runs-on: windows-2025 @@ -249,7 +344,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: windows-latest-cmake-cpu-${{ matrix.arch }} + key: windows-latest-cpu-${{ matrix.arch }} variant: ccache evict-old-files: 1d @@ -310,7 +405,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: windows-latest-cmake-${{ matrix.backend }}-${{ matrix.arch }} + key: windows-latest-${{ matrix.backend }}-${{ matrix.arch }} variant: ccache evict-old-files: 1d @@ -456,7 +551,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: windows-latest-cmake-sycl + key: windows-latest-sycl variant: ccache evict-old-files: 1d @@ -536,7 +631,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: ubuntu-rocm-cmake-${{ matrix.ROCM_VERSION }}-${{ matrix.build }} + key: ubuntu-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }} evict-old-files: 1d - name: Dependencies @@ -616,13 +711,13 @@ jobs: runs-on: windows-2022 env: - HIPSDK_INSTALLER_VERSION: "25.Q3" + HIPSDK_INSTALLER_VERSION: "26.Q1" strategy: matrix: include: - name: "radeon" - gpu_targets: "gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032" + gpu_targets: "gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032" steps: - name: Clone @@ -632,7 +727,7 @@ jobs: - name: Grab rocWMMA package id: grab_rocwmma run: | - curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.0.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.0.0.70001-42~24.04_amd64.deb" + curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70200-43~24.04_amd64.deb" 7z x rocwmma.deb 7z x data.tar @@ -646,7 +741,7 @@ jobs: - name: ccache uses: ggml-org/ccache-action@v1.2.16 with: - key: windows-latest-cmake-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}-x64 + key: windows-latest-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}-x64 evict-old-files: 1d - name: Install ROCm @@ -655,7 +750,7 @@ jobs: run: | $ErrorActionPreference = "Stop" write-host "Downloading AMD HIP SDK Installer" - Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe" + Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-Win11-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe" write-host "Installing AMD HIP SDK" $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru $completed = $proc.WaitForExit(600000) @@ -689,20 +784,20 @@ jobs: cmake -G "Unix Makefiles" -B build -S . ` -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" ` -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" ` - -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.0.1/include/ -Wno-ignored-attributes -Wno-nested-anon-types" ` + -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.2.0/include/ -Wno-ignored-attributes -Wno-nested-anon-types" ` -DCMAKE_BUILD_TYPE=Release ` -DGGML_BACKEND_DL=ON ` -DGGML_NATIVE=OFF ` -DGGML_CPU=OFF ` - -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}" ` + -DGPU_TARGETS="${{ matrix.gpu_targets }}" ` -DGGML_HIP_ROCWMMA_FATTN=ON ` -DGGML_HIP=ON ` -DLLAMA_BUILD_BORINGSSL=ON cmake --build build --target ggml-hip -j ${env:NUMBER_OF_PROCESSORS} md "build\bin\rocblas\library\" md "build\bin\hipblaslt\library" - cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\" - cp "${env:HIP_PATH}\bin\hipblaslt.dll" "build\bin\" + cp "${env:HIP_PATH}\bin\libhipblas.dll" "build\bin\" + cp "${env:HIP_PATH}\bin\libhipblaslt.dll" "build\bin\" cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\" cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\" cp "${env:HIP_PATH}\bin\hipblaslt\library\*" "build\bin\hipblaslt\library\" @@ -872,7 +967,7 @@ jobs: permissions: contents: write # for creating release - runs-on: ubuntu-latest + runs-on: ubuntu-slim needs: - windows @@ -883,6 +978,7 @@ jobs: - ubuntu-22-rocm - ubuntu-22-cpu - ubuntu-22-vulkan + - ubuntu-24-openvino - macOS-arm64 - macOS-x64 - ios-xcode-build @@ -967,6 +1063,7 @@ jobs: - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz) - [Ubuntu x64 (ROCm 7.2)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.2-x64.tar.gz) - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-s390x.tar.gz) + - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ needs.ubuntu-24-openvino.outputs.openvino_version }}-x64.tar.gz) **Windows:** - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-x64.zip) diff --git a/.github/workflows/server-sanitize.yml b/.github/workflows/server-sanitize.yml new file mode 100644 index 0000000000..4c9f447cf8 --- /dev/null +++ b/.github/workflows/server-sanitize.yml @@ -0,0 +1,105 @@ +name: Server (sanitize) + +on: + workflow_dispatch: # allows manual triggering + inputs: + sha: + description: 'Commit SHA1 to build' + required: false + type: string + slow_tests: + description: 'Run slow tests' + required: true + type: boolean + push: + branches: + - master + paths: [ + '.github/workflows/server-sanitize.yml', + '**/CMakeLists.txt', + '**/Makefile', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + 'tools/server/**.*' + ] + +env: + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + LLAMA_LOG_VERBOSITY: 10 + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + +jobs: + server: + runs-on: ubuntu-latest + + strategy: + matrix: + sanitizer: [ADDRESS, UNDEFINED] # THREAD is very slow + build_type: [RelWithDebInfo] + fail-fast: false + + steps: + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get -y install \ + build-essential \ + xxd \ + git \ + cmake \ + curl \ + wget \ + language-pack-en \ + libssl-dev + + - name: Clone + id: checkout + uses: actions/checkout@v6 + with: + fetch-depth: 0 + ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} + + - name: Build + id: cmake_build + run: | + cmake -B build \ + -DLLAMA_BUILD_BORINGSSL=ON \ + -DGGML_SCHED_NO_REALLOC=ON \ + -DGGML_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \ + -DGGML_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \ + -DGGML_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }} \ + -DLLAMA_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \ + -DLLAMA_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \ + -DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }} + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server + + - name: Python setup + id: setup_python + uses: actions/setup-python@v6 + with: + python-version: '3.11' + pip-install: -r tools/server/tests/requirements.txt + + - name: Tests + id: server_integration_tests + if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }} + run: | + cd tools/server/tests + export ${{ matrix.extra_args }} + pytest -v -x -m "not slow" + + - name: Slow tests + id: server_integration_tests_slow + if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} + run: | + cd tools/server/tests + export ${{ matrix.extra_args }} + SLOW_TESTS=1 pytest -v -x diff --git a/.github/workflows/server-metal.yml b/.github/workflows/server-self-hosted.yml similarity index 55% rename from .github/workflows/server-metal.yml rename to .github/workflows/server-self-hosted.yml index 1d707bef44..29bd79690a 100644 --- a/.github/workflows/server-metal.yml +++ b/.github/workflows/server-self-hosted.yml @@ -1,4 +1,4 @@ -name: Server-Metal +name: Server (self-hosted) on: workflow_dispatch: # allows manual triggering @@ -14,7 +14,19 @@ on: push: branches: - master - paths: ['.github/workflows/server-metal.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*'] + paths: [ + '.github/workflows/server-self-hosted.yml', + '**/CMakeLists.txt', + '**/Makefile', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.cu', + '**/*.swift', + '**/*.m', + 'tools/server/**.*' + ] env: LLAMA_LOG_COLORS: 1 @@ -28,7 +40,7 @@ concurrency: jobs: server-metal: - runs-on: [self-hosted, macOS, ARM64] + runs-on: [self-hosted, llama-server, macOS, ARM64] name: server-metal (${{ matrix.wf_name }}) strategy: @@ -71,3 +83,42 @@ jobs: pip install -r requirements.txt export ${{ matrix.extra_args }} pytest -v -x -m "not slow" + + server-cuda: + runs-on: [self-hosted, llama-server, Linux, NVIDIA] + + name: server-cuda (${{ matrix.wf_name }}) + strategy: + matrix: + build_type: [Release] + wf_name: ["GPUx1"] + include: + - build_type: Release + extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1" + wf_name: "GPUx1, backend-sampling" + fail-fast: false + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + with: + fetch-depth: 0 + ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} + + - name: Build + id: cmake_build + run: | + cmake -B build -DGGML_SCHED_NO_REALLOC=ON + cmake --build build --config ${{ matrix.build_type }} -j $(sysctl -n hw.logicalcpu) --target llama-server + + - name: Tests + id: server_integration_tests + if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }} + run: | + cd tools/server/tests + python3 -m venv venv + source venv/bin/activate + pip install -r requirements.txt + export ${{ matrix.extra_args }} + pytest -v -x -m "not slow" diff --git a/.github/workflows/server-webui.yml b/.github/workflows/server-webui.yml index 94899c9376..492107ffd8 100644 --- a/.github/workflows/server-webui.yml +++ b/.github/workflows/server-webui.yml @@ -1,4 +1,3 @@ -# Server WebUI build and tests name: Server WebUI on: @@ -11,10 +10,20 @@ on: push: branches: - master - paths: ['.github/workflows/server-webui.yml', 'tools/server/webui/**.*', 'tools/server/tests/**.*', 'tools/server/public/**'] + paths: [ + '.github/workflows/server-webui.yml', + 'tools/server/webui/**.*', + 'tools/server/tests/**.*', + 'tools/server/public/**' + ] pull_request: types: [opened, synchronize, reopened] - paths: ['.github/workflows/server-webui.yml', 'tools/server/webui/**.*', 'tools/server/tests/**.*', 'tools/server/public/**'] + paths: [ + '.github/workflows/server-webui.yml', + 'tools/server/webui/**.*', + 'tools/server/tests/**.*', + 'tools/server/public/**' + ] env: LLAMA_LOG_COLORS: 1 @@ -29,7 +38,7 @@ concurrency: jobs: webui-check: name: WebUI Checks - runs-on: ubuntu-latest + runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} continue-on-error: true steps: - name: Checkout code diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 99d05226ba..750c29f08e 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -1,4 +1,3 @@ -# Server build and tests name: Server on: @@ -15,10 +14,34 @@ on: push: branches: - master - paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*'] + paths: [ + '.github/workflows/server.yml', + '**/CMakeLists.txt', + '**/Makefile', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.cu', + '**/*.swift', + '**/*.m', + 'tools/server/**.*' + ] pull_request: types: [opened, synchronize, reopened] - paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*'] + paths: [ + '.github/workflows/server.yml', + '**/CMakeLists.txt', + '**/Makefile', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.cu', + '**/*.swift', + '**/*.m', + 'tools/server/**.*' + ] env: LLAMA_LOG_COLORS: 1 @@ -34,17 +57,18 @@ jobs: server: runs-on: ubuntu-latest + name: server (${{ matrix.wf_name }}) strategy: matrix: - sanitizer: [ADDRESS, UNDEFINED] # THREAD is very slow - build_type: [RelWithDebInfo] + build_type: [Release] + wf_name: ["default"] include: - build_type: Release - sanitizer: "" extra_args: "" + wf_name: "default" - build_type: Release - sanitizer: "" extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1" + wf_name: "backend-sampling" fail-fast: false steps: @@ -74,13 +98,7 @@ jobs: run: | cmake -B build \ -DLLAMA_BUILD_BORINGSSL=ON \ - -DGGML_SCHED_NO_REALLOC=ON \ - -DGGML_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \ - -DGGML_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \ - -DGGML_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }} \ - -DLLAMA_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \ - -DLLAMA_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \ - -DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }} + -DGGML_SCHED_NO_REALLOC=ON cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server - name: Python setup diff --git a/.gitignore b/.gitignore index bb122d6924..73954e8f5d 100644 --- a/.gitignore +++ b/.gitignore @@ -124,6 +124,11 @@ poetry.toml # Scripts !/scripts/install-oneapi.bat +# Generated by scripts +/hellaswag_val_full.txt +/winogrande-debiased-eval.csv +/wikitext-2-raw/ + # Test models for lora adapters /lora-tests diff --git a/CODEOWNERS b/CODEOWNERS index 9d252c9b8d..4257f5927a 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -2,27 +2,13 @@ # multiplie collaborators per item can be specified /.devops/*.Dockerfile @ngxson -/.github/actions/ @CISC -/.github/workflows/ @CISC +/.github/actions/ @ggml-org/ci +/.github/workflows/ @ggml-org/ci /ci/ @ggerganov /cmake/ @ggerganov -/common/CMakeLists.txt @ggerganov -/common/arg.* @ggerganov -/common/base64.hpp.* @ggerganov -/common/build-info.* @ggerganov -/common/chat.* @pwilkin -/common/chat-peg-parser.* @aldehir -/common/common.* @ggerganov -/common/console.* @ggerganov -/common/http.* @angt -/common/jinja/ @ngxson @CISC @aldehir -/common/llguidance.* @ggerganov -/common/log.* @ggerganov +/common/ @ggml-org/llama-common +/common/jinja/ @CISC /common/ngram-map.* @srogmann -/common/peg-parser.* @aldehir -/common/sampling.* @ggerganov -/common/speculative.* @ggerganov -/common/unicode.* @aldehir /convert_*.py @CISC /examples/batched.swift/ @ggerganov /examples/batched/ @ggerganov @@ -49,29 +35,28 @@ /examples/speculative/ @ggerganov /ggml/cmake/ @ggerganov /ggml/include/ @ggerganov +/ggml/src/ggml-cann/ @ggml-org/ggml-cann /ggml/src/ggml-common.h @ggerganov /ggml/src/ggml-cpu/ @ggerganov /ggml/src/ggml-cpu/spacemit/ @alex-spacemit -/ggml/src/ggml-cuda/fattn* @JohannesGaessler -/ggml/src/ggml-cuda/mmf.* @JohannesGaessler @am17an -/ggml/src/ggml-cuda/mmq.* @JohannesGaessler -/ggml/src/ggml-cuda/mmvf.* @JohannesGaessler -/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler +/ggml/src/ggml-cuda/ @ggml-org/ggml-cuda /ggml/src/ggml-cuda/fattn-wmma* @IMbackK /ggml/src/ggml-hip/ @IMbackK /ggml/src/ggml-cuda/vendors/hip.h @IMbackK /ggml/src/ggml-impl.h @ggerganov -/ggml/src/ggml-metal/ @ggerganov -/ggml/src/ggml-opencl/ @lhez @max-krasnyansky -/ggml/src/ggml-hexagon/ @max-krasnyansky @lhez +/ggml/src/ggml-metal/ @ggml-org/ggml-metal +/ggml/src/ggml-opencl/ @ggml-org/ggml-opencl +/ggml/src/ggml-hexagon/ @ggml-org/ggml-hexagon /ggml/src/ggml-opt.cpp @JohannesGaessler /ggml/src/ggml-quants.* @ggerganov -/ggml/src/ggml-rpc/ @rgerganov +/ggml/src/ggml-rpc/ @ggml-org/ggml-rpc +/ggml/src/ggml-sycl/ @ggml-org/ggml-sycl /ggml/src/ggml-threading.* @ggerganov -/ggml/src/ggml-vulkan/ @0cc4m +/ggml/src/ggml-vulkan/ @ggml-org/ggml-vulkan /ggml/src/ggml-virtgpu/ @kpouget -/ggml/src/ggml-webgpu/ @reeselevine -/ggml/src/ggml-zdnn/ @taronaeo @Andreas-Krebbel @AlekseiNikiforovIBM +/ggml/src/ggml-webgpu/ @ggml-org/ggml-webgpu +/ggml/src/ggml-zdnn/ @ggml-org/ggml-zdnn @Andreas-Krebbel @AlekseiNikiforovIBM +/ggml/src/ggml-openvino/ @cavusmustafa @wine99 /ggml/src/ggml.c @ggerganov /ggml/src/ggml.cpp @ggerganov /ggml/src/gguf.cpp @JohannesGaessler @Green-Sky @@ -89,16 +74,19 @@ /src/llama-vocab.* @CISC /src/models/ @CISC /tests/ @ggerganov -/tests/test-chat-.* @pwilkin +/tests/test-chat.* @pwilkin +/tests/test-llama-archs.cpp @JohannesGaessler /tools/batched-bench/ @ggerganov /tools/cli/ @ngxson /tools/completion/ @ggerganov -/tools/mtmd/ @ngxson +/tools/mtmd/ @ggml-org/llama-mtmd /tools/perplexity/ @ggerganov +/tools/parser/ @pwilkin /tools/quantize/ @ggerganov -/tools/rpc/ @rgerganov -/tools/server/* @ngxson @ggerganov # no subdir -/tools/server/webui/ @allozaur +/tools/rpc/ @ggml-org/ggml-rpc +/tools/server/* @ggml-org/llama-server # no subdir +/tools/server/tests/ @ggml-org/llama-server +/tools/server/webui/ @ggml-org/llama-webui /tools/tokenize/ @ggerganov /tools/tts/ @ggerganov /vendor/ @ggerganov diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 7545e790f8..fc26289aec 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -30,15 +30,21 @@ Before submitting your PR: - Search for existing PRs to prevent duplicating efforts - llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier - Test your changes: - - Execute [the full CI locally on your machine](ci/README.md) before publishing - - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`) - - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends) - - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` + - Execute [the full CI locally on your machine](ci/README.md) before publishing + - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`) + - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends) + - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` - Create separate PRs for each feature or fix: - - Avoid combining unrelated changes in a single PR - - For intricate features, consider opening a feature request first to discuss and align expectations - - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs + - Avoid combining unrelated changes in a single PR + - For intricate features, consider opening a feature request first to discuss and align expectations + - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs + - In particular, adding new data types (extension of the `ggml_type` enum) carries with it a disproportionate maintenance burden. As such, to add a new quantization type you will need to meet the following *additional* criteria *at minimum*: + - convert a small model to GGUF using the new type and upload it to HuggingFace + - provide [perplexity](https://github.com/ggml-org/llama.cpp/tree/master/tools/perplexity) comparisons to FP16/BF16 (whichever is the native precision) as well as to types of similar size + - provide KL divergence data calculated vs. the FP16/BF16 (whichever is the native precision) version for both the new type as well as types of similar size + - provide [performance data](https://github.com/ggml-org/llama.cpp/tree/master/tools/llama-bench) for the new type in comparison to types of similar size on pure CPU - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly +- If you are a new contributor, limit your open PRs to 1. After submitting your PR: - Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability @@ -159,7 +165,7 @@ Maintainers reserve the right to decline review or close pull requests for any r # Code maintenance -- Existing code should have designated collaborators and/or maintainers specified in the [CODEOWNERS](CODEOWNERS) file reponsible for: +- Existing code should have designated collaborators and/or maintainers specified in the [CODEOWNERS](CODEOWNERS) file responsible for: - Reviewing and merging related PRs - Fixing related bugs - Providing developer guidance/support diff --git a/README.md b/README.md index 5c11f38048..8339105100 100644 --- a/README.md +++ b/README.md @@ -259,6 +259,8 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo - [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server - [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale - [llmaz](https://github.com/InftyAI/llmaz) - ☸️ Easy, advanced inference platform for large language models on Kubernetes. +- [LLMKube](https://github.com/defilantech/llmkube) - Kubernetes operator for llama.cpp with multi-GPU and Apple Silicon Metal + support"
@@ -277,6 +279,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo | [BLAS](docs/build.md#blas-build) | All | | [BLIS](docs/backend/BLIS.md) | All | | [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU | +| [OpenVINO [In Progress]](docs/backend/OPENVINO.md) | Intel CPUs, GPUs, and NPUs | | [MUSA](docs/build.md#musa) | Moore Threads GPU | | [CUDA](docs/build.md#cuda) | Nvidia GPU | | [HIP](docs/build.md#hip) | AMD GPU | @@ -287,7 +290,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo | [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE | | [WebGPU [In Progress]](docs/build.md#webgpu) | All | | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All | -| [Hexagon [In Progress]](docs/backend/hexagon/README.md) | Snapdragon | +| [Hexagon [In Progress]](docs/backend/snapdragon/README.md) | Snapdragon | | [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR | ## Obtaining and quantizing models diff --git a/benches/nemotron/nemotron-dgx-spark.md b/benches/nemotron/nemotron-dgx-spark.md new file mode 100644 index 0000000000..2bce30a30e --- /dev/null +++ b/benches/nemotron/nemotron-dgx-spark.md @@ -0,0 +1,72 @@ +# NVIDIA DGX Spark + +## System info + +```bash +uname --all +Linux spark-17ed 6.11.0-1016-nvidia #16-Ubuntu SMP PREEMPT_DYNAMIC Sun Sep 21 16:52:46 UTC 2025 aarch64 aarch64 aarch64 GNU/Linux + +g++ --version +g++ (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 + +nvidia-smi +Fri Mar 6 11:39:45 2026 ++-----------------------------------------------------------------------------------------+ +| NVIDIA-SMI 580.95.05 Driver Version: 580.95.05 CUDA Version: 13.0 | ++-----------------------------------------+------------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+========================+======================| +| 0 NVIDIA GB10 On | 0000000F:01:00.0 Off | N/A | +| N/A 52C P0 13W / N/A | Not Supported | 0% Default | +| | | N/A | ++-----------------------------------------+------------------------+----------------------+ +``` + +## ggml-org/nemotron-3-super-120b-GGUF + +Model: https://huggingface.co/ggml-org/nemotron-3-super-120b-GGUF + +- `llama-batched-bench` + +main: n_kv_max = 303104, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = 99, n_threads = 20, n_threads_batch = 20 + +| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s | +|-------|--------|------|--------|----------|----------|----------|----------|----------|----------| +| 512 | 32 | 1 | 544 | 1.094 | 468.05 | 1.621 | 19.74 | 2.715 | 200.37 | +| 512 | 32 | 2 | 1088 | 1.463 | 700.16 | 2.437 | 26.26 | 3.900 | 279.01 | +| 512 | 32 | 4 | 2176 | 2.647 | 773.76 | 4.043 | 31.66 | 6.689 | 325.29 | +| 512 | 32 | 8 | 4352 | 5.291 | 774.14 | 6.151 | 41.62 | 11.442 | 380.37 | +| 512 | 32 | 16 | 8704 | 10.603 | 772.62 | 10.385 | 49.30 | 20.987 | 414.72 | +| 512 | 32 | 32 | 17408 | 21.231 | 771.69 | 18.235 | 56.16 | 39.466 | 441.09 | +| 4096 | 32 | 1 | 4128 | 5.340 | 767.05 | 1.616 | 19.81 | 6.956 | 593.47 | +| 4096 | 32 | 2 | 8256 | 10.673 | 767.55 | 2.454 | 26.08 | 13.127 | 628.94 | +| 4096 | 32 | 4 | 16512 | 21.348 | 767.46 | 4.072 | 31.44 | 25.420 | 649.57 | +| 4096 | 32 | 8 | 33024 | 42.714 | 767.15 | 6.277 | 40.78 | 48.991 | 674.08 | +| 4096 | 32 | 16 | 66048 | 85.385 | 767.54 | 10.596 | 48.32 | 95.981 | 688.14 | +| 4096 | 32 | 32 | 132096 | 170.819 | 767.32 | 18.619 | 55.00 | 189.437 | 697.31 | +| 8192 | 32 | 1 | 8224 | 10.690 | 766.32 | 1.619 | 19.76 | 12.310 | 668.10 | +| 8192 | 32 | 2 | 16448 | 21.382 | 766.24 | 2.467 | 25.94 | 23.850 | 689.65 | +| 8192 | 32 | 4 | 32896 | 42.782 | 765.92 | 4.098 | 31.23 | 46.881 | 701.69 | +| 8192 | 32 | 8 | 65792 | 85.582 | 765.77 | 6.368 | 40.20 | 91.951 | 715.52 | +| 8192 | 32 | 16 | 131584 | 171.066 | 766.21 | 10.774 | 47.52 | 181.840 | 723.62 | +| 8192 | 32 | 32 | 263168 | 342.140 | 766.19 | 18.969 | 53.98 | 361.109 | 728.78 | + + +- `llama-bench` + +| model | size | params | backend | n_ubatch | fa | test | t/s | +| ----------------------- | ---------: | ---------: | ---------- | -------: | -: | --------------: | -------------------: | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | pp2048 | 768.84 ± 0.90 | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | tg32 | 19.94 ± 0.16 | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | pp2048 @ d4096 | 764.51 ± 0.50 | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | tg32 @ d4096 | 19.95 ± 0.18 | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | pp2048 @ d8192 | 759.53 ± 0.71 | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | tg32 @ d8192 | 19.83 ± 0.18 | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | pp2048 @ d16384 | 747.98 ± 1.58 | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | tg32 @ d16384 | 19.84 ± 0.18 | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | pp2048 @ d32768 | 724.40 ± 2.70 | +| nemotron 120B.A12B Q4_K | 65.10 GiB | 120.67 B | CUDA | 2048 | 1 | tg32 @ d32768 | 19.45 ± 0.18 | + +build: 04a65daab (8268) diff --git a/ci/run.sh b/ci/run.sh index 96755ea13e..16cd80d56a 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -25,6 +25,9 @@ # # with KLEIDIAI support # GG_BUILD_KLEIDIAI=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt # +# # with OPENVINO support +# GG_BUILD_OPENVINO=1 GG_BUILD_LOW_PERF=1 GGML_OPENVINO_DEVICE=CPU bash ./ci/run.sh ./tmp/results ./tmp/mnt +# if [ -z "$2" ]; then echo "usage: $0 " @@ -46,6 +49,7 @@ cd $sd/../ SRC=`pwd` CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_OPENSSL=OFF -DGGML_SCHED_NO_REALLOC=ON" +CTEST_EXTRA="" if [ ! -z ${GG_BUILD_METAL} ]; then CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON" @@ -165,6 +169,18 @@ if [ -n "${GG_BUILD_KLEIDIAI}" ]; then -DBUILD_SHARED_LIBS=OFF" fi +if [ ! -z ${GG_BUILD_OPENVINO} ]; then + if [ -z ${OpenVINO_DIR} ]; then + echo "OpenVINO_DIR not found, please install OpenVINO via archives and enable it by:" + echo "source /opt/intel/openvino/setupvars.sh" + exit 1 + fi + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_OPENVINO=ON" + + # TODO: fix and re-enable the `test-llama-archs` test below + CTEST_EXTRA="-E test-llama-archs" +fi + ## helpers # download a file if it does not exist or if it is outdated @@ -222,7 +238,7 @@ function gg_run_ctest_debug { (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log - (time ctest --output-on-failure -L main -E "test-opt|test-backend-ops" ) 2>&1 | tee -a $OUT/${ci}-ctest.log + (time ctest --output-on-failure -L main -E "test-opt|test-backend-ops" ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log set +e } @@ -254,9 +270,9 @@ function gg_run_ctest_release { (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log if [ -z ${GG_BUILD_LOW_PERF} ]; then - (time ctest --output-on-failure -L 'main|python' ) 2>&1 | tee -a $OUT/${ci}-ctest.log + (time ctest --output-on-failure -L 'main|python' ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log else - (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log + (time ctest --output-on-failure -L main -E test-opt ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log fi set +e diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 27ca335be3..75c6366c7f 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -47,10 +47,10 @@ add_library(${TARGET} STATIC arg.cpp arg.h base64.hpp - chat-parser.cpp - chat-parser.h - chat-parser-xml-toolcall.h - chat-parser-xml-toolcall.cpp + chat-auto-parser-generator.cpp + chat-auto-parser-helpers.cpp + chat-auto-parser.h + chat-diff-analyzer.cpp chat-peg-parser.cpp chat-peg-parser.h chat.cpp @@ -81,6 +81,8 @@ add_library(${TARGET} STATIC preset.cpp preset.h regex-partial.cpp + reasoning-budget.cpp + reasoning-budget.h regex-partial.h sampling.cpp sampling.h diff --git a/common/arg.cpp b/common/arg.cpp index c2c0b4018b..181943a618 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -732,23 +732,28 @@ static void common_params_print_completion(common_params_context & ctx_arg) { "llama-completion", "llama-convert-llama2c-to-ggml", "llama-cvector-generator", + "llama-debug", + "llama-diffusion-cli", "llama-embedding", "llama-eval-callback", "llama-export-lora", + "llama-finetune", + "llama-fit-params", + "llama-gemma3-cli", "llama-gen-docs", "llama-gguf", "llama-gguf-hash", "llama-gguf-split", - "llama-gritlm", + "llama-idle", "llama-imatrix", - "llama-infill", - "llama-mtmd-cli", - "llama-llava-clip-quantize-cli", + "llama-llava-cli", "llama-lookahead", "llama-lookup", "llama-lookup-create", "llama-lookup-merge", "llama-lookup-stats", + "llama-minicpmv-cli", + "llama-mtmd-cli", "llama-parallel", "llama-passkey", "llama-perplexity", @@ -1279,13 +1284,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_env("LLAMA_ARG_SWA_FULL")); add_opt(common_arg( - {"--ctx-checkpoints", "--swa-checkpoints"}, "N", + {"-ctxcp", "--ctx-checkpoints", "--swa-checkpoints"}, "N", string_format("max number of context checkpoints to create per slot (default: %d)" "[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)", params.n_ctx_checkpoints), [](common_params & params, int value) { params.n_ctx_checkpoints = value; } ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"-cpent", "--checkpoint-every-n-tokens"}, "N", + string_format("create a checkpoint every n tokens during prefill (processing), -1 to disable (default: %d)", params.checkpoint_every_nt), + [](common_params & params, int value) { + params.checkpoint_every_nt = value; + } + ).set_env("LLAMA_ARG_CHECKPOINT_EVERY_NT").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( {"-cram", "--cache-ram"}, "N", string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)" @@ -1578,7 +1590,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_sparam()); add_opt(common_arg( - {"--temp"}, "N", + {"--temp", "--temperature"}, "N", string_format("temperature (default: %.2f)", (double)params.sampling.temp), [](common_params & params, const std::string & value) { params.sampling.temp = std::stof(value); @@ -1611,7 +1623,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_sparam()); add_opt(common_arg( - {"--top-nsigma"}, "N", + {"--top-nsigma", "--top-n-sigma"}, "N", string_format("top-n-sigma sampling (default: %.2f, -1.0 = disabled)", params.sampling.top_n_sigma), [](common_params & params, const std::string & value) { params.sampling.top_n_sigma = std::stof(value); @@ -1634,7 +1646,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_sparam()); add_opt(common_arg( - {"--typical"}, "N", + {"--typical", "--typical-p"}, "N", string_format("locally typical sampling, parameter p (default: %.2f, 1.0 = disabled)", (double)params.sampling.typ_p), [](common_params & params, const std::string & value) { params.sampling.typ_p = std::stof(value); @@ -2399,7 +2411,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.fit_params = false; } else { throw std::runtime_error( - string_format("error: unkown value for --fit: '%s'\n", value.c_str())); + string_format("error: unknown value for --fit: '%s'\n", value.c_str())); } } ).set_env("LLAMA_ARG_FIT")); @@ -2420,11 +2432,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ); } if (split_arg.size() == 1) { - std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoul(split_arg[0]) * 1024*1024); + std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoull(split_arg[0]) * 1024*1024); return; } for (size_t i = 0; i < split_arg.size(); i++) { - params.fit_params_target[i] = std::stoul(split_arg[i]) * 1024*1024; + params.fit_params_target[i] = std::stoull(split_arg[i]) * 1024*1024; } } ).set_env("LLAMA_ARG_FIT_TARGET")); @@ -2520,11 +2532,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex )); add_opt(common_arg( {"-a", "--alias"}, "STRING", - "set alias for model name (to be used by REST API)", + "set model name aliases, comma-separated (to be used by API)", [](common_params & params, const std::string & value) { - params.model_alias = value; + for (auto & alias : string_split(value, ',')) { + alias = string_strip(alias); + if (!alias.empty()) { + params.model_alias.insert(alias); + } + } } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ALIAS")); + add_opt(common_arg( + {"--tags"}, "STRING", + "set model tags, comma-separated (informational, not used for routing)", + [](common_params & params, const std::string & value) { + for (auto & tag : string_split(value, ',')) { + tag = string_strip(tag); + if (!tag.empty()) { + params.model_tags.insert(tag); + } + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TAGS")); add_opt(common_arg( {"-m", "--model"}, "FNAME", ex == LLAMA_EXAMPLE_EXPORT_LORA @@ -2642,7 +2671,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.out_file = value; } - ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_FINETUNE})); + ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_FINETUNE, + LLAMA_EXAMPLE_RESULTS, LLAMA_EXAMPLE_EXPORT_GRAPH_OPS})); add_opt(common_arg( {"-ofreq", "--output-frequency"}, "N", string_format("output the imatrix every N iterations (default: %d)", params.n_out_freq), @@ -2810,6 +2840,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.webui_config_json = read_file(value); } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG_FILE")); + add_opt(common_arg( + {"--webui-mcp-proxy"}, + {"--no-webui-mcp-proxy"}, + string_format("experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: %s)", params.webui_mcp_proxy ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.webui_mcp_proxy = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_MCP_PROXY")); add_opt(common_arg( {"--webui"}, {"--no-webui"}, @@ -2881,6 +2919,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { auto parsed = json::parse(value); for (const auto & item : parsed.items()) { + if (item.key() == "enable_thinking") { + LOG_WRN("Setting 'enable_thinking' via --chat-template-kwargs is deprecated. " + "Use --reasoning on / --reasoning off instead.\n"); + } params.default_template_kwargs[item.key()] = item.value().dump(); } } @@ -3016,14 +3058,39 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.reasoning_format = common_reasoning_format_from_name(value); } ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK")); + add_opt(common_arg( + {"-rea", "--reasoning"}, "[on|off|auto]", + "Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))", + [](common_params & params, const std::string & value) { + if (is_truthy(value)) { + params.enable_reasoning = 1; + params.default_template_kwargs["enable_thinking"] = "true"; + } else if (is_falsey(value)) { + params.enable_reasoning = 0; + params.default_template_kwargs["enable_thinking"] = "false"; + } else if (is_autoy(value)) { + params.enable_reasoning = -1; + } else { + throw std::invalid_argument( + string_format("error: unknown value for --reasoning: '%s'\n", value.c_str())); + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING")); add_opt(common_arg( {"--reasoning-budget"}, "N", - "controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)", + "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)", [](common_params & params, int value) { - if (value != 0 && value != -1) { throw std::invalid_argument("invalid value"); } + if (value < -1) { throw std::invalid_argument("invalid value"); } params.reasoning_budget = value; } ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET")); + add_opt(common_arg( + {"--reasoning-budget-message"}, "MESSAGE", + "message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)", + [](common_params & params, const std::string & value) { + params.reasoning_budget_message = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET_MESSAGE")); add_opt(common_arg( {"--chat-template"}, "JINJA_TEMPLATE", string_format( @@ -3582,6 +3649,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } } ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"--check"}, + string_format("check rather than generate results (default: %s)", params.check ? "true" : "false"), + [](common_params & params) { + params.check = true; + } + ).set_examples({LLAMA_EXAMPLE_RESULTS})); add_opt(common_arg( {"--save-logits"}, string_format("save final logits to files for verification (default: %s)", params.save_logits ? "true" : "false"), diff --git a/common/chat-auto-parser-generator.cpp b/common/chat-auto-parser-generator.cpp new file mode 100644 index 0000000000..f19819494c --- /dev/null +++ b/common/chat-auto-parser-generator.cpp @@ -0,0 +1,454 @@ +#include "chat-auto-parser.h" +#include "chat-peg-parser.h" +#include "chat.h" +#include "common.h" +#include "json-schema-to-grammar.h" +#include "log.h" +#include "nlohmann/json.hpp" + +#include +#include + +using json = nlohmann::ordered_json; + +// Helper to iterate over tools/functions +static void foreach_function(const json & tools, const std::function & fn) { + for (const auto & tool : tools) { + if (!tool.contains("type") || tool.at("type") != "function" || !tool.contains("function")) { + continue; + } + fn(tool); + } +} + +namespace autoparser { + +parser_build_context::parser_build_context(common_chat_peg_builder & p, const templates_params & inputs) : + p(p), + inputs(inputs), + reasoning_parser(p.eps()) {} + +common_chat_params peg_generator::generate_parser(const common_chat_template & tmpl, + const struct templates_params & inputs) { + // Run differential analysis to extract template structure + struct autoparser autoparser; + autoparser.analyze_template(tmpl); + return generate_parser(tmpl, inputs, autoparser); +} + +common_chat_params peg_generator::generate_parser(const common_chat_template & tmpl, + const struct templates_params & inputs, + const autoparser & autoparser) { + // Build the parser using the analysis results + auto parser = autoparser.build_parser(inputs); + + // Create the result structure + common_chat_params data; + data.prompt = common_chat_template_direct_apply(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.preserved_tokens = autoparser.preserved_tokens; + data.parser = parser.save(); + + // Build grammar if tools are present + bool has_tools = + autoparser.tools.format.mode != tool_format::NONE && inputs.tools.is_array() && !inputs.tools.empty(); + std::string trigger_marker = !autoparser.tools.format.section_start.empty() ? autoparser.tools.format.section_start : + autoparser.tools.format.per_call_start; + + bool has_response_format = !inputs.json_schema.empty() && inputs.json_schema.is_object(); + bool include_grammar = has_response_format || (has_tools && + ((inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO && !trigger_marker.empty()) || + inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED)); + + if (include_grammar) { + data.grammar_lazy = !has_response_format && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + // Set grammar triggers based on tool section markers (fall back to per-call markers) + if (data.grammar_lazy) { + data.grammar_triggers = { + { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, trigger_marker } + }; + } + } + + return data; +} + +common_peg_arena autoparser::build_parser(const templates_params & inputs) const { + if (!analysis_complete) { + throw std::invalid_argument("Cannot call build_parser on autoparser without performing analysis first, call analyze_template(...)"); + } + return build_chat_peg_parser([&](common_chat_peg_builder & p) { + // If the template uses Python dict format (single-quoted strings in JSON structures), + // pre-register a json-string rule that accepts both quote styles. This must happen + // before any call to p.json() so that all JSON parsing inherits the flexible rule. + if (tools.format.uses_python_dicts) { + p.rule("json-string", p.quoted_string()); + } + + parser_build_context ctx(p, inputs); + bool extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + bool enable_thinking = inputs.enable_thinking; + + ctx.extracting_reasoning = extract_reasoning && enable_thinking && reasoning.mode != reasoning_mode::NONE; + ctx.content = &content; + + // Build reasoning parser + ctx.reasoning_parser = reasoning.build_parser(ctx); + + bool has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + bool has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty(); + + if (has_response_format) { + auto response_format = p.rule("response-format", p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema))); + return ctx.reasoning_parser + p.space() + p.choice({ + p.literal("```json") + p.space() + response_format + p.space() + p.literal("```"), + response_format + }) + p.end(); + } + + if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && jinja_caps.supports_tool_calls) { + return tools.build_parser(ctx); + } + + return content.build_parser(ctx); + }); +} + +common_peg_parser analyze_reasoning::build_parser(parser_build_context & ctx) const { + auto & p = ctx.p; + + if (!ctx.extracting_reasoning) { + return p.eps(); + } + + bool thinking_forced_open = (mode == reasoning_mode::FORCED_OPEN); + bool thinking_forced_closed = (mode == reasoning_mode::FORCED_CLOSED); + + if (thinking_forced_open || thinking_forced_closed) { + // Thinking is forced open OR forced closed with enable_thinking=true + // In both cases, expect only the closing tag (opening was in template) + // However, since we might have incorrectly detected the open/close pattern, + // we admit an optional starting marker + return p.optional(p.literal(start)) + p.reasoning(p.until(end)) + end; + } + if (mode == reasoning_mode::TAG_BASED || mode == reasoning_mode::TOOLS_ONLY) { + // Standard tag-based reasoning OR tools-only mode (reasoning appears with tools) + // Both use the same tag-based pattern if markers are available + if (!start.empty() && !end.empty()) { + return p.optional(start + p.reasoning(p.until(end)) + end); + } + } else if (mode == reasoning_mode::DELIMITER) { + return p.optional(p.reasoning(p.until(end)) + end); + } + + return p.eps(); +} + +common_peg_parser analyze_content::build_parser(parser_build_context & ctx) const { + auto & p = ctx.p; + + if (is_always_wrapped()) { + if (ctx.extracting_reasoning) { + return ctx.reasoning_parser + start + p.content(p.until(end)) + end + p.end(); + } + return p.content(p.until(start)) + start + p.content(p.until(end)) + end + p.end(); + } + return ctx.reasoning_parser + p.content(p.rest()) + p.end(); +} + +common_peg_parser analyze_content::build_optional_wrapped(parser_build_context & ctx) const { + auto & p = ctx.p; + + if (is_always_wrapped()) { + return p.optional(start + p.content(p.until(end)) + end); + } + return p.eps(); +} + +common_peg_parser analyze_tools::build_parser(parser_build_context & ctx) const { + switch (format.mode) { + case tool_format::JSON_NATIVE: + return build_tool_parser_json_native(ctx); + case tool_format::TAG_WITH_JSON: + return build_tool_parser_tag_json(ctx); + case tool_format::TAG_WITH_TAGGED: + return build_tool_parser_tag_tagged(ctx); + default: + LOG_ERR("[ERROR] Template seems to support tool calls, but failed to determine tool format. Tool calling will not work properly. " + "Check for a fixed template for your model in the models/templates directory of your llama.cpp installation or " + "report an issue at https://github.com/ggml-org/llama.cpp/issues\n"); + return ctx.p.eps(); + } +} + +common_peg_parser analyze_tools::build_tool_parser_json_native(parser_build_context & ctx) const { + auto & p = ctx.p; + const auto & inputs = ctx.inputs; + bool force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED; + + // Build effective field names with dot notation if function_field is set + std::string name_field = format.name_field; + std::string args_field = format.args_field; + + if (!format.function_field.empty() && format.function_field != "function" && + name_field.find('.') == std::string::npos) { + name_field = format.function_field + "." + name_field; + args_field = format.function_field + "." + args_field; + } + + auto tools_parser = p.standard_json_tools( + format.section_start, format.section_end, inputs.tools, inputs.parallel_tool_calls, + inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED, name_field, args_field, format.tools_array_wrapped, + format.fun_name_is_key, format.id_field, format.gen_id_field, format.parameter_order); + + // Handle content wrappers if present + if (ctx.content && ctx.content->is_always_wrapped()) { + auto wrapped_content = ctx.content->build_optional_wrapped(ctx); + return ctx.reasoning_parser + wrapped_content + tools_parser + p.end(); + } + + std::string tool_start = "{"; + if (!format.section_start.empty()) { + tool_start = format.section_start; + } else if (!format.per_call_start.empty()) { + tool_start = format.per_call_start; + } + + return ctx.reasoning_parser + (force_tools ? p.eps() : p.optional(p.content(p.until(tool_start)))) + tools_parser + + p.end(); +} + +common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context & ctx) const { + auto & p = ctx.p; + const auto & inputs = ctx.inputs; + bool force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED; + + common_peg_parser tool_choice = p.choice(); + + foreach_function(inputs.tools, [&](const json & tool) { + const auto & func = tool.at("function"); + std::string name = func.at("name"); + const auto & schema = func.at("parameters"); + + // Build call_id parser based on position (if supported) + common_peg_parser call_id_section = p.eps(); + if (call_id.pos == call_id_position::BETWEEN_FUNC_AND_ARGS && !call_id.prefix.empty() && + !call_id.suffix.empty()) { + call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(call_id.suffix))) + call_id.suffix; + } + + auto func_parser = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) + + call_id_section + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)); + if (!function.close.empty()) { + func_parser = func_parser + function.close; + } + tool_choice |= p.rule("tool-" + name, func_parser); + }); + + auto require_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED; + + common_peg_parser tool_calls = p.eps(); + + if (!format.per_call_start.empty()) { + auto wrapped_call = format.per_call_start + tool_choice + format.per_call_end; + if (inputs.parallel_tool_calls) { + tool_calls = p.trigger_rule("tool-call", wrapped_call + p.zero_or_more(p.space() + wrapped_call)); + } else { + tool_calls = p.trigger_rule("tool-call", wrapped_call); + } + if (!format.section_start.empty()) { + tool_calls = p.trigger_rule("tool-calls", + p.literal(format.section_start) + p.space() + tool_calls + p.space() + + (format.section_end.empty() ? p.end() : p.literal(format.section_end))); + } + } else { + std::string separator = ", "; // Default + if (inputs.parallel_tool_calls) { + tool_calls = p.trigger_rule("tool-call", format.section_start + tool_choice + + p.zero_or_more(separator + tool_choice) + format.section_end); + } else { + tool_calls = p.trigger_rule("tool-call", format.section_start + tool_choice + format.section_end); + } + } + + if (!require_calls) { + tool_calls = p.optional(tool_calls); + } + + std::string trigger_marker = !format.section_start.empty() ? format.section_start : format.per_call_start; + auto content_before_tools = trigger_marker.empty() ? p.eps() : p.until(trigger_marker); + return ctx.reasoning_parser + (force_tools ? p.eps() : p.optional(p.content(content_before_tools))) + tool_calls + + p.end(); +} + +common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_context & ctx) const { + auto & p = ctx.p; + const auto & inputs = ctx.inputs; + bool force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED; + + common_peg_parser tool_choice = p.choice(); + + foreach_function(inputs.tools, [&](const json & tool) { + const auto & func = tool.at("function"); + std::string name = func.at("name"); + const auto & params = func.at("parameters"); + + if (!params.contains("properties") || !params.at("properties").is_object()) { + return; + } + + const auto & properties = params.at("properties"); + std::set required; + if (params.contains("required") && params.at("required").is_array()) { + params.at("required").get_to(required); + } + + // Build parser for each argument, separating required and optional + std::vector required_parsers; + std::vector optional_parsers; + for (const auto & [param_name, param_schema] : properties.items()) { + bool is_required = required.find(param_name) != required.end(); + std::string type = "object"; + auto type_obj = param_schema.contains("type") ? param_schema.at("type") : json::object(); + if (type_obj.is_string()) { + type_obj.get_to(type); + } else if (type_obj.is_object()) { + if (type_obj.contains("type") && type_obj.at("type").is_string()) { + type_obj.at("type").get_to(type); + } + } + + auto arg = p.tool_arg( + p.tool_arg_open(arguments.name_prefix + p.tool_arg_name(p.literal(param_name)) + + arguments.name_suffix) + + arguments.value_prefix + + (type == "string" ? p.tool_arg_string_value(p.schema(p.until(arguments.value_suffix), + "tool-" + name + "-arg-" + param_name + "-schema", + param_schema, true)) : + p.tool_arg_json_value(p.schema( + p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, format.uses_python_dicts)) + + p.space()) + + p.tool_arg_close(p.literal(arguments.value_suffix))); + + auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg); + if (is_required) { + required_parsers.push_back(named_arg); + } else { + optional_parsers.push_back(named_arg); + } + } + + // Build required arg sequence in definition order + common_peg_parser args_seq = p.eps(); + for (size_t i = 0; i < required_parsers.size(); i++) { + if (i > 0) { + args_seq = args_seq + p.space(); + } + args_seq = args_seq + required_parsers[i]; + } + + // Build optional args with flexible ordering + if (!optional_parsers.empty()) { + common_peg_parser any_opt = p.choice(); + for (const auto & opt : optional_parsers) { + any_opt |= opt; + } + args_seq = args_seq + p.repeat(p.space() + any_opt, 0, (int) optional_parsers.size()); + } + + // Build call_id parser based on position (if supported) + common_peg_parser call_id_section = p.eps(); + bool have_call_id = false; + if (call_id.pos == call_id_position::BETWEEN_FUNC_AND_ARGS && !call_id.prefix.empty() && + !call_id.suffix.empty()) { + have_call_id = true; + call_id_section = p.optional(call_id.prefix + p.tool_id(p.until(call_id.suffix)) + call_id.suffix); + } + + bool matched_atomic = false; + common_peg_parser func_parser = p.eps(); + if (!function.name_suffix.empty()) { + func_parser = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) + + call_id_section + p.space() + args_seq; + matched_atomic = true; + } else if (have_call_id) { + func_parser = p.atomic(p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) + + call_id_section) + p.space() + args_seq; + matched_atomic = true; + } else if (!arguments.name_prefix.empty() && properties.size() > 0) { + func_parser = p.atomic(p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) + + call_id_section + p.space() + p.peek(p.literal(arguments.name_prefix))) + args_seq; + matched_atomic = true; + } else { + func_parser = p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) + + call_id_section + p.space() + args_seq; + } + + if (!function.close.empty()) { + func_parser = func_parser + p.space() + p.tool_close(p.literal(function.close)); + } else if (!format.per_call_end.empty()) { + // When there's no func_close but there is a per_call_end marker, use peek() to ensure + // we only emit tool_close when we can actually see the closing marker. This prevents + // premature closing during partial parsing when we've seen e.g. "" (end) or "" prefix that failed to match. + func_parser = func_parser + p.tool_close(p.peek(p.literal(format.per_call_end))); + } else { + func_parser = + func_parser + p.tool_close(p.space()); // force this to process tool closing callbacks in mapper + } + if (!matched_atomic) { + func_parser = p.atomic(func_parser); + } + + tool_choice |= p.rule("tool-" + name, func_parser); + }); + + auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED; + + common_peg_parser tool_calls = p.eps(); + + if (!format.per_call_start.empty()) { + auto wrapped_call = format.per_call_start + p.space() + tool_choice + p.space() + format.per_call_end; + if (inputs.parallel_tool_calls) { + tool_calls = p.trigger_rule("tool-call", wrapped_call + p.zero_or_more(p.space() + wrapped_call)); + } else { + tool_calls = p.trigger_rule("tool-call", wrapped_call); + } + if (!format.section_start.empty()) { + tool_calls = p.trigger_rule("tool-calls", + p.literal(format.section_start) + p.space() + tool_calls + p.space() + + (format.section_end.empty() ? p.end() : p.literal(format.section_end))); + } + } else { + std::string separator = ", "; // Default + + if (inputs.parallel_tool_calls) { + tool_calls = p.trigger_rule("tool-call", format.section_start + p.space() + tool_choice + + p.zero_or_more(separator + tool_choice) + p.space() + + format.section_end); + } else { + tool_calls = p.trigger_rule( + "tool-call", format.section_start + p.space() + tool_choice + p.space() + format.section_end); + } + } + + if (!require_tools) { + tool_calls = p.optional(tool_calls); + } + + std::string trigger_marker = !format.section_start.empty() ? format.section_start : format.per_call_start; + auto content_before_tools = trigger_marker.empty() ? p.eps() : p.until(trigger_marker); + return ctx.reasoning_parser + (force_tools ? p.eps() : p.optional(p.content(content_before_tools))) + tool_calls + + p.end(); +} + +} // namespace autoparser diff --git a/common/chat-auto-parser-helpers.cpp b/common/chat-auto-parser-helpers.cpp new file mode 100644 index 0000000000..787d7bab98 --- /dev/null +++ b/common/chat-auto-parser-helpers.cpp @@ -0,0 +1,347 @@ +#include "chat-auto-parser-helpers.h" + +#include "chat-auto-parser.h" +#include "chat.h" +#include "log.h" +#include "nlohmann/json.hpp" + +#include +#include + +using json = nlohmann::ordered_json; + +std::string trim_whitespace(const std::string & str) { + size_t start = 0; + while (start < str.length() && std::isspace(static_cast(str[start]))) { + start++; + } + + if (start == str.length()) { + return ""; + } + + size_t end = str.length() - 1; + while (end > start && std::isspace(static_cast(str[end]))) { + end--; + } + + return str.substr(start, end - start + 1); +} + +std::string trim_leading_whitespace(const std::string & str) { + size_t start = 0; + while (start < str.length() && std::isspace(static_cast(str[start]))) { + start++; + } + + return str.substr(start); +} + +std::string trim_trailing_whitespace(const std::string & str) { + if (str.empty()) { + return ""; + } + + size_t end = str.length() - 1; + while (end > 0 && std::isspace(static_cast(str[end]))) { + end--; + } + + // If first char is also whitespace, return empty string + if (end == 0 && std::isspace(static_cast(str[0]))) { + return ""; + } + + return str.substr(0, end + 1); +} + +std::string trim_trailing_newlines(const std::string & str) { + size_t end = str.length(); + while (end > 0 && str[end - 1] == '\n') { + end--; + } + + return str.substr(0, end); +} + +static size_t common_prefix_len(const std::string & left, const std::string & right) { + size_t prefix_len = 0; + size_t min_len = std::min(left.length(), right.length()); + while (prefix_len < min_len && left[prefix_len] == right[prefix_len]) { + prefix_len++; + } + return prefix_len; +} + +static size_t common_suffix_len(const std::string & left, const std::string & right) { + size_t suffix_len = 0; + size_t min_len = std::min(left.length(), right.length()); + while (suffix_len < min_len && left[left.length() - 1 - suffix_len] == right[right.length() - 1 - suffix_len]) { + suffix_len++; + } + return suffix_len; +} + +diff_split calculate_diff_split(const std::string & left, const std::string & right) { + diff_split result; + + auto left_seg = segmentize_markers(left); + auto right_seg = segmentize_markers(right); + + if (left_seg.empty()) { + result.right = right; + return result; + } + if (right_seg.empty()) { + result.left = left; + return result; + } + + auto left_start = left_seg.begin(); + auto left_end = --left_seg.end(); + auto right_start = right_seg.begin(); + auto right_end = --right_seg.end(); + + auto test = [&] () { + return left_start != left_end && right_start != right_end; + }; + + bool left_fully_consumed = false; + bool right_fully_consumed = false; + + while (test()) { + bool advanced = false; + if (*left_start == *right_start) { + result.prefix.append(left_start->value); + left_start++; + right_start++; + advanced = true; + } + if (*left_end == *right_end) { + result.suffix = left_end->value + result.suffix; + if (left_start != left_end) { + left_end--; + } else { + left_fully_consumed = true; + } + if (right_start != right_end) { + right_end--; + } else { + right_fully_consumed = true; + } + advanced = true; + } + if (!advanced) { + break; + } + } + + if (left_start == left_end && right_start != right_end) { + if (*left_start == *right_end) { + result.suffix = right_end->value + result.suffix; + right_end--; + left_fully_consumed = true; + } else if (*left_start == *right_start) { + result.prefix.append(right_start->value); + right_start++; + left_fully_consumed = true; + } + } else if (right_start == right_end && left_start != left_end) { + if (*left_end == *right_start) { + result.suffix = left_end->value + result.suffix; + left_end--; + right_fully_consumed = true; + } else if (*left_start == *right_start) { + result.prefix.append(left_start->value); + left_start++; + right_fully_consumed = true; + } + } else if (left_start == left_end && right_start == right_end && *left_start == *right_start && left_start->type == segment_type::MARKER) { + result.prefix.append(right_start->value); + left_fully_consumed = true; + right_fully_consumed = true; + } + + auto eat_segment = [](std::string str, const segment & seg) -> std::string { return std::move(str) + seg.value; }; + + bool can_have_text_suffix = left_end->type == segment_type::TEXT && right_end->type == segment_type::TEXT; + bool can_have_text_prefix = right_start->type == segment_type::TEXT && left_start->type == segment_type::TEXT; + + std::string remainder_left = std::accumulate(left_start, left_fully_consumed ? left_end : ++left_end, std::string(), eat_segment); + std::string remainder_right = std::accumulate(right_start, right_fully_consumed ? right_end : ++right_end, std::string(), eat_segment); + + size_t suffix_len = can_have_text_suffix ? common_suffix_len(remainder_left, remainder_right) : 0; + // avoid overlaps between prefix and suffix + size_t prefix_len = can_have_text_prefix ? common_prefix_len(remainder_left.substr(0, remainder_left.size() - suffix_len), + remainder_right.substr(0, remainder_right.size() - suffix_len)) : 0; + + result.prefix.append(remainder_left.substr(0, prefix_len)); + result.suffix = remainder_left.substr(remainder_left.length() - suffix_len, suffix_len) + result.suffix; + result.left = remainder_left.substr(prefix_len, remainder_left.length() - prefix_len - suffix_len); + result.right = remainder_right.substr(prefix_len, remainder_right.length() - prefix_len - suffix_len); + + if (result.left == "" && result.right == "") { + // degenerate case, no diff + result.prefix = left; + result.suffix = ""; + // pick prefix = all as representation + } + return result; +} + +// Returns the prefix of `full` up until the first occurrence of the common prefix of `left` and `right` +std::string until_common_prefix(const std::string & full, const std::string & left, const std::string & right) { + // Find the common prefix of left and right + size_t common_prefix_len = 0; + size_t min_len = std::min(left.length(), right.length()); + while (common_prefix_len < min_len && left[common_prefix_len] == right[common_prefix_len]) { + common_prefix_len++; + } + + // If there's no common prefix, return empty string + if (common_prefix_len == 0) { + return ""; + } + + // Find the common prefix in the full string + std::string common_prefix = left.substr(0, common_prefix_len); + size_t pos = full.find(common_prefix); + + // If not found, return empty string + if (pos == std::string::npos) { + return ""; + } + + // Return everything before the common prefix + return full.substr(0, pos); +} + +// Returns the suffix of `full` after the last occurrence of the common suffix of `left` and `right` +std::string after_common_suffix(const std::string & full, const std::string & left, const std::string & right) { + // Find the common suffix of left and right (compare from the end) + size_t common_suffix_len = 0; + size_t min_len = std::min(left.length(), right.length()); + while (common_suffix_len < min_len && + left[left.length() - 1 - common_suffix_len] == right[right.length() - 1 - common_suffix_len]) { + common_suffix_len++; + } + + // If there's no common suffix, return empty string + if (common_suffix_len == 0) { + return ""; + } + + // Extract the common suffix + std::string common_suffix = left.substr(left.length() - common_suffix_len); + + // Find the last occurrence of the common suffix in the full string + size_t pos = full.rfind(common_suffix); + + // If not found, return empty string + if (pos == std::string::npos) { + return ""; + } + + // Return everything after the common suffix + return full.substr(pos + common_suffix_len); +} + +// TODO: segmentize will treat a JSON array inside tags as a tag: [{ "fun": { ... } }] will be three markers +// not too worried about that because it hasn't turned out as a problem anywhere, but noting here in case it will +// Might have to put some restrictions on tag contents as well (like "no { }") +std::vector segmentize_markers(const std::string & text) { + std::vector retval; + bool in_marker = false; + char marker_opener = '\0'; + + auto is_marker_opener = [](char c) -> bool { return c == '<' || c == '['; }; + auto is_marker_closer = [](char op, char c) -> bool { return (op == '<' && c == '>') || (op == '[' && c == ']'); }; + + size_t last_border = 0; + + for (size_t cur_pos = 0; cur_pos < text.length(); cur_pos++) { + if (!in_marker && is_marker_opener(text[cur_pos])) { + if (last_border < cur_pos) { + retval.push_back(segment(segment_type::TEXT, text.substr(last_border, cur_pos - last_border))); + } + last_border = cur_pos; + in_marker = true; + marker_opener = text[cur_pos]; + } else if (in_marker && is_marker_closer(marker_opener, text[cur_pos])) { + // no need to check because last_border will always be smaller + retval.push_back(segment(segment_type::MARKER, text.substr(last_border, cur_pos - last_border + 1))); + last_border = cur_pos + 1; + in_marker = false; + marker_opener = '\0'; + } + } + if (last_border < text.length()) { + retval.push_back(segment(segment_type::TEXT, text.substr(last_border))); + } + return retval; +} + +std::vector prune_whitespace_segments(const std::vector & segments) { + std::vector result; + for (const auto & seg : segments) { + if (!trim_whitespace(seg.value).empty()) { + result.push_back(seg); + } + } + return result; +} + +namespace autoparser { + +std::string apply_template(const common_chat_template & tmpl, const template_params & params) { + templates_params tmpl_params; + tmpl_params.messages = params.messages; + tmpl_params.tools = params.tools; + tmpl_params.add_generation_prompt = params.add_generation_prompt; + tmpl_params.enable_thinking = params.enable_thinking; + + if (params.extra_context) { + tmpl_params.extra_context = *params.extra_context; + } + tmpl_params.extra_context["enable_thinking"] = params.enable_thinking; + + try { + return common_chat_template_direct_apply(tmpl, tmpl_params); + } catch (const std::exception & e) { + LOG_DBG("Template application failed: %s\n", e.what()); + return ""; + } +} + +std::optional compare_variants( + const common_chat_template & tmpl, + const template_params & params_A, + const std::function & params_modifier) { + // Create variant B by copying A + template_params params_B = params_A; + + // Apply modifier to create variant B + if (params_modifier) { + params_modifier(params_B); + } + + // Apply template to both variants + std::string output_A = apply_template(tmpl, params_A); + std::string output_B = apply_template(tmpl, params_B); + + // Check for template application failures + if (output_A.empty() || output_B.empty()) { + return std::nullopt; + } + + // Calculate diff and return result with both outputs + compare_variants_result result; + result.diff = calculate_diff_split(output_A, output_B); + result.output_A = output_A; + result.output_B = output_B; + + return result; +} + +} // namespace autoparser + diff --git a/common/chat-auto-parser-helpers.h b/common/chat-auto-parser-helpers.h new file mode 100644 index 0000000000..6e3df79db8 --- /dev/null +++ b/common/chat-auto-parser-helpers.h @@ -0,0 +1,73 @@ +#pragma once + +#include "chat-auto-parser.h" +#include +#include +#include + +std::string trim_whitespace(const std::string & str); +std::string trim_leading_whitespace(const std::string & str); +std::string trim_trailing_whitespace(const std::string & str); +std::string trim_trailing_newlines(const std::string & str); + +// calculate a diff split (longest common prefix, longest common suffix excluding prefix, +// mismatched part on the left, mismatched part on the right) between two strings +// account for markers - align prefix and suffix endings so that they end on markers +// * eg.: +// calculate_diff_split("
", "

Something

") -> +// { "prefix": "" (not: "<"), "suffix": "", "left": "
", "right": "

Something

" } +// calculate_diff_split("Something", "") -> +// { "prefix": "", "suffix": "", "left": "Something", "right": "" } +diff_split calculate_diff_split(const std::string & left, const std::string & right); + +// Returns the prefix of `full` up until the first occurrence of the common prefix of `left` and `right` +// Returns empty string if there's no common prefix +// * eg.: +// until_common_prefix("really want a FUNCTION call", "FUNCTION alpha", "FUNCTION beta") -> "really want a " +// until_common_prefix("", "", "") -> "" +// until_common_prefix("some text", "1234", "abcd") -> "" +// until_common_prefix("one arg two args three args four", "argument alpha", "argument beta") -> "one "" +std::string until_common_prefix(const std::string & full, const std::string & left, const std::string & right); + +// Returns the suffix of `full` after the last occurrence of the common suffix of `left` and `right` +// Returns empty string if there's no common suffix +// Mirror function of `until_common_prefix` +// * eg.: +// after_common_suffix("really want a FUNCTION call", "first FUNCTION", "second FUNCTION") -> " call" +// after_common_suffix("one arg two-args three args four", "alpha-args", "beta-args") -> " three args four" +std::string after_common_suffix(const std::string & full, const std::string & left, const std::string & right); + +// Segmentize text into markers and non-marker fragments +// * eg.: +// segmentize_markers("The site title
Here's some content
" -> +// [ (MARKER, ""), (MARKER, ""), (MARKER, ""), (TEXT, "The site title"), (MARKER, ""), +// (MARKER, ""), (MARKER, "
"), (TEXT, "Here's some "), (MARKER, ""), (TEXT, "content"), (MARKER, ""), +// (MARKER, "
"), (MARKER, ""), (MARKER, "") +// ] +// segmentize_markers("<|tool_call|>[args]{ are here }[/args]<|tool_call_end|>") -> +// [ (MARKER, "<|tool_call|>"), (MARKER, "[args]"), (TEXT, "{ are here }"), (MARKER, "[/args]"), (MARKER, "<|tool_call_end|>") ] +std::vector segmentize_markers(const std::string & text); + +// Prune whitespace-only segments from a vector of segments +// * eg.: +// segmentize_markers("\n\n\n \n\n\n") -> +// X = [ (MARKER, ""), (TEXT, "\n"), (MARKER, ""), (TEXT, "\n"), (MARKER, ""), (TEXT, "\n \n"), +// (MARKER, ""), (TEXT, "\n"), (MARKER, ""), (TEXT, "\n"), (MARKER, "") ] +// prune_whitespace_segments(X) -> [ (MARKER, ""), (MARKER, ""), (MARKER, ""), (MARKER, ""), +// (MARKER, ""), (MARKER, "") ] +std::vector prune_whitespace_segments(const std::vector & segments); + +namespace autoparser { + +// Apply a template with the given parameters, returning the rendered string (empty on failure) +std::string apply_template(const common_chat_template & tmpl, const template_params & params); + +// Factorized differential comparison function +// Takes base params and a single modifier lambda to create variant B +// Returns compare_variants_result containing diff and both outputs, or std::nullopt on failure +std::optional compare_variants( + const common_chat_template & tmpl, + const template_params & params_A, + const std::function & params_modifier); + +} // namespace autoparser diff --git a/common/chat-auto-parser.h b/common/chat-auto-parser.h new file mode 100644 index 0000000000..52c6488f4b --- /dev/null +++ b/common/chat-auto-parser.h @@ -0,0 +1,433 @@ +#pragma once + +#include "chat.h" +#include "common.h" +#include "jinja/caps.h" +#include "peg-parser.h" + +#include +#include +#include +#include +#include + +using json = nlohmann::ordered_json; + +class common_chat_peg_builder; + +// ============================================================================ +// Parameters for template application (low-level, used by diff analysis) +// ============================================================================ +struct template_params { + json messages; + json tools; + bool add_generation_prompt = false; + bool enable_thinking = true; + std::optional extra_context = std::nullopt; +}; + +struct diff_split { + std::string prefix; + std::string suffix; + std::string left; + std::string right; + + bool operator==(struct diff_split & other) const { + return prefix == other.prefix && suffix == other.suffix && left == other.left && right == other.right; + } +}; + +// Result of compare_variants containing diff and original outputs +struct compare_variants_result { + diff_split diff; + std::string output_A; + std::string output_B; +}; + +namespace autoparser { + +// ============================================================================ +// High-level params for parser generation +// ============================================================================ + +struct templates_params { + json messages; + json tools; + common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO; + json json_schema; + bool parallel_tool_calls = true; + common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO; + bool stream = true; + std::string grammar; + bool add_generation_prompt = false; + bool enable_thinking = true; + std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); + json extra_context; + bool add_bos = false; + bool add_eos = false; + bool is_inference = true; + bool add_inference = false; + bool mark_input = true; // whether to mark input strings in the jinja context +}; + +// ============================================================================ +// Analysis Result Enums +// ============================================================================ + +// Reasoning handling mode (derived from R1-R3 comparisons) +enum class reasoning_mode { + NONE, // No reasoning markers detected + TAG_BASED, // Standard tag-based: ... + DELIMITER, // Delimiter-based: [BEGIN FINAL RESPONSE] (reasoning ends at delimiter) + FORCED_OPEN, // Template ends with open reasoning tag (empty start, non-empty end) + FORCED_CLOSED, // Template ends with open reasoning tag on enabled thinking but + // with both opened and closed tag for disabled thinking + TOOLS_ONLY // Only reason on tool calls, not on normal content +}; + +inline std::ostream & operator<<(std::ostream & os, const reasoning_mode & mode) { + switch (mode) { + case reasoning_mode::NONE: + return os << "NONE"; + case reasoning_mode::TAG_BASED: + return os << "TAG_BASED"; + case reasoning_mode::DELIMITER: + return os << "DELIMITER"; + case reasoning_mode::FORCED_OPEN: + return os << "FORCED_OPEN"; + case reasoning_mode::FORCED_CLOSED: + return os << "FORCED_CLOSED"; + case reasoning_mode::TOOLS_ONLY: + return os << "TOOLS_ONLY"; + default: + return os << "UNKNOWN"; + } +} + +// Content wrapping mode (derived from C1 comparison) +enum class content_mode { + PLAIN, // No content markers + ALWAYS_WRAPPED, // Content always wrapped with markers + WRAPPED_WITH_REASONING, // Content wrapped only when reasoning present +}; + +inline std::ostream & operator<<(std::ostream & os, const content_mode & mode) { + switch (mode) { + case content_mode::PLAIN: + return os << "PLAIN"; + case content_mode::ALWAYS_WRAPPED: + return os << "ALWAYS_WRAPPED"; + case content_mode::WRAPPED_WITH_REASONING: + return os << "WRAPPED_WITH_REASONING"; + default: + return os << "UNKNOWN"; + } +} + +// Call ID position in tool calls (for non-JSON formats) +enum class call_id_position { + NONE, // No call ID support detected + PRE_FUNC_NAME, // Call ID before function name: [CALL_ID]id[FUNC]name{args} + BETWEEN_FUNC_AND_ARGS, // Call ID between function and args: [FUNC]name[CALL_ID]id{args} + POST_ARGS, // Call ID after arguments: [FUNC]name{args}[CALL_ID]id +}; + +inline std::ostream & operator<<(std::ostream & os, const call_id_position & pos) { + switch (pos) { + case call_id_position::NONE: + return os << "NONE"; + case call_id_position::PRE_FUNC_NAME: + return os << "PRE_FUNC_NAME"; + case call_id_position::BETWEEN_FUNC_AND_ARGS: + return os << "BETWEEN_FUNC_AND_ARGS"; + case call_id_position::POST_ARGS: + return os << "POST_ARGS"; + default: + return os << "UNKNOWN"; + } +} + +// Tool call format classification (derived from T1-T5, A1-A3 comparisons) +enum class tool_format { + NONE, // No tool support detected + JSON_NATIVE, // Pure JSON: {"name": "X", "arguments": {...}} + TAG_WITH_JSON, // Tag-based with JSON args: {...} + TAG_WITH_TAGGED, // Tag-based with tagged args: value +}; + +inline std::ostream & operator<<(std::ostream & os, const tool_format & format) { + switch (format) { + case tool_format::NONE: + return os << "NONE"; + case tool_format::JSON_NATIVE: + return os << "JSON_NATIVE"; + case tool_format::TAG_WITH_JSON: + return os << "TAG_WITH_JSON"; + case tool_format::TAG_WITH_TAGGED: + return os << "TAG_WITH_TAGGED"; + default: + return os << "UNKNOWN"; + } +} + +// ============================================================================ +// Sub-structs for tool analysis +// ============================================================================ + +struct tool_format_analysis { + tool_format mode = tool_format::NONE; + + std::string section_start; // e.g., "", "[TOOL_CALLS]", "" + std::string section_end; // e.g., "", "" + std::string per_call_start; // e.g., "<|tool_call_begin|>", "" (for multi-call templates) + std::string per_call_end; // e.g., "<|tool_call_end|>", "" + + bool fun_name_is_key = false; // In JSON format function name is JSON key, i.e. { "": { ... arguments ... } } + bool tools_array_wrapped = false; // Tool calls wrapped in JSON array [...] + bool uses_python_dicts = false; // Tool call args use Python dict format (single-quoted strings) + + std::string function_field = "function"; + std::string name_field = "name"; + std::string args_field = "arguments"; + std::string id_field; + std::string gen_id_field; + std::vector parameter_order; +}; + +struct tool_function_analysis { + std::string name_prefix; // e.g., "", "\"", ":0" + std::string close; // e.g., "", "" (for tag-based) +}; + +struct tool_arguments_analysis { + std::string start; // e.g., "<|tool_call_argument_begin|>", "" + std::string end; // e.g., "<|tool_call_argument_end|>", "" + std::string name_prefix; // e.g., "", "\"" + std::string name_suffix; // e.g., ">", "", "\":" + std::string value_prefix; // e.g., "", "", "" + std::string value_suffix; // e.g., "", "", "" + std::string separator; // e.g., "", "\n", "," +}; + +struct tool_id_analysis { + call_id_position pos = call_id_position::NONE; + + std::string prefix; // e.g., "[CALL_ID]" (marker before call ID value) + std::string suffix; // e.g., "" (marker after call ID value, before next section) +}; + +// ============================================================================ +// Parser build context (shared interface for build_parser methods) +// ============================================================================ + +struct analyze_content; + +struct parser_build_context { + common_chat_peg_builder & p; + const templates_params & inputs; + common_peg_parser reasoning_parser; + bool extracting_reasoning = false; + const analyze_content * content = nullptr; + + parser_build_context(common_chat_peg_builder & p, const templates_params & inputs); +}; + +// ============================================================================ +// Base class for analyzers with parser building +// ============================================================================ + +struct analyze_base { + virtual ~analyze_base() = default; + virtual common_peg_parser build_parser(parser_build_context & ctx) const = 0; + + protected: + const common_chat_template * tmpl = nullptr; + + analyze_base() = default; + explicit analyze_base(const common_chat_template & tmpl) : tmpl(&tmpl) {} +}; + +// ============================================================================ +// Reasoning analyzer +// ============================================================================ + +struct analyze_reasoning : analyze_base { + reasoning_mode mode = reasoning_mode::NONE; + + std::string start; // e.g., "", "[THINK]", "<|START_THINKING|>", "" + std::string end; // e.g., "", "[BEGIN FINAL RESPONSE]", "<|END_THINKING|>" + + analyze_reasoning() = default; + analyze_reasoning(const common_chat_template & tmpl, bool supports_tools); + + common_peg_parser build_parser(parser_build_context & ctx) const override; + + private: + // Look for reasoning markers in rendered content + void compare_reasoning_presence(); + + // Compare generation prompt with enable_thinking=true vs false + void compare_thinking_enabled(); + + // Check if reasoning is always possible or only in tool calls + void compare_reasoning_scope(); +}; + +// ============================================================================ +// Content analyzer +// ============================================================================ + +struct analyze_content : analyze_base { + content_mode mode = content_mode::PLAIN; + + std::string start; // e.g., "", ">>>all\n", "" + std::string end; // e.g., "", "" + + bool requires_nonnull_content = false; + + analyze_content() = default; + analyze_content(const common_chat_template & tmpl, const analyze_reasoning & reasoning); + + common_peg_parser build_parser(parser_build_context & ctx) const override; + + bool is_always_wrapped() const; + common_peg_parser build_optional_wrapped(parser_build_context & ctx) const; +}; + +// ============================================================================ +// Tool analyzer +// ============================================================================ + +struct analyze_tools : analyze_base { + tool_format_analysis format; + tool_function_analysis function; + tool_arguments_analysis arguments; + tool_id_analysis call_id; + + analyze_tools() = default; + analyze_tools(const common_chat_template & tmpl, + const jinja::caps & caps, + const analyze_reasoning & reasoning); + + common_peg_parser build_parser(parser_build_context & ctx) const override; + + private: + // Extract tool calling 'haystack' for further analysis and delegate further analysis based on format + void analyze_tool_calls(const analyze_reasoning & reasoning); + + // Analyze format based on position of function and argument name in needle + void analyze_tool_call_format(const std::string & haystack, + const std::string & fun_name_needle, + const std::string & arg_name_needle, + const analyze_reasoning & reasoning); + + // Analyze specifics of JSON native format (entire tool call is a JSON object) + void analyze_tool_call_format_json_native(const std::string & clean_haystack, + const std::string & fun_name_needle, + const std::string & arg_name_needle); + + // Analyze specifics of non-JSON native format (tags for function name or for function name and arguments) + void analyze_tool_call_format_non_json(const std::string & clean_haystack, + const std::string & fun_name_needle); + + // Check for and extract specific per-call markers for non-native-JSON templates with parallel call support + void check_per_call_markers(); + + // Extract function name markers + void extract_function_markers(); + + // Delegates to separate functions for: separator analysis, argument name analysis, argument value analysis + void analyze_arguments(); + + // Extract argument name markers + void extract_argument_name_markers(); + + // Extract argument value markers + void extract_argument_value_markers(); + + // Extract argument separator, if specified (eg. ......) + void extract_argument_separator(); + + // Extract argument wrapper markers, if present (eg. '......') + void extract_args_markers(); + + // Extract call ID markers, if present + void extract_call_id_markers(); + + // Per-format tool parser builders + common_peg_parser build_tool_parser_json_native(parser_build_context & ctx) const; + common_peg_parser build_tool_parser_tag_json(parser_build_context & ctx) const; + common_peg_parser build_tool_parser_tag_tagged(parser_build_context & ctx) const; +}; + +// ============================================================================ +// Main autoparser class +// ============================================================================ + +struct autoparser { + jinja::caps jinja_caps; + analyze_reasoning reasoning; + analyze_content content; + analyze_tools tools; + bool analysis_complete = false; + + // Preserved tokens for tokenizer (union of all non-empty markers) + std::vector preserved_tokens; + + autoparser() = default; + + // Run full differential analysis on a template + void analyze_template(const common_chat_template & tmpl); + + // Build the PEG parser for this template + common_peg_arena build_parser(const templates_params & inputs) const; + + private: + // Collect tokens from entire analysis to preserve + void collect_preserved_tokens(); +}; + +// ============================================================================ +// Parser generator +// ============================================================================ + +class peg_generator { + public: + static common_chat_params generate_parser(const common_chat_template & tmpl, + const struct templates_params & inputs); + + static common_chat_params generate_parser(const common_chat_template & tmpl, + const struct templates_params & inputs, + const autoparser & autoparser); +}; + +} // namespace autoparser + +enum segment_type { TEXT, MARKER }; + +inline std::ostream & operator<<(std::ostream & os, const segment_type & type) { + switch (type) { + case segment_type::TEXT: + return os << "TEXT"; + case segment_type::MARKER: + return os << "MARKER"; + default: + return os << "UNKNOWN"; + } +} + +struct segment { + segment_type type; + std::string value; + + segment(segment_type type, std::string value) : type(type), value(std::move(value)) {} + + bool operator==(const segment & other) const { + return type == other.type && value == other.value; + } + + bool operator!=(const segment & other) const { + return !(*this == other); + } +}; diff --git a/common/chat-diff-analyzer.cpp b/common/chat-diff-analyzer.cpp new file mode 100644 index 0000000000..05b3b6b6a8 --- /dev/null +++ b/common/chat-diff-analyzer.cpp @@ -0,0 +1,1333 @@ +#include "chat-auto-parser.h" +#include "chat-auto-parser-helpers.h" +#include "chat-peg-parser.h" +#include "chat.h" +#include "log.h" +#include "nlohmann/json.hpp" +#include "peg-parser.h" + +#include + +#define ANSI_RESET "\033[0m" +#define ANSI_PURPLE "\033[1m\x1b[38;5;126m" +#define ANSI_ORANGE "\033[1m\x1b[38;5;214m" +#define ANSI_RED "\033[1m\x1b[38;5;196m" + +using json = nlohmann::ordered_json; + +namespace autoparser { + +static const std::string FUN_FIRST = "FFF_FIRST_FUN_F"; +static const std::string FUN_SECOND = "SSS_SECOND_FUN_S"; +static const std::string ARG_FIRST = "AA_ARG_FST_AA"; +static const std::string ARG_SECOND = "BB_ARG_SND_BB"; +static const std::string USER_MSG = "U_USER_MSG Hello END_U"; +static const std::string ASSISTANT_MSG = "A_ASST_MSG I can help END_A"; +static const std::string THINKING_CONTENT = "REASON_PART I am thinking END_R"; + +static std::vector> workarounds( + { // Old reasoning Qwen templates - they don't really display reasoning content, but we still want to + // support reasoning on them + [](const common_chat_template & tmpl, autoparser & analysis) -> void { + if (tmpl.src.find("content.split('')") != std::string::npos && + tmpl.src.find("reasoning_content") == std::string::npos && + analysis.reasoning.mode == reasoning_mode::NONE) { + analysis.reasoning.mode = reasoning_mode::FORCED_OPEN; + analysis.reasoning.start = ""; + analysis.reasoning.end = ""; + analysis.preserved_tokens.push_back(""); + analysis.preserved_tokens.push_back(""); + LOG_DBG(ANSI_ORANGE "[Patch: old Qwen/Deepseek thinking template]\n" ANSI_RESET); + } + }, + // Granite 3.3, with separate reasoning and content markers + [](const common_chat_template & tmpl, autoparser & analysis) -> void { + if (tmpl.src.find("Write your thoughts between and write your response between " + "") != std::string::npos) { + analysis.reasoning.mode = reasoning_mode::TAG_BASED; + analysis.reasoning.start = ""; + analysis.reasoning.end = ""; + analysis.preserved_tokens.push_back(""); + analysis.preserved_tokens.push_back(""); + analysis.content.mode = content_mode::WRAPPED_WITH_REASONING; + analysis.content.start = ""; + analysis.content.end = ""; + analysis.preserved_tokens.push_back(""); + analysis.preserved_tokens.push_back(""); + LOG_DBG(ANSI_ORANGE "[Patch: Granite 3.3]\n" ANSI_RESET); + } + }, + // Cohere Command R+ - content wrapped in <|CHATBOT_TOKEN|>...<|END_OF_TURN_TOKEN|> + [](const common_chat_template & tmpl, autoparser & analysis) -> void { + if (tmpl.src.find("<|CHATBOT_TOKEN|>") != std::string::npos && + tmpl.src.find("<|END_OF_TURN_TOKEN|>") != std::string::npos && analysis.content.start.empty()) { + analysis.content.mode = content_mode::ALWAYS_WRAPPED; + analysis.content.start = "<|CHATBOT_TOKEN|>"; + analysis.content.end = "<|END_OF_TURN_TOKEN|>"; + analysis.preserved_tokens.push_back("<|CHATBOT_TOKEN|>"); + analysis.preserved_tokens.push_back("<|END_OF_TURN_TOKEN|>"); + LOG_DBG(ANSI_ORANGE "[Patch: Cohere Command R+]\n" ANSI_RESET); + } + }, + // Functionary - no tool call section delimiter + [](const common_chat_template & tmpl, autoparser & analysis) -> void { + if (tmpl.src.find("set has_code_interpreter = tools | selectattr(\"type\", \"equalto\", " + "\"code_interpreter\") | list | length > 0") != std::string::npos) { + analysis.content.mode = content_mode::PLAIN; + analysis.content.end = ""; + analysis.tools.function.name_prefix = ""; + analysis.tools.format.section_start = ""; + analysis.tools.format.section_end = ""; + analysis.tools.format.per_call_start = ""); + analysis.preserved_tokens.push_back("<|eom_id|>"); + analysis.preserved_tokens.push_back(""); + analysis.preserved_tokens.push_back(""); + LOG_DBG(ANSI_ORANGE "[Patch: Functionary 3.1]\n" ANSI_RESET); + } + }, + // DeepSeek-R1-Distill-Qwen + [](const common_chat_template & tmpl, autoparser & analysis) -> void { + if (tmpl.src.find( + "{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>'") != + std::string::npos) { + analysis.tools.format.section_start = "<｜tool▁calls▁begin｜>"; + analysis.tools.format.section_end = "<｜tool▁calls▁end｜>"; + analysis.tools.format.per_call_start = "<｜tool▁call▁begin｜>function"; + analysis.tools.function.name_prefix = "<｜tool▁sep｜>"; + analysis.tools.format.per_call_end = "<｜tool▁call▁end｜>"; + analysis.tools.function.close = "```"; + } + } + }); + +// Common JSON structures +static json params_schema = { + { "type", "object" }, + { "properties", + { { ARG_FIRST, { { "type", "string" }, { "description", "First argument" } } }, + { ARG_SECOND, { { "type", "string" }, { "description", "Second argument" } } } } }, + { "required", json::array({}) } +}; + +static json tools = json::array({ + { { "type", "function" }, + { "function", + json{ { "name", FUN_FIRST }, { "description", "Test function foo" }, { "parameters", params_schema } } } }, + { { "type", "function" }, + { "function", + json{ { "name", FUN_SECOND }, { "description", "Test function bar" }, { "parameters", params_schema } } } } +}); + +static json user_msg = json{ + { "role", "user" }, + { "content", USER_MSG } +}; + +static json build_tool_call(const std::string & name, const json & args, const std::string & id = "call00001") { + return json{ + { "id", id }, + { "type", "function" }, + { "function", json{ { "name", name }, { "arguments", args } } } + }; +} + +static json first_tool_call_zero_args = build_tool_call(FUN_FIRST, json::object(), "call00001"); +static json first_tool_call_one_arg = build_tool_call(FUN_FIRST, {{ ARG_FIRST, "XXXX" }}, "call00001"); +static json first_tool_call_one_arg_other_val = build_tool_call(FUN_FIRST, {{ ARG_FIRST, "YYYY" }}, "call00001"); +static json first_tool_call_other_arg = build_tool_call(FUN_FIRST, {{ ARG_SECOND, "YYYY" }}, "call00001"); + +static json first_tool_call = + build_tool_call(FUN_FIRST, json{{ ARG_FIRST, "XXXX" }, { ARG_SECOND, "YYYY" }}, "call00001"); +static json second_tool_call = + build_tool_call(FUN_SECOND, json{ { ARG_FIRST, "XXXX" }, { ARG_SECOND, "YYYY" }}, "call00002"); +static json first_tool_call_alt_id = + build_tool_call(FUN_FIRST, json{{ ARG_FIRST, "XXXX" }, { ARG_SECOND, "YYYY" }}, "call99999"); + +template +static std::string mode_to_str(T mode) { + std::ostringstream os; + os << mode; + return os.str(); +} + +void autoparser::analyze_template(const common_chat_template & tmpl) { + jinja_caps = tmpl.original_caps(); + reasoning = analyze_reasoning(tmpl, jinja_caps.supports_tool_calls); + content = analyze_content(tmpl, reasoning); + tools = analyze_tools(jinja_caps.supports_tool_calls ? analyze_tools(tmpl, jinja_caps, reasoning) : analyze_tools()); + collect_preserved_tokens(); + + for (auto & workaround : workarounds) { + workaround(tmpl, *this); + } + + LOG_DBG("\n--- Reasoning & Content Structure ---\n"); + LOG_DBG("reasoning_mode: %s\n", mode_to_str(reasoning.mode).c_str()); + LOG_DBG("reasoning_start: '%s'\n", reasoning.start.c_str()); + LOG_DBG("reasoning_end: '%s'\n", reasoning.end.c_str()); + LOG_DBG("content_mode: %s\n", mode_to_str(content.mode).c_str()); + LOG_DBG("content_start: '%s'\n", content.start.c_str()); + LOG_DBG("content_end: '%s'\n", content.end.c_str()); + + LOG_DBG("\n--- Tool Call Structure ---\n"); + LOG_DBG("tool_mode: %s\n", mode_to_str(tools.format.mode).c_str()); + LOG_DBG("supports_tools: %s\n", jinja_caps.supports_tools ? "true" : "false"); + LOG_DBG("supports_parallel_calls: %s\n", jinja_caps.supports_parallel_tool_calls ? "true" : "false"); + LOG_DBG("tool_section_start: '%s'\n", tools.format.section_start.c_str()); + LOG_DBG("tool_section_end: '%s'\n", tools.format.section_end.c_str()); + LOG_DBG("per_call_start: '%s'\n", tools.format.per_call_start.c_str()); + LOG_DBG("per_call_end: '%s'\n", tools.format.per_call_end.c_str()); + LOG_DBG("func_name_prefix: '%s'\n", tools.function.name_prefix.c_str()); + LOG_DBG("func_name_suffix: '%s'\n", tools.function.name_suffix.c_str()); + LOG_DBG("func_close: '%s'\n", tools.function.close.c_str()); + LOG_DBG("python_dict_format: %s\n", tools.format.uses_python_dicts ? "true" : "false"); + LOG_DBG("arg_name_prefix: '%s'\n", tools.arguments.name_prefix.c_str()); + LOG_DBG("arg_name_suffix: '%s'\n", tools.arguments.name_suffix.c_str()); + LOG_DBG("arg_value_prefix: '%s'\n", tools.arguments.value_prefix.c_str()); + LOG_DBG("arg_value_suffix: '%s'\n", tools.arguments.value_suffix.c_str()); + LOG_DBG("name_field: '%s'\n", tools.format.name_field.c_str()); + LOG_DBG("args_field: '%s'\n", tools.format.args_field.c_str()); + LOG_DBG("id_field: '%s'\n", tools.format.id_field.c_str()); + LOG_DBG("gen_id_field: '%s'\n", tools.format.gen_id_field.c_str()); + LOG_DBG("parameter_order: '%s'\n", std::accumulate(tools.format.parameter_order.begin(), tools.format.parameter_order.end(), + std::string(""), [] (const std::string & a, const std::string & b) { return a.empty() ? b : a + ", " + b; } + ).c_str()); + + LOG_DBG(ANSI_PURPLE "=== Differential analysis complete ===\n" ANSI_RESET); + analysis_complete = true; +} + +void autoparser::collect_preserved_tokens() { + auto add_token = [this](const std::string & org_token) { + std::string token = trim_whitespace(org_token); + if (!token.empty()) { + // Avoid duplicates + if (std::find(preserved_tokens.begin(), preserved_tokens.end(), token) == preserved_tokens.end()) { + preserved_tokens.push_back(token); + } + } + }; + + add_token(reasoning.start); + add_token(reasoning.end); + add_token(content.start); + add_token(content.end); + add_token(tools.format.section_start); + add_token(tools.format.section_end); + add_token(tools.format.per_call_start); + add_token(tools.format.per_call_end); + add_token(tools.function.name_prefix); + add_token(tools.function.name_suffix); + add_token(tools.function.close); + add_token(tools.arguments.start); + add_token(tools.arguments.end); + add_token(tools.arguments.name_prefix); + add_token(tools.arguments.name_suffix); + add_token(tools.arguments.separator); + add_token(tools.arguments.value_prefix); + add_token(tools.arguments.value_suffix); + add_token(tools.call_id.prefix); + add_token(tools.call_id.suffix); +} + +analyze_reasoning::analyze_reasoning(const common_chat_template & tmpl, bool supports_tools) + : analyze_base(tmpl) { + LOG_DBG(ANSI_PURPLE "=== Starting differential analysis ===\n" ANSI_RESET); + LOG_DBG(ANSI_ORANGE "Phase 1: Reasoning analysis\n" ANSI_RESET); + + compare_reasoning_presence(); + compare_thinking_enabled(); + if (supports_tools) { + compare_reasoning_scope(); + } +} + +void analyze_reasoning::compare_reasoning_presence() { + json user_msg = json{ + { "role", "user" }, + { "content", USER_MSG } + }; + + json assistant_no_reasoning = json{ + { "role", "assistant" }, + { "content", ASSISTANT_MSG } + }; + + json assistant_with_reasoning = json{ + { "role", "assistant" }, + { "content", ASSISTANT_MSG }, + { "reasoning_content", THINKING_CONTENT } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_no_reasoning }); + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto comparison = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_with_reasoning }); }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed, skipping reasoning detection\n" ANSI_RESET, __func__); + return; + } + + const auto & diff = comparison->diff; + + const std::string reasoning_content = THINKING_CONTENT; + + if (!diff.right.empty() && diff.right.find(reasoning_content) != std::string::npos) { + auto parser_delimiter = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.literal(reasoning_content) + p.space() + p.optional(p.tag("post", (p.marker() + p.space())) + p.rest()); + }); + auto parser_wrapped = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("pre", p.marker()) + p.space() + p.literal(reasoning_content) + p.space() + p.tag("post", (p.marker() + p.space())) + p.rest(); + }); + // try the more aggressive parse first, if it fails, fall back to the delimiter one + auto result = parser_wrapped.parse_anywhere_and_extract(comparison->output_B); + if (!result.result.success()) { + result = parser_delimiter.parse_anywhere_and_extract(comparison->output_B); + } + if (result.result.success()) { + if (!result.tags["pre"].empty() && !result.tags["post"].empty()) { + if (parser_wrapped.parse_anywhere_and_extract(diff.right).result.success()) { // both tags in the diff = no forced close + mode = reasoning_mode::TAG_BASED; + } else { + mode = reasoning_mode::FORCED_CLOSED; + } + start = trim_whitespace(result.tags["pre"]); + end = result.tags["post"]; + } else if (!result.tags["post"].empty()) { + mode = reasoning_mode::DELIMITER; + end = result.tags["post"]; + } + } + } +} + +void analyze_reasoning::compare_thinking_enabled() { + json user_msg = json{ + { "role", "user" }, + { "content", USER_MSG } + }; + + template_params params; + params.messages = json::array({ user_msg }); + params.add_generation_prompt = true; + params.enable_thinking = false; + + auto comparison = compare_variants(*tmpl, params, [&](template_params & p) { p.enable_thinking = true; }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET , __func__); + return; + } + + const auto & diff = comparison->diff; + + std::string left_trimmed = trim_whitespace(diff.left); + + if (left_trimmed.empty() && !diff.right.empty()) { + std::string right_trimmed = trim_whitespace(diff.right); + + if (!right_trimmed.empty() && string_ends_with(comparison->output_B, right_trimmed)) { + if (start.empty()) { + start = right_trimmed; + mode = reasoning_mode::FORCED_OPEN; + } + } + } + + if (start.empty() && !end.empty()) { + mode = reasoning_mode::DELIMITER; + } + + // Check for FORCED_CLOSED: when enable_thinking=false produces both start and end markers, + // but enable_thinking=true produces only the start marker + if (!comparison->output_A.empty() && !comparison->output_B.empty()) { + auto parser_start = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.literal(start) + p.space() + p.literal(end) + p.rest(); + }); + auto parser_start_end = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("pre", p.literal(start)) + p.space() + p.negate(p.literal(end)) + p.rest(); + }); + if (!start.empty() && parser_start_end.parse_anywhere_and_extract(comparison->output_A).result.success() && + parser_start.parse_anywhere_and_extract(comparison->output_B).result.success()) { + mode = reasoning_mode::FORCED_CLOSED; + } else if (!end.empty()) { // we extract the starting marker now since we didn't get it earlier + auto result = parser_start_end.parse_anywhere_and_extract(comparison->output_A); + if (result.result.success()) { + start = result.tags["pre"]; + mode = reasoning_mode::FORCED_CLOSED; + } + } + } + + if (start.empty() && end.empty()) { // we might still have the case of "just open" and "just close" + if (!diff.left.empty() && !diff.right.empty()) { + auto seg_A = segmentize_markers(trim_trailing_whitespace(diff.left)); + auto seg_B = segmentize_markers(trim_trailing_whitespace(diff.right)); + if (seg_A.size() == 1 && seg_B.size() == 1) { + mode = reasoning_mode::FORCED_CLOSED; + start = seg_B[0].value; + end = seg_A[0].value; + } + } + } +} + +void analyze_reasoning::compare_reasoning_scope() { + json assistant_reasoning_content = json{ + { "role", "assistant" }, + { "content", ASSISTANT_MSG }, + { "reasoning_content", THINKING_CONTENT } + }; + + json assistant_reasoning_tools = json{ + { "role", "assistant" }, + { "content", nullptr }, + { "reasoning_content", THINKING_CONTENT }, + { "tool_calls", + json::array({ build_tool_call(FUN_FIRST, json{ { ARG_FIRST, "VVVV" }, { ARG_SECOND, "XXXX" } }) }) } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_reasoning_content }); + params.tools = tools; + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto comparison = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_reasoning_tools }); }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__); + return; + } + + std::string reasoning_content = THINKING_CONTENT; + + // Check if reasoning only appears in variant B (with tools) + bool reasoning_in_A = comparison->output_A.find(reasoning_content) != std::string::npos; + bool reasoning_in_B = comparison->output_B.find(reasoning_content) != std::string::npos; + + if (!reasoning_in_A && reasoning_in_B) { + mode = reasoning_mode::TOOLS_ONLY; + LOG_DBG(ANSI_ORANGE "%s: Detected TOOLS_ONLY reasoning mode\n" ANSI_RESET, __func__); + + auto parser_wrapped = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("pre", p.marker()) + p.space() + p.literal(reasoning_content) + p.space() + p.tag("post", (p.marker() + p.space())); + }); + auto result = parser_wrapped.parse_anywhere_and_extract(comparison->output_B); + if (result.result.success()) { + start = result.tags["pre"]; + end = result.tags["post"]; + } else { + auto parser_delimiter = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.literal(reasoning_content) + p.space() + p.optional(p.tag("post", (p.marker() + p.space()))); + }); + result = parser_delimiter.parse_anywhere_and_extract(comparison->output_B); + if (result.result.success()) { + end = result.tags["post"]; + } else { + LOG_DBG(ANSI_ORANGE "%s: Unable to extracft reasoning markers, falling back to reasoning = NONE\n" ANSI_RESET, __func__); + mode = reasoning_mode::NONE; + } + } + } +} + +analyze_content::analyze_content(const common_chat_template & tmpl, const analyze_reasoning & reasoning) + : analyze_base(tmpl) { + LOG_DBG(ANSI_ORANGE "Phase 2: Content analysis\n" ANSI_RESET); + + json assistant_content_only = json{ + { "role", "assistant" }, + { "content", ASSISTANT_MSG } + }; + + json assistant_with_tools = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ build_tool_call("test_func", json{ { "arg1", "value1" } }) }) } + }; + + json assistant_with_reasoning = json{ + { "role", "assistant" }, + { "content", "" }, + { "reasoning_content", THINKING_CONTENT } + }; + + template_params params_content_only; + params_content_only.messages = json::array({ user_msg, assistant_content_only }); + params_content_only.add_generation_prompt = false; + params_content_only.enable_thinking = true; + params_content_only.tools = tools; + + auto comparison_with_tools = compare_variants(tmpl, params_content_only, [&](template_params & p) { + p.messages = json::array({ user_msg, assistant_with_tools }); + }); + + auto comparison_with_reasoning = compare_variants(tmpl, params_content_only, [&](template_params & p) { + p.messages = json::array({ user_msg, assistant_with_reasoning }); + }); + + if (!comparison_with_tools || !comparison_with_reasoning) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__); + return; + } + + const auto & diff_tools = comparison_with_tools->diff; + const auto & diff_reasoning = comparison_with_reasoning->diff; + + std::string response = ASSISTANT_MSG; + + bool found_plain_content = false; + if (trim_whitespace(diff_tools.left) == response) { + auto parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + return p.space() + diff_reasoning.left + p.space() + p.optional(p.marker()) + p.space() + p.end(); + }); + if (parser.parse_and_extract(diff_reasoning.left).result.success()) { + // We only have the content text in the diff (possibly with a stray EOG marker), so no markers + mode = content_mode::PLAIN; + found_plain_content = true; + } else if (reasoning.mode != reasoning_mode::NONE && !reasoning.end.empty()) { + auto post_reasoning_parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + return p.literal(reasoning.end) + p.space() + p.literal(response); + }); + if (post_reasoning_parser.parse_anywhere_and_extract(diff_reasoning.left).result.success()) { + mode = content_mode::PLAIN; + found_plain_content = true; + } + } + } + if (!found_plain_content) { + std::string rdiff = diff_reasoning.left; + if (!reasoning.end.empty() && rdiff.find(reasoning.end) != std::string::npos) { + rdiff = rdiff.substr(rdiff.find(reasoning.end) + reasoning.end.length()); + } + // Take the more promising diff + std::string pure_content = rdiff.length() > diff_tools.left.length() ? rdiff : diff_tools.left; + auto parser_wrapped = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("pre", p.marker()) + p.space() + p.literal(response) + p.space() + p.tag("post", (p.marker() + p.space())) + p.rest(); + }); + auto result = parser_wrapped.parse_anywhere_and_extract(pure_content); + start = result.tags["pre"]; + end = result.tags["post"]; + // TODO: WRAPPED_WITH_REASONING + } + + // Determine content mode + if (!start.empty() || !end.empty()) { + mode = content_mode::ALWAYS_WRAPPED; + // TODO: END_DELIMITED content mode - delimited at end but not at start? + } +} + +bool analyze_content::is_always_wrapped() const { + return mode == content_mode::ALWAYS_WRAPPED && !start.empty() && !end.empty(); +} + +analyze_tools::analyze_tools(const common_chat_template & tmpl, + const jinja::caps & caps, + const analyze_reasoning & reasoning) + : analyze_base(tmpl) { + LOG_DBG(ANSI_ORANGE "Phase 3: Tool call analysis\n" ANSI_RESET); + + analyze_tool_calls(reasoning); + + if (format.mode != tool_format::NONE && format.mode != tool_format::JSON_NATIVE) { + if (caps.supports_parallel_tool_calls) { + check_per_call_markers(); + } + extract_function_markers(); + if (format.mode == tool_format::TAG_WITH_TAGGED) { + analyze_arguments(); + } + extract_argument_separator(); + extract_args_markers(); + extract_call_id_markers(); + } +} + +void analyze_tools::analyze_tool_calls(const analyze_reasoning & reasoning) { + json assistant_no_tools = json{ + { "role", "assistant" }, + { "content", ASSISTANT_MSG } + }; + + json assistant_with_tools = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call }) } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_no_tools }); + params.tools = tools; + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto comparison = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_with_tools }); }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__); + return; + } + + const auto & diff = comparison->diff; + + std::string tool_section = diff.right; + + if (tool_section.empty()) { + return; + } + + analyze_tool_call_format(tool_section, FUN_FIRST, ARG_FIRST, reasoning); +} + +void analyze_tools::analyze_tool_call_format(const std::string & haystack, + const std::string & fun_name_needle, + const std::string & arg_name_needle, + const analyze_reasoning & reasoning) { + if (fun_name_needle.empty() || arg_name_needle.empty() || haystack.empty()) { + return; + } + + enum class json_quote_style { NONE, DOUBLE_QUOTES, SINGLE_QUOTES }; + + auto in_json_haystack = [&haystack](const std::string & needle) -> json_quote_style { + auto parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.choice({ p.literal("{"), p.literal(":") }) << p.choice({ + p.tag("sq", p.literal("'") + p.literal(needle) + p.literal("'")), + p.tag("dq", p.literal("\"") + p.literal(needle) + p.literal("\"")) }); + }); + auto result = parser.parse_anywhere_and_extract(haystack); + if (!result.result.success()) { + return json_quote_style::NONE; + } + return result.tags.count("sq") && !result.tags["sq"].empty() + ? json_quote_style::SINGLE_QUOTES + : json_quote_style::DOUBLE_QUOTES; + }; + + auto fun_quote = in_json_haystack(fun_name_needle); + auto arg_quote = in_json_haystack(arg_name_needle); + + if (fun_quote != json_quote_style::NONE) { + // no need to check further, we're in JSON land + format.mode = tool_format::JSON_NATIVE; + format.uses_python_dicts = (fun_quote == json_quote_style::SINGLE_QUOTES); + } else if (arg_quote != json_quote_style::NONE) { + format.mode = tool_format::TAG_WITH_JSON; + format.uses_python_dicts = (arg_quote == json_quote_style::SINGLE_QUOTES); + } else { + format.mode = tool_format::TAG_WITH_TAGGED; + } + + // first, remove any reasoning markers + std::string clean_haystack = haystack; + if (!reasoning.start.empty()) { + auto pos = haystack.find(reasoning.start); + if (pos != std::string::npos) { + clean_haystack = haystack.substr(0, pos) + haystack.substr(pos + reasoning.start.length()); + } + } + if (!reasoning.end.empty()) { + auto pos = clean_haystack.find(reasoning.end); + if (pos != std::string::npos) { + clean_haystack = clean_haystack.substr(0, pos) + clean_haystack.substr(pos + reasoning.end.length()); + } + } + + if (format.mode == tool_format::JSON_NATIVE) { + analyze_tool_call_format_json_native(clean_haystack, fun_name_needle, arg_name_needle); + } else { + analyze_tool_call_format_non_json(clean_haystack, fun_name_needle); + } + // always relax whitespace requirements on ending markers since they don't influence content + format.section_end = trim_whitespace(format.section_end); + format.per_call_end = trim_whitespace(format.per_call_end); +} + +void analyze_tools::analyze_tool_call_format_json_native(const std::string & clean_haystack, + const std::string & fun_name_needle, + const std::string & arg_name_needle) { + // we might not have the typical OpenAI tool calling structure + int json_start = clean_haystack.find_first_of('{'); + int json_end = clean_haystack.find_last_of('}'); + std::string cut = clean_haystack.substr(json_start, json_end - json_start + 1); + json call_struct = json::parse(cut); + auto register_field = [&](const std::string & prefix, const nlohmann::detail::iteration_proxy_value & subel) { + if (subel.value().is_string() && std::string(subel.value()).find("call0000") != std::string::npos) { + format.id_field = !prefix.empty() ? prefix + "." + subel.key() : subel.key(); + } else if (subel.value().is_string() && std::string(subel.value()) == fun_name_needle) { + format.name_field = !prefix.empty() ? prefix + "." + subel.key() : subel.key(); + } else if (subel.value().dump().find(arg_name_needle) != + std::string::npos) { // handle both string and JSON obj variants + format.args_field = !prefix.empty() ? prefix + "." + subel.key() : subel.key(); + } else if (subel.key().find("id") != std::string::npos) { + // heuristics for generated id field + format.gen_id_field = !prefix.empty() ? prefix + "." + subel.key() : subel.key(); + } + }; + for (const auto & el : call_struct.items()) { + if (el.key() == fun_name_needle) { + format.fun_name_is_key = true; + // When function name is the key, there's no name field and args are direct + format.name_field.clear(); + format.args_field.clear(); + // Don't register this element - the function name IS the key, not a field + } else { + if (el.value().is_object() && + el.value().dump().find(arg_name_needle) == std::string::npos) { // not the args object + format.function_field = el.key(); + for (const auto & subel : el.value().items()) { + register_field(el.key(), subel); + } + } + // Register this element as a potential field + register_field("", el); + } + } + auto array_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("pre", p.literal("[") + p.space()) + p.literal(cut) + p.tag("post", p.space() + p.literal("]")); + }); + + auto ar_parse_res = array_parser.parse_anywhere_and_extract(clean_haystack); + if (ar_parse_res.result.success()) { + format.tools_array_wrapped = true; + json_start -= ar_parse_res.tags["pre"].length(); + json_end += ar_parse_res.tags["post"].length(); + } + json_end++; // we want to move past the closing char for end marker extraction + + std::vector> located_params; + if (!format.name_field.empty()) { + located_params.push_back({ clean_haystack.find(format.name_field), format.name_field }); + } + if (!format.args_field.empty()) { + located_params.push_back({ clean_haystack.find(format.args_field), format.args_field }); + } + if (!format.id_field.empty()) { + located_params.push_back({ clean_haystack.find(format.id_field), format.id_field }); + } + if (!format.gen_id_field.empty()) { + located_params.push_back({ clean_haystack.find(format.gen_id_field), format.gen_id_field }); + } + std::sort(located_params.begin(), located_params.end()); + for (auto & pair : located_params) { + format.parameter_order.push_back(pair.second); + } + // we can immediately extract tool calling markers too + format.section_start = trim_leading_whitespace(clean_haystack.substr(0, json_start)); + format.section_end = trim_whitespace(clean_haystack.substr(json_end)); + // When tools_array_wrapped is true, the closing bracket is part of the array structure, + // not a separate section end marker. Clear tool_section_end to avoid duplicate brackets. + if (format.tools_array_wrapped && format.section_end == "]") { + format.section_end.clear(); + } +} + +void analyze_tools::analyze_tool_call_format_non_json(const std::string & clean_haystack, + const std::string & fun_name_needle) { + // first, let's find out if the function is inside a tag or standalone + auto fun_marker_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("fun_marker", p.choice({ + p.tag("fun_pre", p.literal("<") + p.until_one_of({ ">", fun_name_needle })) + p.literal(fun_name_needle) + + p.tag("fun_post", p.negate(p.space() + p.literal("<")) + p.until(">") + p.literal(">")) + p.space(), + p.tag("fun_pre", p.literal("[") + p.until_one_of({ "]", fun_name_needle })) + p.literal(fun_name_needle) + + p.tag("fun_post", p.negate(p.space() + p.literal("[") + p.until("]") + p.literal("]")) + p.space()) })); + }); + auto fun_res = fun_marker_parser.parse_anywhere_and_extract(clean_haystack); + std::string fun_marker = fun_name_needle; + if (fun_res.result.success()) { + fun_marker = fun_res.tags["fun_marker"]; + } + // now, consume up to two markers, then treat everything up to the function marker as function name prefix + auto per_tool_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("sec_start", p.marker() + p.space()) + p.tag("call_start", p.marker() + p.space()) + + p.tag("fun_pre", p.until(fun_marker)) + fun_marker + p.tag("rest", p.rest()); + }); + auto section_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("sec_start", p.marker() + p.space()) + fun_marker + p.tag("rest", p.rest()); + }); + auto result = per_tool_parser.parse_anywhere_and_extract(clean_haystack); + tagged_parse_result result_end; + if (result.result.success()) { + auto double_closer_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("call_end", p.marker() + p.space()) + p.tag("sec_end", p.marker() + p.space()) + p.end(); + }); + result_end = double_closer_parser.parse_anywhere_and_extract(result.tags["rest"]); + function.name_prefix = fun_res.tags["fun_pre"] + function.name_prefix; + } else { + result = section_parser.parse_anywhere_and_extract(clean_haystack); + auto single_closer_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("sec_end", p.marker() + p.space()) + p.end(); + }); + result_end = single_closer_parser.parse_anywhere_and_extract(result.tags["rest"]); + } + format.per_call_start = result.tags["call_start"]; + format.per_call_end = result_end.tags["call_end"]; + format.section_start = result.tags["sec_start"]; + format.section_end = result_end.tags["sec_end"]; +} + +void analyze_tools::check_per_call_markers() { + json assistant_one_tool = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call }) } + }; + + json assistant_two_tools = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call, second_tool_call }) } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_one_tool }); + params.tools = tools; + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto one_vs_two = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_two_tools }); }); + + if (!one_vs_two) { + LOG_DBG(ANSI_ORANGE "%s: Generating double tool call comparison failed\n" ANSI_RESET, __func__); + return; + } + + diff_split filter_common_call_part = calculate_diff_split(one_vs_two->diff.suffix, one_vs_two->diff.right); + + std::string second_tool_content = trim_leading_whitespace(filter_common_call_part.right); + if (!format.section_start.empty() && + second_tool_content.find(format.section_start) == 0) { + format.per_call_start = format.section_start; + format.per_call_end = format.section_end; + format.section_start.clear(); + format.section_end.clear(); + } +} + +void analyze_tools::extract_function_markers() { + json assistant_nocall = json{ + { "role", "assistant" }, + { "content", ASSISTANT_MSG }, + }; + + json assistant_foofoo = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call }) } + }; + + json assistant_barbar = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ second_tool_call }) } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_foofoo }); + params.tools = tools; + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto comparison = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_barbar }); }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__); + return; + } + + const auto & diff = comparison->diff; + + if (diff.left.find(FUN_FIRST) != std::string::npos && diff.right.find(FUN_SECOND) != std::string::npos) { + std::string prefix_marker; + if (!format.per_call_start.empty()) { + prefix_marker = format.per_call_start; + } else { + prefix_marker = format.section_start; + } + if (!prefix_marker.empty() && diff.prefix.rfind(prefix_marker) != std::string::npos) { + function.name_prefix = + diff.prefix.substr(diff.prefix.rfind(prefix_marker) + prefix_marker.size()); + } + + // Extract name prefix/suffix from diff.left (stop at the next marker boundary) + auto name_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("pre", p.until(FUN_FIRST)) + p.literal(FUN_FIRST) + + p.tag("post", p.zero_or_more(p.negate(p.marker()) + p.any())); + }); + auto name_result = name_parser.parse_and_extract(diff.left); + if (name_result.result.success()) { + function.name_prefix += name_result.tags["pre"]; + function.name_suffix = name_result.tags["post"]; + } + + // Extend name_suffix with content from diff.suffix before args begin + if (format.mode == tool_format::TAG_WITH_JSON) { + // For JSON: name_suffix extends to the first non-marker { or [, including any + // markers along the way. Only applies if there's at least one marker after + // the JSON content (matching the original "stop < seg_suf.size() - 1" guard). + auto suffix_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + auto non_json = p.marker() | (p.negate(p.literal("{")) + p.negate(p.literal("[")) + p.any()); + auto after_json = p.zero_or_more(p.negate(p.marker()) + p.any()) + p.marker(); + return p.tag("ext", p.zero_or_more(non_json)) + after_json; + }); + auto suf_result = suffix_parser.parse_and_extract(diff.suffix); + if (suf_result.result.success()) { + function.name_suffix += suf_result.tags["ext"]; + } + } else { + // For tagged: name_suffix extends to the first marker (arg marker) + auto suffix_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.tag("ext", p.zero_or_more(p.negate(p.marker()) + p.any())); + }); + auto suf_result = suffix_parser.parse_and_extract(diff.suffix); + if (suf_result.result.success()) { + function.name_suffix += suf_result.tags["ext"]; + } + } + + // Extract the closer (between last arg and call/section end marker) + std::string suffix_marker; + if (!format.per_call_end.empty()) { + suffix_marker = format.per_call_end; + } else { + suffix_marker = format.section_end; + } + std::string closer_suffix; + if (suffix_marker.empty()) { + // we'll have to rely on an extra diff with no-calls version + auto notool_comp = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_nocall }); }); + if (notool_comp) { + auto nt_diff = notool_comp->diff; + closer_suffix = nt_diff.left.substr(nt_diff.left.find("YYYY") + 4); + } + } else { + closer_suffix = diff.suffix.substr(0, diff.suffix.find(suffix_marker)); + } + if (!closer_suffix.empty()) { + if (format.mode == tool_format::TAG_WITH_TAGGED) { + // After last arg value, skip the closing arg marker, rest is closer + auto closer_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.until("YYYY") + p.literal("YYYY") + p.space() + + p.marker() + p.space() + + p.tag("close", p.rest()); + }); + auto close_result = closer_parser.parse_and_extract(closer_suffix); + if (close_result.result.success()) { + function.close = close_result.tags["close"]; + } + } else if (format.mode == tool_format::TAG_WITH_JSON) { + // After last arg value, find end of JSON args, rest is closer + auto closer_parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) { + return p.until("YYYY") + p.literal("YYYY") + p.tag("post_val", p.rest()); + }); + auto close_result = closer_parser.parse_and_extract(closer_suffix); + if (close_result.result.success()) { + const auto & post = close_result.tags["post_val"]; + size_t pos = post.find_last_of("}]"); + if (pos != std::string::npos && pos < post.size() - 1) { + function.close = trim_leading_whitespace(post.substr(pos + 1)); + } + } + } + } + function.close = trim_leading_whitespace(function.close); + } +} + +void analyze_tools::analyze_arguments() { + LOG_DBG(ANSI_ORANGE "Phase 4: Argument analysis\n" ANSI_RESET); + + extract_argument_name_markers(); + extract_argument_value_markers(); +} + +void analyze_tools::extract_argument_name_markers() { + json assistant_first_arg = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call_one_arg }) } + }; + + json assistant_second_arg = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call_other_arg }) } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_first_arg }); + params.tools = tools; + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto comparison = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_second_arg }); }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__); + return; + } + + const auto & diff = comparison->diff; + + if (!diff.left.empty() && !diff.right.empty()) { + // Parse both sides to find ARG_FIRST/ARG_SECOND and extract the surrounding structure + auto left_parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + return p.tag("pre", p.until(ARG_FIRST)) + p.literal(ARG_FIRST) + + p.tag("suffix", p.until_one_of({"\"", "X"})); + }); + auto right_parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + return p.tag("pre", p.until(ARG_SECOND)) + p.literal(ARG_SECOND) + + p.tag("suffix", p.until_one_of({"\"", "Y"})); + }); + auto left_result = left_parser.parse_anywhere_and_extract(diff.left); + auto right_result = right_parser.parse_anywhere_and_extract(diff.right); + + if (left_result.result.success() && right_result.result.success() && + !left_result.tags["pre"].empty() && + left_result.tags["pre"] == right_result.tags["pre"] && + left_result.tags["suffix"] == right_result.tags["suffix"]) { + // Name is inside a structure (e.g., JSON key): prefix is the shared wrapper + arguments.name_prefix = trim_whitespace(left_result.tags["pre"]); + arguments.name_suffix = trim_leading_whitespace(left_result.tags["suffix"]); + } else if (diff.left.substr(0, ARG_FIRST.length()) == ARG_FIRST && diff.right.substr(0, ARG_SECOND.length()) == ARG_SECOND) { + // Name is directly in the diff: prefix comes from last marker in diff.prefix + auto pre_parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + auto last_marker = p.marker() + p.zero_or_more(p.negate(p.marker()) + p.any()) + p.end(); + return p.zero_or_more(p.negate(last_marker) + p.any()) + p.tag("name_prefix", last_marker); + }); + auto pre_result = pre_parser.parse_and_extract(diff.prefix); + arguments.name_prefix = pre_result.result.success() + ? pre_result.tags["name_prefix"] : diff.prefix; + + // Suffix extends from after ARG_FIRST to the first marker (+ optional whitespace). + // The marker could be in diff.left itself or in diff.suffix, so we concatenate. + std::string after_first = diff.left.substr(ARG_FIRST.length()) + diff.suffix; + auto suffix_parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + return p.tag("suffix", p.zero_or_more(p.negate(p.marker()) + p.any()) + + p.marker() + p.space()); + }); + auto suf_result = suffix_parser.parse_anywhere_and_extract(after_first); + if (suf_result.result.success()) { + arguments.name_suffix = suf_result.tags["suffix"]; + } + } + } +} + +void analyze_tools::extract_argument_value_markers() { + json assistant_val_X = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call_one_arg }) } + }; + + json assistant_val_Y = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call_one_arg_other_val }) } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_val_X }); + params.tools = tools; + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto comparison = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_val_Y }); }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__); + return; + } + + const auto & diff = comparison->diff; + + if (diff.left == "XXXX" && diff.right == "YYYY") { + std::string arg_name_ending = ARG_FIRST + arguments.name_suffix; + std::string prefix = diff.prefix; + if (prefix.rfind(arg_name_ending) != std::string::npos) { + prefix = prefix.substr(prefix.rfind(arg_name_ending) + arg_name_ending.size()); + } + if (!prefix.empty()) { + // Find the last marker + any trailing non-marker text to end + auto prefix_parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + auto last_marker = p.marker() + p.zero_or_more(p.negate(p.marker()) + p.any()) + p.end(); + return p.zero_or_more(p.negate(last_marker) + p.any()) + p.tag("val_prefix", last_marker); + }); + auto pre_result = prefix_parser.parse_and_extract(prefix); + arguments.value_prefix = pre_result.result.success() ? pre_result.tags["val_prefix"] : prefix; + } + + std::string value_suffix = diff.suffix; + if (!function.close.empty()) { + size_t func_close_pos = value_suffix.find(function.close); + if (func_close_pos != std::string::npos) { + value_suffix = value_suffix.substr(0, func_close_pos); + } + } else if (!format.per_call_end.empty() || !format.section_end.empty()) { + std::string end_marker = + !format.per_call_end.empty() ? format.per_call_end : format.section_end; + size_t end_marker_pos = value_suffix.find(end_marker); + if (end_marker_pos != std::string::npos) { + value_suffix = value_suffix.substr(0, end_marker_pos); + } + } + value_suffix = trim_leading_whitespace(value_suffix); + if (!value_suffix.empty()) { + arguments.value_suffix = value_suffix; + } + } +} + +void analyze_tools::extract_argument_separator() { + json assistant_one_arg = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call_one_arg }) } + }; + + json assistant_two_args = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call }) } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_one_arg }); + params.tools = tools; + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto comparison = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_two_args }); }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__); + return; + } + + const auto & diff = comparison->diff; + + if (!diff.right.empty()) { + std::string separator = until_common_prefix(diff.right, ARG_FIRST, ARG_SECOND); + arguments.separator = separator; + } +} + +void analyze_tools::extract_args_markers() { + json assistant_no_args = json{ + { "role", "assistant"}, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call_zero_args }) } + }; + + json assistant_with_args = json{ + { "role", "assistant"}, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call_one_arg }) } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_no_args }); + params.tools = tools; + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto comparison = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_with_args }); }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__); + return; + } + + const auto & diff = comparison->diff; + + if (format.mode != tool_format::JSON_NATIVE) { + std::string prefix_marker = !format.section_start.empty() ? format.section_start : format.per_call_start; + std::string suffix_marker = !format.section_end.empty() ? format.section_end : format.per_call_end; + // these might happen earlier in the tools section as an example or somewhere else, so we need to find the closest ones + size_t prefix_pos = prefix_marker.empty() ? 0 : diff.prefix.rfind(prefix_marker); + size_t suffix_pos = suffix_marker.empty() ? diff.suffix.size() : diff.suffix.find(suffix_marker); + if (prefix_pos == std::string::npos) { + prefix_pos = 0; + } + if (suffix_pos == std::string::npos) { + suffix_pos = diff.suffix.size(); + } + std::string prefix_cut = diff.prefix.substr(prefix_pos + prefix_marker.size()); + std::string suffix_cut = diff.suffix.substr(0, suffix_pos); + std::string args_start = until_common_prefix(prefix_cut, "{}", "{\"first\":"); + std::string args_end = after_common_suffix(suffix_cut, "{}", "\"XXXX\"}"); + + if (!args_start.empty() || !args_end.empty()) { + size_t find_fun = args_start.find(FUN_FIRST); + if (find_fun != std::string::npos) { + args_start = args_start.substr(find_fun + FUN_FIRST.size(), args_start.size() - find_fun - FUN_FIRST.size()); + } + arguments.start = args_start; + arguments.end = args_end; + } + } +} + +void analyze_tools::extract_call_id_markers() { + json assistant_id1 = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call }) } + }; + + json assistant_id2 = json{ + { "role", "assistant" }, + { "content", "" }, + { "tool_calls", json::array({ first_tool_call_alt_id }) } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_id1 }); + params.tools = tools; + params.add_generation_prompt = false; + params.enable_thinking = true; + + auto comparison = compare_variants( + *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_id2 }); }); + + if (!comparison) { + LOG_DBG(ANSI_ORANGE "%s: Template application failed for call_id detection\n" ANSI_RESET, __func__); + return; + } + + const auto & diff = comparison->diff; + + if (diff.left.empty() && diff.right.empty()) { + return; + } + + std::string id_value_1 = "call00001"; + std::string id_value_2 = "call99999"; + + size_t common_id_prefix_len = 0; + for (size_t i = 0; i < std::min(id_value_1.length(), id_value_2.length()); i++) { + if (id_value_1[i] == id_value_2[i]) { + common_id_prefix_len++; + } else { + break; + } + } + std::string common_id_part = id_value_1.substr(0, common_id_prefix_len); + + // Check if the function name is in the prefix (normal case: BETWEEN_FUNC_AND_ARGS or POST_ARGS) + // or in the suffix (call_id is PRE_FUNC_NAME) + std::string func_name = FUN_FIRST; + size_t func_name_in_prefix = diff.prefix.rfind(func_name); + size_t func_name_in_suffix = diff.suffix.find(func_name); + + // Helper: find the last marker in a string (returns just the marker, not trailing text) + auto find_last_marker = [](const std::string & str) -> std::string { + auto parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + auto last = p.marker() + p.zero_or_more(p.negate(p.marker()) + p.any()) + p.end(); + return p.zero_or_more(p.negate(last) + p.any()) + p.tag("m", p.marker()); + }); + auto res = parser.parse_anywhere_and_extract(str); + return res.result.success() ? res.tags["m"] : ""; + }; + + // Helper: find the first marker in a string + auto find_first_marker = [](const std::string & str) -> std::string { + auto parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + return p.tag("m", p.marker()); + }); + auto res = parser.parse_anywhere_and_extract(str); + return res.result.success() ? res.tags["m"] : ""; + }; + + if (func_name_in_prefix != std::string::npos && func_name_in_suffix == std::string::npos) { + // Function name is only in prefix - call_id is BETWEEN_FUNC_AND_ARGS or POST_ARGS + // Check if args indicator "{" is in prefix or suffix + size_t args_in_prefix = diff.prefix.find('{', func_name_in_prefix); + size_t args_in_suffix = diff.suffix.find('{'); + + if (args_in_suffix != std::string::npos && + (args_in_prefix == std::string::npos || args_in_prefix > diff.prefix.length())) { + // Args are in suffix, so call_id is BETWEEN_FUNC_AND_ARGS + call_id.pos = call_id_position::BETWEEN_FUNC_AND_ARGS; + + // Find call_id_prefix: marker immediately preceding common_id_part (no intervening markers) + std::string after_func = diff.prefix.substr(func_name_in_prefix + func_name.length()); + auto id_prefix_parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + return p.tag("prefix", p.marker()) + + p.zero_or_more(p.negate(p.marker()) + p.negate(p.literal(common_id_part)) + p.any()) + + p.literal(common_id_part); + }); + auto id_res = id_prefix_parser.parse_anywhere_and_extract(after_func); + if (id_res.result.success()) { + call_id.prefix = id_res.tags["prefix"]; + } else { + // Fallback: use the last marker in after_func + call_id.prefix = find_last_marker(after_func); + } + + // Extract call_id_suffix: the first marker in the suffix before args "{" + auto suffix_parser = build_tagged_peg_parser([&](common_peg_parser_builder & p) { + return p.zero_or_more(p.negate(p.marker()) + p.negate(p.literal("{")) + p.any()) + + p.tag("suffix", p.marker()); + }); + auto suf_res = suffix_parser.parse_anywhere_and_extract(diff.suffix); + if (suf_res.result.success()) { + call_id.suffix = suf_res.tags["suffix"]; + } + } else if (args_in_prefix != std::string::npos) { + // Args are in prefix, so call_id is POST_ARGS + call_id.pos = call_id_position::POST_ARGS; + + // Extract last marker between args closing brace and the ID + std::string after_args = diff.prefix.substr(args_in_prefix); + size_t closing_brace = after_args.rfind('}'); + if (closing_brace != std::string::npos) { + std::string between_args_and_id = after_args.substr(closing_brace + 1); + call_id.prefix = find_last_marker(between_args_and_id); + } + + // call_id_suffix: first marker in diff.suffix + call_id.suffix = find_first_marker(diff.suffix); + } + } else if (func_name_in_suffix != std::string::npos && func_name_in_prefix == std::string::npos) { + // Function name is only in suffix - call_id is PRE_FUNC_NAME + call_id.pos = call_id_position::PRE_FUNC_NAME; + + // call_id_prefix: last marker in diff.prefix + call_id.prefix = find_last_marker(diff.prefix); + + // call_id_suffix: first marker in the portion of diff.suffix before func_name + std::string before_func = diff.suffix.substr(0, func_name_in_suffix); + call_id.suffix = find_first_marker(before_func); + } + + // When call_id is detected, per_call_end may have been incorrectly set to include + // the call_id_suffix and sample args. Clear it if it starts with call_id_suffix. + if (call_id.pos != call_id_position::NONE && !call_id.suffix.empty() && + format.per_call_end.find(call_id.suffix) == 0) { + format.per_call_end.clear(); + } +} + +} // namespace autoparser diff --git a/common/chat-parser-xml-toolcall.cpp b/common/chat-parser-xml-toolcall.cpp deleted file mode 100644 index ba359fdbf4..0000000000 --- a/common/chat-parser-xml-toolcall.cpp +++ /dev/null @@ -1,879 +0,0 @@ -#include "chat.h" -#include "chat-parser.h" -#include "common.h" -#include "json-partial.h" -#include "json-schema-to-grammar.h" -#include "log.h" -#include "regex-partial.h" - -using json = nlohmann::ordered_json; - -class xml_toolcall_syntax_exception : public std::runtime_error { - public: - xml_toolcall_syntax_exception(const std::string & message) : std::runtime_error(message) {} -}; - -template -inline void sort_uniq(std::vector &vec) { - std::sort(vec.begin(), vec.end()); - vec.erase(std::unique(vec.begin(), vec.end()), vec.end()); -} - -template -inline bool all_space(const T &str) { - return std::all_of(str.begin(), str.end(), [](unsigned char ch) { return std::isspace(ch); }); -} - -static size_t utf8_truncate_safe(const std::string_view s) { - size_t len = s.size(); - if (len == 0) return 0; - size_t i = len; - for (size_t back = 0; back < 4 && i > 0; ++back) { - --i; - unsigned char c = s[i]; - if ((c & 0x80) == 0) { - return len; - } else if ((c & 0xC0) == 0xC0) { - size_t expected_len = 0; - if ((c & 0xE0) == 0xC0) expected_len = 2; - else if ((c & 0xF0) == 0xE0) expected_len = 3; - else if ((c & 0xF8) == 0xF0) expected_len = 4; - else return i; - if (len - i >= expected_len) { - return len; - } else { - return i; - } - } - } - return len - std::min(len, size_t(3)); -} - -inline void utf8_truncate_safe_resize(std::string &s) { - s.resize(utf8_truncate_safe(s)); -} - -inline std::string_view utf8_truncate_safe_view(const std::string_view s) { - return s.substr(0, utf8_truncate_safe(s)); -} - -static std::optional try_find_2_literal_splited_by_spaces(common_chat_msg_parser & builder, const std::string & literal1, const std::string & literal2) { - if (literal1.size() == 0) return builder.try_find_literal(literal2); - const auto saved_pos = builder.pos(); - while (auto res = builder.try_find_literal(literal1)) { - builder.consume_spaces(); - const auto match_len = std::min(literal2.size(), builder.input().size() - builder.pos()); - if (builder.input().compare(builder.pos(), match_len, literal2, 0, match_len) == 0) { - if (res->prelude.size() != res->groups[0].begin - saved_pos) { - res->prelude = builder.str({saved_pos, res->groups[0].begin}); - } - builder.move_to(builder.pos() + match_len); - res->groups[0].end = builder.pos(); - GGML_ASSERT(res->groups[0].begin != res->groups[0].end); - return res; - } - builder.move_to(res->groups[0].begin + 1); - } - builder.move_to(saved_pos); - return std::nullopt; -} - -/** - * make a GBNF that accept any strings except those containing any of the forbidden strings. - */ -std::string make_gbnf_excluding(std::vector forbids) { - constexpr auto charclass_escape = [](unsigned char c) -> std::string { - if (c == '\\' || c == ']' || c == '^' || c == '-') { - std::string s = "\\"; - s.push_back((char)c); - return s; - } - if (isprint(c)) { - return std::string(1, (char)c); - } - char buf[16]; - snprintf(buf, 15, "\\x%02X", c); - return std::string(buf); - }; - constexpr auto build_expr = [charclass_escape](auto self, const std::vector& forbids, int l, int r, int depth) -> std::string { - std::vector>> children; - int i = l; - while (i < r) { - const std::string &s = forbids[i]; - if ((int)s.size() == depth) { - ++i; - continue; - } - unsigned char c = (unsigned char)s[depth]; - int j = i; - while (j < r && (int)forbids[j].size() > depth && - (unsigned char)forbids[j][depth] == c) { - ++j; - } - children.push_back({c, {i, j}}); - i = j; - } - std::vector alts; - if (!children.empty()) { - std::string cls; - for (auto &ch : children) cls += charclass_escape(ch.first); - alts.push_back(std::string("[^") + cls + "]"); - } - for (auto &ch : children) { - std::string childExpr = self(self, forbids, ch.second.first, ch.second.second, depth+1); - if (!childExpr.empty()) { - std::string quoted_ch = "\""; - if (ch.first == '\\') quoted_ch += "\\\\"; - else if (ch.first == '"') quoted_ch += "\\\""; - else if (isprint(ch.first)) quoted_ch.push_back(ch.first); - else { - char buf[16]; - snprintf(buf, 15, "\\x%02X", ch.first); - quoted_ch += buf; - } - quoted_ch += "\""; - std::string branch = quoted_ch + std::string(" ") + childExpr; - alts.push_back(branch); - } - } - if (alts.empty()) return ""; - std::ostringstream oss; - oss << "( "; - for (size_t k = 0; k < alts.size(); ++k) { - if (k) oss << " | "; - oss << alts[k]; - } - oss << " )"; - return oss.str(); - }; - if (forbids.empty()) return "( . )*"; - sort(forbids.begin(), forbids.end()); - std::string expr = build_expr(build_expr, forbids, 0, forbids.size(), 0); - if (expr.empty()) { - std::string cls; - for (auto &s : forbids) if (!s.empty()) cls += charclass_escape((unsigned char)s[0]); - expr = std::string("( [^") + cls + "] )"; - } - if (forbids.size() == 1) - return expr + "*"; - else - return std::string("( ") + expr + " )*"; -} - -/** - * Build grammar for xml-style tool call - * form.scope_start and form.scope_end can be empty. - * Requires data.format for model-specific hacks. - */ -void build_grammar_xml_tool_call(common_chat_params & data, const json & tools, const struct xml_tool_call_format & form) { - GGML_ASSERT(!form.tool_start.empty()); - GGML_ASSERT(!form.tool_sep.empty()); - GGML_ASSERT(!form.key_start.empty()); - GGML_ASSERT(!form.val_end.empty()); - GGML_ASSERT(!form.tool_end.empty()); - - std::string key_val_sep = form.key_val_sep; - if (form.key_val_sep2) { - key_val_sep += "\n"; - key_val_sep += *form.key_val_sep2; - } - GGML_ASSERT(!key_val_sep.empty()); - - if (tools.is_array() && !tools.empty()) { - data.grammar = build_grammar([&](const common_grammar_builder &builder) { - auto string_arg_val = form.last_val_end ? - builder.add_rule("string-arg-val", make_gbnf_excluding({form.val_end, *form.last_val_end})) : - builder.add_rule("string-arg-val", make_gbnf_excluding({form.val_end})); - - std::vector tool_rules; - for (const auto & tool : tools) { - if (!tool.contains("type") || tool.at("type") != "function" || !tool.contains("function")) { - LOG_WRN("Skipping tool without function: %s", tool.dump(2).c_str()); - continue; - } - const auto & function = tool.at("function"); - if (!function.contains("name") || !function.at("name").is_string()) { - LOG_WRN("Skipping invalid function (invalid name): %s", function.dump(2).c_str()); - continue; - } - if (!function.contains("parameters") || !function.at("parameters").is_object()) { - LOG_WRN("Skipping invalid function (invalid parameters): %s", function.dump(2).c_str()); - continue; - } - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); - - struct parameter_rule { - std::string symbol_name; - bool is_required; - }; - std::vector arg_rules; - if (!parameters.contains("properties") || !parameters.at("properties").is_object()) { - LOG_WRN("Skipping invalid function (invalid properties): %s", function.dump(2).c_str()); - continue; - } else { - std::vector requiredParameters; - if (parameters.contains("required")) { - try { parameters.at("required").get_to(requiredParameters); } - catch (const std::runtime_error&) { - LOG_WRN("Invalid function required parameters, ignoring: %s", function.at("required").dump(2).c_str()); - } - } - sort_uniq(requiredParameters); - for (const auto & [key, value] : parameters.at("properties").items()) { - std::string quoted_key = key; - bool required = std::binary_search(requiredParameters.begin(), requiredParameters.end(), key); - if (form.key_start.back() == '"' && key_val_sep[0] == '"') { - quoted_key = gbnf_format_literal(key); - quoted_key = quoted_key.substr(1, quoted_key.size() - 2); - } - arg_rules.push_back(parameter_rule {builder.add_rule("func-" + name + "-kv-" + key, - gbnf_format_literal(form.key_start) + " " + - gbnf_format_literal(quoted_key) + " " + - gbnf_format_literal(key_val_sep) + " " + - ((value.contains("type") && value["type"].is_string() && value["type"] == "string" && (!form.raw_argval || *form.raw_argval)) ? - (form.raw_argval ? - string_arg_val : - "( " + string_arg_val + " | " + builder.add_schema(name + "-arg-" + key, value) + " )" - ) : - builder.add_schema(name + "-arg-" + key, value) - ) - ), required}); - } - } - - auto next_arg_with_sep = builder.add_rule(name + "-last-arg-end", form.last_val_end ? gbnf_format_literal(*form.last_val_end) : gbnf_format_literal(form.val_end)); - decltype(next_arg_with_sep) next_arg = "\"\""; - for (auto i = arg_rules.size() - 1; /* i >= 0 && */ i < arg_rules.size(); --i) { - std::string include_this_arg = arg_rules[i].symbol_name + " " + next_arg_with_sep; - next_arg = builder.add_rule(name + "-arg-after-" + std::to_string(i), arg_rules[i].is_required ? - include_this_arg : "( " + include_this_arg + " ) | " + next_arg - ); - include_this_arg = gbnf_format_literal(form.val_end) + " " + include_this_arg; - next_arg_with_sep = builder.add_rule(name + "-arg-after-" + std::to_string(i) + "-with-sep", arg_rules[i].is_required ? - include_this_arg : "( " + include_this_arg + " ) | " + next_arg_with_sep - ); - } - - std::string quoted_name = name; - if (form.tool_start.back() == '"' && form.tool_sep[0] == '"') { - quoted_name = gbnf_format_literal(name); - quoted_name = quoted_name.substr(1, quoted_name.size() - 2); - } - quoted_name = gbnf_format_literal(quoted_name); - // Kimi-K2 uses functions.{{ tool_call['function']['name'] }}:{{ loop.index }} as function name - if (data.format == COMMON_CHAT_FORMAT_KIMI_K2) { - quoted_name = "\"functions.\" " + quoted_name + " \":\" [0-9]+"; - } - tool_rules.push_back(builder.add_rule(name + "-call", - gbnf_format_literal(form.tool_start) + " " + - quoted_name + " " + - gbnf_format_literal(form.tool_sep) + " " + - next_arg - )); - } - - auto tool_call_once = builder.add_rule("root-tool-call-once", string_join(tool_rules, " | ")); - auto tool_call_more = builder.add_rule("root-tool-call-more", gbnf_format_literal(form.tool_end) + " " + tool_call_once); - auto call_end = builder.add_rule("root-call-end", form.last_tool_end ? gbnf_format_literal(*form.last_tool_end) : gbnf_format_literal(form.tool_end)); - auto tool_call_multiple_with_end = builder.add_rule("root-tool-call-multiple-with-end", tool_call_once + " " + tool_call_more + "* " + call_end); - builder.add_rule("root", - (form.scope_start.empty() ? "" : gbnf_format_literal(form.scope_start) + " ") + - tool_call_multiple_with_end + "?" + - (form.scope_end.empty() ? "" : " " + gbnf_format_literal(form.scope_end)) - ); - }); - - // grammar trigger for tool call - data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, form.scope_start + form.tool_start }); - } -} - -/** - * Parse XML-Style tool call for given xml_tool_call_format. Return false for invalid syntax and get the position untouched. - * Throws xml_toolcall_syntax_exception if there is invalid syntax and cannot recover the original status for common_chat_msg_parser. - * form.scope_start, form.tool_sep and form.scope_end can be empty. - */ -inline bool parse_xml_tool_calls(common_chat_msg_parser & builder, const struct xml_tool_call_format & form) { - GGML_ASSERT(!form.tool_start.empty()); - GGML_ASSERT(!form.key_start.empty()); - GGML_ASSERT(!form.key_val_sep.empty()); - GGML_ASSERT(!form.val_end.empty()); - GGML_ASSERT(!form.tool_end.empty()); - - // Helper to choose return false or throw error - constexpr auto return_error = [](common_chat_msg_parser & builder, auto &start_pos, const bool &recovery) { - LOG_DBG("Failed to parse XML-Style tool call at position: %s\n", gbnf_format_literal(builder.consume_rest().substr(0, 20)).c_str()); - if (recovery) { - builder.move_to(start_pos); - return false; - } else throw xml_toolcall_syntax_exception("Tool call parsing failed with unrecoverable errors. Try using a grammar to constrain the model’s output."); - }; - // Drop substring from needle to end from a JSON - constexpr auto partial_json = [](std::string &json_str, std::string_view needle = "XML_TOOL_CALL_PARTIAL_FLAG") { - auto pos = json_str.rfind(needle); - if (pos == std::string::npos) { - return false; - } - for (auto i = pos + needle.size(); i < json_str.size(); ++i) { - unsigned char ch = static_cast(json_str[i]); - if (ch != '\'' && ch != '"' && ch != '}' && ch != ':' && !std::isspace(ch)) { - return false; - } - } - if (pos != 0 && json_str[pos - 1] == '"') { - --pos; - } - json_str.resize(pos); - return true; - }; - // Helper to generate a partial argument JSON - constexpr auto gen_partial_json = [partial_json](auto set_partial_arg, auto &arguments, auto &builder, auto &function_name) { - auto rest = builder.consume_rest(); - utf8_truncate_safe_resize(rest); - set_partial_arg(rest, "XML_TOOL_CALL_PARTIAL_FLAG"); - auto tool_str = arguments.dump(); - if (partial_json(tool_str)) { - if (builder.add_tool_call(function_name, "", tool_str)) { - return; - } - } - LOG_DBG("Failed to parse partial XML-Style tool call, fallback to non-partial: %s\n", tool_str.c_str()); - }; - // Helper to find a close (because there may be form.last_val_end or form.last_tool_end) - constexpr auto try_find_close = []( - common_chat_msg_parser & builder, - const std::string & end, - const std::optional & alt_end, - const std::string & end_next, - const std::optional & alt_end_next - ) { - auto saved_pos = builder.pos(); - auto tc = builder.try_find_literal(end); - auto val_end_size = end.size(); - if (alt_end) { - auto pos_1 = builder.pos(); - builder.move_to(saved_pos); - auto tc2 = try_find_2_literal_splited_by_spaces(builder, *alt_end, end_next); - if (alt_end_next) { - builder.move_to(saved_pos); - auto tc3 = try_find_2_literal_splited_by_spaces(builder, *alt_end, *alt_end_next); - if (tc3 && (!tc2 || tc2->prelude.size() > tc3->prelude.size())) { - tc2 = tc3; - } - } - if (tc2 && (!tc || tc->prelude.size() > tc2->prelude.size())) { - tc = tc2; - tc->groups[0].end = std::min(builder.input().size(), tc->groups[0].begin + alt_end->size()); - builder.move_to(tc->groups[0].end); - val_end_size = alt_end->size(); - } else { - builder.move_to(pos_1); - } - } - return std::make_pair(val_end_size, tc); - }; - // Helper to find a val_end or last_val_end, returns matched pattern size - const auto try_find_val_end = [try_find_close, &builder, &form]() { - return try_find_close(builder, form.val_end, form.last_val_end, form.tool_end, form.last_tool_end); - }; - // Helper to find a tool_end or last_tool_end, returns matched pattern size - const auto try_find_tool_end = [try_find_close, &builder, &form]() { - return try_find_close(builder, form.tool_end, form.last_tool_end, form.scope_end, std::nullopt); - }; - - bool recovery = true; - const auto start_pos = builder.pos(); - if (!all_space(form.scope_start)) { - if (auto tc = builder.try_find_literal(form.scope_start)) { - if (all_space(tc->prelude)) { - if (form.scope_start.size() != tc->groups[0].end - tc->groups[0].begin) - throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.scope_start)); - } else { - builder.move_to(start_pos); - return false; - } - } else return false; - } - while (auto tc = builder.try_find_literal(form.tool_start)) { - if (!all_space(tc->prelude)) { - LOG_DBG("XML-Style tool call: Expected %s, but found %s, trying to match next pattern\n", - gbnf_format_literal(form.tool_start).c_str(), - gbnf_format_literal(tc->prelude).c_str() - ); - builder.move_to(tc->groups[0].begin - tc->prelude.size()); - break; - } - - // Find tool name - auto func_name = builder.try_find_literal(all_space(form.tool_sep) ? form.key_start : form.tool_sep); - if (!func_name) { - auto [sz, tc] = try_find_tool_end(); - func_name = tc; - } - if (!func_name) { - // Partial tool name not supported - throw common_chat_msg_partial_exception("incomplete tool_call"); - } - // If the model generate multiple tool call and the first tool call has no argument - if (func_name->prelude.find(form.tool_end) != std::string::npos || (form.last_tool_end ? func_name->prelude.find(*form.last_tool_end) != std::string::npos : false)) { - builder.move_to(func_name->groups[0].begin - func_name->prelude.size()); - auto [sz, tc] = try_find_tool_end(); - func_name = tc; - } - - // Parse tool name - builder.move_to(all_space(form.tool_sep) ? func_name->groups[0].begin : func_name->groups[0].end); - std::string function_name = string_strip(func_name->prelude); - // Kimi-K2 uses functions.{{ tool_call['function']['name'] }}:{{ loop.index }} as function name - if (builder.syntax().format == COMMON_CHAT_FORMAT_KIMI_K2) { - if (string_starts_with(function_name, "functions.")) { - static const std::regex re(":\\d+$"); - if (std::regex_search(function_name, re)) { - function_name = function_name.substr(10, function_name.rfind(":") - 10); - } - } - } - - // Argument JSON - json arguments = json::object(); - - // Helper to generate a partial argument JSON - const auto gen_partial_args = [&](auto set_partial_arg) { - gen_partial_json(set_partial_arg, arguments, builder, function_name); - }; - - // Parse all arg_key/arg_value pairs - while (auto tc = builder.try_find_literal(form.key_start)) { - if (!all_space(tc->prelude)) { - LOG_DBG("XML-Style tool call: Expected %s, but found %s, trying to match next pattern\n", - gbnf_format_literal(form.key_start).c_str(), - gbnf_format_literal(tc->prelude).c_str() - ); - builder.move_to(tc->groups[0].begin - tc->prelude.size()); - break; - } - if (tc->groups[0].end - tc->groups[0].begin != form.key_start.size()) { - auto tool_call_arg = arguments.dump(); - if (tool_call_arg.size() != 0 && tool_call_arg[tool_call_arg.size() - 1] == '}') { - tool_call_arg.resize(tool_call_arg.size() - 1); - } - builder.add_tool_call(function_name, "", tool_call_arg); - throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.key_start)); - } - - // Parse arg_key - auto key_res = builder.try_find_literal(form.key_val_sep); - if (!key_res) { - gen_partial_args([&](auto &rest, auto &needle) {arguments[rest + needle] = "";}); - throw common_chat_msg_partial_exception("Expected " + gbnf_format_literal(form.key_val_sep) + " after " + gbnf_format_literal(form.key_start)); - } - if (key_res->groups[0].end - key_res->groups[0].begin != form.key_val_sep.size()) { - gen_partial_args([&](auto &, auto &needle) {arguments[key_res->prelude + needle] = "";}); - throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.key_val_sep)); - } - auto &key = key_res->prelude; - recovery = false; - - // Parse arg_value - if (form.key_val_sep2) { - if (auto tc = builder.try_find_literal(*form.key_val_sep2)) { - if (!all_space(tc->prelude)) { - LOG_DBG("Failed to parse XML-Style tool call: Unexcepted %s between %s and %s\n", - gbnf_format_literal(tc->prelude).c_str(), - gbnf_format_literal(form.key_val_sep).c_str(), - gbnf_format_literal(*form.key_val_sep2).c_str() - ); - return return_error(builder, start_pos, false); - } - if (tc->groups[0].end - tc->groups[0].begin != form.key_val_sep2->size()) { - gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;}); - throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(*form.key_val_sep2)); - } - } else { - gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;}); - throw common_chat_msg_partial_exception("Expected " + gbnf_format_literal(*form.key_val_sep2) + " after " + gbnf_format_literal(form.key_val_sep)); - } - } - auto val_start = builder.pos(); - - // Test if arg_val is a partial JSON - std::optional value_json = std::nullopt; - if (!form.raw_argval || !*form.raw_argval) { - try { value_json = builder.try_consume_json(); } - catch (const std::runtime_error&) { builder.move_to(val_start); } - // TODO: Delete this when json_partial adds top-level support for null/true/false - if (builder.pos() == val_start) { - const static std::regex number_regex(R"([0-9-][0-9]*(\.\d*)?([eE][+-]?\d*)?)"); - builder.consume_spaces(); - std::string_view sv = utf8_truncate_safe_view(builder.input()); - sv.remove_prefix(builder.pos()); - std::string rest = "a"; - if (sv.size() < 6) rest = sv; - if (string_starts_with("null", rest) || string_starts_with("true", rest) || string_starts_with("false", rest) || std::regex_match(sv.begin(), sv.end(), number_regex)) { - value_json = {123, {"123", "123"}}; - builder.consume_rest(); - } else { - builder.move_to(val_start); - } - } - } - - // If it is a JSON and followed by , parse as json - // cannot support streaming because it may be a plain text starting with JSON - if (value_json) { - auto json_end = builder.pos(); - builder.consume_spaces(); - if (builder.pos() == builder.input().size()) { - if (form.raw_argval && !*form.raw_argval && (value_json->json.is_string() || value_json->json.is_object() || value_json->json.is_array())) { - arguments[key] = value_json->json; - auto json_str = arguments.dump(); - if (!value_json->healing_marker.json_dump_marker.empty()) { - GGML_ASSERT(std::string::npos != json_str.rfind(value_json->healing_marker.json_dump_marker)); - json_str.resize(json_str.rfind(value_json->healing_marker.json_dump_marker)); - } else { - GGML_ASSERT(json_str.back() == '}'); - json_str.resize(json_str.size() - 1); - } - builder.add_tool_call(function_name, "", json_str); - } else { - gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;}); - } - LOG_DBG("Possible JSON arg_value: %s\n", value_json->json.dump().c_str()); - throw common_chat_msg_partial_exception("JSON arg_value detected. Waiting for more tokens for validations."); - } - builder.move_to(json_end); - auto [val_end_size, tc] = try_find_val_end(); - if (tc && all_space(tc->prelude) && value_json->healing_marker.marker.empty()) { - if (tc->groups[0].end - tc->groups[0].begin != val_end_size) { - gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;}); - LOG_DBG("Possible terminated JSON arg_value: %s\n", value_json->json.dump().c_str()); - throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.val_end) + (form.last_val_end ? gbnf_format_literal(*form.last_val_end) : "")); - } else arguments[key] = value_json->json; - } else builder.move_to(val_start); - } - - // If not, parse as plain text - if (val_start == builder.pos()) { - if (auto [val_end_size, value_plain] = try_find_val_end(); value_plain) { - auto &value_str = value_plain->prelude; - if (form.trim_raw_argval) value_str = string_strip(value_str); - if (value_plain->groups[0].end - value_plain->groups[0].begin != val_end_size) { - gen_partial_args([&](auto &, auto &needle) {arguments[key] = value_str + needle;}); - throw common_chat_msg_partial_exception( - "Expected " + gbnf_format_literal(form.val_end) + - " after " + gbnf_format_literal(form.key_val_sep) + - (form.key_val_sep2 ? " " + gbnf_format_literal(*form.key_val_sep2) : "") - ); - } - arguments[key] = value_str; - } else { - if (form.trim_raw_argval) { - gen_partial_args([&](auto &rest, auto &needle) {arguments[key] = string_strip(rest) + needle;}); - } else { - gen_partial_args([&](auto &rest, auto &needle) {arguments[key] = rest + needle;}); - } - throw common_chat_msg_partial_exception( - "Expected " + gbnf_format_literal(form.val_end) + - " after " + gbnf_format_literal(form.key_val_sep) + - (form.key_val_sep2 ? " " + gbnf_format_literal(*form.key_val_sep2) : "") - ); - } - } - } - - // Consume closing tag - if (auto [tool_end_size, tc] = try_find_tool_end(); tc) { - if (!all_space(tc->prelude)) { - LOG_DBG("Failed to parse XML-Style tool call: Expected %s, but found %s\n", - gbnf_format_literal(form.tool_end).c_str(), - gbnf_format_literal(tc->prelude).c_str() - ); - return return_error(builder, start_pos, recovery); - } - if (tc->groups[0].end - tc->groups[0].begin == tool_end_size) { - // Add the parsed tool call - if (!builder.add_tool_call(function_name, "", arguments.dump())) { - throw common_chat_msg_partial_exception("Failed to add XML-Style tool call"); - } - recovery = false; - continue; - } - } - - auto tool_call_arg = arguments.dump(); - if (tool_call_arg.size() != 0 && tool_call_arg[tool_call_arg.size() - 1] == '}') { - tool_call_arg.resize(tool_call_arg.size() - 1); - } - builder.add_tool_call(function_name, "", tool_call_arg); - throw common_chat_msg_partial_exception("Expected " + gbnf_format_literal(form.tool_end) + " after " + gbnf_format_literal(form.val_end)); - } - if (auto tc = builder.try_find_literal(form.scope_end)) { - if (!all_space(tc->prelude)) { - LOG_DBG("Failed to parse XML-Style tool call: Expected %s, but found %s\n", - gbnf_format_literal(form.scope_end).c_str(), - gbnf_format_literal(tc->prelude).c_str() - ); - return return_error(builder, start_pos, recovery); - } - } else { - if (all_space(form.scope_end)) return true; - builder.consume_spaces(); - if (builder.pos() == builder.input().size()) - throw common_chat_msg_partial_exception("incomplete tool calls"); - LOG_DBG("Failed to parse XML-Style tool call: Expected %s, but found %s\n", - gbnf_format_literal(form.scope_end).c_str(), - gbnf_format_literal(builder.consume_rest()).c_str() - ); - return return_error(builder, start_pos, recovery); - } - - return true; -} - -/** - * Parse XML-Style tool call for given xml_tool_call_format. Return false for invalid syntax and get the position untouched. - * May cause std::runtime_error if there is invalid syntax because partial valid tool call is already sent out to client. - * form.scope_start, form.tool_sep and form.scope_end can be empty. - */ -bool common_chat_msg_parser::try_consume_xml_tool_calls(const struct xml_tool_call_format & form) { - auto pos = pos_; - auto tsize = result_.tool_calls.size(); - try { return parse_xml_tool_calls(*this, form); } - catch (const xml_toolcall_syntax_exception&) {} - move_to(pos); - result_.tool_calls.resize(tsize); - return false; -} - -/** - * Parse content uses reasoning and XML-Style tool call - * TODO: Note that form.allow_toolcall_in_think is not tested yet. If anyone confirms it works, this comment can be removed. - */ -inline void parse_msg_with_xml_tool_calls(common_chat_msg_parser & builder, const struct xml_tool_call_format & form, const std::string & start_think = "", const std::string & end_think = "") { - constexpr auto rstrip = [](std::string &s) { - s.resize(std::distance(s.begin(), std::find_if(s.rbegin(), s.rend(), [](unsigned char ch) { return !std::isspace(ch); }).base())); - }; - // Erase substring from l to r, along with additional spaces nearby - constexpr auto erase_spaces = [](auto &str, size_t l, size_t r) { - while (/* l > -1 && */ --l < str.size() && std::isspace(static_cast(str[l]))); - ++l; - while (++r < str.size() && std::isspace(static_cast(str[r]))); - if (l < r) str[l] = '\n'; - if (l + 1 < r) str[l + 1] = '\n'; - if (l != 0) l += 2; - str.erase(l, r - l); - return l; - }; - constexpr auto trim_suffix = [](std::string &content, std::initializer_list list) { - auto best_match = content.size(); - for (auto pattern: list) { - if (pattern.size() == 0) continue; - for (auto match_idx = content.size() - std::min(pattern.size(), content.size()); content.size() > match_idx; match_idx++) { - auto match_len = content.size() - match_idx; - if (content.compare(match_idx, match_len, pattern.data(), match_len) == 0 && best_match > match_idx) { - best_match = match_idx; - } - } - } - if (content.size() > best_match) { - content.erase(best_match); - } - }; - const auto trim_potential_partial_word = [&start_think, &end_think, &form, trim_suffix](std::string &content) { - return trim_suffix(content, { - start_think, end_think, form.scope_start, form.tool_start, form.tool_sep, form.key_start, - form.key_val_sep, form.key_val_sep2 ? form.key_val_sep2->c_str() : "", - form.val_end, form.last_val_end ? form.last_val_end->c_str() : "", - form.tool_end, form.last_tool_end ? form.last_tool_end->c_str() : "", - form.scope_end - }); - }; - - - // Trim leading spaces without affecting keyword matching - static const common_regex spaces_regex("\\s*"); - { - auto tc = builder.consume_regex(spaces_regex); - auto spaces = builder.str(tc.groups[0]); - auto s1 = spaces.size(); - trim_potential_partial_word(spaces); - auto s2 = spaces.size(); - builder.move_to(builder.pos() - (s1 - s2)); - } - - // Parse content - bool reasoning_unclosed = builder.syntax().thinking_forced_open; - std::string unclosed_reasoning_content(""); - for (;;) { - auto tc = try_find_2_literal_splited_by_spaces(builder, form.scope_start, form.tool_start); - std::string content; - std::string tool_call_start; - - if (tc) { - content = std::move(tc->prelude); - tool_call_start = builder.str(tc->groups[0]); - LOG_DBG("Matched tool start: %s\n", gbnf_format_literal(tool_call_start).c_str()); - } else { - content = builder.consume_rest(); - utf8_truncate_safe_resize(content); - } - - // Handle unclosed think block - if (reasoning_unclosed) { - if (auto pos = content.find(end_think); pos == std::string::npos && builder.pos() != builder.input().size()) { - unclosed_reasoning_content += content; - if (!(form.allow_toolcall_in_think && tc)) { - unclosed_reasoning_content += tool_call_start; - continue; - } - } else { - reasoning_unclosed = false; - std::string reasoning_content; - if (pos == std::string::npos) { - reasoning_content = std::move(content); - } else { - reasoning_content = content.substr(0, pos); - content.erase(0, pos + end_think.size()); - } - if (builder.pos() == builder.input().size() && all_space(content)) { - rstrip(reasoning_content); - trim_potential_partial_word(reasoning_content); - rstrip(reasoning_content); - if (reasoning_content.empty()) { - rstrip(unclosed_reasoning_content); - trim_potential_partial_word(unclosed_reasoning_content); - rstrip(unclosed_reasoning_content); - if (unclosed_reasoning_content.empty()) continue; - } - } - if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE || builder.syntax().reasoning_in_content) { - builder.add_content(start_think); - builder.add_content(unclosed_reasoning_content); - builder.add_content(reasoning_content); - if (builder.pos() != builder.input().size() || !all_space(content)) - builder.add_content(end_think); - } else { - builder.add_reasoning_content(unclosed_reasoning_content); - builder.add_reasoning_content(reasoning_content); - } - unclosed_reasoning_content.clear(); - } - } - - // Handle multiple think block - bool toolcall_in_think = false; - for (auto think_start = content.find(start_think); think_start != std::string::npos; think_start = content.find(start_think, think_start)) { - if (auto think_end = content.find(end_think, think_start + start_think.size()); think_end != std::string::npos) { - if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) { - auto reasoning_content = content.substr(think_start + start_think.size(), think_end - think_start - start_think.size()); - builder.add_reasoning_content(reasoning_content); - think_start = erase_spaces(content, think_start, think_end + end_think.size() - 1); - } else { - think_start = think_end + end_think.size() - 1; - } - } else { - // This start is in thinking block, skip this tool call - // This start is in thinking block - if (form.allow_toolcall_in_think) { - unclosed_reasoning_content = content.substr(think_start + start_think.size()); - } else { - unclosed_reasoning_content = content.substr(think_start + start_think.size()) + tool_call_start; - } - reasoning_unclosed = true; - content.resize(think_start); - toolcall_in_think = true; - } - } - - if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) { - rstrip(content); - // Handle unclosed token from content: delete all token - if (auto pos = content.rfind(end_think); pos != std::string::npos) { - while (pos != std::string::npos) { - pos = erase_spaces(content, pos, pos + end_think.size() - 1); - pos = content.rfind(end_think, pos); - } - } - // Strip if needed - if (content.size() > 0 && std::isspace(static_cast(content[0]))) { - content = string_strip(content); - } - } - - // remove potential partial suffix - if (builder.pos() == builder.input().size() && builder.is_partial()) { - if (unclosed_reasoning_content.empty()) { - rstrip(content); - trim_potential_partial_word(content); - rstrip(content); - } else { - rstrip(unclosed_reasoning_content); - trim_potential_partial_word(unclosed_reasoning_content); - rstrip(unclosed_reasoning_content); - } - } - - // consume unclosed_reasoning_content if allow_toolcall_in_think is set - if (form.allow_toolcall_in_think && !unclosed_reasoning_content.empty()) { - if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) { - builder.add_reasoning_content(unclosed_reasoning_content); - } else { - if (content.empty()) { - content = start_think + unclosed_reasoning_content; - } else { - content += "\n\n" + start_think; - content += unclosed_reasoning_content; - } - } - unclosed_reasoning_content.clear(); - } - - // Add content - if (!content.empty()) { - // If there are multiple content blocks - if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content && builder.result().content.size() != 0) { - builder.add_content("\n\n"); - } - builder.add_content(content); - } - - // This start is in thinking block and toolcall_in_think not set, skip this tool call - if (toolcall_in_think && !form.allow_toolcall_in_think) { - continue; - } - - // There is no tool call and all content is parsed - if (!tc) { - GGML_ASSERT(builder.pos() == builder.input().size()); - GGML_ASSERT(unclosed_reasoning_content.empty()); - if (!form.allow_toolcall_in_think) GGML_ASSERT(!reasoning_unclosed); - break; - } - - builder.move_to(tc->groups[0].begin); - if (builder.try_consume_xml_tool_calls(form)) { - auto end_of_tool = builder.pos(); - builder.consume_spaces(); - if (builder.pos() != builder.input().size()) { - builder.move_to(end_of_tool); - if (!builder.result().content.empty()) { - builder.add_content("\n\n"); - } - } - } else { - static const common_regex next_char_regex("."); - auto c = builder.str(builder.consume_regex(next_char_regex).groups[0]); - rstrip(c); - builder.add_content(c); - } - } -} - -/** - * Parse content uses reasoning and XML-Style tool call - */ -void common_chat_msg_parser::consume_reasoning_with_xml_tool_calls(const struct xml_tool_call_format & form, const std::string & start_think, const std::string & end_think) { - parse_msg_with_xml_tool_calls(*this, form, start_think, end_think); -} diff --git a/common/chat-parser-xml-toolcall.h b/common/chat-parser-xml-toolcall.h deleted file mode 100644 index b309fb6670..0000000000 --- a/common/chat-parser-xml-toolcall.h +++ /dev/null @@ -1,45 +0,0 @@ -#pragma once - -#include "chat.h" - -#include - -#include -#include -#include - - -// Sample config: -// MiniMax-M2 (left): \n\nvalue\n...\n... -// GLM 4.5 (right): function_name\nkey\nvalue\n -struct xml_tool_call_format { - std::string scope_start; // \n // \n // can be empty - std::string tool_start; // - std::string tool_sep; // \">\n // \n // can be empty only for parse_xml_tool_calls - std::string key_start; // - std::string key_val_sep; // \"> // \n - std::string val_end; // \n // \n - std::string tool_end; // \n // \n - std::string scope_end; // // // can be empty - // Set this if there can be dynamic spaces inside key_val_sep. - // e.g. key_val_sep= key_val_sep2= for GLM4.5 - std::optional key_val_sep2 = std::nullopt; - // Set true if argval should only be raw string. e.g. Hello "world" hi - // Set false if argval should only be json string. e.g. "Hello \"world\" hi" - // Defaults to std::nullopt, both will be allowed. - std::optional raw_argval = std::nullopt; - std::optional last_val_end = std::nullopt; - std::optional last_tool_end = std::nullopt; - bool trim_raw_argval = false; - bool allow_toolcall_in_think = false; -}; - -// make a GBNF that accept any strings except those containing any of the forbidden strings. -std::string make_gbnf_excluding(std::vector forbids); - -/** - * Build grammar for xml-style tool call - * form.scope_start and form.scope_end can be empty. - * Requires data.format for model-specific hacks. - */ -void build_grammar_xml_tool_call(common_chat_params & data, const nlohmann::ordered_json & tools, const struct xml_tool_call_format & form); diff --git a/common/chat-parser.cpp b/common/chat-parser.cpp deleted file mode 100644 index 060578f0b7..0000000000 --- a/common/chat-parser.cpp +++ /dev/null @@ -1,1649 +0,0 @@ -#include "chat-parser.h" -#include "chat-peg-parser.h" -#include "common.h" -#include "log.h" -#include "peg-parser.h" -#include "regex-partial.h" - -#include -#include -#include -#include -#include -#include -#include - -using json = nlohmann::ordered_json; - -static void parse_prefixed_json_tool_call_array(common_chat_msg_parser & builder, - const common_regex & prefix, - size_t rstrip_prefix = 0) { - static const std::vector> args_paths = { { "arguments" } }; - if (auto res = builder.try_find_regex(prefix)) { - builder.move_back(rstrip_prefix); - auto tool_calls = builder.consume_json_with_dumped_args(args_paths); - if (!builder.add_tool_calls(tool_calls.value) || tool_calls.is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call array"); - } - } else { - builder.add_content(builder.consume_rest()); - } -} - -static std::string wrap_code_as_arguments(common_chat_msg_parser & builder, const std::string & code) { - std::string arguments; - if (builder.is_partial()) { - arguments = (json{ - { "code", code + builder.healing_marker() } - }) - .dump(); - auto idx = arguments.find(builder.healing_marker()); - if (idx != std::string::npos) { - arguments.resize(idx); - } - } else { - arguments = (json{ - { "code", code } - }) - .dump(); - } - return arguments; -} - -/** - * Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between. - * Aggregates the prefix, suffix and in-between text into the content. - */ -static void parse_json_tool_calls( - common_chat_msg_parser & builder, - const std::optional & block_open, - const std::optional & function_regex_start_only, - const std::optional & function_regex, - const common_regex & close_regex, - const std::optional & block_close, - bool allow_raw_python = false, - const std::function & get_function_name = - nullptr) { - auto parse_tool_calls = [&]() { - size_t from = std::string::npos; - auto first = true; - while (true) { - auto start_pos = builder.pos(); - auto res = function_regex_start_only && first ? builder.try_consume_regex(*function_regex_start_only) : - function_regex ? builder.try_find_regex(*function_regex, from) : - std::nullopt; - - if (res) { - std::string name; - if (get_function_name) { - name = get_function_name(*res); - } else { - GGML_ASSERT(res->groups.size() == 2); - name = builder.str(res->groups[1]); - } - first = false; - if (name.empty()) { - // get_function_name signalled us that we should skip this match and treat it as content. - from = res->groups[0].begin + 1; - continue; - } - from = std::string::npos; - - auto maybe_raw_python = name == "python" && allow_raw_python; - if (builder.input()[builder.pos()] == '{' || !maybe_raw_python) { - if (auto arguments = builder.try_consume_json_with_dumped_args({ {} })) { - if (!builder.add_tool_call(name, "", arguments->value) || arguments->is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - builder.consume_regex(close_regex); - } - continue; - } - if (maybe_raw_python) { - auto arguments = wrap_code_as_arguments(builder, builder.consume_rest()); - if (!builder.add_tool_call(name, "", arguments)) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - return; - } - throw common_chat_msg_partial_exception("incomplete tool call"); - } else { - builder.move_to(start_pos); - } - break; - } - if (block_close) { - builder.consume_regex(*block_close); - } - builder.consume_spaces(); - builder.add_content(builder.consume_rest()); - }; - if (block_open) { - if (auto res = builder.try_find_regex(*block_open)) { - parse_tool_calls(); - } else { - builder.add_content(builder.consume_rest()); - } - } else { - parse_tool_calls(); - } -} - -common_chat_msg_parser::common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_parser_params & syntax) - : input_(input), is_partial_(is_partial), syntax_(syntax) -{ - result_.role = "assistant"; - - while (true) { - std::string id = std::to_string(std::rand()); - if (input.find(id) == std::string::npos) { - healing_marker_ = id; - break; - } - } -} - -std::string common_chat_msg_parser::str(const common_string_range & rng) const { - GGML_ASSERT(rng.begin <= rng.end); - return input_.substr(rng.begin, rng.end - rng.begin); -} - -void common_chat_msg_parser::add_content(const std::string &content) { - result_.content += content; -} - -void common_chat_msg_parser::add_reasoning_content(const std::string &reasoning_content) { - result_.reasoning_content += reasoning_content; -} - -bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::string & id, const std::string & arguments) { - if (name.empty()) { - return false; - } - - common_chat_tool_call tool_call; - tool_call.name = name; - tool_call.arguments = arguments; - tool_call.id = id; - - // LOG_DBG("Tool call arguments:\n\traw: %s\n\tresult: %s\n", arguments.c_str(), tool_call.arguments.c_str()); - result_.tool_calls.emplace_back(tool_call); - - return true; -} -bool common_chat_msg_parser::add_tool_call(const json & tool_call) { - std::string name = tool_call.contains("name") ? tool_call.at("name") : ""; - std::string id = tool_call.contains("id") ? tool_call.at("id") : ""; - std::string arguments = ""; - if (tool_call.contains("arguments")) { - if (tool_call.at("arguments").is_object()) { - arguments = tool_call.at("arguments").dump(); - } else { - arguments = tool_call.at("arguments"); - } - } - - return add_tool_call(name, id, arguments); -} - -bool common_chat_msg_parser::add_tool_calls(const json & arr) { - for (const auto & item : arr) { - if (!add_tool_call(item)) { - return false; - } - } - return true; -} - -bool common_chat_msg_parser::add_tool_call_short_form(const json & tool_call) { - if (!tool_call.is_object() || tool_call.size() != 1) { - return false; - } - - // Get the tool name (the single key in the object) - auto it = tool_call.begin(); - std::string name = it.key(); - - if (name.empty()) { - return false; - } - - // Get the arguments (the nested object) - const json & args_json = it.value(); - std::string arguments = ""; - - if (args_json.is_object()) { - arguments = args_json.dump(); - } else if (args_json.is_string()) { - arguments = args_json; - } else if (!args_json.is_null()) { - // For other types, convert to string representation - arguments = args_json.dump(); - } - - return add_tool_call(name, "", arguments); -} -void common_chat_msg_parser::finish() { - if (!is_partial_ && pos_ != input_.size()) { - throw std::runtime_error("Unexpected content at end of input");// + input_.substr(pos_)); - } -} - -bool common_chat_msg_parser::consume_spaces() { - const auto length = input_.size(); - auto consumed = false; - while (pos_ < length && std::isspace(input_[pos_])) { - ++pos_; - consumed = true; - } - return consumed; -} - -bool common_chat_msg_parser::try_consume_literal(const std::string & literal) { - auto pos = pos_; - for (auto i = 0u; i < literal.size(); ++i) { - if (pos >= input_.size()) { - return false; - } - if (input_[pos] != literal[i]) { - return false; - } - ++pos; - } - pos_ = pos; - return true; -} - -std::optional common_chat_msg_parser::try_find_literal(const std::string & literal) { - auto idx = input_.find(literal, pos_); - if (idx != std::string::npos) { - find_regex_result res; - res.prelude = input_.substr(pos_, idx - pos_); - auto end = idx + literal.size(); - res.groups.emplace_back(common_string_range{idx, end}); - move_to(end); - return res; - } - if (is_partial_) { - idx = string_find_partial_stop(input_, literal); - if (idx != std::string::npos && idx >= pos_) { - find_regex_result res; - res.prelude = input_.substr(pos_, idx - pos_); - auto end = input_.size(); - res.groups.emplace_back(common_string_range{idx, end}); - move_to(end); - return res; - } - } - return std::nullopt; -} - -void common_chat_msg_parser::consume_literal(const std::string & literal) { - if (!try_consume_literal(literal)) { - throw common_chat_msg_partial_exception(literal); - } -} - -bool common_chat_msg_parser::try_parse_reasoning(const std::string & start_think, const std::string & end_think) { - std::string pending_reasoning_prefix; - - if (syntax_.reasoning_format == COMMON_REASONING_FORMAT_NONE) { - return false; - } - - auto set_reasoning_prefix = [&](size_t prefix_pos) { - if (!syntax_.thinking_forced_open || syntax_.reasoning_in_content) { - return; - } - if (prefix_pos + start_think.size() > input_.size()) { - pending_reasoning_prefix.clear(); - return; - } - // Capture the exact literal that opened the reasoning section so we can - // surface it back to callers. This ensures formats that force the - // reasoning tag open (e.g. DeepSeek R1) retain their original prefix - // instead of dropping it during parsing. - pending_reasoning_prefix = input_.substr(prefix_pos, start_think.size()); - }; - - auto handle_reasoning = [&](const std::string & reasoning, bool closed) { - auto stripped_reasoning = string_strip(reasoning); - if (stripped_reasoning.empty()) { - return; - } - if (syntax_.reasoning_in_content) { - add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "" : start_think); - add_content(stripped_reasoning); - if (closed) { - add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "" : end_think); - } - } else { - if (!pending_reasoning_prefix.empty()) { - add_reasoning_content(pending_reasoning_prefix); - pending_reasoning_prefix.clear(); - } - add_reasoning_content(stripped_reasoning); - } - }; - - const size_t saved_pos = pos_; - const size_t saved_content_size = result_.content.size(); - const size_t saved_reasoning_size = result_.reasoning_content.size(); - - auto restore_state = [&]() { - move_to(saved_pos); - result_.content.resize(saved_content_size); - result_.reasoning_content.resize(saved_reasoning_size); - }; - - // Allow leading whitespace to be preserved as content when reasoning is present at the start - size_t cursor = pos_; - size_t whitespace_end = cursor; - while (whitespace_end < input_.size() && std::isspace(static_cast(input_[whitespace_end]))) { - ++whitespace_end; - } - - if (whitespace_end >= input_.size()) { - restore_state(); - if (syntax_.thinking_forced_open) { - auto rest = input_.substr(saved_pos); - if (!rest.empty()) { - handle_reasoning(rest, /* closed */ !is_partial()); - } - move_to(input_.size()); - return true; - } - return false; - } - - cursor = whitespace_end; - const size_t remaining = input_.size() - cursor; - const size_t start_prefix = std::min(start_think.size(), remaining); - const bool has_start_tag = input_.compare(cursor, start_prefix, start_think, 0, start_prefix) == 0; - - if (has_start_tag && start_prefix < start_think.size()) { - move_to(input_.size()); - return true; - } - - if (has_start_tag) { - if (whitespace_end > pos_) { - add_content(input_.substr(pos_, whitespace_end - pos_)); - } - set_reasoning_prefix(cursor); - cursor += start_think.size(); - } else if (syntax_.thinking_forced_open) { - cursor = whitespace_end; - } else { - restore_state(); - return false; - } - while (true) { - if (cursor >= input_.size()) { - move_to(input_.size()); - return true; - } - - size_t end_pos = input_.find(end_think, cursor); - if (end_pos == std::string::npos) { - std::string_view remaining_view(input_.data() + cursor, input_.size() - cursor); - size_t partial_off = string_find_partial_stop(remaining_view, end_think); - size_t reasoning_end = partial_off == std::string::npos ? input_.size() : cursor + partial_off; - if (reasoning_end > cursor) { - handle_reasoning(input_.substr(cursor, reasoning_end - cursor), /* closed */ partial_off == std::string::npos && !is_partial()); - } - move_to(input_.size()); - return true; - } - - if (end_pos > cursor) { - handle_reasoning(input_.substr(cursor, end_pos - cursor), /* closed */ true); - } else { - handle_reasoning("", /* closed */ true); - } - - cursor = end_pos + end_think.size(); - - while (cursor < input_.size() && std::isspace(static_cast(input_[cursor]))) { - ++cursor; - } - - const size_t next_remaining = input_.size() - cursor; - if (next_remaining == 0) { - move_to(cursor); - return true; - } - - const size_t next_prefix = std::min(start_think.size(), next_remaining); - if (input_.compare(cursor, next_prefix, start_think, 0, next_prefix) == 0) { - if (next_prefix < start_think.size()) { - move_to(input_.size()); - return true; - } - set_reasoning_prefix(cursor); - cursor += start_think.size(); - continue; - } - - move_to(cursor); - return true; - } -} - -std::string common_chat_msg_parser::consume_rest() { - auto rest = input_.substr(pos_); - pos_ = input_.size(); - return rest; -} - -// Tries to find the regex, consumes it (pos right after it) and gives the prelude (right before it) and the groups to the callback. -std::optional common_chat_msg_parser::try_find_regex(const common_regex & regex, size_t from, bool add_prelude_to_content) { - auto m = regex.search(input_, from == std::string::npos ? pos_ : from); - if (m.type == COMMON_REGEX_MATCH_TYPE_NONE) { - return std::nullopt; - } - auto prelude = input_.substr(pos_, m.groups[0].begin - pos_); - pos_ = m.groups[0].end; - - if (add_prelude_to_content) { - add_content(prelude); - } - if (m.type == COMMON_REGEX_MATCH_TYPE_PARTIAL) { - if (is_partial()) { - throw common_chat_msg_partial_exception(regex.str()); - } - return std::nullopt; - } - return find_regex_result{prelude, m.groups}; -} - -common_chat_msg_parser::find_regex_result common_chat_msg_parser::consume_regex(const common_regex & regex) { - if (auto result = try_consume_regex(regex)) { - return *result; - } - throw common_chat_msg_partial_exception(regex.str()); -} - -std::optional common_chat_msg_parser::try_consume_regex(const common_regex & regex) { - auto m = regex.search(input_, pos_); - if (m.type == COMMON_REGEX_MATCH_TYPE_NONE) { - return std::nullopt; - } - if (m.type == COMMON_REGEX_MATCH_TYPE_PARTIAL) { - if (is_partial()) { - throw common_chat_msg_partial_exception(regex.str()); - } - return std::nullopt; - } - if (m.groups[0].begin != pos_) { - // Didn't match at the current position. - return std::nullopt; - } - pos_ = m.groups[0].end; - - return find_regex_result { - /* .prelude = */ "", - m.groups, - }; -} - -std::optional common_chat_msg_parser::try_consume_json() { - auto it = input_.cbegin() + pos_; - const auto end = input_.cend(); - common_json result; - if (!common_json_parse(it, end, healing_marker_, result)) { - return std::nullopt; - } - pos_ = std::distance(input_.cbegin(), it); - if (result.healing_marker.marker.empty()) { - // No healing marker, just return the parsed json - return result; - } - if (!is_partial()) { - throw common_chat_msg_partial_exception("JSON"); - } - return result; -} - -common_json common_chat_msg_parser::consume_json() { - if (auto result = try_consume_json()) { - return *result; - } - throw common_chat_msg_partial_exception("JSON"); -} - -common_chat_msg_parser::consume_json_result common_chat_msg_parser::consume_json_with_dumped_args( - const std::vector> & args_paths, - const std::vector> & content_paths -) { - if (auto result = try_consume_json_with_dumped_args(args_paths, content_paths)) { - return *result; - } - throw common_chat_msg_partial_exception("JSON"); -} - -std::optional common_chat_msg_parser::try_consume_json_with_dumped_args( - const std::vector> & args_paths, - const std::vector> & content_paths -) { - auto partial = try_consume_json(); - if (!partial) { - return std::nullopt; - } - auto is_arguments_path = [&](const std::vector & path) { - return std::find(args_paths.begin(), args_paths.end(), path) != args_paths.end(); - }; - auto is_content_path = [&](const std::vector & path) { - return std::find(content_paths.begin(), content_paths.end(), path) != content_paths.end(); - }; - - if (partial->healing_marker.marker.empty()) { - if (args_paths.empty()) { - // No arguments to dump, and JSON was parsed fully. - return consume_json_result { - partial->json, - /* .is_partial = */ false, - }; - } - if (is_arguments_path({})) { - // Entire JSON is the arguments and was parsed fully. - return consume_json_result { - partial->json.dump(/* indent */ -1, /* indent_char */ ' ', /* ensure_ascii */ true), - /* .is_partial = */ false, - }; - } - } - - LOG_DBG("Parsed partial JSON: %s (json_healing_marker: %s)\n", partial->json.dump().c_str(), partial->healing_marker.json_dump_marker.c_str()); - - auto found_healing_marker = false; - std::vector path; - std::function remove_unsupported_healings_and_dump_args = [&](const json & j) -> json { - if (is_arguments_path(path)) { - auto arguments = j.dump(/* indent */ -1, /* indent_char */ ' ', /* ensure_ascii */ true); - if (is_partial() && !partial->healing_marker.marker.empty()) { - auto idx = arguments.find(partial->healing_marker.json_dump_marker); - if (idx != std::string::npos) { - arguments.resize(idx); - found_healing_marker = true; - } - if (arguments == "\"") { - // This happens because of completing `:"$magic` after `"arguments"` - arguments = ""; - } - } - return arguments; - } - if (is_content_path(path)) { - if (!j.is_string()) { - throw std::runtime_error("Content path must be a string"); - } - std::string str = j; - auto idx = str.find(partial->healing_marker.marker); // not using json_dump_marker as we're inside a string - if (idx != std::string::npos) { - str.resize(idx); - found_healing_marker = true; - } - return str; - } - if (j.is_object()) { - auto obj = json::object(); - for (const auto & p : j.items()) { - const auto & key = p.key(); - const auto & value = p.value(); - const std::string key_str = key; // NOLINT - auto idx = key_str.find(healing_marker_); - if (idx != std::string::npos) { - found_healing_marker = true; - break; - } - path.push_back(key_str); - if (value.is_string()) { - const std::string value_str = value; - if (value_str.find(healing_marker_) != std::string::npos) { - found_healing_marker = true; - if (is_content_path(path)) { - if (partial->healing_marker.marker == partial->healing_marker.json_dump_marker) { - // The healing occurred inside the string: good. Otherwise we just ditch the entire key/value pair. - obj[key] = remove_unsupported_healings_and_dump_args(value); - } - } - break; - } - obj[key] = value; - } else { - obj[key] = remove_unsupported_healings_and_dump_args(value); - } - path.pop_back(); - } - return obj; - } - if (j.is_array()) { - auto arr = json::array(); - for (const auto & value : j) { - if (value.is_string()) { - std::string str = value; - auto idx = str.find(healing_marker_); - if (idx != std::string::npos) { - // Don't heal array values that aren't in the arguments. - found_healing_marker = true; - break; - } - } - arr.push_back(remove_unsupported_healings_and_dump_args(value)); - } - return arr; - } - return j; - }; - - auto cleaned = remove_unsupported_healings_and_dump_args(partial->json); - LOG_DBG("Cleaned up JSON %s to %s (json_healing_marker : '%s')\n", partial->json.dump().c_str(), cleaned.dump().c_str(), partial->healing_marker.json_dump_marker.c_str()); - return consume_json_result { - cleaned, - /* .is_partial = */ found_healing_marker, - }; -} - -void common_chat_msg_parser::clear_tools() { - result_.tool_calls.clear(); -} - -/** - * All common_chat_parse_* moved from chat.cpp to chat-parser.cpp below - * to reduce incremental compile time for parser changes. - */ -static void common_chat_parse_generic(common_chat_msg_parser & builder) { - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - static const std::vector> content_paths = { - {"response"}, - }; - static const std::vector> args_paths = { - {"tool_call", "arguments"}, - {"tool_calls", "arguments"}, - }; - auto data = builder.consume_json_with_dumped_args(args_paths, content_paths); - if (data.value.contains("tool_calls")) { - if (!builder.add_tool_calls(data.value.at("tool_calls")) || data.is_partial) { - throw common_chat_msg_partial_exception("incomplete tool calls"); - } - } else if (data.value.contains("tool_call")) { - if (!builder.add_tool_call(data.value.at("tool_call")) || data.is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - } else if (data.value.contains("response")) { - const auto & response = data.value.at("response"); - builder.add_content(response.is_string() ? response.template get() : response.dump(2)); - if (data.is_partial) { - throw common_chat_msg_partial_exception("incomplete response"); - } - } else { - throw common_chat_msg_partial_exception("Expected 'tool_call', 'tool_calls' or 'response' in JSON"); - } -} - -static void common_chat_parse_mistral_nemo(common_chat_msg_parser & builder) { - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - - static const common_regex prefix(regex_escape("[TOOL_CALLS]")); - parse_prefixed_json_tool_call_array(builder, prefix); -} - -static void common_chat_parse_magistral(common_chat_msg_parser & builder) { - builder.try_parse_reasoning("[THINK]", "[/THINK]"); - - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - - static const common_regex prefix(regex_escape("[TOOL_CALLS]")); - parse_prefixed_json_tool_call_array(builder, prefix); -} - -static void common_chat_parse_command_r7b(common_chat_msg_parser & builder) { - builder.try_parse_reasoning("<|START_THINKING|>", "<|END_THINKING|>"); - - static const common_regex start_action_regex("<\\|START_ACTION\\|>"); - static const common_regex end_action_regex("<\\|END_ACTION\\|>"); - static const common_regex start_response_regex("<\\|START_RESPONSE\\|>"); - static const common_regex end_response_regex("<\\|END_RESPONSE\\|>"); - - if (auto res = builder.try_find_regex(start_action_regex)) { - // If we didn't extract thoughts, prelude includes them. - auto tool_calls = builder.consume_json_with_dumped_args({{"parameters"}}); - for (const auto & tool_call : tool_calls.value) { - std::string name = tool_call.contains("tool_name") ? tool_call.at("tool_name") : ""; - std::string id = tool_call.contains("tool_call_id") ? tool_call.at("tool_call_id") : ""; - std::string arguments = tool_call.contains("parameters") ? tool_call.at("parameters") : ""; - if (!builder.add_tool_call(name, id, arguments) || tool_calls.is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - } - if (tool_calls.is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - builder.consume_regex(end_action_regex); - } else if (auto res = builder.try_find_regex(start_response_regex)) { - if (!builder.try_find_regex(end_response_regex)) { - builder.add_content(builder.consume_rest()); - throw common_chat_msg_partial_exception(end_response_regex.str()); - } - } else { - builder.add_content(builder.consume_rest()); - } -} - -static void common_chat_parse_llama_3_1(common_chat_msg_parser & builder, bool with_builtin_tools = false) { - builder.try_parse_reasoning("", ""); - - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - - static const common_regex function_regex( - "\\s*\\{\\s*(?:\"type\"\\s*:\\s*\"function\"\\s*,\\s*)?\"name\"\\s*:\\s*\"([^\"]+)\"\\s*,\\s*\"parameters\"\\s*: "); - static const common_regex close_regex("\\}\\s*"); - - static const common_regex function_name_regex("\\s*(\\w+)\\s*\\.\\s*call\\("); - static const common_regex arg_name_regex("\\s*(\\w+)\\s*=\\s*"); - - if (with_builtin_tools) { - static const common_regex builtin_call_regex("<\\|python_tag\\|>"); - if (auto res = builder.try_find_regex(builtin_call_regex)) { - auto fun_res = builder.consume_regex(function_name_regex); - auto function_name = builder.str(fun_res.groups[1]); - - common_healing_marker healing_marker; - json args = json::object(); - while (true) { - if (auto arg_res = builder.try_consume_regex(arg_name_regex)) { - auto arg_name = builder.str(arg_res->groups[1]); - auto partial = builder.consume_json(); - args[arg_name] = partial.json; - healing_marker.marker = partial.healing_marker.marker; - healing_marker.json_dump_marker = partial.healing_marker.json_dump_marker; - builder.consume_spaces(); - if (!builder.try_consume_literal(",")) { - break; - } - } else { - break; - } - } - builder.consume_literal(")"); - builder.consume_spaces(); - - auto arguments = args.dump(); - if (!builder.add_tool_call(function_name, "", arguments)) { - throw common_chat_msg_partial_exception("Incomplete tool call"); - } - return; - } - } - parse_json_tool_calls( - builder, - /* block_open= */ std::nullopt, - /* function_regex_start_only= */ function_regex, - /* function_regex= */ std::nullopt, - close_regex, - std::nullopt); - -} - -static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) { - builder.try_parse_reasoning("", ""); - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - - static const common_regex tool_calls_begin("(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)"); - static const common_regex tool_calls_end("<｜tool▁calls▁end｜>"); - static const common_regex function_regex("(?:<｜tool▁call▁begin｜>)?function<｜tool▁sep｜>([^\n]+)\n```json\n"); - static const common_regex close_regex("```[\\s\\r\\n]*<｜tool▁call▁end｜>"); - - parse_json_tool_calls( - builder, - /* block_open= */ tool_calls_begin, - /* function_regex_start_only= */ std::nullopt, - function_regex, - close_regex, - tool_calls_end); -} - -static void common_chat_parse_deepseek_v3_1_content(common_chat_msg_parser & builder) { - static const common_regex function_regex("(?:<｜tool▁call▁begin｜>)?([^\\n<]+)(?:<｜tool▁sep｜>)"); - - static const common_regex close_regex("(?:[\\s]*)?<｜tool▁call▁end｜>"); - static const common_regex tool_calls_begin("(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)"); - static const common_regex tool_calls_end("<｜tool▁calls▁end｜>"); - - if (!builder.syntax().parse_tool_calls) { - LOG_DBG("%s: not parse_tool_calls\n", __func__); - builder.add_content(builder.consume_rest()); - return; - } - - LOG_DBG("%s: parse_tool_calls\n", __func__); - - parse_json_tool_calls( - builder, - /* block_open= */ tool_calls_begin, - /* function_regex_start_only= */ std::nullopt, - function_regex, - close_regex, - tool_calls_end); -} - -static void common_chat_parse_deepseek_v3_1(common_chat_msg_parser & builder) { - // DeepSeek V3.1 outputs reasoning content between "" and "" tags, followed by regular content - // First try to parse using the standard reasoning parsing method - LOG_DBG("%s: thinking_forced_open: %s\n", __func__, std::to_string(builder.syntax().thinking_forced_open).c_str()); - - auto start_pos = builder.pos(); - auto found_end_think = builder.try_find_literal(""); - builder.move_to(start_pos); - - if (builder.syntax().thinking_forced_open && !builder.is_partial() && !found_end_think) { - LOG_DBG("%s: no end_think, not partial, adding content\n", __func__); - common_chat_parse_deepseek_v3_1_content(builder); - } else if (builder.try_parse_reasoning("", "")) { - // If reasoning was parsed successfully, the remaining content is regular content - LOG_DBG("%s: parsed reasoning, adding content\n", __func__); - // <｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>NAME\n```json\nJSON\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜> - common_chat_parse_deepseek_v3_1_content(builder); - } else { - if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE) { - LOG_DBG("%s: reasoning_format none, adding content\n", __func__); - common_chat_parse_deepseek_v3_1_content(builder); - return; - } - // If no reasoning tags found, check if we should treat everything as reasoning - if (builder.syntax().thinking_forced_open) { - // If thinking is forced open but no tags found, treat everything as reasoning - LOG_DBG("%s: thinking_forced_open, adding reasoning content\n", __func__); - builder.add_reasoning_content(builder.consume_rest()); - } else { - LOG_DBG("%s: no thinking_forced_open, adding content\n", __func__); - // <｜tool▁call▁begin｜>NAME<｜tool▁sep｜>JSON<｜tool▁call▁end｜> - common_chat_parse_deepseek_v3_1_content(builder); - } - } -} - -static void common_chat_parse_minimax_m2(common_chat_msg_parser & builder) { - static const xml_tool_call_format form { - /* form.scope_start = */ "", - /* form.tool_start = */ "", - /* form.key_start = */ "", - /* form.val_end = */ "", - /* form.tool_end = */ "", - /* form.scope_end = */ "", - }; - builder.consume_reasoning_with_xml_tool_calls(form, "", ""); -} - -static void common_chat_parse_kimi_k2(common_chat_msg_parser & builder) { - static const xml_tool_call_format form = ([]() { - xml_tool_call_format form {}; - form.scope_start = "<|tool_calls_section_begin|>"; - form.tool_start = "<|tool_call_begin|>"; - form.tool_sep = "<|tool_call_argument_begin|>{"; - form.key_start = "\""; - form.key_val_sep = "\":"; - form.val_end = ","; - form.tool_end = "}<|tool_call_end|>"; - form.scope_end = "<|tool_calls_section_end|>"; - form.raw_argval = false; - form.last_val_end = ""; - form.allow_toolcall_in_think = true; - return form; - })(); - builder.consume_reasoning_with_xml_tool_calls(form, "", ""); -} - -static void common_chat_parse_apriel_1_5(common_chat_msg_parser & builder) { - static const xml_tool_call_format form = ([]() { - xml_tool_call_format form {}; - form.scope_start = "["; - form.tool_start = "{\"name\": \""; - form.tool_sep = "\", \"arguments\": {"; - form.key_start = "\""; - form.key_val_sep = "\": "; - form.val_end = ", "; - form.tool_end = "}, "; - form.scope_end = "]"; - form.raw_argval = false; - form.last_val_end = ""; - form.last_tool_end = "}"; - return form; - })(); - builder.consume_reasoning_with_xml_tool_calls(form, "", ""); -} - -static void common_chat_parse_xiaomi_mimo(common_chat_msg_parser & builder) { - static const xml_tool_call_format form = ([]() { - xml_tool_call_format form {}; - form.scope_start = ""; - form.tool_start = "\n{\"name\": \""; - form.tool_sep = "\", \"arguments\": {"; - form.key_start = "\""; - form.key_val_sep = "\": "; - form.val_end = ", "; - form.tool_end = "}\n"; - form.scope_end = ""; - form.raw_argval = false; - form.last_val_end = ""; - return form; - })(); - builder.consume_reasoning_with_xml_tool_calls(form); -} - -static void common_chat_parse_gpt_oss(common_chat_msg_parser & builder) { - static const std::string constraint = "(?: (<\\|constrain\\|>)?([a-zA-Z0-9_-]+))"; - static const std::string recipient("(?: to=functions\\.([^<\\s]+))"); - - static const common_regex start_regex("<\\|start\\|>assistant"); - static const common_regex analysis_regex("<\\|channel\\|>analysis"); - static const common_regex final_regex("<\\|channel\\|>final" + constraint + "?"); - static const common_regex preamble_regex("<\\|channel\\|>commentary"); - static const common_regex tool_call1_regex(recipient + "<\\|channel\\|>(analysis|commentary)" + constraint + "?"); - static const common_regex tool_call2_regex("<\\|channel\\|>(analysis|commentary)" + recipient + constraint + "?"); - - auto consume_end = [&](bool include_end = false) { - if (auto res = builder.try_find_literal("<|end|>")) { - return res->prelude + (include_end ? builder.str(res->groups[0]) : ""); - } - return builder.consume_rest(); - }; - - auto handle_tool_call = [&](const std::string & name) { - if (auto args = builder.try_consume_json_with_dumped_args({{}})) { - if (builder.syntax().parse_tool_calls) { - if (!builder.add_tool_call(name, "", args->value) || args->is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - } else if (args->is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - } - }; - - auto regex_match = [](const common_regex & regex, const std::string & input) -> std::optional { - auto match = regex.search(input, 0, true); - if (match.type == COMMON_REGEX_MATCH_TYPE_FULL) { - return match; - } - return std::nullopt; - }; - - do { - auto header_start_pos = builder.pos(); - auto content_start = builder.try_find_literal("<|message|>"); - if (!content_start) { - throw common_chat_msg_partial_exception("incomplete header"); - } - - auto header = content_start->prelude; - - if (auto match = regex_match(tool_call1_regex, header)) { - auto group = match->groups[1]; - auto name = header.substr(group.begin, group.end - group.begin); - handle_tool_call(name); - continue; - } - - if (auto match = regex_match(tool_call2_regex, header)) { - auto group = match->groups[2]; - auto name = header.substr(group.begin, group.end - group.begin); - handle_tool_call(name); - continue; - } - - if (regex_match(analysis_regex, header)) { - builder.move_to(header_start_pos); - if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE || builder.syntax().reasoning_in_content) { - builder.add_content(consume_end(true)); - } else { - builder.try_parse_reasoning("<|channel|>analysis<|message|>", "<|end|>"); - } - continue; - } - - if(regex_match(final_regex, header) || regex_match(preamble_regex, header)) { - builder.add_content(consume_end()); - continue; - } - - // Possibly a malformed message, attempt to recover by rolling - // back to pick up the next <|start|> - LOG_DBG("%s: unknown header from message: %s\n", __func__, header.c_str()); - builder.move_to(header_start_pos); - } while (builder.try_find_regex(start_regex, std::string::npos, false)); - - auto remaining = builder.consume_rest(); - if (!remaining.empty()) { - LOG_DBG("%s: content after last message: %s\n", __func__, remaining.c_str()); - } -} - -static void common_chat_parse_glm_4_5(common_chat_msg_parser & builder) { - static const xml_tool_call_format form { - /* form.scope_start = */ "", - /* form.tool_start = */ "", - /* form.tool_sep = */ "", - /* form.key_start = */ "", - /* form.key_val_sep = */ "", - /* form.val_end = */ "", - /* form.tool_end = */ "", - /* form.scope_end = */ "", - /* form.key_val_sep2 = */ "", - }; - builder.consume_reasoning_with_xml_tool_calls(form, "", ""); -} - -static void common_chat_parse_firefunction_v2(common_chat_msg_parser & builder) { - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - static const common_regex prefix(regex_escape(" functools[")); - parse_prefixed_json_tool_call_array(builder, prefix, /* rstrip_prefix= */ 1); -} - -static void common_chat_parse_functionary_v3_2(common_chat_msg_parser & builder) { - static const common_regex function_regex_start_only(R"((\w+\n\{|python\n|all\n))"); - static const common_regex function_regex(R"(>>>(\w+\n\{|python\n|all\n))"); - static const common_regex close_regex(R"(\s*)"); - - parse_json_tool_calls( - builder, - std::nullopt, - function_regex_start_only, - function_regex, - close_regex, - std::nullopt, - /* allow_raw_python= */ true, - /* get_function_name= */ [&](const auto & res) -> std::string { - auto at_start = res.groups[0].begin == 0; - auto name = builder.str(res.groups[1]); - if (!name.empty() && name.back() == '{') { - // Unconsume the opening brace '{' to ensure the JSON parsing goes well. - builder.move_back(1); - } - auto idx = name.find_last_not_of("\n{"); - name = name.substr(0, idx + 1); - if (at_start && name == "all") { - return ""; - } - return name; - }); -} - -static void common_chat_parse_functionary_v3_1_llama_3_1(common_chat_msg_parser & builder) { - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - // This version of Functionary still supports the llama 3.1 tool call format for the python tool. - static const common_regex python_tag_regex(regex_escape("<|python_tag|>")); - - static const common_regex function_regex(R"()"); - static const common_regex close_regex(R"()"); - - parse_json_tool_calls( - builder, - /* block_open= */ std::nullopt, - /* function_regex_start_only= */ std::nullopt, - function_regex, - close_regex, - std::nullopt); - - if (auto res = builder.try_find_regex(python_tag_regex)) { - auto arguments = wrap_code_as_arguments(builder, builder.consume_rest()); - builder.add_tool_call("python", "", arguments); - return; - } -} - -static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) { - builder.try_parse_reasoning("", ""); - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - - static const common_regex open_regex( - "(?:" - "(```(?:xml|json)?\\n\\s*)?" // match 1 (block_start) - "(" // match 2 (open_tag) - "" - "|" - "|" - "|" - "|" - "|" - "|" - "|" - ")?" - "(\\s*\\{\\s*\"name\")" // match 3 (named tool call) - ")" - "|]+)>" // match 4 (function name) - "|" // match 5 (function name again) - ); - - while (auto res = builder.try_find_regex(open_regex)) { - const auto & block_start = res->groups[1]; - std::string block_end = block_start.empty() ? "" : "```"; - - const auto & open_tag = res->groups[2]; - std::string close_tag; - - if (!res->groups[3].empty()) { - builder.move_to(res->groups[3].begin); - close_tag = open_tag.empty() ? "" : "value) || tool_call->is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - builder.consume_spaces(); - builder.consume_literal(close_tag); - builder.consume_spaces(); - if (!block_end.empty()) { - builder.consume_literal(block_end); - builder.consume_spaces(); - } - } else { - throw common_chat_msg_partial_exception("failed to parse tool call"); - } - } else { - auto function_name = builder.str(res->groups[4]); - if (function_name.empty()) { - function_name = builder.str(res->groups[5]); - } - GGML_ASSERT(!function_name.empty()); - - close_tag = ""; - - if (auto arguments = builder.try_consume_json_with_dumped_args({{}})) { - if (!builder.add_tool_call(function_name, "", arguments->value) || arguments->is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - builder.consume_spaces(); - builder.consume_literal(close_tag); - builder.consume_spaces(); - if (!block_end.empty()) { - builder.consume_literal(block_end); - builder.consume_spaces(); - } - } - } - } - - builder.add_content(builder.consume_rest()); -} - -static void common_chat_parse_granite(common_chat_msg_parser & builder) { - // Parse thinking tags - static const common_regex start_think_regex(regex_escape("")); - static const common_regex end_think_regex(regex_escape("")); - // Granite models output partial tokens such as "<" and "groups[0].begin); - builder.try_find_regex(end_think_regex, std::string::npos, false); - // Restore position for try_parse_reasoning() - builder.move_to(res->groups[0].begin); - } - builder.try_parse_reasoning("", ""); - - // Parse response tags - static const common_regex start_response_regex(regex_escape("")); - static const common_regex end_response_regex(regex_escape("")); - // Granite models output partial tokens such as "<" and "")); - if (auto res = builder.try_find_regex(tool_call_regex)) { - builder.move_to(res->groups[0].end); - - // Expect JSON array of tool calls - if (auto tool_call = builder.try_consume_json_with_dumped_args({{{"arguments"}}})) { - if (!builder.add_tool_calls(tool_call->value) || tool_call->is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - } - } else { - builder.add_content(builder.consume_rest()); - } -} - -static void common_chat_parse_nemotron_v2(common_chat_msg_parser & builder) { - // Parse thinking tags - builder.try_parse_reasoning("", ""); - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - - // Look for tool calls - static const common_regex tool_call_regex(regex_escape("")); - if (auto res = builder.try_find_regex(tool_call_regex)) { - builder.move_to(res->groups[0].end); - - // Expect JSON array of tool calls - auto tool_calls_data = builder.consume_json(); - if (tool_calls_data.json.is_array()) { - if (!builder.try_consume_literal("")) { - throw common_chat_msg_partial_exception("Incomplete tool call"); - } - builder.add_tool_calls(tool_calls_data.json); - } else { - throw common_chat_msg_partial_exception("Incomplete tool call"); - } - } - builder.add_content(builder.consume_rest()); -} - -static void common_chat_parse_apertus(common_chat_msg_parser & builder) { - // Parse thinking tags - builder.try_parse_reasoning("<|inner_prefix|>", "<|inner_suffix|>"); - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - - // Look for tool calls - static const common_regex tool_call_regex(regex_escape("<|tools_prefix|>")); - if (auto res = builder.try_find_regex(tool_call_regex)) { - builder.move_to(res->groups[0].end); - - auto tool_calls_data = builder.consume_json(); - if (tool_calls_data.json.is_array()) { - builder.consume_spaces(); - if (!builder.try_consume_literal("<|tools_suffix|>")) { - throw common_chat_msg_partial_exception("Incomplete tool call"); - } - for (const auto & value : tool_calls_data.json) { - if (value.is_object()) { - builder.add_tool_call_short_form(value); - } - } - } else { - throw common_chat_msg_partial_exception("Incomplete tool call"); - } - } - builder.add_content(builder.consume_rest()); -} - - -static void common_chat_parse_lfm2(common_chat_msg_parser & builder) { - if (!builder.syntax().parse_tool_calls) { - builder.add_content(builder.consume_rest()); - return; - } - - // LFM2 format: <|tool_call_start|>[{"name": "get_current_time", "arguments": {"location": "Paris"}}]<|tool_call_end|> - static const common_regex tool_call_start_regex(regex_escape("<|tool_call_start|>")); - static const common_regex tool_call_end_regex(regex_escape("<|tool_call_end|>")); - - // Loop through all tool calls - while (auto res = builder.try_find_regex(tool_call_start_regex, std::string::npos, /* add_prelude_to_content= */ true)) { - builder.move_to(res->groups[0].end); - - // Parse JSON array format: [{"name": "...", "arguments": {...}}] - auto tool_calls_data = builder.consume_json(); - - // Consume end marker - builder.consume_spaces(); - if (!builder.try_consume_regex(tool_call_end_regex)) { - throw common_chat_msg_partial_exception("Expected <|tool_call_end|>"); - } - - // Process each tool call in the array - if (tool_calls_data.json.is_array()) { - for (const auto & tool_call : tool_calls_data.json) { - if (!tool_call.is_object()) { - throw common_chat_msg_partial_exception("Tool call must be an object"); - } - - if (!tool_call.contains("name")) { - throw common_chat_msg_partial_exception("Tool call missing 'name' field"); - } - - std::string function_name = tool_call.at("name"); - std::string arguments = "{}"; - - if (tool_call.contains("arguments")) { - if (tool_call.at("arguments").is_object()) { - arguments = tool_call.at("arguments").dump(); - } else if (tool_call.at("arguments").is_string()) { - arguments = tool_call.at("arguments"); - } - } - - if (!builder.add_tool_call(function_name, "", arguments)) { - throw common_chat_msg_partial_exception("Incomplete tool call"); - } - } - } else { - throw common_chat_msg_partial_exception("Expected JSON array for tool calls"); - } - - // Consume any trailing whitespace after this tool call - builder.consume_spaces(); - } - - // Consume any remaining content after all tool calls - auto remaining = builder.consume_rest(); - if (!string_strip(remaining).empty()) { - builder.add_content(remaining); - } -} - -static void common_chat_parse_seed_oss(common_chat_msg_parser & builder) { - static const xml_tool_call_format form { - /* form.scope_start = */ "", - /* form.tool_start = */ "", - /* form.key_start = */ "", - /* form.val_end = */ "", - /* form.tool_end = */ "", - /* form.scope_end = */ "", - }; - builder.consume_reasoning_with_xml_tool_calls(form, "", ""); -} - -static void common_chat_parse_solar_open(common_chat_msg_parser & builder) { - builder.try_parse_reasoning("<|think|>", "<|end|><|begin|>assistant<|content|>"); - - // TODO: Tool calling - - builder.add_content(builder.consume_rest()); -} - -static void common_chat_parse_exaone_moe_content(common_chat_msg_parser & builder) { - // 1) { "name": "...", "arguments": {...} } - // 2) { "id": "...", "type": "function", "function": { "name": "...", "arguments": {...} } } - static const common_regex tool_call_open(R"(]*>)"); - - if (!builder.syntax().parse_tool_calls) { - LOG_DBG("%s: not parse_tool_calls\n", __func__); - builder.add_content(builder.consume_rest()); - return; - } - - LOG_DBG("%s: parse_tool_calls\n", __func__); - - // Find all blocks - while (auto first = builder.try_find_regex(tool_call_open, std::string::npos, /* add_prelude_to_content= */ true)) { - builder.move_to(first->groups[0].end); - builder.consume_spaces(); - - builder.try_consume_literal("```json"); - builder.try_consume_literal("```"); - builder.consume_spaces(); - - // Consume JSON object - auto data = builder.consume_json(); - - builder.consume_spaces(); - builder.try_consume_literal("```"); - builder.consume_spaces(); - - if (!builder.try_consume_literal("")) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - builder.consume_spaces(); - - // Extract name and arguments - std::string name; - std::string id; - nlohmann::ordered_json arguments; - - const auto extract_args = [&](const nlohmann::ordered_json & obj) -> bool { - if (!obj.contains("name") || !obj.contains("arguments")) { - return false; - } - name = obj.at("name").get(); - arguments = obj.at("arguments"); - if (obj.contains("id") && obj.at("id").is_string()) { - id = obj.at("id").get(); - } - return true; - }; - - if (!extract_args(data.json)) { - if (data.json.contains("function") && data.json.at("function").is_object()) { - auto fn = data.json.at("function"); - extract_args(fn); - if (id.empty() && data.json.contains("id") && data.json.at("id").is_string()) { - id = data.json.at("id").get(); - } - } - } - - // If name is empty, treat the JSON object as content - if (name.empty()) { - LOG_DBG("%s: tool call missing name, treating as content\n", __func__); - builder.add_content(data.json.dump()); - continue; - } - - std::string args_str = arguments.dump(); - if (!builder.add_tool_call(name, id, args_str)) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - } - - builder.add_content(builder.consume_rest()); -} - -static void common_chat_parse_exaone_moe(common_chat_msg_parser & builder) { - LOG_DBG("%s: parsing exaone_moe\n", __func__); - // EXAONE MoE outputs reasoning content between "" and "" tags, followed by regular content - // First try to parse using the standard reasoning parsing method - LOG_DBG("%s: thinking_forced_open: %s\n", __func__, std::to_string(builder.syntax().thinking_forced_open).c_str()); - - auto start_pos = builder.pos(); - auto found_end_think = builder.try_find_literal(""); - builder.move_to(start_pos); - - if (builder.syntax().thinking_forced_open && !builder.is_partial() && !found_end_think) { - LOG_DBG("%s: no end_think, not partial, adding content\n", __func__); - common_chat_parse_exaone_moe_content(builder); - } else if (builder.try_parse_reasoning("", "")) { - // If reasoning was parsed successfully, the remaining content is regular content - LOG_DBG("%s: parsed reasoning, adding content\n", __func__); - common_chat_parse_exaone_moe_content(builder); - } else { - if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE) { - LOG_DBG("%s: reasoning_format none, adding content\n", __func__); - common_chat_parse_exaone_moe_content(builder); - return; - } - // If no reasoning tags found, check if we should treat everything as reasoning - if (builder.syntax().thinking_forced_open) { - // If thinking is forced open but no tags found, treat everything as reasoning - LOG_DBG("%s: thinking_forced_open, adding reasoning content\n", __func__); - builder.add_reasoning_content(builder.consume_rest()); - } else { - LOG_DBG("%s: no thinking_forced_open, adding content\n", __func__); - common_chat_parse_exaone_moe_content(builder); - } - } -} - -static void common_chat_parse_content_only(common_chat_msg_parser & builder) { - builder.try_parse_reasoning("", ""); - builder.add_content(builder.consume_rest()); -} - -static void common_chat_parse(common_chat_msg_parser & builder) { - LOG_DBG("Parsing input with format %s: %s\n", common_chat_format_name(builder.syntax().format), builder.input().c_str()); - - switch (builder.syntax().format) { - case COMMON_CHAT_FORMAT_CONTENT_ONLY: - common_chat_parse_content_only(builder); - break; - case COMMON_CHAT_FORMAT_GENERIC: - common_chat_parse_generic(builder); - break; - case COMMON_CHAT_FORMAT_MISTRAL_NEMO: - common_chat_parse_mistral_nemo(builder); - break; - case COMMON_CHAT_FORMAT_MAGISTRAL: - common_chat_parse_magistral(builder); - break; - case COMMON_CHAT_FORMAT_LLAMA_3_X: - common_chat_parse_llama_3_1(builder); - break; - case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: - common_chat_parse_llama_3_1(builder, /* with_builtin_tools= */ true); - break; - case COMMON_CHAT_FORMAT_DEEPSEEK_R1: - common_chat_parse_deepseek_r1(builder); - break; - case COMMON_CHAT_FORMAT_DEEPSEEK_V3_1: - common_chat_parse_deepseek_v3_1(builder); - break; - case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: - common_chat_parse_functionary_v3_2(builder); - break; - case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: - common_chat_parse_functionary_v3_1_llama_3_1(builder); - break; - case COMMON_CHAT_FORMAT_HERMES_2_PRO: - common_chat_parse_hermes_2_pro(builder); - break; - case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: - common_chat_parse_firefunction_v2(builder); - break; - case COMMON_CHAT_FORMAT_COMMAND_R7B: - common_chat_parse_command_r7b(builder); - break; - case COMMON_CHAT_FORMAT_GRANITE: - common_chat_parse_granite(builder); - break; - case COMMON_CHAT_FORMAT_GPT_OSS: - common_chat_parse_gpt_oss(builder); - break; - case COMMON_CHAT_FORMAT_SEED_OSS: - common_chat_parse_seed_oss(builder); - break; - case COMMON_CHAT_FORMAT_NEMOTRON_V2: - common_chat_parse_nemotron_v2(builder); - break; - case COMMON_CHAT_FORMAT_APERTUS: - common_chat_parse_apertus(builder); - break; - case COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS: - common_chat_parse_lfm2(builder); - break; - case COMMON_CHAT_FORMAT_MINIMAX_M2: - common_chat_parse_minimax_m2(builder); - break; - case COMMON_CHAT_FORMAT_GLM_4_5: - common_chat_parse_glm_4_5(builder); - break; - case COMMON_CHAT_FORMAT_KIMI_K2: - common_chat_parse_kimi_k2(builder); - break; - case COMMON_CHAT_FORMAT_APRIEL_1_5: - common_chat_parse_apriel_1_5(builder); - break; - case COMMON_CHAT_FORMAT_XIAOMI_MIMO: - common_chat_parse_xiaomi_mimo(builder); - break; - case COMMON_CHAT_FORMAT_SOLAR_OPEN: - common_chat_parse_solar_open(builder); - break; - case COMMON_CHAT_FORMAT_EXAONE_MOE: - common_chat_parse_exaone_moe(builder); - break; - default: - throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format)); - } - builder.finish(); -} - -common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_parser_params & syntax) { - if (syntax.format == COMMON_CHAT_FORMAT_PEG_SIMPLE || - syntax.format == COMMON_CHAT_FORMAT_PEG_NATIVE || - syntax.format == COMMON_CHAT_FORMAT_PEG_CONSTRUCTED) { - return common_chat_peg_parse(syntax.parser, input, is_partial, syntax); - } - common_chat_msg_parser builder(input, is_partial, syntax); - try { - common_chat_parse(builder); - } catch (const common_chat_msg_partial_exception & ex) { - LOG_DBG("Partial parse: %s\n", ex.what()); - if (!is_partial) { - builder.clear_tools(); - builder.move_to(0); - common_chat_parse_content_only(builder); - } - } - auto msg = builder.result(); - if (!is_partial) { - LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat({msg}).at(0).dump().c_str()); - } - return msg; -} - -common_chat_msg common_chat_peg_parse(const common_peg_arena & parser, const std::string & input, bool is_partial, const common_chat_parser_params & syntax) { - if (parser.empty()) { - throw std::runtime_error("Failed to parse due to missing parser definition."); - } - - LOG_DBG("Parsing input with format %s: %s\n", common_chat_format_name(syntax.format), input.c_str()); - - common_peg_parse_context ctx(input, is_partial); - auto result = parser.parse(ctx); - if (result.fail()) { - throw std::runtime_error(std::string("Failed to parse input at pos ") + std::to_string(result.end)); - } - - common_chat_msg msg; - msg.role = "assistant"; - - if (syntax.format == COMMON_CHAT_FORMAT_PEG_NATIVE) { - auto mapper = common_chat_peg_native_mapper(msg); - mapper.from_ast(ctx.ast, result); - } else if (syntax.format == COMMON_CHAT_FORMAT_PEG_CONSTRUCTED) { - auto mapper = common_chat_peg_constructed_mapper(msg); - mapper.from_ast(ctx.ast, result); - } else { - // Generic mapper - auto mapper = common_chat_peg_mapper(msg); - mapper.from_ast(ctx.ast, result); - } - if (!is_partial) { - LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat({msg}).at(0).dump().c_str()); - } - return msg; -} diff --git a/common/chat-parser.h b/common/chat-parser.h deleted file mode 100644 index 3ed9c30a2b..0000000000 --- a/common/chat-parser.h +++ /dev/null @@ -1,133 +0,0 @@ -#pragma once - -#include "chat.h" -#include "chat-parser-xml-toolcall.h" -#include "json-partial.h" -#include "regex-partial.h" - -#include - -#include -#include -#include - -class common_chat_msg_partial_exception : public std::runtime_error { - public: - common_chat_msg_partial_exception(const std::string & message) : std::runtime_error(message) {} -}; - -class common_chat_msg_parser { - std::string input_; - bool is_partial_; - common_chat_parser_params syntax_; // TODO: rename to params - std::string healing_marker_; - - size_t pos_ = 0; - common_chat_msg result_; - - public: - common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_parser_params & syntax); - const std::string & input() const { return input_; } - size_t pos() const { return pos_; } - const std::string & healing_marker() const { return healing_marker_; } - const bool & is_partial() const { return is_partial_; } - const common_chat_msg & result() const { return result_; } - const common_chat_parser_params & syntax() const { return syntax_; } - - void move_to(size_t pos) { - if (pos > input_.size()) { - throw std::runtime_error("Invalid position!"); - } - pos_ = pos; - } - void move_back(size_t n) { - if (pos_ < n) { - throw std::runtime_error("Can't move back that far!"); - } - pos_ -= n; - } - - // Get the substring of the input at the given range - std::string str(const common_string_range & rng) const; - - // Appends to the result.content field - void add_content(const std::string & content); - - // Appends to the result.reasoning_content field - void add_reasoning_content(const std::string & reasoning_content); - - // Adds a tool call to the result. If the tool call is too incomplete (e.g. name empty), it won't add anything. - bool add_tool_call(const std::string & name, const std::string & id, const std::string & arguments); - - // Adds a tool call using the "name", "id" and "arguments" fields of the json object - bool add_tool_call(const nlohmann::ordered_json & tool_call); - - // Adds an array of tool calls using their "name", "id" and "arguments" fields. - bool add_tool_calls(const nlohmann::ordered_json & arr); - - // Adds a tool call using the short form: { "tool_name": { "arg1": val, "arg2": val } } - bool add_tool_call_short_form(const nlohmann::ordered_json & tool_call); - - void finish(); - - bool consume_spaces(); - - void consume_literal(const std::string & literal); - - bool try_parse_reasoning(const std::string & start_think, const std::string & end_think); - - std::string consume_rest(); - - struct find_regex_result { - std::string prelude; - std::vector groups; - }; - - std::optional try_find_regex(const common_regex & regex, size_t from = std::string::npos, bool add_prelude_to_content = true); - - bool try_consume_literal(const std::string & literal); - - std::optional try_find_literal(const std::string & literal); - - find_regex_result consume_regex(const common_regex & regex); - - std::optional try_consume_regex(const common_regex & regex); - - std::optional try_consume_json(); - common_json consume_json(); - - struct consume_json_result { - nlohmann::ordered_json value; - bool is_partial; - }; - - /* - Consume (possibly partial) json and converts specific subtrees to (possibly truncated) JSON strings. - - By default, object keys can't be truncated, nor can string values (their corresponding key is removed, - e.g. `{"foo": "bar", "baz": "b` -> `{"foo": "bar"}` - - But one can allow subpaths to be kept truncated, and possibly json-dumped to truncated json strings - - with `content_paths={{"foo"}}` -> `{"foo": "b` -> {"foo": "b"}` - - with `args_paths={{"foo"}}` -> `{"foo": {"b` -> `{"foo": "{b"}` - */ - consume_json_result consume_json_with_dumped_args( - const std::vector> & args_paths = {}, - const std::vector> & content_paths = {} - ); - std::optional try_consume_json_with_dumped_args( - const std::vector> & args_paths = {}, - const std::vector> & content_paths = {} - ); - - /** - * Parse XML-Style tool call for given xml_tool_call_format. Return false for invalid syntax and get the position untouched. - * form.scope_start, form.tool_sep and form.scope_end can be empty. - */ - bool try_consume_xml_tool_calls(const struct xml_tool_call_format & form); - - // Parse content uses reasoning and XML-Style tool call - void consume_reasoning_with_xml_tool_calls(const struct xml_tool_call_format & form, const std::string & start_think = "", const std::string & end_think = ""); - - void clear_tools(); -}; diff --git a/common/chat-peg-parser.cpp b/common/chat-peg-parser.cpp index 1bcba9cd86..4c5bb6218d 100644 --- a/common/chat-peg-parser.cpp +++ b/common/chat-peg-parser.cpp @@ -1,13 +1,17 @@ #include "chat-peg-parser.h" +#include "chat-auto-parser.h" +#include "ggml.h" +#include "peg-parser.h" + #include -using json = nlohmann::json; +using ordered_json = nlohmann::ordered_json; static std::string_view trim_trailing_space(std::string_view sv, int max = -1) { int count = 0; while (!sv.empty() && std::isspace(static_cast(sv.back()))) { - if (max != -1 && count <= max) { + if (max != -1 && count >= max) { break; } sv.remove_suffix(1); @@ -16,109 +20,820 @@ static std::string_view trim_trailing_space(std::string_view sv, int max = -1) { return sv; } -void common_chat_peg_mapper::from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result) { +static std::string_view trim_leading_space(std::string_view sv, int max = -1) { + int count = 0; + while (!sv.empty() && std::isspace(static_cast(sv.front()))) { + if (max != -1 && count >= max) { + break; + } + sv.remove_prefix(1); + count++; + } + return sv; +} + +static std::string_view trim(std::string_view sv) { + return trim_trailing_space(trim_leading_space(sv, 1)); +} + +// Count the number of unclosed '{' braces in a JSON-like string, +// properly skipping braces inside quoted strings. +static int json_brace_depth(const std::string & s) { + int depth = 0; + bool in_string = false; + bool escaped = false; + for (char c : s) { + if (escaped) { + escaped = false; + continue; + } + if (c == '\\' && in_string) { + escaped = true; + continue; + } + if (c == '"') { + in_string = !in_string; + continue; + } + if (!in_string) { + if (c == '{') { + depth++; + } else if (c == '}') { + depth--; + } + } + } + return depth; +} + +// JSON-escape a string and return the inner content (without surrounding quotes). +static std::string escape_json_string_inner(const std::string & s) { + std::string escaped = ordered_json(s).dump(); + if (escaped.size() >= 2 && escaped.front() == '"' && escaped.back() == '"') { + return escaped.substr(1, escaped.size() - 2); + } + return escaped; +} + +// Convert Python-style single-quoted strings to JSON double-quoted strings +// Only converts outer string delimiters, properly handling escape sequences: +// - {'key': 'value'} -> {"key": "value"} +// - {'code': 'print(\'hello\')'} -> {"code": "print('hello')"} +// - {'msg': 'He said "hi"'} -> {"msg": "He said \"hi\""} +static std::string normalize_quotes_to_json(const std::string & input) { + std::string result; + result.reserve(input.size() + 16); // May need extra space for escaping + + bool in_single_quoted = false; + bool in_double_quoted = false; + + for (size_t i = 0; i < input.size(); ++i) { + char c = input[i]; + + // Handle escape sequences + if (c == '\\' && i + 1 < input.size()) { + char next = input[i + 1]; + + if (in_single_quoted) { + // Inside a single-quoted string being converted to double quotes + if (next == '\'') { + // \' -> ' (escaped single quote becomes unescaped in double-quoted string) + result += '\''; + ++i; + continue; + } + if (next == '"') { + // \" stays as \" (already escaped, works in double-quoted string) + result += "\\\""; + ++i; + continue; + } + // Other escapes (\n, \\, etc.): pass through both characters + result += c; + result += next; + ++i; + continue; + } + + if (in_double_quoted) { + // Inside a double-quoted string - pass through escape sequences as-is + result += c; + result += next; + ++i; + continue; + } + + // Outside any string - just pass through the backslash + result += c; + continue; + } + + // Handle quote characters + if (c == '"') { + if (in_single_quoted) { + // Unescaped double quote inside single-quoted string -> must escape for JSON + result += "\\\""; + } else { + // Double quote as string delimiter or outside strings + in_double_quoted = !in_double_quoted; + result += c; + } + } else if (c == '\'') { + if (in_double_quoted) { + // Single quote inside double-quoted string -> pass through + result += c; + } else if (in_single_quoted) { + // Closing single quote -> convert to double quote + in_single_quoted = false; + result += '"'; + } else { + // Opening single quote -> convert to double quote + in_single_quoted = true; + result += '"'; + } + } else { + result += c; + } + } + + return result; +} + +void tag_based_peg_mapper::from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result) { arena.visit(result, [this](const common_peg_ast_node & node) { - map(node); + if (!node.tag.empty()) { + tags[node.tag] = std::string(node.text); + } }); } -void common_chat_peg_mapper::map(const common_peg_ast_node & node) { - bool is_reasoning = node.tag == common_chat_peg_builder::REASONING; - bool is_content = node.tag == common_chat_peg_builder::CONTENT; +tagged_parse_result tagged_peg_parser::parse_and_extract(const std::string & input, common_peg_parse_flags extra_flags) const { + common_peg_parse_context ctx(input, flags | extra_flags); + auto parse_result = arena.parse(ctx); - if (is_reasoning) { - result.reasoning_content = std::string(trim_trailing_space(node.text)); + tag_based_peg_mapper mapper; + mapper.from_ast(ctx.ast, parse_result); + + return { std::move(parse_result), std::move(mapper.tags) }; +} + +tagged_parse_result tagged_peg_parser::parse_anywhere_and_extract(const std::string & input) const { + if (input.empty()) { + return parse_and_extract(input); } + for (size_t i = 0; i < input.size(); i++) { + common_peg_parse_context ctx(input, flags); + auto parse_result = arena.parse(ctx, i); + if (parse_result.success() || i == input.size() - 1) { + tag_based_peg_mapper mapper; + mapper.from_ast(ctx.ast, parse_result); + return { std::move(parse_result), std::move(mapper.tags) }; + } + } + GGML_ABORT("Should not happen"); +} - if (is_content) { - result.content = std::string(trim_trailing_space(node.text)); +tagged_peg_parser build_tagged_peg_parser( + const std::function & fn) { + common_peg_parser_builder builder; + builder.set_root(fn(builder)); + return { builder.build() }; +} + +common_peg_parser common_chat_peg_builder::tag_with_safe_content(const std::string & tag_name, + const std::string & marker, + const common_peg_parser & p) { + if (marker.empty()) { + return zero_or_more(choice({ p, rule(tag_name, content(any())) })); + } + auto content_chunk = rule(tag_name, content(negate(literal(marker)) + any() + until(marker))); + return zero_or_more(choice({ p, content_chunk })); +} + +std::string & common_chat_peg_mapper::args_target() { + return (current_tool && !current_tool->name.empty()) ? current_tool->arguments : args_buffer; +} + +void common_chat_peg_mapper::from_ast(const common_peg_ast_arena & arena, + const common_peg_parse_result & parse_result_arg) { + arena.visit(parse_result_arg, [this](const common_peg_ast_node & node) { map(node); }); + // Flush any pending tool call that was started but never got a name + // This happens during partial parsing when the tool call is incomplete + if (pending_tool_call.has_value() && !pending_tool_call->name.empty()) { + if (!args_buffer.empty()) { + pending_tool_call->arguments = args_buffer; + } + if (closing_quote_pending && !pending_tool_call->arguments.empty()) { + pending_tool_call->arguments += "\""; + } + result.tool_calls.push_back(pending_tool_call.value()); + pending_tool_call.reset(); } } -void common_chat_peg_native_mapper::map(const common_peg_ast_node & node) { - common_chat_peg_mapper::map(node); +void common_chat_peg_mapper::map(const common_peg_ast_node & node) { + // Handle reasoning/content tags + bool is_reasoning = node.tag == common_chat_peg_builder::REASONING; + bool is_content = node.tag == common_chat_peg_builder::CONTENT; - bool is_tool_open = node.tag == common_chat_peg_native_builder::TOOL_OPEN; - bool is_tool_name = node.tag == common_chat_peg_native_builder::TOOL_NAME; - bool is_tool_id = node.tag == common_chat_peg_native_builder::TOOL_ID; - bool is_tool_args = node.tag == common_chat_peg_native_builder::TOOL_ARGS; + if (is_reasoning) { // GPT OSS can have more than 1 reasoning block, so concatenate here + result.reasoning_content += std::string(node.text); + } + + if (is_content) { + // Concatenate content from multiple content nodes (e.g., when reasoning markers + // are preserved before content markers in reasoning_format=NONE mode) + result.content += std::string(node.text); + } + + // Handle tool-related tags (supporting both JSON and tagged formats) + bool is_tool_open = node.tag == common_chat_peg_builder::TOOL_OPEN; + bool is_tool_close = node.tag == common_chat_peg_builder::TOOL_CLOSE; + bool is_tool_name = node.tag == common_chat_peg_builder::TOOL_NAME; + bool is_tool_id = node.tag == common_chat_peg_builder::TOOL_ID; + bool is_tool_args = node.tag == common_chat_peg_builder::TOOL_ARGS; + bool is_arg_open = node.tag == common_chat_peg_builder::TOOL_ARG_OPEN; + bool is_arg_close = node.tag == common_chat_peg_builder::TOOL_ARG_CLOSE; + bool is_arg_name = node.tag == common_chat_peg_builder::TOOL_ARG_NAME; + bool is_arg_value = node.tag == common_chat_peg_builder::TOOL_ARG_VALUE; + bool is_arg_string_value = node.tag == common_chat_peg_builder::TOOL_ARG_STRING_VALUE; if (is_tool_open) { - result.tool_calls.emplace_back(); - current_tool = &result.tool_calls.back(); + pending_tool_call = common_chat_tool_call(); + current_tool = &pending_tool_call.value(); + arg_count = 0; + args_buffer.clear(); + closing_quote_pending = false; } if (is_tool_id && current_tool) { - current_tool->id = std::string(trim_trailing_space(node.text)); + auto text = trim_trailing_space(node.text); + if (text.size() >= 2 && text.front() == '"' && text.back() == '"') { + text = text.substr(1, text.size() - 2); + } + current_tool->id = std::string(text); } if (is_tool_name && current_tool) { current_tool->name = std::string(trim_trailing_space(node.text)); + // Now that we have the name, populate the arguments from the buffer + if (!args_buffer.empty()) { + current_tool->arguments = args_buffer; + args_buffer.clear(); + } else if (current_tool->arguments.empty()) { + current_tool->arguments = "{"; + } + // Add the tool call to results so streaming can see it + if (pending_tool_call.has_value()) { + result.tool_calls.push_back(pending_tool_call.value()); + pending_tool_call.reset(); + current_tool = &result.tool_calls.back(); + } } if (is_tool_args && current_tool) { - current_tool->arguments = std::string(trim_trailing_space(node.text)); - } -} - -void common_chat_peg_constructed_mapper::map(const common_peg_ast_node & node) { - common_chat_peg_mapper::map(node); - - bool is_tool_open = node.tag == common_chat_peg_constructed_builder::TOOL_OPEN; - bool is_tool_name = node.tag == common_chat_peg_constructed_builder::TOOL_NAME; - bool is_tool_close = node.tag == common_chat_peg_constructed_builder::TOOL_CLOSE; - bool is_arg_open = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_OPEN; - bool is_arg_close = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_CLOSE; - bool is_arg_name = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_NAME; - bool is_arg_string = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_STRING_VALUE; - bool is_arg_json = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_JSON_VALUE; - - if (is_tool_open) { - result.tool_calls.emplace_back(); - current_tool = &result.tool_calls.back(); - arg_count = 0; - } - - if (is_tool_name) { - current_tool->name = std::string(node.text); - current_tool->arguments = "{"; + // For JSON format: arguments come as a complete JSON object + // For tagged format: built up from individual arg_name/arg_value nodes + auto text = trim_trailing_space(node.text); + if (!text.empty() && text.front() == '{') { + args_target() = std::string(text); + } } if (is_arg_open) { - needs_closing_quote = false; + closing_quote_pending = false; } if (is_arg_name && current_tool) { + std::string arg_entry; if (arg_count > 0) { - current_tool->arguments += ","; + arg_entry = ","; } - current_tool->arguments += json(trim_trailing_space(node.text)).dump() + ":"; + arg_entry += ordered_json(trim(node.text)).dump() + ":"; ++arg_count; + + auto & target = args_target(); + if (target.empty()) { + target = "{"; + } + target += arg_entry; } - if (is_arg_string && current_tool) { - // Serialize to JSON, but exclude the end quote - std::string dumped = json(trim_trailing_space(node.text)).dump(); - current_tool->arguments += dumped.substr(0, dumped.size() - 1); - needs_closing_quote = true; + if ((is_arg_value || is_arg_string_value) && current_tool) { + std::string value_content = std::string(trim_trailing_space(trim_leading_space(node.text, 1), 1)); + + std::string value_to_add; + if (value_content.empty() && is_arg_string_value) { + // Empty string value - arg_close will add the closing quote + value_to_add = "\""; + closing_quote_pending = true; + } else if (!value_content.empty() && is_arg_string_value) { + // Schema declares this as string type - always treat as literal string value + if (!closing_quote_pending) { + value_to_add = "\""; + closing_quote_pending = true; + } + value_to_add += escape_json_string_inner(value_content); + } else if (!value_content.empty()) { + // For potential containers, normalize Python-style single quotes to JSON double quotes + bool is_potential_container = value_content[0] == '[' || value_content[0] == '{'; + if (is_potential_container) { + value_content = normalize_quotes_to_json(value_content); + } + + // Try to parse as JSON value (number, bool, null, object, array) + try { + ordered_json parsed = ordered_json::parse(value_content); + if (parsed.is_string()) { + // Don't add closing quote yet (added by arg_close) for monotonic streaming + std::string escaped = parsed.dump(); + if (!escaped.empty() && escaped.back() == '"') { + escaped.pop_back(); + } + value_to_add = escaped; + closing_quote_pending = true; + } else { + // Non-string values: use raw content to preserve whitespace for monotonicity + value_to_add = value_content; + } + } catch (...) { + if (node.is_partial && is_potential_container) { + // Partial container: pass through the already-normalized content + value_to_add = value_content; + } else { + // Not valid JSON - treat as string value + if (!closing_quote_pending) { + value_to_add = "\""; + closing_quote_pending = true; + } + value_to_add += escape_json_string_inner(value_content); + } + } + } + + args_target() += value_to_add; } if (is_arg_close && current_tool) { - if (needs_closing_quote) { - current_tool->arguments += "\""; - needs_closing_quote = false; + if (closing_quote_pending) { + args_target() += "\""; + closing_quote_pending = false; } } - if (is_arg_json && current_tool) { - current_tool->arguments += std::string(trim_trailing_space(node.text)); - } - if (is_tool_close && current_tool) { - if (needs_closing_quote) { - current_tool->arguments += "\""; - needs_closing_quote = false; + // Flush buffer to arguments if tool name was never seen + if (current_tool->name.empty() && !args_buffer.empty()) { + current_tool->arguments = args_buffer; + args_buffer.clear(); + } + // Close any pending string quote + if (closing_quote_pending) { + current_tool->arguments += "\""; + closing_quote_pending = false; + } + // Close any unclosed braces (accounts for nested objects) + for (int d = json_brace_depth(current_tool->arguments); d > 0; d--) { + current_tool->arguments += "}"; + } + // Add tool call to results if named; otherwise discard + if (pending_tool_call.has_value()) { + if (!current_tool->name.empty()) { + result.tool_calls.push_back(pending_tool_call.value()); + } + pending_tool_call.reset(); } - current_tool->arguments += "}"; } } + +common_peg_parser common_chat_peg_builder::standard_constructed_tools( + const std::map & markers, + const ordered_json & tools, + bool parallel_tool_calls, + bool force_tool_calls) { + if (!tools.is_array() || tools.empty()) { + return eps(); + } + + // Extract markers with defaults + auto get_marker = [&markers](const std::string & key, const std::string & default_val = "") -> std::string { + auto it = markers.find(key); + return it != markers.end() ? it->second : default_val; + }; + + std::string section_start = get_marker("tool_call_start_marker", ""); + std::string section_end = get_marker("tool_call_end_marker", ""); + std::string func_opener = get_marker("function_opener", ""); + std::string func_closer = get_marker("function_closer", ""); + std::string param_key_prefix = get_marker("parameter_key_prefix", ""); + std::string param_closer = get_marker("parameter_closer", ""); + + // Build tool choices for tagged format + auto tool_choices = choice(); + + for (const auto & tool_def : tools) { + if (!tool_def.contains("function")) { + continue; + } + const auto & function = tool_def.at("function"); + std::string name = function.at("name"); + ordered_json params = function.contains("parameters") ? function.at("parameters") : ordered_json::object(); + + // Build argument parsers + auto args = eps(); + if (params.contains("properties") && !params["properties"].empty()) { + auto arg_choice = choice(); + for (const auto & el : params["properties"].items()) { + const std::string & prop_name = el.key(); + + auto arg_name_parser = + choice({ literal(prop_name), literal("\"" + prop_name + "\""), literal("'" + prop_name + "'") }); + + auto arg_rule = tool_arg(tool_arg_open(literal(param_key_prefix)) + tool_arg_name(arg_name_parser) + + literal(param_key_suffix) + tool_arg_value(until(param_closer)) + + tool_arg_close(literal(param_closer))); + arg_choice |= arg_rule; + } + args = zero_or_more(arg_choice + space()); + } + + // Build function parser: args + auto tool_parser = tool(tool_open(literal(func_opener) + tool_name(literal(name)) + literal(func_name_suffix)) + + space() + tool_args(args) + space() + tool_close(literal(func_closer))); + + tool_choices |= rule("tool-" + name, tool_parser); + } + + // Build the section with markers + auto section = + parallel_tool_calls ? + trigger_rule("tool-call", literal(section_start) + space() + one_or_more(tool_choices + space()) + + literal(section_end)) : + trigger_rule("tool-call", literal(section_start) + space() + tool_choices + space() + literal(section_end)); + + return force_tool_calls ? section : optional(section); +} + +// Python-style tool calls: name(arg1="value1", arg2=123) +// Used only by LFM2 for now, so we don't merge it into autoparser +common_peg_parser common_chat_peg_builder::python_style_tool_calls( + const ordered_json & tools, + bool parallel_tool_calls) { + if (!tools.is_array() || tools.empty()) { + return eps(); + } + + auto tool_choices = choice(); + + for (const auto & tool_def : tools) { + if (!tool_def.contains("function")) { + continue; + } + const auto & function = tool_def.at("function"); + std::string name = function.at("name"); + ordered_json params = function.contains("parameters") ? function.at("parameters") : ordered_json::object(); + + auto args = eps(); + if (params.contains("properties") && !params["properties"].empty()) { + auto arg_choice = choice(); + for (const auto & el : params["properties"].items()) { + const std::string & prop_name = el.key(); + const auto & prop_def = el.value(); + bool is_string_type = (prop_def.contains("type") && prop_def["type"] == "string"); + + auto arg_name_parser = literal(prop_name); + + common_peg_parser arg_value_parser = eps(); + auto string_value_parser = choice({ + literal("\"") + tool_arg_string_value(string_content('"')) + literal("\""), + literal("'") + tool_arg_string_value(string_content('\'')) + literal("'") + }); + + if (is_string_type) { + arg_value_parser = string_value_parser; + } else { + arg_value_parser = tool_arg_value(python_value()); + } + + // Full argument: name="value" or name=value + auto arg_rule = tool_arg( + tool_arg_open(eps()) + + tool_arg_name(arg_name_parser) + + literal("=") + + arg_value_parser + + tool_arg_close(eps()) + ); + arg_choice |= arg_rule; + } + + args = arg_choice + zero_or_more("," + space() + arg_choice); + } + + auto tool_parser = tool(tool_open(tool_name(literal(name)) + literal("(")) + + space() + tool_args(args) + space() + tool_close(literal(")")) + ); + + tool_choices |= rule("tool-" + name, tool_parser); + } + + if (parallel_tool_calls) { + return "[" + space() + tool_choices + zero_or_more("," + space() + tool_choices) + space() + "]"; + } + return "[" + space() + tool_choices + space() + "]"; +} + +// Helper: Parse dot notation key into prefix and field name +static std::pair parse_key_spec(const std::string & key) { + auto dot_pos = key.find('.'); + if (dot_pos == std::string::npos) { + return {"", key}; // Top-level field + } + return {key.substr(0, dot_pos), key.substr(dot_pos + 1)}; +} + +// Mode 1: function_is_key — parse {"function_name": {...}} +common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key( + const ordered_json & tools, + const std::string & args_key, + const std::string & effective_args_key, + const std::string & call_id_key, + const std::string & gen_call_id_key) { + + auto tool_choices = choice(); + + for (const auto & tool_def : tools) { + if (!tool_def.contains("function")) { + continue; + } + const auto & function = tool_def.at("function"); + std::string name = function.at("name"); + ordered_json params = function.contains("parameters") ? function.at("parameters") : ordered_json::object(); + + // Build inner object fields + std::vector inner_fields; + + if (!call_id_key.empty()) { + auto id_parser = atomic( + literal("\"" + call_id_key + "\"") + space() + literal(":") + space() + + literal("\"") + tool_id(string_content('"')) + literal("\"") + ); + inner_fields.push_back(optional(id_parser + space() + optional(literal(",") + space()))); + } + + if (!gen_call_id_key.empty()) { + auto gen_id_parser = atomic( + literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() + + choice({ + literal("\"") + tool_id(string_content('"')) + literal("\""), + tool_id(json_number()) + }) + ); + inner_fields.push_back(optional(gen_id_parser + space() + optional(literal(",") + space()))); + } + + // Arguments — either wrapped in args_key or parsed directly + common_peg_parser args_parser = eps(); + if (args_key.empty()) { + args_parser = tool_args(schema(json(), "tool-" + name + "-schema", params)); + } else { + args_parser = literal("\"" + effective_args_key + "\"") + space() + literal(":") + space() + + tool_args(schema(json(), "tool-" + name + "-schema", params)); + } + inner_fields.push_back(args_parser); + + // Build inner object parser + common_peg_parser inner_object = eps(); + if (args_key.empty() && inner_fields.size() == 1) { + inner_object = inner_fields[0]; + } else { + inner_object = literal("{") + space(); + for (size_t i = 0; i < inner_fields.size(); i++) { + inner_object = inner_object + inner_fields[i]; + if (i < inner_fields.size() - 1) { + inner_object = inner_object + space(); + } + } + inner_object = inner_object + space() + literal("}"); + } + + auto tool_parser = tool( + tool_open(literal("{")) + space() + + literal("\"") + tool_name(literal(name)) + literal("\"") + + space() + literal(":") + space() + + inner_object + + space() + tool_close(literal("}")) + ); + + tool_choices |= rule("tool-" + name, tool_parser); + } + + return tool_choices; +} + +// Mode 2: Nested keys (dot notation like "function.name") +common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys( + const ordered_json & tools, + const std::string & effective_name_key, + const std::string & effective_args_key, + const std::string & call_id_key, + const std::string & gen_call_id_key) { + + auto tool_choices = choice(); + + auto name_spec = parse_key_spec(effective_name_key); + auto args_spec = parse_key_spec(effective_args_key); + + std::string nested_prefix = !name_spec.first.empty() ? name_spec.first : args_spec.first; + std::string nested_name_field = !name_spec.first.empty() ? name_spec.second : effective_name_key; + std::string nested_args_field = !args_spec.first.empty() ? args_spec.second : effective_args_key; + + for (const auto & tool_def : tools) { + if (!tool_def.contains("function")) { + continue; + } + const auto & function = tool_def.at("function"); + std::string name = function.at("name"); + ordered_json params = function.contains("parameters") ? function.at("parameters") : ordered_json::object(); + + auto nested_name = literal("\"" + nested_name_field + "\"") + space() + literal(":") + space() + + literal("\"") + tool_name(literal(name)) + literal("\""); + auto nested_args = literal("\"" + nested_args_field + "\"") + space() + literal(":") + space() + + tool_args(schema(json(), "tool-" + name + "-schema", params)); + + auto nested_object = literal("{") + space() + + nested_name + space() + literal(",") + space() + + nested_args + + space() + literal("}"); + + // Format: { id?, "function": {...} } + auto tool_parser_body = tool_open(literal("{")) + space(); + + if (!call_id_key.empty()) { + auto id_spec = parse_key_spec(call_id_key); + if (id_spec.first.empty()) { + auto id_parser = atomic( + literal("\"" + call_id_key + "\"") + space() + literal(":") + space() + + literal("\"") + tool_id(string_content('"')) + literal("\"") + ); + tool_parser_body = tool_parser_body + optional(id_parser + space() + literal(",") + space()); + } + } + + if (!gen_call_id_key.empty()) { + auto gen_id_spec = parse_key_spec(gen_call_id_key); + if (gen_id_spec.first.empty()) { + auto gen_id_parser = atomic( + literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() + + choice({ + literal("\"") + tool_id(string_content('"')) + literal("\""), + tool_id(json_number()) + }) + ); + tool_parser_body = tool_parser_body + optional(gen_id_parser + space() + literal(",") + space()); + } + } + + auto nested_field = literal("\"" + nested_prefix + "\"") + space() + literal(":") + space() + nested_object; + tool_parser_body = tool_parser_body + nested_field + space() + tool_close(literal("}")); + + tool_choices |= rule("tool-" + name, tool(tool_parser_body)); + } + + return tool_choices; +} + +// Mode 3: Flat keys with optional ID fields and parameter ordering +common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys( + const ordered_json & tools, + const std::string & effective_name_key, + const std::string & effective_args_key, + const std::string & call_id_key, + const std::string & gen_call_id_key, + const std::vector & parameters_order) { + + auto tool_choices = choice(); + auto name_key_parser = literal("\"" + effective_name_key + "\""); + auto args_key_parser = literal("\"" + effective_args_key + "\""); + + for (const auto & tool_def : tools) { + if (!tool_def.contains("function")) { + continue; + } + const auto & function = tool_def.at("function"); + std::string name = function.at("name"); + ordered_json params = function.contains("parameters") ? function.at("parameters") : ordered_json::object(); + + auto tool_name_ = name_key_parser + space() + literal(":") + space() + + literal("\"") + tool_name(literal(name)) + literal("\""); + auto tool_args_ = args_key_parser + space() + literal(":") + space() + + tool_args(schema(json(), "tool-" + name + "-schema", params)); + + // Build ID parsers if keys are provided + common_peg_parser id_parser = eps(); + if (!call_id_key.empty()) { + id_parser = atomic( + literal("\"" + call_id_key + "\"") + space() + literal(":") + space() + + choice({ + literal("\"") + tool_id(string_content('"')) + literal("\""), + tool_id(json_number()) + }) + ); + } + + common_peg_parser gen_id_parser = eps(); + if (!gen_call_id_key.empty()) { + gen_id_parser = atomic( + literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() + + choice({ + literal("\"") + tool_id(string_content('"')) + literal("\""), + tool_id(json_number()) + }) + ); + } + + // Create (parser, key) pairs for all fields, then sort by parameters_order + std::vector> parser_pairs; + parser_pairs.emplace_back(tool_name_, effective_name_key); + parser_pairs.emplace_back(tool_args_, effective_args_key); + if (!call_id_key.empty()) { + parser_pairs.emplace_back(optional(id_parser), call_id_key); + } + if (!gen_call_id_key.empty()) { + parser_pairs.emplace_back(optional(gen_id_parser), gen_call_id_key); + } + + std::sort(parser_pairs.begin(), parser_pairs.end(), + [¶meters_order](const auto & a, const auto & b) { + auto pos_a = std::find(parameters_order.begin(), parameters_order.end(), a.second); + auto pos_b = std::find(parameters_order.begin(), parameters_order.end(), b.second); + size_t idx_a = (pos_a == parameters_order.end()) ? parameters_order.size() : std::distance(parameters_order.begin(), pos_a); + size_t idx_b = (pos_b == parameters_order.end()) ? parameters_order.size() : std::distance(parameters_order.begin(), pos_b); + return idx_a < idx_b; + }); + + auto ordered_body = tool_open(literal("{")) + space(); + for (size_t i = 0; i < parser_pairs.size(); i++) { + ordered_body = ordered_body + parser_pairs[i].first; + if (i < parser_pairs.size() - 1) { + ordered_body = ordered_body + space() + literal(",") + space(); + } + } + ordered_body = ordered_body + space() + tool_close(literal("}")); + + tool_choices |= rule("tool-" + name, tool(ordered_body)); + } + + return tool_choices; +} + +common_peg_parser common_chat_peg_builder::standard_json_tools( + const std::string & section_start, + const std::string & section_end, + const ordered_json & tools, + bool parallel_tool_calls, + bool force_tool_calls, + const std::string & name_key, + const std::string & args_key, + bool array_wrapped, + bool function_is_key, + const std::string & call_id_key, + const std::string & gen_call_id_key, + const std::vector & parameters_order) { + if (!tools.is_array() || tools.empty()) { + return eps(); + } + + std::string effective_name_key = name_key.empty() ? "name" : name_key; + std::string effective_args_key = args_key.empty() ? "arguments" : args_key; + + // Dispatch to the appropriate builder based on the JSON layout mode + common_peg_parser tool_choices = eps(); + if (function_is_key) { + tool_choices = build_json_tools_function_is_key(tools, args_key, effective_args_key, call_id_key, gen_call_id_key); + } else { + auto name_spec = parse_key_spec(effective_name_key); + auto args_spec = parse_key_spec(effective_args_key); + if (!name_spec.first.empty() || !args_spec.first.empty()) { + tool_choices = build_json_tools_nested_keys(tools, effective_name_key, effective_args_key, call_id_key, gen_call_id_key); + } else { + tool_choices = build_json_tools_flat_keys(tools, effective_name_key, effective_args_key, call_id_key, gen_call_id_key, parameters_order); + } + } + + // Build the section with markers + auto tool_calls = tool_choices; + if (parallel_tool_calls) { + tool_calls = tool_calls + zero_or_more(space() + literal(",") + space() + tool_choices); + } + + if (array_wrapped) { + tool_calls = literal("[") + space() + tool_calls + space() + literal("]"); + } + + auto section = + trigger_rule("tool-call", literal(section_start) + space() + tool_calls + space() + literal(section_end)); + + return force_tool_calls ? section : optional(section); +} diff --git a/common/chat-peg-parser.h b/common/chat-peg-parser.h index b84cbed206..a497508d2f 100644 --- a/common/chat-peg-parser.h +++ b/common/chat-peg-parser.h @@ -3,22 +3,9 @@ #include "chat.h" #include "peg-parser.h" -class common_chat_peg_builder : public common_peg_parser_builder { - public: - static constexpr const char * REASONING_BLOCK = "reasoning-block"; - static constexpr const char * REASONING = "reasoning"; - static constexpr const char * CONTENT = "content"; - - common_peg_parser reasoning_block(const common_peg_parser & p) { return tag(REASONING_BLOCK, p); } - common_peg_parser reasoning(const common_peg_parser & p) { return tag(REASONING, p); } - common_peg_parser content(const common_peg_parser & p) { return tag(CONTENT, p); } -}; - -inline common_peg_arena build_chat_peg_parser(const std::function & fn) { - common_chat_peg_builder builder; - builder.set_root(fn(builder)); - return builder.build(); -} +#include +#include +#include class common_chat_peg_mapper { public: @@ -26,80 +13,169 @@ class common_chat_peg_mapper { common_chat_peg_mapper(common_chat_msg & msg) : result(msg) {} + virtual ~common_chat_peg_mapper() = default; + virtual void from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result); virtual void map(const common_peg_ast_node & node); + private: + // Tool call handling state + std::optional pending_tool_call; // Tool call waiting for name + common_chat_tool_call * current_tool = nullptr; + int arg_count = 0; + bool closing_quote_pending = false; + std::string args_buffer; // Buffer to delay arguments until tool name is known + + // Returns a reference to the active argument destination string. + // Before tool_name is known, writes go to args_buffer; after, to current_tool->arguments. + std::string & args_target(); }; -class common_chat_peg_native_builder : public common_chat_peg_builder { - public: - static constexpr const char * TOOL = "tool"; - static constexpr const char * TOOL_OPEN = "tool-open"; - static constexpr const char * TOOL_CLOSE = "tool-close"; - static constexpr const char * TOOL_ID = "tool-id"; - static constexpr const char * TOOL_NAME = "tool-name"; - static constexpr const char * TOOL_ARGS = "tool-args"; +struct content_structure; +struct tool_call_structure; +class common_chat_peg_builder : public common_peg_parser_builder { + public: + // Tag constants (from former common_chat_peg_base_builder) + static constexpr const char * REASONING_BLOCK = "reasoning-block"; + static constexpr const char * REASONING = "reasoning"; + static constexpr const char * CONTENT = "content"; + + // Tag constants + static constexpr const char * TOOL = "tool"; + static constexpr const char * TOOL_OPEN = "tool-open"; + static constexpr const char * TOOL_CLOSE = "tool-close"; + static constexpr const char * TOOL_ID = "tool-id"; + static constexpr const char * TOOL_NAME = "tool-name"; + static constexpr const char * TOOL_ARGS = "tool-args"; + static constexpr const char * TOOL_ARG = "tool-arg"; + static constexpr const char * TOOL_ARG_OPEN = "tool-arg-open"; + static constexpr const char * TOOL_ARG_CLOSE = "tool-arg-close"; + static constexpr const char * TOOL_ARG_NAME = "tool-arg-name"; + static constexpr const char * TOOL_ARG_VALUE = "tool-arg-value"; + static constexpr const char * TOOL_ARG_STRING_VALUE = "tool-arg-string-value"; // For schema-declared string types + + // Low-level tag methods (from former common_chat_peg_base_builder) + common_peg_parser reasoning_block(const common_peg_parser & p) { return tag(REASONING_BLOCK, p); } + + common_peg_parser reasoning(const common_peg_parser & p) { return tag(REASONING, p); } + + common_peg_parser content(const common_peg_parser & p) { return tag(CONTENT, p); } + + common_peg_parser tag_with_safe_content(const std::string & tag_name, + const std::string & marker, + const common_peg_parser & p); + + // Low-level tag methods common_peg_parser tool(const common_peg_parser & p) { return tag(TOOL, p); } common_peg_parser tool_open(const common_peg_parser & p) { return atomic(tag(TOOL_OPEN, p)); } common_peg_parser tool_close(const common_peg_parser & p) { return atomic(tag(TOOL_CLOSE, p)); } common_peg_parser tool_id(const common_peg_parser & p) { return atomic(tag(TOOL_ID, p)); } common_peg_parser tool_name(const common_peg_parser & p) { return atomic(tag(TOOL_NAME, p)); } common_peg_parser tool_args(const common_peg_parser & p) { return tag(TOOL_ARGS, p); } -}; - -class common_chat_peg_native_mapper : public common_chat_peg_mapper { - common_chat_tool_call * current_tool; - - public: - common_chat_peg_native_mapper(common_chat_msg & msg) : common_chat_peg_mapper(msg) {} - - void map(const common_peg_ast_node & node) override; -}; - -inline common_peg_arena build_chat_peg_native_parser(const std::function & fn) { - common_chat_peg_native_builder builder; - builder.set_root(fn(builder)); - return builder.build(); -} - -class common_chat_peg_constructed_builder : public common_chat_peg_builder { - public: - static constexpr const char * TOOL = "tool"; - static constexpr const char * TOOL_OPEN = "tool-open"; - static constexpr const char * TOOL_CLOSE = "tool-close"; - static constexpr const char * TOOL_NAME = "tool-name"; - static constexpr const char * TOOL_ARG = "tool-arg"; - static constexpr const char * TOOL_ARG_OPEN = "tool-arg-open"; - static constexpr const char * TOOL_ARG_CLOSE = "tool-arg-close"; - static constexpr const char * TOOL_ARG_NAME = "tool-arg-name"; - static constexpr const char * TOOL_ARG_STRING_VALUE = "tool-arg-string-value"; - static constexpr const char * TOOL_ARG_JSON_VALUE = "tool-arg-json-value"; - - common_peg_parser tool(const common_peg_parser & p) { return tag(TOOL, p); } - common_peg_parser tool_open(const common_peg_parser & p) { return atomic(tag(TOOL_OPEN, p)); } - common_peg_parser tool_close(const common_peg_parser & p) { return atomic(tag(TOOL_CLOSE, p)); } - common_peg_parser tool_name(const common_peg_parser & p) { return atomic(tag(TOOL_NAME, p)); } common_peg_parser tool_arg(const common_peg_parser & p) { return tag(TOOL_ARG, p); } common_peg_parser tool_arg_open(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_OPEN, p)); } common_peg_parser tool_arg_close(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_CLOSE, p)); } common_peg_parser tool_arg_name(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_NAME, p)); } + common_peg_parser tool_arg_value(const common_peg_parser & p) { return tag(TOOL_ARG_VALUE, p); } + + // Use for schema-declared string types - won't be treated as potential JSON container common_peg_parser tool_arg_string_value(const common_peg_parser & p) { return tag(TOOL_ARG_STRING_VALUE, p); } - common_peg_parser tool_arg_json_value(const common_peg_parser & p) { return tag(TOOL_ARG_JSON_VALUE, p); } + common_peg_parser tool_arg_json_value(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_VALUE, p)); } + + // Legacy-compatible helper for building standard JSON tool calls + // Used by tests and manual parsers + // name_key/args_key: JSON key names for function name and arguments + // Empty or "name"/"arguments" will accept both common variations + // Supports dot notation for nested objects (e.g., "function.name") + // array_wrapped: if true, tool calls are wrapped in JSON array [...] + // function_is_key: if true, function name is the JSON key (e.g., {"func_name": {...}}) + // call_id_key: JSON key for string call ID (e.g., "id") + // gen_call_id_key: JSON key for generated integer call ID (e.g., "tool_call_id") + // parameters_order: order in which JSON fields should be parsed + common_peg_parser standard_json_tools(const std::string & section_start, + const std::string & section_end, + const nlohmann::ordered_json & tools, + bool parallel_tool_calls, + bool force_tool_calls, + const std::string & name_key = "", + const std::string & args_key = "", + bool array_wrapped = false, + bool function_is_key = false, + const std::string & call_id_key = "", + const std::string & gen_call_id_key = "", + const std::vector & parameters_order = {}); + + // Legacy-compatible helper for building XML/tagged style tool calls + // Used by tests and manual parsers + common_peg_parser standard_constructed_tools(const std::map & markers, + const nlohmann::ordered_json & tools, + bool parallel_tool_calls, + bool force_tool_calls); + + // Helper for Python-style function call format: name(arg1="value1", arg2=123) + // Used by LFM2 and similar templates + common_peg_parser python_style_tool_calls(const nlohmann::ordered_json & tools, + bool parallel_tool_calls); + + private: + // Implementation helpers for standard_json_tools — one per JSON tool call layout mode + common_peg_parser build_json_tools_function_is_key(const nlohmann::ordered_json & tools, + const std::string & args_key, + const std::string & effective_args_key, + const std::string & call_id_key, + const std::string & gen_call_id_key); + + common_peg_parser build_json_tools_nested_keys(const nlohmann::ordered_json & tools, + const std::string & effective_name_key, + const std::string & effective_args_key, + const std::string & call_id_key, + const std::string & gen_call_id_key); + + common_peg_parser build_json_tools_flat_keys(const nlohmann::ordered_json & tools, + const std::string & effective_name_key, + const std::string & effective_args_key, + const std::string & call_id_key, + const std::string & gen_call_id_key, + const std::vector & parameters_order); }; -class common_chat_peg_constructed_mapper : public common_chat_peg_mapper { - common_chat_tool_call * current_tool; - int arg_count = 0; - bool needs_closing_quote = false; - - public: - common_chat_peg_constructed_mapper(common_chat_msg & msg) : common_chat_peg_mapper(msg) {} - - void map(const common_peg_ast_node & node) override; -}; - -inline common_peg_arena build_chat_peg_constructed_parser(const std::function & fn) { - common_chat_peg_constructed_builder builder; - builder.set_root(fn(builder)); - return builder.build(); +inline common_peg_arena build_chat_peg_parser( + const std::function & fn) { + common_chat_peg_builder builder; + builder.set_root(fn(builder)); + return builder.build(); } + +class tag_based_peg_mapper { + public: + std::map tags; + + void from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result); +}; + +struct tagged_parse_result { + common_peg_parse_result result; + std::map tags; +}; + +struct tagged_peg_parser { + common_peg_arena arena; + common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_NONE; + + tagged_peg_parser & withDebug() { + flags |= COMMON_PEG_PARSE_FLAG_DEBUG; + return *this; + } + + tagged_peg_parser & withoutDebug() { + flags = flags & ~COMMON_PEG_PARSE_FLAG_DEBUG; + return *this; + } + + tagged_parse_result parse_and_extract(const std::string & input, common_peg_parse_flags extra_flags = COMMON_PEG_PARSE_FLAG_NONE) const; + tagged_parse_result parse_anywhere_and_extract(const std::string & input) const; +}; + +tagged_peg_parser build_tagged_peg_parser( + const std::function & fn); + diff --git a/common/chat.cpp b/common/chat.cpp index 52780c59ad..cfd5df30a7 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -1,24 +1,25 @@ #include "chat.h" -#include "chat-parser.h" + +#include "chat-auto-parser.h" #include "chat-peg-parser.h" #include "common.h" -#include "json-partial.h" +#include "ggml.h" #include "json-schema-to-grammar.h" #include "log.h" -#include "regex-partial.h" -#include "jinja/parser.h" #include "jinja/value.h" #include "jinja/runtime.h" #include "jinja/caps.h" +#include "peg-parser.h" -#include #include -#include +#include +#include #include #include -#include + #include +#include #include #include #include @@ -26,14 +27,26 @@ using json = nlohmann::ordered_json; static std::string format_time(const std::chrono::system_clock::time_point & now, const std::string & format) { - auto time = std::chrono::system_clock::to_time_t(now); - auto local_time = *std::localtime(&time); + auto time = std::chrono::system_clock::to_time_t(now); + auto local_time = *std::localtime(&time); std::ostringstream ss; ss << std::put_time(&local_time, format.c_str()); auto res = ss.str(); return res; } +static json safe_args_parse(const std::string & to_parse) { + std::string stripped = to_parse; + if (to_parse.at(0) == '"' && to_parse.at(to_parse.length() - 1) == '"') { + stripped = to_parse.substr(1, to_parse.length() - 1); + } + try { + return json::parse(stripped); + } catch (json::exception & e) { + return stripped; + } +} + static std::string string_diff(const std::string & last, const std::string & current) { if (last.empty()) { return current; @@ -116,7 +129,7 @@ json common_chat_msg::to_json_oaicompat(bool concat_typed_text) const { {"type", "function"}, {"function", { {"name", tool_call.name}, - {"arguments", tool_call.arguments}, + {"arguments", json(tool_call.arguments)}, }}, }; if (!tool_call.id.empty()) { @@ -133,7 +146,8 @@ json common_chat_msg::to_json_oaicompat(bool concat_typed_text) const { return jmsg; } -std::vector common_chat_msg_diff::compute_diffs(const common_chat_msg & msg_prv, const common_chat_msg & msg_new) { +std::vector common_chat_msg_diff::compute_diffs(const common_chat_msg & msg_prv, + const common_chat_msg & msg_new) { std::vector diffs; if (msg_new.tool_calls.size() > msg_prv.tool_calls.size()) { diffs.reserve(msg_new.tool_calls.size() - msg_prv.tool_calls.size() + 3); @@ -143,38 +157,56 @@ std::vector common_chat_msg_diff::compute_diffs(const comm // TODO: these can become expensive for long messages - how to optimize? if (msg_prv.reasoning_content != msg_new.reasoning_content) { - auto & diff = diffs.emplace_back(); + auto & diff = diffs.emplace_back(); diff.reasoning_content_delta = string_diff(msg_prv.reasoning_content, msg_new.reasoning_content); } if (msg_prv.content != msg_new.content) { - auto & diff = diffs.emplace_back(); + auto & diff = diffs.emplace_back(); diff.content_delta = string_diff(msg_prv.content, msg_new.content); } if (msg_new.tool_calls.size() < msg_prv.tool_calls.size()) { - throw std::runtime_error("Invalid diff: now finding less tool calls!"); + std::string err = "Invalid diff: now finding less tool calls!\n"; + err += " Previous (" + std::to_string(msg_prv.tool_calls.size()) + "):\n"; + for (const auto & tc : msg_prv.tool_calls) { + err += " - name: '" + tc.name + "', args: '" + tc.arguments + "'\n"; + } + err += " Current (" + std::to_string(msg_new.tool_calls.size()) + "):\n"; + for (const auto & tc : msg_new.tool_calls) { + err += " - name: '" + tc.name + "', args: '" + tc.arguments + "'\n"; + } + err += " Current msg text content:\n" + msg_new.content + "\n"; + throw std::runtime_error(err); } if (!msg_prv.tool_calls.empty()) { - const auto idx = msg_prv.tool_calls.size() - 1; + const auto idx = msg_prv.tool_calls.size() - 1; const auto & pref = msg_prv.tool_calls[idx]; const auto & newf = msg_new.tool_calls[idx]; - if (pref.name != newf.name) { - throw std::runtime_error("Invalid diff: tool call mismatch!"); + // Allow tool name to change during incremental parsing: + // - empty -> non-empty (initial discovery) + // - prefix -> longer string (name grows as more input is parsed) + if (pref.name != newf.name && !pref.name.empty() && !newf.name.empty()) { + // Check if one is a prefix of the other (for incremental parsing where names grow or shrink) + bool is_prefix = (newf.name.rfind(pref.name, 0) == 0); + if (!is_prefix) { + LOG_ERR("Tool call mismatch: prev='%s' new='%s'\n", pref.name.c_str(), newf.name.c_str()); + throw std::runtime_error("Invalid diff: tool call mismatch!"); + } } const auto args_diff = string_diff(pref.arguments, newf.arguments); - if (!args_diff.empty() || pref.id != newf.id) { - auto & diff = diffs.emplace_back(); + if (!args_diff.empty() || pref.id != newf.id || pref.name != newf.name) { + auto & diff = diffs.emplace_back(); diff.tool_call_index = idx; - if (pref.id != newf.id) { - diff.tool_call_delta.id = newf.id; + if (pref.id != newf.id || pref.name != newf.name) { + diff.tool_call_delta.id = newf.id; diff.tool_call_delta.name = newf.name; } diff.tool_call_delta.arguments = args_diff; } } for (size_t idx = msg_prv.tool_calls.size(); idx < msg_new.tool_calls.size(); ++idx) { - auto & diff = diffs.emplace_back(); + auto & diff = diffs.emplace_back(); diff.tool_call_index = idx; diff.tool_call_delta = msg_new.tool_calls[idx]; } @@ -184,94 +216,14 @@ std::vector common_chat_msg_diff::compute_diffs(const comm using chat_template_caps = jinja::caps; -struct common_chat_template { - jinja::program prog; - std::string bos_tok; - std::string eos_tok; - std::string src; - chat_template_caps caps; - - common_chat_template(const std::string & src, const std::string & bos_token, const std::string & eos_token) { - jinja::lexer lexer; - auto lexer_res = lexer.tokenize(src); - this->prog = jinja::parse_from_tokens(lexer_res); - - this->src = lexer_res.source; - this->bos_tok = bos_token; - this->eos_tok = eos_token; - - this->caps = jinja::caps_get(prog); - // LOG_INF("%s: caps:\n%s\n", __func__, this->caps.to_string().c_str()); - } - - const std::string & source() const { return src; } - const std::string & bos_token() const { return bos_tok; } - const std::string & eos_token() const { return eos_tok; } - - // TODO: this is ugly, refactor it somehow - json add_system(const json & messages, const std::string & system_prompt) const { - GGML_ASSERT(messages.is_array()); - auto msgs_copy = messages; - if (!caps.supports_system_role) { - if (msgs_copy.empty()) { - msgs_copy.insert(msgs_copy.begin(), json{ - {"role", "user"}, - {"content", system_prompt} - }); - } else { - auto & first_msg = msgs_copy[0]; - if (!first_msg.contains("content")) { - first_msg["content"] = ""; - } - first_msg["content"] = system_prompt + "\n\n" - + first_msg["content"].get(); - } - } else { - if (msgs_copy.empty() || msgs_copy[0].at("role") != "system") { - msgs_copy.insert(msgs_copy.begin(), json{ - {"role", "system"}, - {"content", system_prompt} - }); - } else if (msgs_copy[0].at("role") == "system") { - msgs_copy[0]["content"] = system_prompt; - } - } - return msgs_copy; - } - - chat_template_caps original_caps() const { - return caps; - } - -}; - struct common_chat_templates { bool add_bos; bool add_eos; - bool has_explicit_template; // Model had builtin template or template overridde was specified. - std::unique_ptr template_default; // always set (defaults to chatml) + bool has_explicit_template; // Model had builtin template or template overridde was specified. + std::unique_ptr template_default; // always set (defaults to chatml) std::unique_ptr template_tool_use; }; -struct templates_params { - json messages; - json tools; - common_chat_tool_choice tool_choice; - json json_schema; - bool parallel_tool_calls; - common_reasoning_format reasoning_format; - bool stream; - std::string grammar; - bool add_generation_prompt = true; - bool enable_thinking = true; - std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); - json extra_context; - bool add_bos; - bool add_eos; - bool is_inference = true; - bool mark_input = true; // whether to mark input strings in the jinja context -}; - common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) { if (tool_choice == "auto") { return COMMON_CHAT_TOOL_CHOICE_AUTO; @@ -286,23 +238,24 @@ common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::strin } bool common_chat_templates_support_enable_thinking(const common_chat_templates * chat_templates) { - common_chat_templates_inputs dummy_inputs; + common_chat_templates_inputs inputs; + inputs.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; common_chat_msg msg; - msg.role = "user"; + msg.role = "user"; msg.content = "test"; - dummy_inputs.messages = {msg}; - dummy_inputs.enable_thinking = false; - const auto rendered_no_thinking = common_chat_templates_apply(chat_templates, dummy_inputs); - dummy_inputs.enable_thinking = true; - const auto rendered_with_thinking = common_chat_templates_apply(chat_templates, dummy_inputs); - return rendered_no_thinking.prompt != rendered_with_thinking.prompt; + inputs.messages = { msg }; + inputs.enable_thinking = true; + inputs.add_generation_prompt = true; + inputs.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; + + auto params = common_chat_templates_apply(chat_templates, inputs); + return params.supports_thinking; } std::vector common_chat_msgs_parse_oaicompat(const json & messages) { std::vector msgs; try { - if (!messages.is_array()) { throw std::invalid_argument("Expected 'messages' to be an array, got " + messages.dump()); } @@ -318,7 +271,7 @@ std::vector common_chat_msgs_parse_oaicompat(const json & messa } msg.role = message.at("role"); - auto has_content = message.contains("content"); + auto has_content = message.contains("content"); auto has_tool_calls = message.contains("tool_calls"); if (has_content) { const auto & content = message.at("content"); @@ -339,7 +292,9 @@ std::vector common_chat_msgs_parse_oaicompat(const json & messa msg.content_parts.push_back(msg_part); } } else if (!content.is_null()) { - throw std::invalid_argument("Invalid 'content' type: expected string or array, got " + content.dump() + " (ref: https://github.com/ggml-org/llama.cpp/issues/8367)"); + throw std::invalid_argument("Invalid 'content' type: expected string or array, got " + + content.dump() + + " (ref: https://github.com/ggml-org/llama.cpp/issues/8367)"); } } if (has_tool_calls) { @@ -359,8 +314,13 @@ std::vector common_chat_msgs_parse_oaicompat(const json & messa if (!fc.contains("name")) { throw std::invalid_argument("Missing tool call name: " + tool_call.dump()); } - tc.name = fc.at("name"); - tc.arguments = fc.at("arguments"); + tc.name = fc.at("name"); + const auto & args = fc.at("arguments"); + if (args.is_string()) { + tc.arguments = args; + } else { + tc.arguments = args.dump(); + } if (tool_call.contains("id")) { tc.id = tool_call.at("id"); } @@ -368,7 +328,9 @@ std::vector common_chat_msgs_parse_oaicompat(const json & messa } } if (!has_content && !has_tool_calls) { - throw std::invalid_argument("Expected 'content' or 'tool_calls' (ref: https://github.com/ggml-org/llama.cpp/issues/8367 & https://github.com/ggml-org/llama.cpp/issues/12279)"); + throw std::invalid_argument( + "Expected 'content' or 'tool_calls' (ref: https://github.com/ggml-org/llama.cpp/issues/8367 & " + "https://github.com/ggml-org/llama.cpp/issues/12279)"); } if (message.contains("reasoning_content")) { msg.reasoning_content = message.at("reasoning_content"); @@ -474,12 +436,13 @@ json common_chat_tools_to_json_oaicompat(const std::vector & t auto result = json::array(); for (const auto & tool : tools) { result.push_back({ - {"type", "function"}, - {"function", { - {"name", tool.name}, - {"description", tool.description}, - {"parameters", json::parse(tool.parameters)}, - }}, + { "type", "function" }, + { "function", + { + { "name", tool.name }, + { "description", tool.description }, + { "parameters", json::parse(tool.parameters) }, + } }, }); } return result; @@ -497,16 +460,20 @@ json common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff) { json tool_call; tool_call["index"] = diff.tool_call_index; if (!diff.tool_call_delta.id.empty()) { - tool_call["id"] = diff.tool_call_delta.id; + tool_call["id"] = diff.tool_call_delta.id; tool_call["type"] = "function"; } - json function = json::object(); - if (!diff.tool_call_delta.name.empty()) { - function["name"] = diff.tool_call_delta.name; + if (!diff.tool_call_delta.name.empty() || !diff.tool_call_delta.arguments.empty()) { + json function = json::object(); + if (!diff.tool_call_delta.name.empty()) { + function["name"] = diff.tool_call_delta.name; + } + if (!diff.tool_call_delta.arguments.empty()) { + function["arguments"] = diff.tool_call_delta.arguments; + } + tool_call["function"] = function; } - function["arguments"] = diff.tool_call_delta.arguments; - tool_call["function"] = function; - delta["tool_calls"] = json::array({tool_call}); + delta["tool_calls"] = json::array({ tool_call }); } return delta; } @@ -515,13 +482,13 @@ bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) { if (use_jinja) { try { common_chat_msg msg; - msg.role = "user"; + msg.role = "user"; msg.content = "test"; auto tmpls = common_chat_templates_init(/* model= */ nullptr, tmpl); common_chat_templates_inputs inputs; - inputs.messages = {msg}; + inputs.messages = { msg }; common_chat_templates_apply(tmpls.get(), inputs); return true; @@ -530,28 +497,28 @@ bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) { return false; } } - llama_chat_message chat[] = {{"user", "test"}}; + llama_chat_message chat[] = { + { "user", "test" } + }; const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0); return res >= 0; } -std::string common_chat_format_single( - const struct common_chat_templates * tmpls, - const std::vector & past_msg, - const common_chat_msg & new_msg, - bool add_ass, - bool use_jinja) { - +std::string common_chat_format_single(const struct common_chat_templates * tmpls, + const std::vector & past_msg, + const common_chat_msg & new_msg, + bool add_ass, + bool use_jinja) { common_chat_templates_inputs inputs; inputs.use_jinja = use_jinja; - inputs.add_bos = tmpls->add_bos; - inputs.add_eos = tmpls->add_eos; + inputs.add_bos = tmpls->add_bos; + inputs.add_eos = tmpls->add_eos; std::string fmt_past_msg; if (!past_msg.empty()) { - inputs.messages = past_msg; + inputs.messages = past_msg; inputs.add_generation_prompt = false; - fmt_past_msg = common_chat_templates_apply(tmpls, inputs).prompt; + fmt_past_msg = common_chat_templates_apply(tmpls, inputs).prompt; } std::ostringstream ss; // if the past_msg ends with a newline, we must preserve it in the formatted version @@ -561,37 +528,39 @@ std::string common_chat_format_single( // format chat with new_msg inputs.messages.push_back(new_msg); inputs.add_generation_prompt = add_ass; - auto fmt_new_msg = common_chat_templates_apply(tmpls, inputs).prompt; + auto fmt_new_msg = common_chat_templates_apply(tmpls, inputs).prompt; // get the diff part ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size()); return ss.str(); } -std::string common_chat_format_example(const struct common_chat_templates * tmpls, bool use_jinja, const std::map & chat_template_kwargs) { +std::string common_chat_format_example(const struct common_chat_templates * tmpls, + bool use_jinja, + const std::map & chat_template_kwargs) { common_chat_templates_inputs inputs; - inputs.use_jinja = use_jinja; - inputs.add_bos = tmpls->add_bos; - inputs.add_eos = tmpls->add_eos; + inputs.use_jinja = use_jinja; + inputs.add_bos = tmpls->add_bos; + inputs.add_eos = tmpls->add_eos; inputs.chat_template_kwargs = chat_template_kwargs; - auto add_simple_msg = [&](auto role, auto content) { + auto add_simple_msg = [&](auto role, auto content) { common_chat_msg msg; - msg.role = role; + msg.role = role; msg.content = content; inputs.messages.push_back(msg); }; - add_simple_msg("system", "You are a helpful assistant"); - add_simple_msg("user", "Hello"); + add_simple_msg("system", "You are a helpful assistant"); + add_simple_msg("user", "Hello"); add_simple_msg("assistant", "Hi there"); - add_simple_msg("user", "How are you?"); + add_simple_msg("user", "How are you?"); return common_chat_templates_apply(tmpls, inputs).prompt; } -#define CHATML_TEMPLATE_SRC \ - "{%- for message in messages -%}\n" \ +#define CHATML_TEMPLATE_SRC \ + "{%- for message in messages -%}\n" \ " {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' -}}\n" \ - "{%- endfor -%}\n" \ - "{%- if add_generation_prompt -%}\n" \ - " {{- '<|im_start|>assistant\n' -}}\n" \ + "{%- endfor -%}\n" \ + "{%- if add_generation_prompt -%}\n" \ + " {{- '<|im_start|>assistant\n' -}}\n" \ "{%- endif -%}" void common_chat_templates_free(struct common_chat_templates * tmpls) { @@ -609,19 +578,16 @@ std::string common_chat_templates_source(const struct common_chat_templates * tm return tmpls->template_tool_use->source(); } return ""; - } else { - LOG_DBG("%s: unknown template variant: %s\n", __func__, variant.c_str()); } + LOG_DBG("%s: unknown template variant: %s\n", __func__, variant.c_str()); } return tmpls->template_default->source(); } -common_chat_templates_ptr common_chat_templates_init( - const struct llama_model * model, - const std::string & chat_template_override, - const std::string & bos_token_override, - const std::string & eos_token_override) -{ +common_chat_templates_ptr common_chat_templates_init(const struct llama_model * model, + const std::string & chat_template_override, + const std::string & bos_token_override, + const std::string & eos_token_override) { std::string default_template_src; std::string template_tool_use_src; @@ -630,7 +596,7 @@ common_chat_templates_ptr common_chat_templates_init( GGML_ASSERT(model != nullptr); const auto * str = llama_model_chat_template(model, /* name */ nullptr); if (str) { - default_template_src = str; + default_template_src = str; has_explicit_template = true; } str = llama_model_chat_template(model, /* name */ "tool_use"); @@ -652,34 +618,40 @@ common_chat_templates_ptr common_chat_templates_init( // TODO @ngxson : this is a temporary hack to prevent chat template from throwing an error // Ref: https://github.com/ggml-org/llama.cpp/pull/15230#issuecomment-3173959633 if (default_template_src.find("<|channel|>") != std::string::npos - // search for the error message and patch it - && default_template_src.find("in message.content or") != std::string::npos) { + // search for the error message and patch it + && default_template_src.find("in message.content or") != std::string::npos) { string_replace_all(default_template_src, - "{%- if \"<|channel|>analysis<|message|>\" in message.content or \"<|channel|>final<|message|>\" in message.content %}", - "{%- if false %}"); + "{%- if \"<|channel|>analysis<|message|>\" in message.content or " + "\"<|channel|>final<|message|>\" in message.content %}", + "{%- if false %}"); } // TODO @aldehir : this is a temporary fix, pending Minja changes // Ref: https://github.com/ggml-org/llama.cpp/pull/17713#issuecomment-3631342664 if (default_template_src.find("[TOOL_CALLS]") != std::string::npos - // search for the error message and patch it - && default_template_src.find("if (message['content'] is none or") != std::string::npos) { + // search for the error message and patch it + && default_template_src.find("if (message['content'] is none or") != std::string::npos) { string_replace_all(default_template_src, - "{%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %}", - "{%- if false %}"); + "{%- if (message['content'] is none or message['content'] == '' or " + "message['content']|length == 0) and (message['tool_calls'] is not defined or " + "message['tool_calls'] is none or message['tool_calls']|length == 0) %}", + "{%- if false %}"); } std::string token_bos = bos_token_override; std::string token_eos = eos_token_override; - bool add_bos = false; - bool add_eos = false; + bool add_bos = false; + bool add_eos = false; if (model) { - const auto * vocab = llama_model_get_vocab(model); - const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) { + const auto * vocab = llama_model_get_vocab(model); + const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) { if (token == LLAMA_TOKEN_NULL) { - if (default_template_src.find(jinja_variable_name) != std::string::npos - || template_tool_use_src.find(jinja_variable_name) != std::string::npos) { - LOG_WRN("common_chat_templates_init: warning: vocab does not have a %s token, jinja template won't work as intended.\n", name); + if (default_template_src.find(jinja_variable_name) != std::string::npos || + template_tool_use_src.find(jinja_variable_name) != std::string::npos) { + LOG_WRN( + "common_chat_templates_init: warning: vocab does not have a %s token, jinja template won't " + "work as intended.\n", + name); } return std::string(); } @@ -687,13 +659,13 @@ common_chat_templates_ptr common_chat_templates_init( }; token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token"); token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token"); - add_bos = llama_vocab_get_add_bos(vocab); - add_eos = llama_vocab_get_add_eos(vocab); + add_bos = llama_vocab_get_add_bos(vocab); + add_eos = llama_vocab_get_add_eos(vocab); } common_chat_templates_ptr tmpls(new common_chat_templates()); tmpls->has_explicit_template = has_explicit_template; - tmpls->add_bos = add_bos; - tmpls->add_eos = add_eos; + tmpls->add_bos = add_bos; + tmpls->add_eos = add_eos; try { tmpls->template_default = std::make_unique(default_template_src, token_bos, token_eos); } catch (const std::exception & e) { @@ -714,35 +686,12 @@ common_chat_templates_ptr common_chat_templates_init( const char * common_chat_format_name(common_chat_format format) { switch (format) { - case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only"; - case COMMON_CHAT_FORMAT_GENERIC: return "Generic"; - case COMMON_CHAT_FORMAT_MISTRAL_NEMO: return "Mistral Nemo"; - case COMMON_CHAT_FORMAT_MAGISTRAL: return "Magistral"; - case COMMON_CHAT_FORMAT_LLAMA_3_X: return "Llama 3.x"; - case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: return "Llama 3.x with builtin tools"; - case COMMON_CHAT_FORMAT_DEEPSEEK_R1: return "DeepSeek R1"; - case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2"; - case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2"; - case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1"; - case COMMON_CHAT_FORMAT_DEEPSEEK_V3_1: return "DeepSeek V3.1"; - case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro"; - case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B"; - case COMMON_CHAT_FORMAT_GRANITE: return "Granite"; - case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS"; - case COMMON_CHAT_FORMAT_SEED_OSS: return "Seed-OSS"; - case COMMON_CHAT_FORMAT_NEMOTRON_V2: return "Nemotron V2"; - case COMMON_CHAT_FORMAT_APERTUS: return "Apertus"; - case COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS: return "LFM2 with JSON tools"; - case COMMON_CHAT_FORMAT_MINIMAX_M2: return "MiniMax-M2"; - case COMMON_CHAT_FORMAT_GLM_4_5: return "GLM 4.5"; - case COMMON_CHAT_FORMAT_KIMI_K2: return "Kimi K2"; - case COMMON_CHAT_FORMAT_APRIEL_1_5: return "Apriel 1.5"; - case COMMON_CHAT_FORMAT_XIAOMI_MIMO: return "Xiaomi MiMo"; - case COMMON_CHAT_FORMAT_SOLAR_OPEN: return "Solar Open"; - case COMMON_CHAT_FORMAT_EXAONE_MOE: return "EXAONE MoE"; - case COMMON_CHAT_FORMAT_PEG_SIMPLE: return "peg-simple"; - case COMMON_CHAT_FORMAT_PEG_NATIVE: return "peg-native"; - case COMMON_CHAT_FORMAT_PEG_CONSTRUCTED: return "peg-constructed"; + case COMMON_CHAT_FORMAT_CONTENT_ONLY: + return "Content-only"; + case COMMON_CHAT_FORMAT_PEG_SIMPLE: + return "peg-simple"; + case COMMON_CHAT_FORMAT_PEG_NATIVE: + return "peg-native"; default: throw std::runtime_error("Unknown chat format"); } @@ -750,10 +699,14 @@ const char * common_chat_format_name(common_chat_format format) { const char * common_reasoning_format_name(common_reasoning_format format) { switch (format) { - case COMMON_REASONING_FORMAT_NONE: return "none"; - case COMMON_REASONING_FORMAT_AUTO: return "auto"; - case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek"; - case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: return "deepseek-legacy"; + case COMMON_REASONING_FORMAT_NONE: + return "none"; + case COMMON_REASONING_FORMAT_AUTO: + return "auto"; + case COMMON_REASONING_FORMAT_DEEPSEEK: + return "deepseek"; + case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: + return "deepseek-legacy"; default: throw std::runtime_error("Unknown reasoning format"); } @@ -762,11 +715,14 @@ const char * common_reasoning_format_name(common_reasoning_format format) { common_reasoning_format common_reasoning_format_from_name(const std::string & format) { if (format == "none") { return COMMON_REASONING_FORMAT_NONE; - } else if (format == "auto") { + } + if (format == "auto") { return COMMON_REASONING_FORMAT_AUTO; - } else if (format == "deepseek") { + } + if (format == "deepseek") { return COMMON_REASONING_FORMAT_DEEPSEEK; - } else if (format == "deepseek-legacy") { + } + if (format == "deepseek-legacy") { return COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; } throw std::runtime_error("Unknown reasoning format: " + format); @@ -782,7 +738,8 @@ static void foreach_function(const json & tools, const std::function & fn) { +static void foreach_parameter(const json & function, + const std::function & fn) { if (!function.contains("parameters") || !function.at("parameters").is_object()) { return; } @@ -790,7 +747,7 @@ static void foreach_parameter(const json & function, const std::function required; if (params.contains("required") && params.at("required").is_array()) { params.at("required").get_to(required); @@ -801,19 +758,19 @@ static void foreach_parameter(const json & function, const std::function & messages_override = std::nullopt, - const std::optional & tools_override = std::nullopt, - const std::optional & additional_context = std::nullopt) -{ + const autoparser::templates_params & inputs, + const std::optional & messages_override, + const std::optional & tools_override, + const std::optional & additional_context) { jinja::context ctx(tmpl.source()); nlohmann::ordered_json inp = nlohmann::ordered_json{ {"messages", messages_override.has_value() ? *messages_override : inputs.messages}, {"bos_token", tmpl.bos_token()}, {"eos_token", tmpl.eos_token()}, + {"enable_thinking", inputs.enable_thinking}, }; if (tools_override.has_value() || !inputs.tools.empty()) { inp["tools"] = tools_override.has_value() ? *tools_override : inputs.tools; @@ -839,7 +796,7 @@ static std::string apply( // render jinja::runtime runtime(ctx); const jinja::value results = runtime.execute(tmpl.prog); - auto parts = runtime.gather_string_parts(results); + auto parts = jinja::runtime::gather_string_parts(results); std::string result = parts->as_string().str(); @@ -853,265 +810,8 @@ static std::string apply( return result; } -static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - auto tool_call_schemas = json::array(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - auto tool_schema = json { - {"type", "object"}, - {"properties", { - {"name", { - {"type", "string"}, - {"const", function.at("name")}, - }}, - {"arguments", function.at("parameters")}, - }}, - {"required", json::array({"name", "arguments"})}, - }; - if (function.contains("description")) { - tool_schema["description"] = function.at("description"); - } - if (inputs.parallel_tool_calls) { - tool_schema.at("properties")["id"] = { - {"type", "string"}, - {"minLength", 4}, - }; - tool_schema.at("required").push_back("id"); - } - tool_call_schemas.emplace_back(tool_schema); - }); - const auto tool_call = - inputs.parallel_tool_calls - ? json { - {"type", "object"}, - {"properties", { - {"tool_calls", { - {"type", "array"}, - {"items", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json { - {"anyOf", tool_call_schemas}, - }}, - {"minItems", 1}, - }}, - }}, - {"required", json::array({"tool_calls"})}, - } - : json { - {"type", "object"}, - {"properties", { - {"tool_call", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json { - {"anyOf", tool_call_schemas}, - }}, - }}, - {"required", json::array({"tool_call"})}, - }; - const auto schema = - inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED - ? json { - {"anyOf", json::array({ - tool_call, - { - {"type", "object"}, - {"properties", { - {"response", inputs.json_schema.is_null() - ? json {{"type", "string"}} - : inputs.json_schema - }, - }}, - {"required", json::array({"response"})}, - }, - })} - } - : tool_call; - - data.grammar_lazy = false; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - builder.add_schema("root", schema); - }); - - auto tweaked_messages = tmpl.add_system( - inputs.messages, - "Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request"); - - // ensure all messages has "content" field - for (auto & message : tweaked_messages) { - if (!message.contains("content") || message["content"].is_null()) { - message["content"] = ""; - } - } - - data.prompt = apply(tmpl, inputs, /* messages_override= */ tweaked_messages); - data.format = COMMON_CHAT_FORMAT_GENERIC; - return data; -} - -static common_chat_params common_chat_params_init_mistral_nemo(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - auto schemas = json::array(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - schemas.push_back({ - {"type", "object"}, - {"properties", { - // Important note: the model is probably trained to take a JSON stringified arguments value. - // It's hard to constrain that for now (while reusing the JSON schema conversion), so we're just expecting a plain object. - {"name", { - {"type", "string"}, - {"const", function.at("name")}, - }}, - {"arguments", function.at("parameters")}, - {"id", { - {"type", "string"}, - // Nemo's template expects a 9-character alphanumeric ID. - {"pattern", "^[a-zA-Z0-9]{9}$"}, - }}, - }}, - {"required", json::array({"name", "arguments", "id"})}, - }); - }); - auto schema = json { - {"type", "array"}, - {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, - {"minItems", 1}, - }; - if (!inputs.parallel_tool_calls) { - schema["maxItems"] = 1; - } - builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema)); - }); - data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]"}); - data.preserved_tokens = { - "[TOOL_CALLS]", - }; - data.prompt = apply(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO; - return data; -} - - -// Case-insensitive find -static size_t ifind_string(const std::string & haystack, const std::string & needle, size_t pos = 0) { - auto it = std::search( - haystack.begin() + pos, haystack.end(), - needle.begin(), needle.end(), - [](char a, char b) { return std::tolower(a) == std::tolower(b); } - ); - return (it == haystack.end()) ? std::string::npos : std::distance(haystack.begin(), it); -} - -static common_chat_params common_chat_params_init_lfm2(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - const auto is_json_schema_provided = !inputs.json_schema.is_null(); - const auto is_grammar_provided = !inputs.grammar.empty(); - const auto are_tools_provided = inputs.tools.is_array() && !inputs.tools.empty(); - - // the logic requires potentially modifying the messages - auto tweaked_messages = inputs.messages; - - auto replace_json_schema_marker = [](json & messages) -> bool { - static std::string marker1 = "force json schema.\n"; - static std::string marker2 = "force json schema."; - - if (messages.empty() || messages.at(0).at("role") != "system") { - return false; - } - - std::string content = messages.at(0).at("content"); - - for (const auto & marker : {marker1, marker2}) { - const auto pos = ifind_string(content, marker); - if (pos != std::string::npos) { - content.replace(pos, marker.length(), ""); - // inject modified content back into the messages - messages.at(0).at("content") = content; - return true; - } - } - - return false; - }; - - // Lfm2 model does not natively work with json, but can generally understand the tools structure - // - // Example of the pytorch dialog structure: - // <|startoftext|><|im_start|>system - // List of tools: <|tool_list_start|>[{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|tool_list_end|><|im_end|> - // <|im_start|>user - // What is the current status of candidate ID 12345?<|im_end|> - // <|im_start|>assistant - // <|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|> - // <|im_start|>tool - // <|tool_response_start|>{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}<|tool_response_end|><|im_end|> - // <|im_start|>assistant - // The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|> - // - // For the llama server compatibility with json tools semantic, - // the client can add "Follow json schema." line into the system message prompt to force the json output. - // - if (are_tools_provided && (is_json_schema_provided || is_grammar_provided)) { - // server/utils.hpp prohibits that branch for the custom grammar anyways - throw std::runtime_error("Tools call must not use \"json_schema\" or \"grammar\", use non-tool invocation if you want to use custom grammar"); - } else if (are_tools_provided && replace_json_schema_marker(tweaked_messages)) { - LOG_INF("%s: Using tools to build a grammar\n", __func__); - - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - auto schemas = json::array(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - schemas.push_back({ - {"type", "object"}, - {"properties", { - {"name", { - {"type", "string"}, - {"const", function.at("name")}, - }}, - {"arguments", function.at("parameters")}, - }}, - {"required", json::array({"name", "arguments", "id"})}, - }); - }); - auto schema = json { - {"type", "array"}, - {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, - {"minItems", 1}, - }; - if (!inputs.parallel_tool_calls) { - schema["maxItems"] = 1; - } - - builder.add_rule("root", "\"<|tool_call_start|>\"" + builder.add_schema("tool_calls", schema) + "\"<|tool_call_end|>\""); - }); - // model has no concept of tool selection mode choice, - // if the system prompt rendered correctly it will produce a tool call - // the grammar goes inside the tool call body - data.grammar_lazy = true; - data.grammar_triggers = {{COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, "\\s*<\\|tool_call_start\\|>\\s*\\["}}; - data.preserved_tokens = {"<|tool_call_start|>", "<|tool_call_end|>"}; - data.format = COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS; - } else if (are_tools_provided && (!is_json_schema_provided && !is_grammar_provided)) { - LOG_INF("%s: Using tools without json schema or grammar\n", __func__); - // output those tokens - data.preserved_tokens = {"<|tool_call_start|>", "<|tool_call_end|>"}; - } else if (is_json_schema_provided) { - LOG_INF("%s: Using provided json schema to build a grammar\n", __func__); - data.grammar = json_schema_to_grammar(inputs.json_schema); - } else if (is_grammar_provided) { - LOG_INF("%s: Using provided grammar\n", __func__); - data.grammar = inputs.grammar; - } else { - LOG_INF("%s: Using content relying on the template\n", __func__); - } - - data.prompt = apply(tmpl, inputs, /* messages_override= */ tweaked_messages); - LOG_DBG("%s: Prompt: %s\n", __func__, data.prompt.c_str()); - - return data; -} - -static common_chat_params common_chat_params_init_ministral_3(const common_chat_template & tmpl, const struct templates_params & inputs) { +static common_chat_params common_chat_params_init_ministral_3(const common_chat_template & tmpl, + const autoparser::templates_params & inputs) { common_chat_params data; // Build up messages to follow the format: https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512/blob/main/chat_template.jinja @@ -1129,8 +829,8 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_ // If message contains `reasoning_content`, add it as a block of type `thinking` if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) { content.push_back({ - {"type", "thinking"}, - {"thinking", msg.at("reasoning_content").get()}, + { "type", "thinking" }, + { "thinking", msg.at("reasoning_content").get() }, }); } @@ -1138,8 +838,8 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_ if (msg.contains("content")) { if (msg.at("content").is_string()) { content.push_back({ - {"type", "text"}, - {"text", msg.at("content").get()}, + { "type", "text" }, + { "text", msg.at("content").get() }, }); } else if (msg.at("content").is_array()) { auto blocks = msg.at("content"); @@ -1147,32 +847,37 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_ } } - auto adjusted = msg; + auto adjusted = msg; adjusted["content"] = content; adjusted.erase("reasoning_content"); adjusted_messages.push_back(adjusted); } - auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; - auto include_grammar = true; + auto include_grammar = true; - data.prompt = apply(tmpl, inputs, /* messages_override = */ adjusted_messages); - data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; - data.preserved_tokens = { + data.supports_thinking = true; + data.thinking_start_tag = "[THINK]"; + data.thinking_end_tag = "[/THINK]"; + data.prompt = common_chat_template_direct_apply(tmpl, inputs, /* messages_override = */ adjusted_messages); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.preserved_tokens = { "[THINK]", "[/THINK]", "[TOOL_CALLS]", "[ARGS]", }; - auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) { - auto reasoning = extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps(); + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + auto reasoning = + extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps(); // Response format parser if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) { // Ministral wants to emit json surrounded by code fences - return reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) << "```"; + return reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) + << "```"; } // Tool call parser @@ -1180,17 +885,16 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_ auto tool_choice = p.choice(); foreach_function(inputs.tools, [&](const json & tool) { const auto & function = tool.at("function"); - std::string name = function.at("name"); - const auto & schema = function.at("parameters"); + std::string name = function.at("name"); + const auto & schema = function.at("parameters"); - tool_choice |= p.rule("tool-" + name, - p.tool_open(p.tool_name(p.literal(name)) + "[ARGS]") - + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)) - ); + tool_choice |= + p.rule("tool-" + name, p.tool_open(p.tool_name(p.literal(name)) + "[ARGS]") + + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema))); }); - auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0; - auto max_calls = inputs.parallel_tool_calls ? -1 : 1; + auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0; + auto max_calls = inputs.parallel_tool_calls ? -1 : 1; auto tool_calls = p.trigger_rule("tool-call", p.repeat("[TOOL_CALLS]" + tool_choice, min_calls, max_calls)); return reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls; @@ -1209,820 +913,40 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_ data.grammar = build_grammar([&](const common_grammar_builder & builder) { foreach_function(inputs.tools, [&](const json & tool) { const auto & function = tool.at("function"); - auto schema = function.at("parameters"); + auto schema = function.at("parameters"); builder.resolve_refs(schema); }); parser.build_grammar(builder, data.grammar_lazy); }); data.grammar_triggers = { - {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]"} + { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]" } }; } return data; } -static common_chat_params common_chat_params_init_magistral(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - data.prompt = apply(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_MAGISTRAL; - data.preserved_tokens = { - "[THINK]", - "[/THINK]", - }; - - if (inputs.tools.is_array() && !inputs.tools.empty()) { - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - auto schemas = json::array(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - schemas.push_back({ - {"type", "object"}, - {"properties", { - {"name", { - {"type", "string"}, - {"const", function.at("name")}, - }}, - {"arguments", function.at("parameters")}, - {"id", { - {"type", "string"}, - {"pattern", "^[a-zA-Z0-9]{9}$"}, - }}, - }}, - {"required", json::array({"name", "arguments", "id"})}, - }); - }); - auto schema = json { - {"type", "array"}, - {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, - {"minItems", 1}, - }; - if (!inputs.parallel_tool_calls) { - schema["maxItems"] = 1; - } - builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema)); - }); - data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]"}); - data.preserved_tokens.push_back("[TOOL_CALLS]"); - } else { - data.grammar_lazy = false; - if (!inputs.json_schema.is_null()) { - if (!inputs.grammar.empty()) { - throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both"); - } - data.grammar = json_schema_to_grammar(inputs.json_schema); - } else { - data.grammar = inputs.grammar; - } - } - - return data; -} - -static common_chat_params common_chat_params_init_command_r7b(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - auto adjusted_messages = json::array(); - for (const auto & msg : inputs.messages) { - auto has_reasoning_content = msg.contains("reasoning_content") && msg.at("reasoning_content").is_string(); - auto has_tool_calls = msg.contains("tool_calls") && msg.at("tool_calls").is_array(); - if (has_reasoning_content && has_tool_calls) { - auto adjusted_message = msg; - adjusted_message["tool_plan"] = msg.at("reasoning_content"); - adjusted_message.erase("reasoning_content"); - adjusted_messages.push_back(adjusted_message); - } else { - adjusted_messages.push_back(msg); - } - } - data.prompt = apply(tmpl, inputs, /* messages_override= */ adjusted_messages); - data.format = COMMON_CHAT_FORMAT_COMMAND_R7B; - if (string_ends_with(data.prompt, "<|START_THINKING|>")) { - if (!inputs.enable_thinking) { - data.prompt += "<|END_THINKING|>"; - } else { - data.thinking_forced_open = true; - } - } else if (!inputs.enable_thinking && string_ends_with(data.prompt, "<|CHATBOT_TOKEN|>")) { - data.prompt += "<|START_THINKING|><|END_THINKING|>"; - } - - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - auto schemas = json::array(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - schemas.push_back({ - {"type", "object"}, - {"properties", { - {"tool_call_id", { - {"type", "string"}, - // Command-R's template expects an integer string. - {"pattern", "^[0-9]{1,10}$"}, - }}, - {"tool_name", { - {"type", "string"}, - {"const", function.at("name")}, - }}, - {"parameters", function.at("parameters")}, - }}, - {"required", json::array({"tool_call_id", "tool_name", "parameters"})}, - }); - }); - auto schema = json { - {"type", "array"}, - {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, - {"minItems", 1}, - }; - if (!inputs.parallel_tool_calls) { - schema["maxItems"] = 1; - } - builder.add_rule("root", - std::string(data.thinking_forced_open ? "( \"<|END_THINKING|>\" space )? " : "") + - "\"<|START_ACTION|>\" " + builder.add_schema("tool_calls", schema) + " \"<|END_ACTION|>\""); - }); - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, - // If thinking_forced_open, then we capture the tag in the grammar, - // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar) - std::string(data.thinking_forced_open ? "[\\s\\S]*?(<\\|END_THINKING\\|>\\s*)" : "(?:<\\|START_THINKING\\|>[\\s\\S]*?<\\|END_THINKING\\|>\\s*)?") + - "(<\\|START_ACTION\\|>)[\\s\\S]*" - }); - data.preserved_tokens = { - "<|START_ACTION|>", - "<|END_ACTION|>", - "<|START_RESPONSE|>", - "<|END_RESPONSE|>", - "<|START_THINKING|>", - "<|END_THINKING|>", - }; - return data; -} - -static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector & expected_properties) { - if (!parameters.is_object() || !parameters.contains("type") || parameters.at("type") != "object" || !parameters.contains("properties") || !parameters.contains("required")) { - throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties"); - } - const auto & parameters_properties = parameters.at("properties"); - const auto & parameters_required = parameters.at("required"); - for (const auto & prop : expected_properties) { - if (!parameters_properties.contains(prop)) { - throw std::runtime_error("Parameters of tool " + name + " is missing property: " + prop); // NOLINT - } - if (std::find(parameters_required.begin(), parameters_required.end(), json(prop)) == parameters_required.end()) { - throw std::runtime_error("Parameters of tool " + name + " must have property marked as required: " + prop); // NOLINT - } - } - if (parameters_properties.size() != expected_properties.size()) { - throw std::runtime_error("Parameters of tool " + name + " must only have these properties:" + string_join(expected_properties, ", ")); - } -} - -static common_chat_params common_chat_params_init_llama_3_x(const common_chat_template & tmpl, const struct templates_params & inputs, bool allow_python_tag_builtin_tools) { - auto builtin_tools = json::array(); - common_chat_params data; - if (!inputs.tools.is_null()) { - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - std::vector tool_rules; - - auto handle_builtin_tool = [&](const std::string & name, const json & parameters) { - if (name == "wolfram_alpha" || name == "web_search" || name == "brave_search") { - // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py - // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py - expect_tool_parameters(name, parameters, {"query"}); - } else if (name == "python" || name == "code_interpreter") { - // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py - expect_tool_parameters(name, parameters, {"code"}); - } else { - return false; - } - - std::vector kvs; - for (const auto & [key, value] : parameters.at("properties").items()) { - kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value)); // NOLINT - } - - tool_rules.push_back( - builder.add_rule( - name + "-call", - "\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\"")); - builtin_tools.push_back(name); - - return true; - }; - - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); - - // https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime - if (allow_python_tag_builtin_tools) { - handle_builtin_tool(name, parameters); - } - tool_rules.push_back( - builder.add_rule( - name + "-call", - "\"{\" space " - "( \"\\\"type\\\"\" space \":\" space \"\\\"function\\\"\" space \",\" space )? " - " \"\\\"name\\\"\" space \":\" space \"\\\"" + name + "\\\"\" space \",\" space " - " \"\\\"parameters\\\"\" space \":\" space " + builder.add_schema(name + "-args", parameters) + " " - "\"}\" space")); - }); - // Small models may hallucinate function names so we match anything (*at the start*) that looks like the JSON of a function call, regardless of the name. - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, - "(\\{\\s*(?:\"type\"\\s*:\\s*\"function\"\\s*,\\s*)?\"name\"\\s*:\\s*\")[\\s\\S]*", // + name + "\"[\\s\\S]*", - }); - if (!builtin_tools.empty()) { - data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|python_tag|>"}); - data.preserved_tokens.push_back("<|python_tag|>"); - } - // Allow a few empty lines on top of the usual constrained json schema space rule. - builder.add_rule("root", string_join(tool_rules, " | ")); - data.additional_stops.push_back("<|eom_id|>"); - }); - data.format = allow_python_tag_builtin_tools && !builtin_tools.empty() - ? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS - : COMMON_CHAT_FORMAT_LLAMA_3_X; - } else { - data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - } - data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, /* tools_override= */ std::nullopt, json { - {"date_string", format_time(inputs.now, "%d %b %Y")}, - {"tools_in_user_message", false}, - {"builtin_tools", builtin_tools}, - }); - return data; -} - -static common_chat_params common_chat_params_init_nemotron_v2(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - // Generate the prompt using the apply() function with the template - data.prompt = apply(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_NEMOTRON_V2; - - // Handle thinking tags appropriately based on inputs.enable_thinking - if (string_ends_with(data.prompt, "\n")) { - if (!inputs.enable_thinking) { - data.prompt += ""; - } else { - data.thinking_forced_open = true; - } - } - - // When tools are present, build grammar for the format, similar to CommandR, but without tool call ID - if (!inputs.tools.is_null() && inputs.tools.is_array() && !inputs.tools.empty()) { - data.grammar_lazy = true; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - auto schemas = json::array(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - schemas.push_back({ - { "type", "object" }, - { "properties", - { - { "name", - { - { "type", "string" }, - { "const", function.at("name") }, - } }, - { "arguments", function.at("parameters") }, - } }, - { "required", json::array({ "name", "arguments" }) }, - }); - }); - auto schema = json{ - { "type", "array" }, - { "items", schemas.size() == 1 ? schemas[0] : json{ { "anyOf", schemas } } }, - { "minItems", 1 }, - }; - if (!inputs.parallel_tool_calls) { - schema["maxItems"] = 1; - } - builder.add_rule("root", - std::string(data.thinking_forced_open ? "( \"\" space )? " : "") + - "\"\" " + builder.add_schema("tool_calls", schema) + - " \"\""); - }); - data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, - // If thinking_forced_open, then we capture the tag in the grammar, - // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar) - std::string(data.thinking_forced_open ? - "[\\s\\S]*?(\\s*)" : - "(?:[\\s\\S]*?\\s*)?") + - "()[\\s\\S]*" }); - } - return data; -} - -static common_chat_params common_chat_params_init_qwen3_coder(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - data.prompt = apply(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_PEG_CONSTRUCTED; - - // Nemotron Nano 3 and Step-3.5-Flash use the Qwen3 Coder tool calling with thinking - bool supports_reasoning = (tmpl.source().find("") != std::string::npos); - - // Handle thinking tags appropriately based on inputs.enable_thinking - if (supports_reasoning && string_ends_with(data.prompt, "\n")) { - if (!inputs.enable_thinking) { - data.prompt += ""; - } else { - data.thinking_forced_open = true; - } - } - - data.preserved_tokens = { - "", - "", - }; - - if (supports_reasoning) { - data.preserved_tokens.insert(data.preserved_tokens.end(), {"", ""}); - } - - auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); - auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; - auto include_grammar = true; - - auto parser = build_chat_peg_constructed_parser([&](auto & p) { - auto reasoning = p.eps(); - if (supports_reasoning && inputs.enable_thinking && extract_reasoning) { - auto reasoning_content = p.reasoning(p.until("")) + ("" | p.end()); - if (data.thinking_forced_open) { - reasoning = reasoning_content; - } - } - - // Response format parser - if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) { - return reasoning << p.content(p.schema(p.json(), "response-format", inputs.json_schema)); - } - - // Tool call parser - if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) { - auto tool_choice = p.choice(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - - auto schema_info = common_schema_info(); - schema_info.resolve_refs(parameters); - - auto tool_open = "\n"; - auto tool_close = p.literal("\n"); - auto args = p.sequence(); - auto arg_string = p.rule("xml-arg-string", p.until_one_of({ - "\n", - "\n" - })); - - foreach_parameter(function, [&](const auto & param_name, const json & param_schema, bool is_required) { - auto rule_name = "tool-" + name + "-arg-" + param_name; - - auto arg_open = "\n"; - auto arg_close = p.literal("\n"); - auto arg_value = p.eps(); - - if (schema_info.resolves_to_string(param_schema)) { - arg_value = p.tool_arg_string_value(arg_string) + "\n"; - } else { - arg_value = p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", param_schema)); - } - - // Model may or my not close with - auto arg_rule = p.rule(rule_name, p.tool_arg_open(arg_open) + arg_value + p.optional(p.tool_arg_close(arg_close))); - args += p.repeat(arg_rule, /* min = */ is_required ? 1 : 0, /* max = */ 1); - }); - - tool_choice |= p.rule("tool-" + name, p.tool_open(tool_open) + args + p.tool_close(tool_close)); - }); - - auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0; - auto max_calls = inputs.parallel_tool_calls ? -1 : 1; - auto tool_call = p.rule("tool-call", "\n" + tool_choice + "" + p.space()); - auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(tool_call, /* min = */ min_calls, /* max = */ max_calls)); - - return reasoning << p.content(p.until("")) << tool_calls; - } - - // Content only parser - include_grammar = false; - return reasoning << p.content(p.rest()); - }); - - data.parser = parser.save(); - - if (include_grammar) { - data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; - - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - auto schema = function.at("parameters"); - builder.resolve_refs(schema); - }); - parser.build_grammar(builder, data.grammar_lazy); - }); - - data.grammar_triggers = { - {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, ""} - }; - } - - return data; -} - - -static common_chat_params common_chat_params_init_apertus(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - // Generate the prompt using the apply() function with the template - data.prompt = apply(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_APERTUS; - - // Handle thinking tags appropriately based on inputs.enable_thinking - if (string_ends_with(data.prompt, "<|inner_prefix|>")) { - if (!inputs.enable_thinking) { - data.prompt += "<|inner_suffix|>"; - } else { - data.thinking_forced_open = true; - } - } - - // When tools are present, build grammar for the <|tools_prefix|> format - if (!inputs.tools.is_null() && inputs.tools.is_array() && !inputs.tools.empty()) { - data.grammar_lazy = true; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - auto schemas = json::array(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - schemas.push_back({ - { "type", "object" }, - { "properties", - { - { function.at("name"), function.at("parameters") } - } }, - { "required", json::array({ function.at("name") }) }, - }); - }); - auto schema = json{ - { "type", "array" }, - { "items", schemas.size() == 1 ? schemas[0] : json{ { "anyOf", schemas } } }, - { "minItems", 1 }, - }; - if (!inputs.parallel_tool_calls) { - schema["maxItems"] = 1; - } - builder.add_rule("root", - std::string(data.thinking_forced_open ? "( \"<|inner_suffix|>\" space )? " : "") + - "\"<|tools_prefix|>\"" + builder.add_schema("tool_calls", schema) + "\"<|tools_suffix|>\""); - }); - data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, - // If thinking_forced_open, then we capture the <|inner_suffix|> tag in the grammar, - // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar) - std::string(data.thinking_forced_open ? - "[\\s\\S]*?(<\\|inner_suffix\\|>\\s*)" : - "(?:<\\|inner_prefix\\|>[\\s\\S]*?<\\|inner_suffix\\|>\\s*)?") + - "(<\\|tools_prefix\\|>)[\\s\\S]*" }); - data.preserved_tokens = { - "<|system_start|>", - "<|system_end|>", - "<|developer_start|>", - "<|developer_end|>", - "<|user_start|>", - "<|user_end|>", - "<|assistant_start|>", - "<|assistant_end|>", - "<|inner_prefix|>", - "<|inner_suffix|>", - "<|tools_prefix|>", - "<|tools_suffix|>", - }; - } - return data; -} - -static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - auto prompt = apply(tmpl, inputs); - - // Hacks to fix the official (broken) prompt. - // It is advisable to use --chat-template-file models/templates/llama-cpp-deepseek-r1.jinja instead, - // until the official template is fixed. - if (tmpl.source().find("{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}") != std::string::npos) { - // Don't leave the chat dangling after tool results - if (string_ends_with(prompt, "<｜tool▁outputs▁end｜>")) { - prompt += "<｜end▁of▁sentence｜>"; - if (inputs.add_generation_prompt) { - prompt += "<｜Assistant｜>"; - } - } - // Fix up tool call delta example added by Minja - prompt = std::regex_replace( - prompt, - std::regex("(<｜tool▁call▁end｜>)[\\s\\r\\n]*(<｜tool▁outputs▁begin｜>|<｜User｜>)"), - "$1<｜tool▁calls▁end｜><｜end▁of▁sentence｜>$2"); - } - data.prompt = prompt; - data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1; - if (string_ends_with(data.prompt, "\n")) { - if (!inputs.enable_thinking) { - data.prompt += ""; - } else { - data.thinking_forced_open = true; - } - } - - if (inputs.tools.is_array() && !inputs.tools.empty()) { - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED && inputs.json_schema.is_null(); - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - std::vector tool_rules; - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); - tool_rules.push_back(builder.add_rule(name + "-call", - "( \"<｜tool▁call▁begin｜>\" )? \"function<｜tool▁sep｜>" + name + "\\n" - "```json\\n\" " + builder.add_schema(name + "-args", parameters) + " " - "\"```<｜tool▁call▁end｜>\"")); - }); - // Distill Qwen 7B & 32B models seem confused re/ syntax of their tool call opening tag, - // so we accept common variants (then it's all constrained) - builder.add_rule("root", - std::string(data.thinking_forced_open ? "( \"\" space )? " : "") + - "( \"<｜tool▁calls▁begin｜>\" | \"<｜tool_calls_begin｜>\" | \"<｜tool calls begin｜>\" | \"<｜tool\\\\_calls\\\\_begin｜>\" | \"<｜tool▁calls｜>\" ) " - "(" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " " - "\"<｜tool▁calls▁end｜>\"" - " space"); - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, - // If thinking_forced_open, then we capture the tag in the grammar, - // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar) - std::string(data.thinking_forced_open ? "[\\s\\S]*?(\\s*)" : "(?:[\\s\\S]*?\\s*)?") + - "(<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)[\\s\\S]*" - }); - data.preserved_tokens = { - "", - "", - "<｜tool▁calls▁begin｜>", - "<｜tool▁call▁begin｜>", - "<｜tool▁sep｜>", - "<｜tool▁call▁end｜>", - "<｜tool▁calls▁end｜", - }; - }); - } - return data; -} - -static common_chat_params common_chat_params_init_deepseek_v3_1(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - // Pass thinking context for DeepSeek V3.1 template - json additional_context = { - {"thinking", inputs.enable_thinking}, - }; - - auto prompt = apply(tmpl, inputs, - /* messages_override= */ inputs.messages, - /* tools_override= */ std::nullopt, - additional_context); - data.prompt = prompt; - data.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - if (string_ends_with(data.prompt, "")) { - if (!inputs.enable_thinking) { - data.prompt += ""; - } else { - data.thinking_forced_open = true; - } - } - if (inputs.tools.is_array() && !inputs.tools.empty()) { - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED && inputs.json_schema.is_null(); - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - std::vector tool_rules; - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); - tool_rules.push_back(builder.add_rule(name + "-call", - "( \"<｜tool▁call▁begin｜>\" )? \"" + name + "<｜tool▁sep｜>" - "\" " + builder.add_schema(name + "-args", parameters) + " " - "\"<｜tool▁call▁end｜>\"")); - }); - // Distill Qwen 7B & 32B models seem confused re/ syntax of their tool call opening tag, - // so we accept common variants (then it's all constrained) - builder.add_rule("root", - std::string(data.thinking_forced_open ? "( \"\" space )? " : "") + - "( \"<｜tool▁calls▁begin｜>\" | \"<｜tool_calls_begin｜>\" | \"<｜tool calls begin｜>\" | \"<｜tool\\\\_calls\\\\_begin｜>\" | \"<｜tool▁calls｜>\" ) " - "(" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " " - "\"<｜tool▁calls▁end｜>\"" - " space"); - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, - // If thinking_forced_open, then we capture the tag in the grammar, - // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar) - std::string(data.thinking_forced_open ? "[\\s\\S]*?(\\s*)" : "(?:[\\s\\S]*?\\s*)?") + - "(<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)[\\s\\S]*" - }); - data.preserved_tokens = { - "", - "", - "<｜tool▁calls▁begin｜>", - "<｜tool▁call▁begin｜>", - "<｜tool▁sep｜>", - "<｜tool▁call▁end｜>", - "<｜tool▁calls▁end｜>", - }; - }); - } - return data; -} - -static common_chat_params common_chat_params_init_minimax_m2(const common_chat_template & tmpl, const struct templates_params & params) { - common_chat_params data; - data.grammar_lazy = params.tools.is_array() && !params.tools.empty() && params.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - - data.prompt = apply(tmpl, params); - data.format = COMMON_CHAT_FORMAT_MINIMAX_M2; - - // Handle thinking tags based on prompt ending - if (string_ends_with(data.prompt, "\n")) { - if (!params.enable_thinking) { - // Close the thinking tag immediately if thinking is disabled - data.prompt += "\n\n"; - } else { - // Mark thinking as forced open (template started with ) - data.thinking_forced_open = true; - } - } - - // Preserve MiniMax-M2 special tokens - data.preserved_tokens = { - "", - "", - "", - "", - }; - - // build grammar for tool call - static const xml_tool_call_format form { - /* form.scope_start = */ "\n", - /* form.tool_start = */ "\n", - /* form.key_start = */ "", - /* form.val_end = */ "\n", - /* form.tool_end = */ "\n", - /* form.scope_end = */ "", - }; - build_grammar_xml_tool_call(data, params.tools, form); - - return data; -} - -static common_chat_params common_chat_params_init_kimi_k2(const common_chat_template & tmpl, const struct templates_params & params) { - common_chat_params data; - data.grammar_lazy = params.tools.is_array() && !params.tools.empty() && params.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - - data.prompt = apply(tmpl, params); - data.format = COMMON_CHAT_FORMAT_KIMI_K2; - - data.preserved_tokens = { - "", - "", - "<|tool_calls_section_begin|>", - "<|tool_call_begin|>", - "<|tool_call_argument_begin|>", - "<|tool_call_end|>", - "<|tool_calls_section_end|>", - "<|im_end|>", - "<|im_system|>", - "<|im_middle|>", - }; - - data.additional_stops.insert(data.additional_stops.end(), { - "<|im_end|>", - "<|im_middle|>" - }); - // build grammar for tool call - static const xml_tool_call_format form = ([]() { - xml_tool_call_format form {}; - form.scope_start = "<|tool_calls_section_begin|>"; - form.tool_start = "<|tool_call_begin|>"; - form.tool_sep = "<|tool_call_argument_begin|>{"; - form.key_start = "\""; - form.key_val_sep = "\": "; - form.val_end = ", "; - form.tool_end = "}<|tool_call_end|>"; - form.scope_end = "<|tool_calls_section_end|>"; - form.raw_argval = false; - form.last_val_end = ""; - return form; - })(); - build_grammar_xml_tool_call(data, params.tools, form); - - return data; -} - -static common_chat_params common_chat_params_init_apriel_1_5(const common_chat_template & tmpl, const struct templates_params & params) { - common_chat_params data; - data.grammar_lazy = params.tools.is_array() && !params.tools.empty() && params.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - - data.prompt = apply(tmpl, params); - data.format = COMMON_CHAT_FORMAT_APRIEL_1_5; - - data.preserved_tokens = { - "", - "", - "", - "", - }; - - // build grammar for tool call - static const xml_tool_call_format form = ([]() { - xml_tool_call_format form {}; - form.scope_start = "["; - form.tool_start = "{\"name\": \""; - form.tool_sep = "\", \"arguments\": {"; - form.key_start = "\""; - form.key_val_sep = "\": "; - form.val_end = ", "; - form.tool_end = "}, "; - form.scope_end = "]"; - form.raw_argval = false; - form.last_val_end = ""; - form.last_tool_end = "}"; - return form; - })(); - build_grammar_xml_tool_call(data, params.tools, form); - - return data; -} - -static common_chat_params common_chat_params_init_xiaomi_mimo(const common_chat_template & tmpl, const struct templates_params & params) { - common_chat_params data; - data.grammar_lazy = params.tools.is_array() && !params.tools.empty() && params.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - - data.prompt = apply(tmpl, params); - data.format = COMMON_CHAT_FORMAT_XIAOMI_MIMO; - - data.preserved_tokens = { - "", - "", - }; - - // build grammar for tool call - static const xml_tool_call_format form = ([]() { - xml_tool_call_format form {}; - form.scope_start = "\n"; - form.tool_start = "\n{\"name\": \""; - form.tool_sep = "\", \"arguments\": {"; - form.key_start = "\""; - form.key_val_sep = "\": "; - form.val_end = ", "; - form.tool_end = "}\n"; - form.scope_end = ""; - form.raw_argval = false; - form.last_val_end = ""; - return form; - })(); - build_grammar_xml_tool_call(data, params.tools, form); - - return data; -} - -static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl, const struct templates_params & inputs) { +static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl, + const autoparser::templates_params & inputs) { common_chat_params data; // Copy reasoning to the "thinking" field as expected by the gpt-oss template auto adjusted_messages = json::array(); for (const auto & msg : inputs.messages) { auto has_reasoning_content = msg.contains("reasoning_content") && msg.at("reasoning_content").is_string(); - auto has_tool_calls = msg.contains("tool_calls") && msg.at("tool_calls").is_array(); + auto has_tool_calls = msg.contains("tool_calls") && msg.at("tool_calls").is_array(); if (has_reasoning_content && has_tool_calls) { - auto adjusted_message = msg; + auto adjusted_message = msg; adjusted_message["thinking"] = msg.at("reasoning_content"); - adjusted_message.erase("content"); adjusted_messages.push_back(adjusted_message); } else { adjusted_messages.push_back(msg); } } - auto prompt = apply(tmpl, inputs, /* messages_override= */ adjusted_messages); + auto prompt = common_chat_template_direct_apply(tmpl, inputs, /* messages_override= */ adjusted_messages); // Check if we need to replace the return token with end token during // inference and without generation prompt. For more details see: @@ -2035,696 +959,449 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp } } - data.prompt = prompt; - data.format = COMMON_CHAT_FORMAT_GPT_OSS; + data.prompt = prompt; + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.supports_thinking = true; // These special tokens are required to parse properly, so we include them // even if parse_tool_calls is false. data.preserved_tokens = { - "<|channel|>", - "<|constrain|>", - "<|message|>", - "<|start|>", - "<|end|>", + "<|channel|>", "<|constrain|>", "<|message|>", "<|start|>", "<|end|>", }; - if (!inputs.json_schema.is_null()) { - data.grammar_lazy = false; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - auto schema = inputs.json_schema; - builder.resolve_refs(schema); + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && has_tools; - auto not_end = builder.add_rule("not-end", - "[^<] | \"<\" [^|] | \"<|\" [^e] | \"<|e\" [^n] | \"<|en\" [^d] | \"<|end\" [^|] | \"<|end|\" [^>]"); - auto analysis = builder.add_rule("analysis", - "\"<|channel|>analysis<|message|>\" ( " + not_end + " )* \"<|end|>\""); - auto constraint = builder.add_rule("constraint", "\"<|constrain|>\"? [a-zA-Z0-9_-]+"); - auto final = builder.add_rule("final", - "\"<|channel|>final\" ( \" \" " + constraint + " )? \"<|message|>\" " + - builder.add_schema("response", schema) - ); + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + const std::string END = "<|end|>"; + const std::string START = "<|start|>"; + const std::string MESSAGE = "<|message|>"; + const std::string CHANNEL = "<|channel|>"; + const std::string CONSTRAIN = "<|constrain|>"; + const std::string START_ASSISTANT = START + "assistant"; + const std::string CHANNEL_ANALYSIS = CHANNEL + "analysis"; + const std::string CHANNEL_COMMENTARY = CHANNEL + "commentary"; + const std::string CHANNEL_FINAL = CHANNEL + "final"; - builder.add_rule("root", "( " + analysis + " \"<|start|>assistant\" )? " + final); - }); - } + auto the_end = END | p.end(); - if (inputs.tools.is_array() && !inputs.tools.empty()) { - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - // tool calls can appear in commentary or analysis channels - auto channel = builder.add_rule("channel", "\"<|channel|>\" ( \"commentary\" | \"analysis\" )"); + const std::string analysis_header = CHANNEL_ANALYSIS + MESSAGE; + auto segment_content = p.until(END); + auto analysis_segment = extract_reasoning ? + p.literal(analysis_header) + p.reasoning(segment_content) + p.until(END) + the_end : + p.content(analysis_header + p.until(END) + the_end); - std::vector tool_rules_recipient_in_role; - std::vector tool_rules_recipient_in_channel; - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); + auto channel_header_content = p.until_one_of({ " to=functions.", MESSAGE }); + auto content_header = p.choice({ p.literal(CHANNEL_COMMENTARY), p.literal(CHANNEL_FINAL) }); + auto content_segment = p.rule("content-segment", content_header + channel_header_content + MESSAGE + + p.content(segment_content) + the_end); - tool_rules_recipient_in_role.push_back( - builder.add_rule(name + "-call", - "\"" + name + "\"" + channel + " \" <|constrain|>json\"? \"<|message|>\" " + - builder.add_schema(name + "-args", parameters) - ) - ); - - tool_rules_recipient_in_channel.push_back( - builder.add_rule(name + "-call", - "\"" + name + "\"" + " \" <|constrain|>json\"? \"<|message|>\" " + - builder.add_schema(name + "-args", parameters) - ) - ); - }); - - auto recipient_in_channel = builder.add_rule("recipient_in_channel", - channel + " \" to=functions.\" ( " + - string_join(tool_rules_recipient_in_channel, " | ") + " )" - ); - - if (data.grammar_lazy) { - auto recipient_in_role = builder.add_rule("recipient_in_role", - "\"<|start|>assistant\"? \" to=functions.\" ( " + - string_join(tool_rules_recipient_in_role, " | ") + " )" - ); - - builder.add_rule("root", recipient_in_role + " | " + recipient_in_channel); - } else { - auto not_end = builder.add_rule("not-end", - "[^<] | \"<\" [^|] | \"<|\" [^e] | \"<|e\" [^n] | \"<|en\" [^d] | \"<|end\" [^|] | \"<|end|\" [^>]"); - auto analysis = builder.add_rule("analysis", - "\"<|channel|>analysis<|message|>\" ( " + not_end + " )* \"<|end|>\""); - auto commentary = builder.add_rule("commentary", - "\"<|channel|>commentary<|message|>\" ( " + not_end + " )* \"<|end|>\""); - - auto recipient_in_role = builder.add_rule("recipient_in_role", - "\" to=functions.\" ( " + string_join(tool_rules_recipient_in_role, " | ") + " )" - ); - - builder.add_rule("root", - "( " + analysis + " \"<|start|>assistant\" )? " + - "( " + commentary + " \"<|start|>assistant\" )? " + - "( " + recipient_in_role + " | " + recipient_in_channel + " )" - ); - } - - // Trigger on tool calls that appear in the commentary channel - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, - "<\\|channel\\|>(?:commentary|analysis) to" - }); - - // Trigger tool calls that appear in the role section, either at the - // start or in the middle. - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, - "^ to" - }); - - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, - "<\\|start\\|>assistant to" - }); - }); - } - - return data; -} - -static common_chat_params common_chat_params_init_glm_4_5(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - data.grammar_lazy = inputs.tools.is_array() && !inputs.tools.empty() && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - - std::string prompt = apply(tmpl, inputs); - - // match the existing trimming behavior - if (inputs.add_bos && string_starts_with(prompt, tmpl.bos_token())) { - prompt.erase(0, tmpl.bos_token().size()); - } - if (inputs.add_eos && string_ends_with(prompt, tmpl.eos_token())) { - prompt.erase(prompt.size() - tmpl.eos_token().size()); - } - if (string_ends_with(prompt, "")) { - if (!inputs.enable_thinking) { - prompt += ""; - } else { - data.thinking_forced_open = true; - } - } - - // add GLM preserved tokens - data.preserved_tokens = { - "<|endoftext|>", - "[MASK]", - "[gMASK]", - "[sMASK]", - "", - "", - "<|system|>", - "<|user|>", - "<|assistant|>", - "<|observation|>", - "<|begin_of_image|>", - "<|end_of_image|>", - "<|begin_of_video|>", - "<|end_of_video|>", - "<|begin_of_audio|>", - "<|end_of_audio|>", - "<|begin_of_transcription|>", - "<|end_of_transcription|>", - "<|code_prefix|>", - "<|code_middle|>", - "<|code_suffix|>", - "/nothink", - "", - "", - "", - "", - "", - "", - "", - "" - }; - - // extra GLM 4.5 stop word - data.additional_stops.insert(data.additional_stops.end(), { - "<|user|>", - "<|observation|>" - }); - - // build grammar for tool call - static const xml_tool_call_format form { - /* form.scope_start = */ "", - /* form.tool_start = */ "\n", - /* form.tool_sep = */ "\n", - /* form.key_start = */ "", - /* form.key_val_sep = */ "\n", - /* form.val_end = */ "\n", - /* form.tool_end = */ "\n", - /* form.scope_end = */ "", - }; - build_grammar_xml_tool_call(data, inputs.tools, form); - - data.prompt = prompt; - data.format = COMMON_CHAT_FORMAT_GLM_4_5; - return data; -} - -static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) { - LOG_DBG("%s\n", __func__); - common_chat_params data; - const std::optional additional_context = json { - {"datetime", format_time(inputs.now, "%b %d %Y %H:%M:%S GMT")}, - {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))}, - }; - data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, /* tools_override =*/ std::nullopt, additional_context); - if (inputs.tools.is_array() && !inputs.tools.empty()) { - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - auto schemas = json::array(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - schemas.push_back({ - {"type", "object"}, - {"properties", { - {"name", { - {"type", "string"}, - {"const", function.at("name")}, - }}, - {"arguments", function.at("parameters")}, - }}, - {"required", json::array({"name", "arguments", "id"})}, - }); - }); - auto schema = json { - {"type", "array"}, - {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, - {"minItems", 1}, - }; - if (!inputs.parallel_tool_calls) { - schema["maxItems"] = 1; - } - builder.add_rule("root", "\" functools\"? " + builder.add_schema("tool_calls", schema)); - }); - data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, " functools["}); - data.preserved_tokens = { - " functools[", - }; - data.format = COMMON_CHAT_FORMAT_FIREFUNCTION_V2; - } else { - data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - } - return data; -} - -static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const struct templates_params & inputs) { - // >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}... - // Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar - // If the function is python, we also allow raw python code (if the line after `python\n` doesn't start w/ opening `{`), which the model seems to prefer for multiline code. - common_chat_params data; - data.prompt = apply(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2; - if (inputs.tools.is_array() && !inputs.tools.empty()) { - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - std::vector first_tool_rules; - std::vector subsequent_tool_rules; - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); - std::string args_pattern = "[\\s\\S]*"; - auto args_rule = builder.add_schema(name + "-args", parameters); - if (name == "python") { - args_rule = builder.add_rule(name + "-maybe-raw-args", args_rule + " | [^{] .*"); - } else { - args_pattern = "\\{" + args_pattern; - } - auto call_rule = builder.add_rule(name + "-call", "\"" + name + "\\n\" " + args_rule); - first_tool_rules.push_back(call_rule); - if (inputs.parallel_tool_calls) { - subsequent_tool_rules.push_back(builder.add_rule(name + "-call2", "\">>>\" " + call_rule)); - } - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, - "((?:[\\s\\S]+?>>>)?" + regex_escape(name) + "\n)" + args_pattern, - }); - }); - data.preserved_tokens = { - "<|end_header_id|>", - }; - auto first_rule = first_tool_rules.empty() ? "" : builder.add_rule("first_tool_call", string_join(first_tool_rules, " | ")) + " space"; - if (inputs.parallel_tool_calls) { - auto subsequent_rule = builder.add_rule("subsequent_tool_call", string_join(subsequent_tool_rules, " | ")) + " space"; - builder.add_rule("root", first_rule + " (" + subsequent_rule + ")*"); - } else { - builder.add_rule("root", first_rule); - } - - }); - } - return data; -} - -static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct templates_params & inputs) { - // https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt - common_chat_params data; - - if (!inputs.tools.is_null()) { - std::string python_code_argument_name; - auto has_raw_python = false; - - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - std::vector tool_rules; - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - const auto & parameters = function.at("parameters"); - std::string name = function.at("name"); - if (name == "python" || name == "ipython") { - if (!parameters.contains("type")) { - throw std::runtime_error("Missing type in python tool"); - } - has_raw_python = true; - const auto & type = parameters.at("type"); - if (type == "object") { - auto properties = parameters.at("properties"); - for (auto it = properties.begin(); it != properties.end(); ++it) { - if (it.value().at("type") == "string") { - if (!python_code_argument_name.empty()) { - throw std::runtime_error("Multiple string arguments found in python tool"); - } - python_code_argument_name = it.key(); - } - } - if (python_code_argument_name.empty()) { - throw std::runtime_error("No string argument found in python tool"); - } - } else if (type != "string") { - throw std::runtime_error("Invalid type in python tool: " + type.dump()); - } - } - tool_rules.push_back(builder.add_rule(name + "-call", "\"\" " + builder.add_schema(name + "-args", parameters) + " \"\" space")); - }); - if (has_raw_python) { - tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*")); - data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|python_tag|>"}); - data.preserved_tokens.push_back("<|python_tag|>"); - } - auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space"; - builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call); - data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "\n")) { - if (!extra_context["enable_thinking"]) { - data.prompt += ""; - } else { - data.thinking_forced_open = true; - } - } - - if (!inputs.tools.is_null()) { - // (content)?({"name": "foo", "arguments": {"a": 1}})* - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - std::vector tool_rules; - std::vector tool_call_alts; - std::vector escaped_names; - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); - tool_rules.push_back(builder.add_schema(name + "-call", { - {"type", "object"}, - {"properties", json { - {"name", json {{"const", name}}}, - {"arguments", parameters}, - }}, - {"required", json::array({"name", "arguments"})}, - })); - tool_call_alts.push_back(builder.add_rule( - name + "-function-tag", - "\"\" space " + - builder.add_schema(name + "-args", parameters) + " " - "\"\" space")); - - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_WORD, - "", - }); - auto escaped_name = regex_escape(name); - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, - " alt_tags { - any_tool_call, - "\"\" space " + any_tool_call + " \"\"", - // The rest is just to accommodate common "good bad" outputs. - "\"\" space " + any_tool_call + " \"\"", - "\"\" space " + any_tool_call + " \"\"", - "\"\" space " + any_tool_call + " \"\"", - "\"\" space " + any_tool_call + " \"\"", - "\"\" space " + any_tool_call + " \"\"", - "\"\" space " + any_tool_call + " \"\"", - }; - auto wrappable_tool_call = builder.add_rule("wrappable_tool_call", "( " + string_join(alt_tags, " | ") + " ) space"); - tool_call_alts.push_back(wrappable_tool_call); - tool_call_alts.push_back( - "( \"```\\n\" | \"```json\\n\" | \"```xml\\n\" ) space " + wrappable_tool_call + " space \"```\" space "); - auto tool_call = builder.add_rule("tool_call", string_join(tool_call_alts, " | ")); - builder.add_rule("root", - std::string(data.thinking_forced_open ? "( \"\" space )? " : "") + - (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call)); - // Trigger on some common known "good bad" outputs (only from the start and with a json that's about a specific argument name to avoid false positives) - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, - // If thinking_forced_open, then we capture the tag in the grammar, - // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar) - std::string(data.thinking_forced_open ? "(\\s*)" : "") + ( - "\\s*(" - "(?:" - "||||)?" - "\\s*\\{\\s*\"name\"\\s*:\\s*\"(?:" + string_join(escaped_names, "|") + ")\"" - ")" - ")" - ), - }); - data.preserved_tokens = { - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "```", - "```json", - "```xml", - }; - }); - } - - return data; -} - -static common_chat_params common_chat_params_init_granite(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - // Pass thinking context for Granite template - json additional_context = { - {"thinking", inputs.enable_thinking}, - }; - - data.prompt = apply(tmpl, inputs, /* messages_override= */ std::nullopt, /* tools_override= */ std::nullopt, additional_context); - data.format = COMMON_CHAT_FORMAT_GRANITE; - - if (string_ends_with(data.prompt, "\n") || string_ends_with(data.prompt, "")) { - if (!inputs.enable_thinking) { - data.prompt += ""; - } else { - data.thinking_forced_open = true; - } - } - - if (!inputs.tools.is_null()) { - // Granite uses <|tool_call|> followed by JSON list - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - std::vector tool_rules; - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); - tool_rules.push_back(builder.add_rule(name + "-call", builder.add_schema(name + -"-args", { - {"type", "object"}, - {"properties", { - {"name", {{"const", name}}}, - {"arguments", parameters}, - }}, - {"required", json::array({"name", "arguments"})}, - }))); - }); - - auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")); - auto tool_list = builder.add_rule("tool_list", "\"[\" space " + tool_call + " (\",\" space " + tool_call + ")* space \"]\""); - - if (data.thinking_forced_open) { - builder.add_rule("root", "\"\" space \"\" space [^<]* \"\" space \"<|tool_call|>\" space " + tool_list); - } else { - builder.add_rule("root", "\"<|tool_call|>\" space " + tool_list); - } - - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_WORD, - "<|tool_call|>" - }); - - data.preserved_tokens = { - "", - "", - "", - "", - "<|tool_call|>", - }; - }); - } else { - // Handle thinking tags for non-tool responses - if (data.thinking_forced_open && inputs.enable_thinking) { - data.grammar_lazy = false; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - builder.add_rule("root", "\"\" space \"\" space .* \"\" space"); - }); - data.preserved_tokens = { - "", - "", - "", - "", - }; - } - } - - return data; -} - -static common_chat_params common_chat_params_init_solar_open(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - // Copy `reasoning_content` to `reasoning` - auto adjusted_messages = json::array(); - for (const auto & msg : inputs.messages) { - if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) { - auto adjusted_message = msg; - adjusted_message["reasoning"] = msg.at("reasoning_content"); - adjusted_message.erase("reasoning_content"); - adjusted_messages.push_back(adjusted_message); - } else { - adjusted_messages.push_back(msg); - } - } - - auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); - auto include_grammar = true; - - auto prompt = apply(tmpl, inputs, /* messages_override= */ adjusted_messages); - - // Check if we need to replace the flush token with end token during inference and without generation prompt. - if (inputs.is_inference && !inputs.add_generation_prompt) { - static constexpr std::string_view return_token = "<|flush|>"; - static constexpr std::string_view end_token = "<|end|>"; - if (size_t pos = prompt.rfind(return_token); pos != std::string::npos) { - prompt.replace(pos, return_token.length(), end_token); - } - } - - data.prompt = prompt; - data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; - data.preserved_tokens = { - "<|think|>", - "<|content|>", - "<|begin|>", - "<|end|>", - "<|tool_calls|>", - "<|tool_call:begin|>", - "<|tool_call:end|>", - "<|tool_call:name|>", - "<|tool_call:args|>", - }; - - auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) { - auto lit_think = p.atomic(p.literal("<|think|>")); - auto lit_assistant_begin = p.atomic(p.literal("<|begin|>assistant")); - auto lit_content = p.atomic(p.literal("<|content|>")); - auto lit_end = p.atomic(p.literal("<|end|>")); - auto parser_until_end = p.until("<|end|>"); - - // reasoning <- "<|think|>" (!"<|end|>" .)* - auto parser_reasoning = p.rule("reasoning", lit_think + p.reasoning(parser_until_end)); - - // content <- "<|content|>" (!"<|end|>" .)* - auto parser_content = p.rule("content", lit_content + p.content(parser_until_end)); - - // wrap_choice(items) <- item-choice wrapped* - // item-choice <- items[0] / ... / items[n] - // wrapped <- "<|end|><|begin|>assistant" item-choice - auto wrap_choice = [&](const std::vector & items) { - auto choice = p.choice(items); - return choice + p.zero_or_more(lit_end + lit_assistant_begin + choice); - }; - - // wrap_seq(items) <- item[0] "<|end|><|begin|>assistant" item[1] ... - auto wrap_seq = [&](const std::vector & items) { - auto seq = p.sequence(); - for (auto i = 0u; i < items.size(); i++) { - if (i == 0) { - seq += items[i]; - continue; - } - seq += lit_end + lit_assistant_begin + items[i]; - } - return seq; - }; - - // Response format parser - if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) { - auto parser_response_format = lit_content + p.content(p.schema(p.json(), "response-format", inputs.json_schema)); - return p.choice({ - wrap_seq({parser_reasoning, parser_response_format}), - wrap_seq({parser_response_format}) - }); + if (!inputs.json_schema.is_null()) { + auto final_header = p.literal(CHANNEL_FINAL); + auto constraint = p.optional(p.space() + p.literal(CONSTRAIN) + channel_header_content); + return p.optional(analysis_segment) + final_header + constraint + MESSAGE + + p.content(p.schema(p.json(), "response-format", inputs.json_schema)); } - auto lit_tool_call_begin = p.literal("<|tool_call:begin|>"); - auto lit_tool_call_name = p.literal("<|tool_call:name|>"); - auto lit_tool_call_args = p.literal("<|tool_call:args|>"); - auto lit_tool_call_end = p.literal("<|tool_call:end|>"); + auto segment = p.optional(START_ASSISTANT + p.space()) + p.choice({ content_segment, analysis_segment }); + auto contents = p.optional(segment + p.repeat(p.optional(p.space()) + segment, 0, -1)) + p.end(); // Tool call parser if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) { - auto parser_tool_call = p.choice(); + auto tool_choice = p.choice(); + foreach_function(inputs.tools, [&](const json & tool) { const auto & function = tool.at("function"); - std::string name = function.at("name"); - const auto & schema = function.at("parameters"); + std::string name = function.at("name"); + const auto & params = function.at("parameters"); - // tool(name, schema) <- name "<|tool_call:args|>" schema - parser_tool_call |= p.rule("tool-" + name, - p.atomic(p.tool_name(p.literal(name)) + lit_tool_call_args) - + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema))); + // Tool call can appear as: + // 1. In role header: " to=functions.NAME<|channel|>..." + // 2. In channel: "<|channel|>(analysis|commentary) to=functions.NAME..." + auto func_name = p.literal(" to=functions.") + p.tool_name(p.literal(name)); + + auto channel = p.literal(CHANNEL_COMMENTARY) | p.literal(CHANNEL_ANALYSIS); + auto constraint = p.space() + p.optional(p.literal(CONSTRAIN) + channel_header_content); + auto args = p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", params)); + + // Pattern 1: recipient in role header + // " to=functions.NAME<|channel|>(analysis|commentary)[constraint]<|message|>ARGS" + auto tool_in_role = p.tool(p.tool_open(func_name + channel) + constraint + MESSAGE + args); + + // Pattern 2: recipient in channel header + // "<|channel|>(analysis|commentary) to=functions.NAME[constraint]<|message|>ARGS" + auto tool_in_channel = p.tool(channel + p.tool_open(func_name + constraint + MESSAGE) + args); + + tool_choice |= tool_in_role | tool_in_channel; }); auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0; auto max_calls = inputs.parallel_tool_calls ? -1 : 1; - // tool-calls <- "<|tool_calls|>" tool-call+ - // tool-call <- "<|tool_call:begin|> call-id "<|tool_call:name|>" &([^<]+ "<|tool_call:args|>") tool-choice "<|tool_call:end|>" - // call-id <- [a-zA-Z0-9_-]+ - // tool-choice <- tool(t[0].name, t[0].schema) / ... / tool(t[n].name, t[n].schema) - auto parser_tool_calls = p.trigger_rule("tool-calls", - p.atomic(p.literal("<|tool_calls|>")) - + p.repeat( - p.tool_open( - lit_tool_call_begin - + p.tool_id(p.chars("[a-zA-Z0-9_-]", 1, -1)) - + lit_tool_call_name - + p.peek(p.chars("[^<]", 1, -1) + lit_tool_call_args)) - + parser_tool_call - + p.tool_close(lit_tool_call_end), - /* min = */ 1, - /* max = */ max_calls)); + auto role_start = p.optional(p.space() + p.literal(START_ASSISTANT)); + auto tool_call = p.rule("tool-call", p.repeat(role_start + tool_choice, min_calls, max_calls) + p.end()); - if (min_calls == 1) { - // If required, then try any combination of the reasoning, content, and tool call - return p.choice({ - wrap_seq({parser_reasoning, parser_content, parser_tool_calls}), - wrap_seq({parser_reasoning, parser_tool_calls}), - wrap_seq({parser_content, parser_tool_calls}), - wrap_seq({parser_tool_calls}) - }); + return p.choice({ p.trigger_rule("single-tool", tool_call), p.trigger_rule("tools", p.one_or_more(segment) + tool_call) }); + } + + return contents; + }); + + data.parser = parser.save(); + + if (include_grammar) { + data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + data.grammar_triggers = { + { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^(?:<\\|start\\|>assistant\\s*)?(\\s+to=functions)" }, + { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "(?:<\\|end\\|>)(?:<\\|start\\|>assistant\\s*)?(\\s+to=functions)" }, + { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, + "(?:<\\|start\\|>assistant\\s*)?(<\\|channel\\|>(?:commentary|analysis)\\s+to=functions)" } + }; + } + + return data; +} + +// Functionary v3.2 - uses recipient-based format: >>>recipient\n{content} +static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, + const autoparser::templates_params & inputs) { + common_chat_params data; + + data.prompt = common_chat_template_direct_apply(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.preserved_tokens = { + ">>>all", + }; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + // Functionary v3.2 format: + // - Normal content: >>>all\n{content} + // - Tool calls: >>>function_name\n{json_args} + // Generation prompt ends with ">>>" so model outputs recipient immediately + + // Build content parser for >>>all\n{content} + // When tools are present, content stops before the next ">>>" (tool call) + // When no tools, content goes until end + auto content_until_tool = p.literal(">>>all\n") + p.content(p.until(">>>")); + auto content_until_end = p.literal(">>>all\n") + p.content(p.rest()); + + // If no tools or tool_choice is NONE, just parse content + if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { + // When no tools, just match the prefix and capture everything after + return content_until_end + p.end(); + } + + // Build tool call parsers for each available function + auto tool_choice = p.choice(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + const auto & schema = function.at("parameters"); + + // Tool format: >>>function_name\n{json_args} + auto tool_parser = p.tool( + p.tool_open(p.literal(">>>") + p.tool_name(p.literal(name)) + p.literal("\n")) + + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)) + ); + + tool_choice |= p.rule("tool-" + name, tool_parser); + }); + + auto content_only = content_until_end; + auto tools_only = p.trigger_rule("tools", p.one_or_more(tool_choice)); + auto content_and_tools = content_until_tool + tools_only; + + if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) { + if (inputs.parallel_tool_calls) { + return p.choice({ content_and_tools, tools_only }) + p.end(); + } + return p.choice({ content_until_tool + tool_choice, tools_only }) + p.end(); + } + if (inputs.parallel_tool_calls) { + return p.choice({ content_and_tools, content_only, tools_only }) + p.end(); + } + auto content_and_tool = content_until_tool + tool_choice; + return p.choice({ content_and_tool, content_only, tool_choice }) + p.end(); + }); + + data.parser = parser.save(); + + if (include_grammar) { + data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + // Grammar trigger for when the model starts outputting a tool call + // (after the initial ">>>" in the generation prompt but recipient other than "all") + data.grammar_triggers = { + { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, ">>>(?!all)" } + }; + } + + return data; +} + +// Kimi K2 Thinking - uses unique tool call ID format: functions.: +// The ID contains both the function name and an incrementing counter +static common_chat_params common_chat_params_init_kimi_k2(const common_chat_template & tmpl, + const autoparser::templates_params & inputs) { + common_chat_params data; + + data.prompt = common_chat_template_direct_apply(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.supports_thinking = true; + data.thinking_start_tag = ""; + data.thinking_end_tag = ""; + data.preserved_tokens = { + "<|tool_calls_section_begin|>", + "<|tool_calls_section_end|>", + "<|tool_call_begin|>", + "<|tool_call_argument_begin|>", + "<|tool_call_end|>", + "", + "", + }; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + // Kimi K2 Thinking format: + // - Reasoning: {reasoning} + // - Content: text after reasoning + // - Tool calls section: + // <|tool_calls_section_begin|> + // <|tool_call_begin|>functions.:<|tool_call_argument_begin|>{json_args}<|tool_call_end|> + // ... + // <|tool_calls_section_end|> + // The ID format is: functions.: where counter is 0, 1, 2, ... + + // Tool call markers + const std::string SECTION_BEGIN = "<|tool_calls_section_begin|>"; + const std::string SECTION_END = "<|tool_calls_section_end|>"; + const std::string CALL_BEGIN = "<|tool_call_begin|>"; + const std::string ARGS_BEGIN = "<|tool_call_argument_begin|>"; + const std::string CALL_END = "<|tool_call_end|>"; + + const std::string THINK_START = ""; + const std::string THINK_END = ""; + + auto end = p.end(); + + // Note: this model is CRAZY. It can diverge from its supposed tool calling pattern in so many ways it's not funny. + // For example, it can call tools at the end of reasoning without closing reasoning... + auto reasoning = extract_reasoning ? p.optional(THINK_START + p.reasoning( + p.until_one_of({ THINK_END, "<|tool_calls_section_begin|>", "<|tool_call_begin|>" })) + + p.optional(p.literal(THINK_END))) : p.eps(); + + + // Content only parser (no tools) + if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { + return reasoning + p.content(p.rest()) + end; + } + + // Build tool call parsers for each available function + // The ID format is: functions.: + // We need to match: functions.: + auto tool_choice = p.choice(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + const auto & schema = function.at("parameters"); + + // Match: functions.: + // Capture the full call id (functions.:) using tool_id tag + auto tool_id = p.tool_id(p.literal("functions.") + p.tool_name(p.literal(name)) + p.literal(":") + p.chars("[0-9]", 1, -1)); + auto tool_parser = p.tool( + p.tool_open(tool_id + p.literal(ARGS_BEGIN)) + + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)) + + p.tool_close(p.optional((p.literal(CALL_END)))) + ); + + tool_choice |= p.rule("tool-" + name, tool_parser); + }); + + // Tool calls section: <|tool_calls_section_begin|> tool_calls <|tool_calls_section_end|> + auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0; + auto max_calls = inputs.parallel_tool_calls ? -1 : 1; + // Use trigger_rule so grammar generator knows where to start generating rules + auto tool_calls = p.rule("tool-calls", + p.optional(p.literal(SECTION_BEGIN)) + + p.trigger_rule("tool-call", p.repeat(CALL_BEGIN + tool_choice, min_calls, max_calls) + + p.optional(p.literal(SECTION_END))) + ); + + auto content_before_tools = p.content(p.until_one_of({ SECTION_BEGIN, CALL_BEGIN })); + + return reasoning + content_before_tools + tool_calls + end; + }); + + data.parser = parser.save(); + + if (include_grammar) { + data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + data.grammar_triggers = { + { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|tool_call_begin|>" } + }; + } + + return data; +} + +// LFM2 format: +// - Reasoning: {reasoning} (optional, only if enable_thinking is true) +// - Content: text after reasoning (optional) +// - Tool calls: <|tool_call_start|>[function_name(arg1="value1", arg2="value2")]<|tool_call_end|> +// Tool calls can appear multiple times (parallel tool calls) +static common_chat_params common_chat_params_init_lfm2(const common_chat_template & tmpl, + const autoparser::templates_params & inputs) { + common_chat_params data; + + data.prompt = common_chat_template_direct_apply(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.supports_thinking = true; + data.preserved_tokens = { + "<|tool_list_start|>", + "<|tool_list_end|>", + "<|tool_call_start|>", + "<|tool_call_end|>", + "", + "", + }; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + + + const std::string TOOL_CALL_START = "<|tool_call_start|>"; + const std::string TOOL_CALL_END = "<|tool_call_end|>"; + const std::string THINK_START = ""; + const std::string THINK_END = ""; + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + + auto end = p.end(); + + auto reasoning = p.eps(); + if (extract_reasoning && inputs.enable_thinking) { + reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END); + } + + if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { + return reasoning + p.content(p.rest()) + end; + } + + auto tool_calls = p.rule("tool-calls", + p.trigger_rule("tool-call", p.literal(TOOL_CALL_START) + + p.python_style_tool_calls(inputs.tools, inputs.parallel_tool_calls) + + p.literal(TOOL_CALL_END) + ) + ); + + auto content = p.content(p.until(TOOL_CALL_START)); + + return reasoning + content + tool_calls + end; + }); + + data.parser = parser.save(); + + if (include_grammar) { + data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + data.grammar_triggers = { + { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOL_CALL_START } + }; + } + + return data; +} + +static common_chat_params common_chat_params_init_gigachat_v3( + const common_chat_template & tmpl, + const autoparser::templates_params & inputs) { + + common_chat_params data; + + data.prompt = common_chat_template_direct_apply(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.supports_thinking = false; + data.preserved_tokens = { + "<|message_sep|>\n\n", + "<|role_sep|>\n", + }; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + auto tool_call_start_prefix = "<|message_sep|>\n\nfunction call<|role_sep|>\n"; + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) { + // Build a choice of all available tools + auto tool_choice = p.choice(); + for (const auto & tool : inputs.tools) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + const auto & schema = function.at("parameters"); + + auto tool_name = p.json_member("name", "\"" + p.tool_name(p.literal(name)) + "\""); + auto tool_args = p.json_member("arguments", p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema))); + + auto tool_open = p.tool_open(p.literal("{") << tool_name); + + tool_choice |= p.rule("tool-" + name, tool_open << "," << tool_args << "}"); } - return wrap_choice({parser_reasoning, parser_content, parser_tool_calls}); + // Define the tool call structure + auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0; + auto max_calls = 1; // parallel toolcalls are not supported + auto tool_call = p.rule("tool-call", p.literal(tool_call_start_prefix) + tool_choice); + auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(tool_call, /* min = */ min_calls, /* max = */ max_calls)); + + return p.content(p.until("<|message_sep|>\n\n")) << tool_calls; } // Content only parser include_grammar = false; - return wrap_choice({parser_reasoning, parser_content}); + return p.content(p.rest()); + }); data.parser = parser.save(); @@ -2742,191 +1419,25 @@ static common_chat_params common_chat_params_init_solar_open(const common_chat_t }); data.grammar_triggers = { - {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|tool_calls|>"} + {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, tool_call_start_prefix} }; } - return data; } -static common_chat_params common_chat_params_init_exaone_moe(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - data.prompt = apply(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_EXAONE_MOE; - if (string_ends_with(data.prompt, "\n")) { - if (!inputs.enable_thinking) { - data.prompt += "\n\n"; - } else { - data.thinking_forced_open = true; - } - } - - if (inputs.tools.is_array() && !inputs.tools.empty()) { - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED && inputs.json_schema.is_null(); - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - std::vector tool_rules; - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); - // Expect: {"name": "", "arguments": {...}} - tool_rules.push_back(builder.add_rule( - name + "-call", - "\"\" space " + - builder.add_schema(name + "-obj", json{ - {"type", "object"}, - {"properties", { - {"name", json{{"const", name}}}, - {"arguments", parameters}, - }}, - {"required", json::array({"name", "arguments"})}, - }) + - " space \"\" space")); - }); - - auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")); - builder.add_rule("root", - std::string(data.thinking_forced_open ? "( \"\" space )? " : "") + - (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call)); - - data.grammar_triggers.push_back({ - COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL, - std::string(data.thinking_forced_open ? "[\\s\\S]*?(\\s*)?" : "") + - "()[\\s\\S]*" - }); - data.preserved_tokens = { - "", - "", - "", - "", - }; - }); - } - - return data; -} - -static common_chat_params common_chat_params_init_translate_gemma(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - - // This template does not support tools or reasoning - // we just need to transform the messages into the correct schema - - templates_params inputs_new = inputs; - json & messages = inputs_new.messages; - - // default to chat_template_kwargs, or en-GB if not specified - std::string default_src_lang = inputs.extra_context.value("source_lang_code", "en-GB"); - std::string default_tgt_lang = inputs.extra_context.value("target_lang_code", "en-GB"); - - GGML_ASSERT(messages.is_array()); - for (auto & message : messages) { - if (message.contains("role") && message["role"].get() != "user") { - continue; - } - if (!message.contains("content")) { - message["content"] = json::array(); - } - if (message.contains("content") && !message["content"].is_array()) { - auto content_str = message["content"].get(); - // default to en-GB if not specified (to make common_chat_format_example works) - auto src_lang = message.contains("source_lang_code") - ? message["source_lang_code"].get() : default_src_lang; - auto tgt_lang = message.contains("target_lang_code") - ? message["target_lang_code"].get() : default_tgt_lang; - message["content"] = json::array({ - json{ - {"type", "text"}, - {"text", content_str}, - {"source_lang_code", src_lang}, - {"target_lang_code", tgt_lang}, - } - }); - } - } - - data.prompt = apply(tmpl, inputs_new, std::nullopt, std::nullopt); - data.format = COMMON_CHAT_FORMAT_GENERIC; - - return data; -} - -static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) { - common_chat_params data; - data.prompt = apply(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - data.grammar_lazy = false; - if (!inputs.json_schema.is_null()) { - if (!inputs.grammar.empty()) { - throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both"); - } - data.grammar = json_schema_to_grammar(inputs.json_schema); - } else { - data.grammar = inputs.grammar; - } - return data; -} - -static common_chat_params common_chat_params_init_seed_oss( - const common_chat_template & tmpl, - templates_params & params, - const common_chat_templates_inputs & inputs) -{ - common_chat_params data; - data.prompt = apply(tmpl, params); - data.format = COMMON_CHAT_FORMAT_SEED_OSS; - if (string_ends_with(data.prompt, "")) { - if (!inputs.enable_thinking) { - data.prompt += ""; - } else { - data.thinking_forced_open = true; - } - } - - if (params.tools.is_array() && !params.tools.empty()) { - data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; - data.grammar = build_grammar([&](const common_grammar_builder & builder) { - std::vector tool_rules; - foreach_function(params.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - builder.resolve_refs(parameters); - - // Create rule for Seed-OSS function call format - std::string param_rules; - if (parameters.contains("properties")) { - for (const auto & [key, value] : parameters.at("properties").items()) { - param_rules += "\"\"" + builder.add_schema(name + "-arg-" + key, value) + - "\"\""; - } - } - - tool_rules.push_back(builder.add_rule(name + "-call", - "\"\" space \"\" space " + - param_rules + - " \"\" space \"\"")); - }); - - data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "" }); - - data.preserved_tokens = { - "", "", "", "", - "", "", - }; - - builder.add_rule("root", string_join(tool_rules, " | ")); - }); - } - return data; -} - -// various workarounds for known issues with certain templates or model behaviors -// TODO @ngxson : improve this (how?) namespace workaround { +static void map_developer_role_to_system(json & messages) { + for (auto & message : messages) { + if (message.contains("role")) { + if (message["role"] == "developer") { + message["role"] = "system"; + } + } + } +} + + // if first message is system and template does not support it, merge it with next message static void system_message_not_supported(json & messages) { if (!messages.empty() && messages.front().at("role") == "system") { @@ -2944,6 +1455,15 @@ static void system_message_not_supported(json & messages) { } } +static void requires_non_null_content(json & messages) { + GGML_ASSERT(messages.is_array()); + for (auto & message : messages) { + if (message.contains("tool_calls") && !message.contains("content")) { + message["content"] = ""; + } + } +} + static void func_args_not_string(json & messages) { GGML_ASSERT(messages.is_array()); for (auto & message : messages) { @@ -2964,71 +1484,21 @@ static void func_args_not_string(json & messages) { } } -static void move_tool_calls_to_content(json & messages, int indent_spaces = 2) { - GGML_ASSERT(messages.is_array()); - for (auto & message : messages) { - if (message.contains("tool_calls")) { - auto tool_calls_new = json{ - {"tool_calls", message.at("tool_calls")} - }; - message.erase("tool_calls"); - auto content = message.at("content"); - std::string content_new = content.is_null() ? "" : content.get(); - message["content"] = content_new + tool_calls_new.dump(indent_spaces, ' ', false, json::error_handler_t::replace); - } - } } -// TODO @ngxson : we may remove support for generic schema in the future -static void use_generic_schema(json & messages) { - GGML_ASSERT(messages.is_array()); - for (auto & message : messages) { - if (message.contains("tool_calls") && message.at("tool_calls").is_array()) { - auto & tool_calls = message.at("tool_calls"); - for (auto & tool_call : tool_calls) { - if (tool_call.contains("type") && tool_call.at("type") == "function" && - tool_call.contains("function") && tool_call.at("function").is_object()) { - // Copy values before erasing to avoid use-after-free - json name_value; - json arguments_value; - json id_value; - const auto & function = tool_call.at("function"); - if (function.contains("name")) { - name_value = function.at("name"); - } - if (function.contains("arguments")) { - arguments_value = function.at("arguments"); - } - if (tool_call.contains("id")) { - id_value = tool_call.at("id"); - } - // Now safely erase and assign in the correct order - tool_call.erase("type"); - tool_call.erase("function"); - tool_call.erase("id"); - // Reassign in desired order: name, arguments, id - if (!name_value.is_null()) { - tool_call["name"] = name_value; - } - if (!arguments_value.is_null()) { - tool_call["arguments"] = arguments_value; - } - if (!id_value.is_null()) { - tool_call["id"] = id_value; - } - } - } - } - } +static json common_chat_extra_context() { + json ctx = json::object(); + std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); + std::string datetime_str = format_time(now, "%b %d %Y"); + std::string date_str = format_time(now, "%d %b %Y"); + ctx["datetime"] = datetime_str; + ctx["date_string"] = date_str; + return ctx; } -} // namespace workaround - -static common_chat_params common_chat_templates_apply_jinja( - const struct common_chat_templates * tmpls, - const struct common_chat_templates_inputs & inputs) -{ - templates_params params; +static common_chat_params common_chat_templates_apply_jinja(const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs) { + autoparser::templates_params params; params.tools = common_chat_tools_to_json_oaicompat(inputs.tools); const auto & tmpl = params.tools.is_array() && tmpls->template_tool_use ? *tmpls->template_tool_use @@ -3045,11 +1515,24 @@ static common_chat_params common_chat_templates_apply_jinja( params.add_bos = tmpls->add_bos; params.add_eos = tmpls->add_eos; + if (src.find("<|channel|>") == std::string::npos) { + // map developer to system for all models except for GPT-OSS + workaround::map_developer_role_to_system(params.messages); + } + workaround::func_args_not_string(params.messages); + if (!tmpl.original_caps().supports_system_role) { workaround::system_message_not_supported(params.messages); } - params.extra_context = json::object(); + if (tmpl.original_caps().supports_tool_calls) { + // some templates will require the content field in tool call messages + // to still be non-null, this puts an empty string everywhere where the + // content field is null + workaround::requires_non_null_content(params.messages); + } + + params.extra_context = common_chat_extra_context(); for (auto el : inputs.chat_template_kwargs) { params.extra_context[el.first] = json::parse(el.second); } @@ -3058,229 +1541,98 @@ static common_chat_params common_chat_templates_apply_jinja( params.json_schema = json::parse(inputs.json_schema); } - if (inputs.parallel_tool_calls && !tmpl.original_caps().supports_parallel_tool_calls) { - LOG_DBG("Disabling parallel_tool_calls because the template does not support it\n"); - params.parallel_tool_calls = false; - } else { - params.parallel_tool_calls = inputs.parallel_tool_calls; - } + // if (inputs.parallel_tool_calls && !tmpl.original_caps().supports_parallel_tool_calls) { + // LOG_DBG("Disabling parallel_tool_calls because the template does not support it\n"); + // params.parallel_tool_calls = false; + // } else { + params.parallel_tool_calls = inputs.parallel_tool_calls; + //} if (params.tools.is_array()) { if (params.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && !params.grammar.empty()) { throw std::runtime_error("Cannot specify grammar with tools"); } if (caps.supports_tool_calls && !caps.supports_tools) { - LOG_WRN("Template supports tool calls but does not natively describe tools. The fallback behaviour used may produce bad results, inspect prompt w/ --verbose & consider overriding the template.\n"); + LOG_WRN( + "Template supports tool calls but does not natively describe tools. The fallback behaviour used may " + "produce bad results, inspect prompt w/ --verbose & consider overriding the template.\n"); } } - // DeepSeek V3.1: detect based on specific patterns in the template - if (src.find("message['prefix'] is defined and message['prefix'] and thinking") != std::string::npos && - params.json_schema.is_null()) { - return common_chat_params_init_deepseek_v3_1(tmpl, params); - } - - // DeepSeek R1: use handler in all cases except json schema (thinking / tools). - if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos && params.json_schema.is_null()) { - return common_chat_params_init_deepseek_r1(tmpl, params); - } - - // Command R7B: : use handler in all cases except json schema (thinking / tools). - if (src.find("<|END_THINKING|><|START_ACTION|>") != std::string::npos && params.json_schema.is_null()) { - workaround::func_args_not_string(params.messages); - return common_chat_params_init_command_r7b(tmpl, params); - } - - // Granite (IBM) - detects thinking / tools support - if (src.find("elif thinking") != std::string::npos && src.find("<|tool_call|>") != std::string::npos) { - workaround::func_args_not_string(params.messages); - workaround::use_generic_schema(params.messages); - workaround::move_tool_calls_to_content(params.messages); - return common_chat_params_init_granite(tmpl, params); - } - - // GLM 4.5: detect by and tags (check before Hermes since both use ) - if (src.find("[gMASK]") != std::string::npos && - src.find("") != std::string::npos && - src.find("") != std::string::npos && - params.json_schema.is_null()) { - workaround::func_args_not_string(params.messages); - if (!params.extra_context.contains("clear_thinking")) { - // by default, do not clear reasoning_content (added since GLM-4.7) - params.extra_context["clear_thinking"] = false; - } - return common_chat_params_init_glm_4_5(tmpl, params); - } - - // Qwen3-Coder XML format detection (must come before Hermes 2 Pro) - // Detect via XML markers: , , and blocks. - // Also matches Step-3.5-Flash and Nemotron 3 Nano which use the same output format. - if (src.find("") != std::string::npos && - src.find("") != std::string::npos && - src.find("# Tools") != std::string::npos && - src.find("") != std::string::npos && - src.find("") != std::string::npos && - src.find("") != std::string::npos && - src.find("") != std::string::npos) { - return common_chat_params_init_xiaomi_mimo(tmpl, params); - } - - // EXAONE MoE format detection - if (src.find("") != std::string::npos && - src.find("") != std::string::npos && - src.find("<|tool_declare|>") != std::string::npos) { - return common_chat_params_init_exaone_moe(tmpl, params); - } - - // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools) - if (src.find("") != std::string::npos && params.json_schema.is_null()) { - return common_chat_params_init_hermes_2_pro(tmpl, params); - } - - // GPT-OSS - if (src.find("<|channel|>") != std::string::npos) { - return common_chat_params_init_gpt_oss(tmpl, params); - } - - // Seed-OSS - if (src.find("") != std::string::npos) { - workaround::func_args_not_string(params.messages); - return common_chat_params_init_seed_oss(tmpl, params, inputs); - } - - // Nemotron v2 - if (src.find("") != std::string::npos) { - return common_chat_params_init_nemotron_v2(tmpl, params); - } - - // Apertus format detection - if (src.find("<|system_start|>") != std::string::npos && src.find("<|tools_prefix|>") != std::string::npos) { - return common_chat_params_init_apertus(tmpl, params); - } - - // LFM2 (w/ tools) - if (src.find("List of tools: <|tool_list_start|>[") != std::string::npos && - src.find("]<|tool_list_end|>") != std::string::npos) { - return common_chat_params_init_lfm2(tmpl, params); - } - - // MiniMax-M2 format detection - if (src.find("]~!b[") != std::string::npos && src.find("]~b]") != std::string::npos) { - workaround::func_args_not_string(params.messages); - return common_chat_params_init_minimax_m2(tmpl, params); - } - - // Kimi K2 format detection - if (src.find("<|im_system|>tool_declare<|im_middle|>") != std::string::npos && - src.find("<|tool_calls_section_begin|>") != std::string::npos && - src.find("## Return of") != std::string::npos) { - return common_chat_params_init_kimi_k2(tmpl, params); - } - - // Apriel 1.5 format detection - if (src.find("") != std::string::npos && - src.find("") != std::string::npos && - src.find("") != std::string::npos && - src.find("<|assistant|>") != std::string::npos && - src.find("<|tool_result|>") != std::string::npos && - src.find("[") != std::string::npos && - src.find("]") != std::string::npos) { - return common_chat_params_init_apriel_1_5(tmpl, params); - } - - // Solar Open - if (src.find("<|tool_response:begin|>") != std::string::npos && - src.find("<|tool_response:name|>") != std::string::npos && - src.find("<|tool_response:result|>") != std::string::npos) { - return common_chat_params_init_solar_open(tmpl, params); - } - - // Use generic handler when mixing tools + JSON schema. - // TODO: support that mix in handlers below. - if ((params.tools.is_array() && params.json_schema.is_object())) { - return common_chat_params_init_generic(tmpl, params); - } - - // Functionary prepends "all\n" to plain content outputs, so we use its handler in all cases. - if (src.find(">>>all") != std::string::npos) { - return common_chat_params_init_functionary_v3_2(tmpl, params); - } - - // Firefunction v2 requires datetime and functions in the context even w/o tools, so we also use its handler in all cases. - if (src.find(" functools[") != std::string::npos) { - return common_chat_params_init_firefunction_v2(tmpl, params); - } - - // Functionary v3.1 (w/ tools) - if (src.find("<|start_header_id|>") != std::string::npos - && src.find("ipython<|end_header_id|>") != std::string::npos) { - auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos; - workaround::func_args_not_string(params.messages); - return common_chat_params_init_llama_3_x(tmpl, params, allow_python_tag_builtin_tools); - } - - // Ministral/Mistral Large 3 - if (src.find("[SYSTEM_PROMPT]") != std::string::npos && - src.find("[TOOL_CALLS]") != std::string::npos && - src.find("[ARGS]") != std::string::npos) { + // Ministral/Mistral Large 3 - uses special reasoning structure fixes, can't use autoparser + // Note: Mistral Small 3.2 uses [CALL_ID] which Ministral doesn't have, so we can distinguish them + if (src.find("[SYSTEM_PROMPT]") != std::string::npos && src.find("[TOOL_CALLS]") != std::string::npos && + src.find("[ARGS]") != std::string::npos && src.find("[CALL_ID]") == std::string::npos) { + LOG_DBG("Using specialized template: Ministral/Magistral Large 3\n"); return common_chat_params_init_ministral_3(tmpl, params); } - if (src.find("[THINK]") != std::string::npos && src.find("[/THINK]") != std::string::npos) { - return common_chat_params_init_magistral(tmpl, params); + // GPT-OSS - has unique channel-based structure that needs dedicated handler + if (src.find("<|channel|>") != std::string::npos) { + LOG_DBG("Using specialized template: GPT-OSS\n"); + return common_chat_params_init_gpt_oss(tmpl, params); } - // Solar Open - if (src.find("<|tool_response:begin|>") != std::string::npos && - src.find("<|tool_response:name|>") != std::string::npos && - src.find("<|tool_response:result|>") != std::string::npos) { - return common_chat_params_init_solar_open(tmpl, params); + // Functionary v3.2 - uses recipient-based format with >>>recipient\n{content} + // Detection: template has ">>>all" for content and ">>>" prefix for tool calls + if (src.find(">>>all") != std::string::npos && src.find(">>>${recipient}") != std::string::npos) { + LOG_DBG("Using specialized template: Functionary v3.2\n"); + return common_chat_params_init_functionary_v3_2(tmpl, params); } - // TranslateGemma - if (src.find("[source_lang_code]") != std::string::npos && - src.find("[target_lang_code]") != std::string::npos) { - return common_chat_params_init_translate_gemma(tmpl, params); + // Kimi K2 Thinking - uses unique tool call ID format: functions.: + // Detection: template has "<|tool_calls_section_begin|>" and "functions." prefix in tool call IDs + if (src.find("<|tool_calls_section_begin|>") != std::string::npos && + src.find("<|tool_call_begin|>") != std::string::npos) { + LOG_DBG("Using specialized template: Kimi K2 Thinking\n"); + return common_chat_params_init_kimi_k2(tmpl, params); } - // Plain handler (no tools) - if (params.tools.is_null() || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { - return common_chat_params_init_without_tools(tmpl, params); + // LFM2 - uses <|tool_list_start|>/<|tool_list_end|> markers and <|tool_call_start|>[name(args)]<|tool_call_end|> format + // Detection: template has "<|tool_list_start|>" and "<|tool_list_end|>" markers + if (src.find("<|tool_list_start|>") != std::string::npos && + src.find("<|tool_list_end|>") != std::string::npos) { + LOG_DBG("Using specialized template: LFM2\n"); + return common_chat_params_init_lfm2(tmpl, params); } - // Mistral Nemo (w/ tools) - if (src.find("[TOOL_CALLS]") != std::string::npos) { - workaround::func_args_not_string(params.messages); - return common_chat_params_init_mistral_nemo(tmpl, params); + // GigaChatV3 format detection + if (src.find("<|role_sep|>") != std::string::npos && + src.find("<|message_sep|>") != std::string::npos && + src.find("<|function_call|>") == std::string::npos + ) { + LOG_DBG("Using specialized template: GigaChatV3\n"); + return common_chat_params_init_gigachat_v3(tmpl, params); } - // Generic fallback - workaround::func_args_not_string(params.messages); - workaround::use_generic_schema(params.messages); - workaround::move_tool_calls_to_content(params.messages); - return common_chat_params_init_generic(tmpl, params); + try { + LOG_DBG("Using differential autoparser\n"); + struct autoparser::autoparser autoparser; + autoparser.analyze_template(tmpl); + auto auto_params = autoparser::peg_generator::generate_parser(tmpl, params, autoparser); + auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE; + if (auto_params.supports_thinking) { + auto_params.thinking_start_tag = autoparser.reasoning.start; + auto_params.thinking_end_tag = autoparser.reasoning.end; + // FORCED_OPEN and FORCED_CLOSED both put in the generation prompt + // (FORCED_CLOSED forces empty when thinking is disabled, + // but forces open when thinking is enabled) + auto_params.thinking_forced_open = + autoparser.reasoning.mode == autoparser::reasoning_mode::FORCED_OPEN || + autoparser.reasoning.mode == autoparser::reasoning_mode::FORCED_CLOSED; + } + return auto_params; + } catch (const std::exception & e) { + throw std::invalid_argument(std::string("Unable to generate parser for this template. Automatic parser generation failed: ") + e.what()); + } } // Legacy template route (adhoc C++ implementation of known templates), forward to llama_chat_apply_template. -static common_chat_params common_chat_templates_apply_legacy( - const struct common_chat_templates * tmpls, - const struct common_chat_templates_inputs & inputs) -{ - size_t alloc_size = 0; +static common_chat_params common_chat_templates_apply_legacy(const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs) { + size_t alloc_size = 0; std::vector chat; - std::vector contents; + std::vector contents; for (const auto & msg : inputs.messages) { auto content = msg.content; @@ -3290,25 +1642,27 @@ static common_chat_params common_chat_templates_apply_legacy( continue; } if (!content.empty()) { - content += "\n";; + content += "\n"; + ; } content += part.text; } contents.emplace_back(std::move(content)); } for (size_t i = 0; i < contents.size(); ++i) { - const auto & msg = inputs.messages[i]; + const auto & msg = inputs.messages[i]; const auto & content = contents[i]; - chat.push_back({msg.role.c_str(), content.c_str()}); + chat.push_back({ msg.role.c_str(), content.c_str() }); size_t msg_size = msg.role.size() + content.size(); - alloc_size += msg_size + (msg_size / 4); // == msg_size * 1.25 but avoiding float ops + alloc_size += msg_size + (msg_size / 4); // == msg_size * 1.25 but avoiding float ops } std::vector buf(alloc_size); // run the first time to get the total output length const auto & src = tmpls->template_default->source(); - int32_t res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt, buf.data(), buf.size()); + int32_t res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt, + buf.data(), buf.size()); // error: chat template is not supported if (res < 0) { @@ -3320,7 +1674,8 @@ static common_chat_params common_chat_templates_apply_legacy( // if it turns out that our buffer is too small, we resize it if ((size_t) res > buf.size()) { buf.resize(res); - res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt, buf.data(), buf.size()); + res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt, buf.data(), + buf.size()); } // for safety, we check the result again @@ -3338,14 +1693,76 @@ static common_chat_params common_chat_templates_apply_legacy( return params; } -common_chat_params common_chat_templates_apply( - const struct common_chat_templates * tmpls, - const struct common_chat_templates_inputs & inputs) -{ +common_chat_params common_chat_templates_apply(const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs) { GGML_ASSERT(tmpls != nullptr); - return inputs.use_jinja - ? common_chat_templates_apply_jinja(tmpls, inputs) - : common_chat_templates_apply_legacy(tmpls, inputs); + return inputs.use_jinja ? common_chat_templates_apply_jinja(tmpls, inputs) : + common_chat_templates_apply_legacy(tmpls, inputs); +} + +common_chat_msg common_chat_parse(const std::string & input, + bool is_partial, + const common_chat_parser_params & params) { + return common_chat_peg_parse(params.parser, input, is_partial, params); +} + +common_chat_msg common_chat_peg_parse(const common_peg_arena & src_parser, + const std::string & input, + bool is_partial, + const common_chat_parser_params & params) { + const common_peg_arena & parser = src_parser.empty() ? + build_chat_peg_parser([](common_chat_peg_builder & p) { return p.content(p.rest()) + p.end(); }) : + src_parser; + + if (src_parser.empty()) { + LOG_DBG("No parser definition detected, assuming pure content parser."); + } + + LOG_DBG("Parsing PEG input with format %s: %s\n", common_chat_format_name(params.format), input.c_str()); + + common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_LENIENT; + if (params.debug) { + flags |= COMMON_PEG_PARSE_FLAG_DEBUG; + } + + common_peg_parse_context ctx(input, flags); + auto result = parser.parse(ctx); + + if (result.fail()) { + // During partial parsing, return partial results if any AST nodes were captured + // This allows streaming to work correctly for formats like FUNC_MARKDOWN_CODE_BLOCK + if (is_partial && result.end > 0) { + // Try to extract any partial results from what was successfully parsed + common_chat_msg msg; + msg.role = "assistant"; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + if (ctx.is_debug()) { + fprintf(stderr, "\nAST for partial parse (fail):\n%s\n", ctx.ast.dump().c_str()); + fflush(stderr); + } + return msg; + } + throw std::runtime_error(std::string("Failed to parse input at pos ") + std::to_string(result.end) + ": " + + input.substr(result.end)); + } + + common_chat_msg msg; + msg.role = "assistant"; + + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + if (ctx.is_debug()) { + fprintf(stderr, "\nAST for %s parse:\n%s\n", is_partial ? "partial" : "full", ctx.ast.dump().c_str()); + fflush(stderr); + } + + if (!is_partial) { + LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat({ msg }).at(0).dump().c_str()); + } + return msg; } std::map common_chat_templates_get_caps(const common_chat_templates * chat_templates) { @@ -3353,3 +1770,4 @@ std::map common_chat_templates_get_caps(const common_chat_tem GGML_ASSERT(chat_templates->template_default != nullptr); return chat_templates->template_default->caps.to_map(); } + diff --git a/common/chat.h b/common/chat.h index 6f0b9409ec..930987cf77 100644 --- a/common/chat.h +++ b/common/chat.h @@ -3,17 +3,30 @@ #pragma once #include "common.h" +#include "jinja/parser.h" +#include "nlohmann/json_fwd.hpp" #include "peg-parser.h" -#include +#include "jinja/runtime.h" +#include "jinja/caps.h" +#include "nlohmann/json.hpp" + #include +#include +#include #include #include -#include + +using chat_template_caps = jinja::caps; +using json = nlohmann::ordered_json; #include struct common_chat_templates; +namespace autoparser { +struct templates_params; +} // namespace autoparser + struct common_chat_tool_call { std::string name; std::string arguments; @@ -38,21 +51,85 @@ struct common_chat_msg_content_part { } }; +struct common_chat_template { + jinja::program prog; + std::string bos_tok; + std::string eos_tok; + std::string src; + chat_template_caps caps; + + common_chat_template(const std::string & src, const std::string & bos_token, const std::string & eos_token) { + jinja::lexer lexer; + auto lexer_res = lexer.tokenize(src); + this->prog = jinja::parse_from_tokens(lexer_res); + + this->src = lexer_res.source; + this->bos_tok = bos_token; + this->eos_tok = eos_token; + + this->caps = jinja::caps_get(prog); + // LOG_INF("%s: caps:\n%s\n", __func__, this->caps.to_string().c_str()); + } + + const std::string & source() const { return src; } + const std::string & bos_token() const { return bos_tok; } + const std::string & eos_token() const { return eos_tok; } + + // TODO: this is ugly, refactor it somehow + json add_system(const json & messages, const std::string & system_prompt) const { + GGML_ASSERT(messages.is_array()); + auto msgs_copy = messages; + if (!caps.supports_system_role) { + if (msgs_copy.empty()) { + msgs_copy.insert(msgs_copy.begin(), json{ + {"role", "user"}, + {"content", system_prompt} + }); + } else { + auto & first_msg = msgs_copy[0]; + if (!first_msg.contains("content")) { + first_msg["content"] = ""; + } + first_msg["content"] = system_prompt + "\n\n" + + first_msg["content"].get(); + } + } else { + if (msgs_copy.empty() || msgs_copy[0].at("role") != "system") { + msgs_copy.insert(msgs_copy.begin(), json{ + {"role", "system"}, + {"content", system_prompt} + }); + } else if (msgs_copy[0].at("role") == "system") { + msgs_copy[0]["content"] = system_prompt; + } + } + return msgs_copy; + } + + chat_template_caps original_caps() const { + return caps; + } + +}; + struct common_chat_msg { - std::string role; - std::string content; + std::string role; + std::string content; std::vector content_parts; - std::vector tool_calls; - std::string reasoning_content; - std::string tool_name; - std::string tool_call_id; + std::vector tool_calls; + std::string reasoning_content; + std::string tool_name; + std::string tool_call_id; nlohmann::ordered_json to_json_oaicompat(bool concat_typed_text = false) const; bool empty() const { - return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() && tool_name.empty() && tool_call_id.empty(); + return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() && + tool_name.empty() && tool_call_id.empty(); } - void set_tool_call_ids(std::vector & ids_cache, const std::function & gen_tool_call_id) { + + void set_tool_call_ids(std::vector & ids_cache, + const std::function & gen_tool_call_id) { for (auto i = 0u; i < tool_calls.size(); i++) { if (ids_cache.size() <= i) { auto id = tool_calls[i].id; @@ -64,32 +141,28 @@ struct common_chat_msg { tool_calls[i].id = ids_cache[i]; } } + bool operator==(const common_chat_msg & other) const { - return role == other.role - && content == other.content - && content_parts == other.content_parts - && tool_calls == other.tool_calls - && reasoning_content == other.reasoning_content - && tool_name == other.tool_name - && tool_call_id == other.tool_call_id; - } - bool operator!=(const common_chat_msg & other) const { - return !(*this == other); + return role == other.role && content == other.content && content_parts == other.content_parts && + tool_calls == other.tool_calls && reasoning_content == other.reasoning_content && + tool_name == other.tool_name && tool_call_id == other.tool_call_id; } + + bool operator!=(const common_chat_msg & other) const { return !(*this == other); } }; struct common_chat_msg_diff { - std::string reasoning_content_delta; - std::string content_delta; - size_t tool_call_index = std::string::npos; + std::string reasoning_content_delta; + std::string content_delta; + size_t tool_call_index = std::string::npos; common_chat_tool_call tool_call_delta; - static std::vector compute_diffs(const common_chat_msg & msg_prv, const common_chat_msg & msg_new); + static std::vector compute_diffs(const common_chat_msg & msg_prv, + const common_chat_msg & msg_new); bool operator==(const common_chat_msg_diff & other) const { - return content_delta == other.content_delta - && tool_call_index == other.tool_call_index - && tool_call_delta == other.tool_call_delta; + return content_delta == other.content_delta && tool_call_index == other.tool_call_index && + tool_call_delta == other.tool_call_delta; } }; @@ -107,64 +180,41 @@ enum common_chat_tool_choice { enum common_chat_format { COMMON_CHAT_FORMAT_CONTENT_ONLY, - COMMON_CHAT_FORMAT_GENERIC, - COMMON_CHAT_FORMAT_MISTRAL_NEMO, - COMMON_CHAT_FORMAT_MAGISTRAL, - COMMON_CHAT_FORMAT_LLAMA_3_X, - COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS, - COMMON_CHAT_FORMAT_DEEPSEEK_R1, - COMMON_CHAT_FORMAT_FIREFUNCTION_V2, - COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2, - COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1, - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - COMMON_CHAT_FORMAT_HERMES_2_PRO, - COMMON_CHAT_FORMAT_COMMAND_R7B, - COMMON_CHAT_FORMAT_GRANITE, - COMMON_CHAT_FORMAT_GPT_OSS, - COMMON_CHAT_FORMAT_SEED_OSS, - COMMON_CHAT_FORMAT_NEMOTRON_V2, - COMMON_CHAT_FORMAT_APERTUS, - COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS, - COMMON_CHAT_FORMAT_GLM_4_5, - COMMON_CHAT_FORMAT_MINIMAX_M2, - COMMON_CHAT_FORMAT_KIMI_K2, - COMMON_CHAT_FORMAT_APRIEL_1_5, - COMMON_CHAT_FORMAT_XIAOMI_MIMO, - COMMON_CHAT_FORMAT_SOLAR_OPEN, - COMMON_CHAT_FORMAT_EXAONE_MOE, // These are intended to be parsed by the PEG parser COMMON_CHAT_FORMAT_PEG_SIMPLE, COMMON_CHAT_FORMAT_PEG_NATIVE, - COMMON_CHAT_FORMAT_PEG_CONSTRUCTED, - COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats + COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats }; struct common_chat_templates_inputs { - std::vector messages; - std::string grammar; - std::string json_schema; - bool add_generation_prompt = true; - bool use_jinja = true; + std::vector messages; + std::string grammar; + std::string json_schema; + bool add_generation_prompt = true; + bool use_jinja = true; // Parameters below only supported when use_jinja is true - std::vector tools; - common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO; - bool parallel_tool_calls = false; - common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE; // TODO: refactor this to "bool enable_thinking" - bool enable_thinking = true; - std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); - std::map chat_template_kwargs; - bool add_bos = false; - bool add_eos = false; + std::vector tools; + common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO; + bool parallel_tool_calls = false; + common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE; // TODO: refactor this to "bool enable_thinking" + bool enable_thinking = true; + std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); + std::map chat_template_kwargs; + bool add_bos = false; + bool add_eos = false; }; struct common_chat_params { common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; std::string prompt; std::string grammar; - bool grammar_lazy = false; + bool grammar_lazy = false; bool thinking_forced_open = false; + bool supports_thinking = false; + std::string thinking_start_tag; // e.g., "" + std::string thinking_end_tag; // e.g., "" std::vector grammar_triggers; std::vector preserved_tokens; std::vector additional_stops; @@ -174,13 +224,14 @@ struct common_chat_params { // per-message parsing syntax // should be derived from common_chat_params struct common_chat_parser_params { - common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE; // TODO: refactor this to "bool parse_reasoning" + common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE; // TODO: refactor this to "bool parse_reasoning" // Whether reasoning_content should be inlined in the content (e.g. for reasoning_format=deepseek in stream mode) - bool reasoning_in_content = false; - bool thinking_forced_open = false; - bool parse_tool_calls = true; - common_peg_arena parser = {}; + bool reasoning_in_content = false; + bool thinking_forced_open = false; + bool parse_tool_calls = true; + bool debug = false; // Enable debug output for PEG parser + common_peg_arena parser = {}; common_chat_parser_params() = default; common_chat_parser_params(const common_chat_params & chat_params) { format = chat_params.format; @@ -193,45 +244,42 @@ bool common_chat_verify_template(const std::string & tmpl, bool use_jinja); void common_chat_templates_free(struct common_chat_templates * tmpls); -struct common_chat_templates_deleter { void operator()(common_chat_templates * tmpls) { common_chat_templates_free(tmpls); } }; +struct common_chat_templates_deleter { + void operator()(common_chat_templates * tmpls) { common_chat_templates_free(tmpls); } +}; typedef std::unique_ptr common_chat_templates_ptr; -common_chat_templates_ptr common_chat_templates_init( - const struct llama_model * model, - const std::string & chat_template_override, - const std::string & bos_token_override = "", - const std::string & eos_token_override = ""); +common_chat_templates_ptr common_chat_templates_init(const struct llama_model * model, + const std::string & chat_template_override, + const std::string & bos_token_override = "", + const std::string & eos_token_override = ""); bool common_chat_templates_was_explicit(const struct common_chat_templates * tmpls); std::string common_chat_templates_source(const struct common_chat_templates * tmpls, const std::string & variant = ""); - -struct common_chat_params common_chat_templates_apply( - const struct common_chat_templates * tmpls, - const struct common_chat_templates_inputs & inputs); +struct common_chat_params common_chat_templates_apply(const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs); // Format single message, while taking into account the position of that message in chat history -std::string common_chat_format_single( - const struct common_chat_templates * tmpls, - const std::vector & past_msg, - const common_chat_msg & new_msg, - bool add_ass, - bool use_jinja); +std::string common_chat_format_single(const struct common_chat_templates * tmpls, + const std::vector & past_msg, + const common_chat_msg & new_msg, + bool add_ass, + bool use_jinja); // Returns an example of formatted chat -std::string common_chat_format_example( - const struct common_chat_templates * tmpls, - bool use_jinja, - const std::map & chat_template_kwargs); +std::string common_chat_format_example(const struct common_chat_templates * tmpls, + bool use_jinja, + const std::map & chat_template_kwargs); -const char* common_chat_format_name(common_chat_format format); -common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_parser_params & syntax); -common_chat_msg common_chat_peg_parse(const common_peg_arena & parser, const std::string & input, bool is_partial, const common_chat_parser_params & syntax); +const char * common_chat_format_name(common_chat_format format); +common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_parser_params & params); +common_chat_msg common_chat_peg_parse(const common_peg_arena & src_parser, const std::string & input, bool is_partial, const common_chat_parser_params & params); // used by arg and server -const char * common_reasoning_format_name(common_reasoning_format format); -common_reasoning_format common_reasoning_format_from_name(const std::string & format); +const char * common_reasoning_format_name(common_reasoning_format format); +common_reasoning_format common_reasoning_format_from_name(const std::string & format); common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice); @@ -250,3 +298,10 @@ nlohmann::ordered_json common_chat_msg_diff_to_json_oaicompat(const common_chat_ // get template caps, useful for reporting to server /props endpoint std::map common_chat_templates_get_caps(const common_chat_templates * chat_templates); + +std::string common_chat_template_direct_apply( + const common_chat_template & tmpl, + const autoparser::templates_params & inputs, + const std::optional & messages_override = std::nullopt, + const std::optional & tools_override = std::nullopt, + const std::optional & additional_context = std::nullopt); diff --git a/common/common.cpp b/common/common.cpp index 53bddc4ef2..cc423d3439 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -676,7 +676,7 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) { size_t offset = 0; while (offset < filename.size()) { - utf8_parse_result result = parse_utf8_codepoint(filename, offset); + utf8_parse_result result = common_parse_utf8_codepoint(filename, offset); if (result.status != utf8_parse_result::SUCCESS) { return false; diff --git a/common/common.h b/common/common.h index cc35c70609..870490dd69 100644 --- a/common/common.h +++ b/common/common.h @@ -104,6 +104,8 @@ enum llama_example { LLAMA_EXAMPLE_DIFFUSION, LLAMA_EXAMPLE_FINETUNE, LLAMA_EXAMPLE_FIT_PARAMS, + LLAMA_EXAMPLE_RESULTS, + LLAMA_EXAMPLE_EXPORT_GRAPH_OPS, LLAMA_EXAMPLE_COUNT, }; @@ -234,6 +236,14 @@ struct common_params_sampling { std::vector logit_bias; // logit biases to apply std::vector logit_bias_eog; // pre-calculated logit biases for EOG tokens + // reasoning budget sampler parameters + // these are populated by the server/CLI based on chat template params + int32_t reasoning_budget_tokens = -1; // -1 = disabled, >= 0 = token budget + bool reasoning_budget_activate_immediately = false; + std::vector reasoning_budget_start; // start tag token sequence + std::vector reasoning_budget_end; // end tag token sequence + std::vector reasoning_budget_forced; // forced sequence (message + end tag) + bool backend_sampling = false; bool has_logit_bias() const { @@ -413,7 +423,8 @@ struct common_params { struct common_params_model model; - std::string model_alias = ""; // model alias // NOLINT + std::set model_alias; // model aliases // NOLINT + std::set model_tags; // model tags (informational, not used for routing) // NOLINT std::string hf_token = ""; // HF token // NOLINT std::string prompt = ""; // NOLINT std::string system_prompt = ""; // NOLINT @@ -458,6 +469,8 @@ struct common_params { bool kl_divergence = false; // compute KL divergence + bool check = false; // check rather than generate results for llama-results + bool usage = false; // print usage bool completion = false; // print source-able completion script bool use_color = false; // use color to distinguish generations and inputs @@ -518,14 +531,15 @@ struct common_params { std::string cls_sep = "\t"; // separator of classification sequences // server params - int32_t port = 8080; // server listens on this network port - int32_t timeout_read = 600; // http read timeout in seconds - int32_t timeout_write = timeout_read; // http write timeout in seconds - int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) - int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting - bool cache_prompt = true; // whether to enable prompt caching - int32_t n_ctx_checkpoints = 8; // max number of context checkpoints per slot - int32_t cache_ram_mib = 8192; // -1 = no limit, 0 - disable, 1 = 1 MiB, etc. + int32_t port = 8080; // server listens on this network port + int32_t timeout_read = 600; // http read timeout in seconds + int32_t timeout_write = timeout_read; // http write timeout in seconds + int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) + int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting + bool cache_prompt = true; // whether to enable prompt caching + int32_t n_ctx_checkpoints = 32; // max number of context checkpoints per slot + int32_t checkpoint_every_nt = 8192; // make a checkpoint every n tokens during prefill + int32_t cache_ram_mib = 8192; // -1 = no limit, 0 - disable, 1 = 1 MiB, etc. std::string hostname = "127.0.0.1"; std::string public_path = ""; // NOLINT @@ -534,7 +548,9 @@ struct common_params { bool use_jinja = true; // NOLINT bool enable_chat_template = true; common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; + int enable_reasoning = -1; // -1 = auto, 0 = disable, 1 = enable int reasoning_budget = -1; + std::string reasoning_budget_message; // message injected before end tag when budget exhausted bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response int sleep_idle_seconds = -1; // if >0, server will sleep after this many seconds of idle time @@ -547,6 +563,7 @@ struct common_params { // webui configs bool webui = true; + bool webui_mcp_proxy = false; std::string webui_config_json; // "advanced" endpoints are disabled by default for better security @@ -871,7 +888,7 @@ std::string common_detokenize( // Embedding utils // -// TODO: repace embd_norm with an enum +// TODO: replace embd_norm with an enum void common_embd_normalize(const float * inp, float * out, int n, int embd_norm); float common_embd_similarity_cos(const float * embd1, const float * embd2, int n); @@ -913,7 +930,7 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; // MoE utils // -const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_(ch|)exps"; +const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate|gate_up)_(ch|)exps"; inline std::string llm_ffn_exps_block_regex(int idx) { return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX); diff --git a/common/console.cpp b/common/console.cpp index 2ea178f81e..a770416ab7 100644 --- a/common/console.cpp +++ b/common/console.cpp @@ -80,6 +80,8 @@ namespace console { static termios initial_state; #endif + static completion_callback completion_cb = nullptr; + // // Init and cleanup // @@ -493,7 +495,7 @@ namespace console { } static void set_line_contents(std::string new_line, std::string & line, std::vector & widths, size_t & char_pos, - size_t & byte_pos) { + size_t & byte_pos, int cursor_byte_pos = -1) { move_to_line_start(char_pos, byte_pos, widths); clear_current_line(widths); @@ -503,6 +505,7 @@ namespace console { char_pos = 0; size_t idx = 0; + int back_width = 0; while (idx < line.size()) { size_t advance = 0; char32_t cp = decode_utf8(line, idx, advance); @@ -511,8 +514,15 @@ namespace console { if (real_width < 0) real_width = 0; widths.push_back(real_width); idx += advance; - ++char_pos; - byte_pos = idx; + if (cursor_byte_pos >= 0 && static_cast(cursor_byte_pos) < idx) { + back_width += real_width; + } else { + ++char_pos; + byte_pos = idx; + } + } + if (cursor_byte_pos >= 0) { + move_cursor(-back_width); } } @@ -784,6 +794,20 @@ namespace console { break; } + if (completion_cb && input_char == '\t') { + auto candidates = completion_cb(line, byte_pos); + + if (!candidates.empty()) { + if (candidates.size() > 1 || candidates[0].first != line) { + // TODO?: Display all candidates + set_line_contents(candidates[0].first, line, widths, char_pos, byte_pos, candidates[0].second); + } else { + // TODO: Move cursor to new byte_pos + } + continue; + } + } + if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D */) { end_of_stream = true; break; @@ -1062,6 +1086,10 @@ namespace console { return readline_advanced(line, multiline_input); } + void set_completion_callback(completion_callback cb) { + completion_cb = cb; + } + namespace spinner { static const char LOADING_CHARS[] = {'|', '/', '-', '\\'}; static std::condition_variable cv_stop; diff --git a/common/console.h b/common/console.h index fad6d39531..72781bea6f 100644 --- a/common/console.h +++ b/common/console.h @@ -4,7 +4,9 @@ #include "common.h" +#include #include +#include enum display_type { DISPLAY_TYPE_RESET = 0, @@ -21,6 +23,9 @@ namespace console { void set_display(display_type display); bool readline(std::string & line, bool multiline_input); + using completion_callback = std::function>(std::string_view, size_t)>; + void set_completion_callback(completion_callback cb); + namespace spinner { void start(); void stop(); diff --git a/common/debug.h b/common/debug.h index 0c55963258..e563b40d68 100644 --- a/common/debug.h +++ b/common/debug.h @@ -18,7 +18,7 @@ template void common_debug_print_tensor(uint8_t * data, ggml // prints tensors that are processed in the computation graph // by default prints all tensors, but can be configured by creating a `base_callback_data` instance with // non-empty filter_patterns. See examples/debug.ccp for possible usage patterns -// The template parameter determins whether an error should be thrown whenever a NaN is encountered +// The template parameter determines whether an error should be thrown whenever a NaN is encountered // in a tensor (useful for stopping debug sessions on first erroneous tensor) // The callback data will be passed as the third parameter (user_data) template bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data); diff --git a/common/http.h b/common/http.h index e8ed56f952..d3daccd6bf 100644 --- a/common/http.h +++ b/common/http.h @@ -7,6 +7,7 @@ struct common_http_url { std::string user; std::string password; std::string host; + int port; std::string path; }; @@ -47,6 +48,20 @@ static common_http_url common_http_parse_url(const std::string & url) { parts.host = rest; parts.path = "/"; } + + auto colon_pos = parts.host.find(':'); + + if (colon_pos != std::string::npos) { + parts.port = std::stoi(parts.host.substr(colon_pos + 1)); + parts.host = parts.host.substr(0, colon_pos); + } else if (parts.scheme == "http") { + parts.port = 80; + } else if (parts.scheme == "https") { + parts.port = 443; + } else { + throw std::runtime_error("unsupported URL scheme: " + parts.scheme); + } + return parts; } @@ -68,7 +83,7 @@ static std::pair common_http_client(const std: } #endif - httplib::Client cli(parts.scheme + "://" + parts.host); + httplib::Client cli(parts.scheme + "://" + parts.host + ":" + std::to_string(parts.port)); if (!parts.user.empty()) { cli.set_basic_auth(parts.user, parts.password); diff --git a/common/jinja/README.md b/common/jinja/README.md index 7059105ee3..8291240767 100644 --- a/common/jinja/README.md +++ b/common/jinja/README.md @@ -63,7 +63,7 @@ The llama.cpp Jinja engine introduces `jinja::string` (see `jinja/string.h`), wh - **One-to-many** (e.g., split): result is marked `is_input` **only if ALL** input parts are marked `is_input` - **Many-to-one** (e.g., join): same as one-to-many -For string concatenation, string parts will be appended to the new string as-is, while perserving the `is_input` flag. +For string concatenation, string parts will be appended to the new string as-is, while preserving the `is_input` flag. **Enabling Input Marking:** diff --git a/common/jinja/caps.cpp b/common/jinja/caps.cpp index dbaaed500a..1158d5e5d6 100644 --- a/common/jinja/caps.cpp +++ b/common/jinja/caps.cpp @@ -1,3 +1,4 @@ +#include "log.h" #include "value.h" #include "runtime.h" #include "caps.h" @@ -36,12 +37,16 @@ static void caps_try_execute(jinja::program & prog, auto tools = ctx.get_val("tools"); bool success = false; + std::string result; try { jinja::runtime runtime(ctx); - runtime.execute(prog); + auto results = runtime.execute(prog); + auto parts = jinja::runtime::gather_string_parts(results); + result = parts->as_string().str(); success = true; } catch (const std::exception & e) { JJ_DEBUG("Exception during execution: %s", e.what()); + result = ""; // ignore exceptions during capability analysis } @@ -90,6 +95,8 @@ caps caps_get(jinja::program & prog) { return v->stats.ops.find(op_name) != v->stats.ops.end(); }; + JJ_DEBUG("%s\n", ">>> Running capability check: typed content"); + // case: typed content support caps_try_execute( prog, @@ -120,6 +127,7 @@ caps caps_get(jinja::program & prog) { } ); + JJ_DEBUG("%s\n", ">>> Running capability check: system prompt"); // case: system prompt support caps_try_execute( @@ -150,7 +158,9 @@ caps caps_get(jinja::program & prog) { } ); - // case: tools support + JJ_DEBUG("%s\n", ">>> Running capability check: single tool support"); + + // case: tools support: single call caps_try_execute( prog, [&]() { @@ -162,10 +172,10 @@ caps caps_get(jinja::program & prog) { }, { {"role", "assistant"}, - {"content", "Assistant message"}, + {"content", ""}, // Some templates expect content to be empty with tool calls {"tool_calls", json::array({ { - {"id", "call1"}, + {"id", "call00001"}, {"type", "function"}, {"function", { {"name", "tool1"}, @@ -173,19 +183,18 @@ caps caps_get(jinja::program & prog) { {"arg", "value"} }} }} - }, - { - {"id", "call2"}, - {"type", "function"}, - {"function", { - {"name", "tool2"}, - {"arguments", { - {"arg", "value"} - }} - }} } })} }, + { + {"role", "tool"}, + {"content", "Tool response"}, + {"tool_call_id", "call00001"} + }, + { + {"role", "assistant"}, + {"content", "The tool response was 'tool response'"} + }, { {"role", "user"}, {"content", "User message"}, @@ -199,7 +208,7 @@ caps caps_get(jinja::program & prog) { {"name", "tool"}, {"type", "function"}, {"function", { - {"name", "tool"}, + {"name", "tool1"}, {"description", "Tool description"}, {"parameters", { {"type", "object"}, @@ -224,6 +233,7 @@ caps caps_get(jinja::program & prog) { auto & tool_name = tools->at(0)->at("function")->at("name"); caps_print_stats(tool_name, "tools[0].function.name"); + caps_print_stats(tools, "tools"); if (!tool_name->stats.used) { result.supports_tools = false; } @@ -233,6 +243,93 @@ caps caps_get(jinja::program & prog) { if (!tool_calls->stats.used) { result.supports_tool_calls = false; } + } + ); + + JJ_DEBUG("%s\n", ">>> Running capability check: parallel tool support"); + + // case: tools support: parallel calls + caps_try_execute( + prog, + [&]() { + // messages + return json::array({ + { + {"role", "user"}, + {"content", "User message"}, + }, + { + {"role", "assistant"}, + {"content", ""}, // Some templates expect content to be empty with tool calls + {"tool_calls", json::array({ + { + {"id", "call00001"}, + {"type", "function"}, + {"function", { + {"name", "tool1"}, + {"arguments", { + {"arg", "value"} + }} + }} + }, + { + {"id", "call00002"}, + {"type", "function"}, + {"function", { + {"name", "tool1"}, + {"arguments", { + {"arg", "value"} + }} + }} + } + })} + }, + { + {"role", "tool"}, + {"content", "Tool response"}, + {"tool_call_id", "call00001"} + }, + { + {"role", "assistant"}, + {"content", "The tool response was 'tool response'"} + }, + { + {"role", "user"}, + {"content", "User message"}, + }, + }); + }, + [&]() { + // tools + return json::array({ + { + {"name", "tool"}, + {"type", "function"}, + {"function", { + {"name", "tool1"}, + {"description", "Tool description"}, + {"parameters", { + {"type", "object"}, + {"properties", { + {"arg", { + {"type", "string"}, + {"description", "Arg description"}, + }}, + }}, + {"required", json::array({ "arg" })}, + }}, + }}, + }, + }); + }, + [&](bool success, value & messages, value & /*tools*/) { + if (!success) { + result.supports_parallel_tool_calls = false; + return; + } + + auto & tool_calls = messages->at(1)->at("tool_calls");; + caps_print_stats(tool_calls, "messages[1].tool_calls"); // check for second tool call usage auto & tool_call_1 = tool_calls->at(1)->at("function"); @@ -243,6 +340,8 @@ caps caps_get(jinja::program & prog) { } ); + JJ_DEBUG("%s\n", ">>> Running capability check: preserve reasoning"); + // case: preserve reasoning content in chat history caps_try_execute( prog, diff --git a/common/jinja/runtime.cpp b/common/jinja/runtime.cpp index c93e182a7e..af2282c546 100644 --- a/common/jinja/runtime.cpp +++ b/common/jinja/runtime.cpp @@ -114,8 +114,10 @@ value binary_expression::execute_impl(context & ctx) { // Logical operators if (op.value == "and") { + JJ_DEBUG("Executing logical test: %s AND %s", left->type().c_str(), right->type().c_str()); return left_val->as_bool() ? right->execute(ctx) : std::move(left_val); } else if (op.value == "or") { + JJ_DEBUG("Executing logical test: %s OR %s", left->type().c_str(), right->type().c_str()); return left_val->as_bool() ? std::move(left_val) : right->execute(ctx); } @@ -721,6 +723,8 @@ value member_expression::execute_impl(context & ctx) { int64_t arr_size = 0; if (is_val(object)) { arr_size = object->as_array().size(); + } else if (is_val(object)) { + arr_size = object->as_string().length(); } if (is_stmt(this->property)) { @@ -836,7 +840,7 @@ value call_expression::execute_impl(context & ctx) { for (auto & arg_stmt : this->args) { auto arg_val = arg_stmt->execute(ctx); JJ_DEBUG(" Argument type: %s", arg_val->type().c_str()); - args.push_back(std::move(arg_val)); + args.push_back(arg_val); } // execute callee value callee_val = callee->execute(ctx); diff --git a/common/jinja/value.h b/common/jinja/value.h index 07e447ff69..6cbedefd96 100644 --- a/common/jinja/value.h +++ b/common/jinja/value.h @@ -12,8 +12,8 @@ #include #include #include -#include #include +#include namespace jinja { diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 2f67c74d79..c7057d651d 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -27,11 +27,11 @@ static std::string build_repetition(const std::string & item_rule, int min_items if (separator_rule.empty()) { if (min_items == 1 && !has_max) { return item_rule + "+"; - } else if (min_items == 0 && !has_max) { - return item_rule + "*"; - } else { - return item_rule + "{" + std::to_string(min_items) + "," + (has_max ? std::to_string(max_items) : "") + "}"; } + if (min_items == 0 && !has_max) { + return item_rule + "*"; + } + return item_rule + "{" + std::to_string(min_items) + "," + (has_max ? std::to_string(max_items) : "") + "}"; } auto result = item_rule + " " + build_repetition("(" + separator_rule + " " + item_rule + ")", min_items == 0 ? 0 : min_items - 1, has_max ? max_items - 1 : max_items); @@ -41,7 +41,7 @@ static std::string build_repetition(const std::string & item_rule, int min_items return result; } -static void _build_min_max_int(int64_t min_value, int64_t max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) { +static void build_min_max_int(int64_t min_value, int64_t max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) { auto has_min = min_value != std::numeric_limits::min(); auto has_max = max_value != std::numeric_limits::max(); @@ -128,14 +128,14 @@ static void _build_min_max_int(int64_t min_value, int64_t max_value, std::string if (has_min && has_max) { if (min_value < 0 && max_value < 0) { out << "\"-\" ("; - _build_min_max_int(-max_value, -min_value, out, decimals_left, /* top_level= */ true); + build_min_max_int(-max_value, -min_value, out, decimals_left, /* top_level= */ true); out << ")"; return; } if (min_value < 0) { out << "\"-\" ("; - _build_min_max_int(0, -min_value, out, decimals_left, /* top_level= */ true); + build_min_max_int(0, -min_value, out, decimals_left, /* top_level= */ true); out << ") | "; min_value = 0; } @@ -159,7 +159,7 @@ static void _build_min_max_int(int64_t min_value, int64_t max_value, std::string if (has_min) { if (min_value < 0) { out << "\"-\" ("; - _build_min_max_int(std::numeric_limits::min(), -min_value, out, decimals_left, /* top_level= */ false); + build_min_max_int(std::numeric_limits::min(), -min_value, out, decimals_left, /* top_level= */ false); out << ") | [0] | [1-9] "; more_digits(0, decimals_left - 1); } else if (min_value == 0) { @@ -194,7 +194,7 @@ static void _build_min_max_int(int64_t min_value, int64_t max_value, std::string } digit_range(c, c); out << " ("; - _build_min_max_int(std::stoll(min_s.substr(1)), std::numeric_limits::max(), out, less_decimals, /* top_level= */ false); + build_min_max_int(std::stoll(min_s.substr(1)), std::numeric_limits::max(), out, less_decimals, /* top_level= */ false); out << ")"; if (c < '9') { out << " | "; @@ -213,10 +213,10 @@ static void _build_min_max_int(int64_t min_value, int64_t max_value, std::string more_digits(0, less_decimals); out << " | "; } - _build_min_max_int(0, max_value, out, decimals_left, /* top_level= */ true); + build_min_max_int(0, max_value, out, decimals_left, /* top_level= */ true); } else { out << "\"-\" ("; - _build_min_max_int(-max_value, std::numeric_limits::max(), out, decimals_left, /* top_level= */ false); + build_min_max_int(-max_value, std::numeric_limits::max(), out, decimals_left, /* top_level= */ false); out << ")"; } return; @@ -232,7 +232,7 @@ struct BuiltinRule { std::vector deps; }; -std::unordered_map PRIMITIVE_RULES = { +static std::unordered_map PRIMITIVE_RULES = { {"boolean", {"(\"true\" | \"false\") space", {}}}, {"decimal-part", {"[0-9]{1,16}", {}}}, {"integral-part", {"[0] | [1-9] [0-9]{0,15}", {}}}, @@ -247,7 +247,7 @@ std::unordered_map PRIMITIVE_RULES = { {"null", {"\"null\" space", {}}}, }; -std::unordered_map STRING_FORMAT_RULES = { +static std::unordered_map STRING_FORMAT_RULES = { {"date", {"[0-9]{4} \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}}, {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9]{3} )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}}, {"date-time", {"date \"T\" time", {"date", "time"}}}, @@ -260,22 +260,26 @@ static bool is_reserved_name(const std::string & name) { static const std::unordered_set RESERVED_NAMES = [] { std::unordered_set s; s.insert("root"); - for (const auto & p : PRIMITIVE_RULES) s.insert(p.first); - for (const auto & p : STRING_FORMAT_RULES) s.insert(p.first); + for (const auto & p : PRIMITIVE_RULES) { + s.insert(p.first); + } + for (const auto & p : STRING_FORMAT_RULES) { + s.insert(p.first); + } return s; }(); return RESERVED_NAMES.find(name) != RESERVED_NAMES.end(); } -std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+"); -std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"\\\\]"); -std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]"); -std::unordered_map GRAMMAR_LITERAL_ESCAPES = { +static std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+"); +static std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"\\\\]"); +static std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]"); +static std::unordered_map GRAMMAR_LITERAL_ESCAPES = { {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"}, {'\\', "\\\\"} }; -std::unordered_set NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'}; -std::unordered_set ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'}; +static std::unordered_set NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'}; +static std::unordered_set ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'}; static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function & replacement) { std::smatch match; @@ -322,19 +326,19 @@ private: if (_rules.find(esc_name) == _rules.end() || _rules[esc_name] == rule) { _rules[esc_name] = rule; return esc_name; - } else { - int i = 0; - while (_rules.find(esc_name + std::to_string(i)) != _rules.end() && _rules[esc_name + std::to_string(i)] != rule) { - i++; - } - std::string key = esc_name + std::to_string(i); - _rules[key] = rule; - return key; } + int i = 0; + while (_rules.find(esc_name + std::to_string(i)) != _rules.end() && _rules[esc_name + std::to_string(i)] != rule) { + i++; + } + std::string key = esc_name + std::to_string(i); + _rules[key] = rule; + return key; } std::string _generate_union_rule(const std::string & name, const std::vector & alt_schemas) { std::vector rules; + rules.reserve(alt_schemas.size()); for (size_t i = 0; i < alt_schemas.size(); i++) { rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i))); } @@ -398,6 +402,7 @@ private: flush_literal(); std::vector results; + results.reserve(ret.size()); for (const auto & item : ret) { results.push_back(to_rule(item)); } @@ -551,7 +556,7 @@ private: TrieNode() : is_end_of_string(false) {} void insert(const std::string & string) { - auto node = this; + auto *node = this; for (char c : string) { node = &node->children[c]; } @@ -676,7 +681,7 @@ private: if (ks.empty()) { return res; } - std::string k = ks[0]; + const std::string& k = ks[0]; std::string kv_rule_name = prop_kv_rule_names[k]; std::string comma_ref = "( \",\" space " + kv_rule_name + " )"; if (first_is_optional) { @@ -779,13 +784,13 @@ public: std::string pointer = ref.substr(ref.find('#') + 1); std::vector tokens = string_split(pointer, "/"); for (size_t i = 1; i < tokens.size(); ++i) { - std::string sel = tokens[i]; + const std::string& sel = tokens[i]; if (target.is_object() && target.contains(sel)) { target = target[sel]; } else if (target.is_array()) { size_t sel_index; try { - sel_index = std::stoul(sel); + sel_index = std::stoull(sel); } catch (const std::invalid_argument & e) { sel_index = target.size(); } @@ -802,7 +807,7 @@ public: _refs[ref] = target; } } else { - for (auto & kv : n.items()) { + for (const auto & kv : n.items()) { visit_refs(kv.value()); } } @@ -812,7 +817,7 @@ public: visit_refs(schema); } - std::string _generate_constant_rule(const json & value) { + static std::string _generate_constant_rule(const json & value) { return format_literal(value.dump()); } @@ -823,10 +828,12 @@ public: if (schema.contains("$ref")) { return _add_rule(rule_name, _resolve_ref(schema["$ref"])); - } else if (schema.contains("oneOf") || schema.contains("anyOf")) { + } + if (schema.contains("oneOf") || schema.contains("anyOf")) { std::vector alt_schemas = schema.contains("oneOf") ? schema["oneOf"].get>() : schema["anyOf"].get>(); return _add_rule(rule_name, _generate_union_rule(name, alt_schemas)); - } else if (schema_type.is_array()) { + } + if (schema_type.is_array()) { std::vector schema_types; for (const auto & t : schema_type) { json schema_copy(schema); @@ -834,15 +841,18 @@ public: schema_types.push_back(schema_copy); } return _add_rule(rule_name, _generate_union_rule(name, schema_types)); - } else if (schema.contains("const")) { + } + if (schema.contains("const")) { return _add_rule(rule_name, _generate_constant_rule(schema["const"]) + " space"); - } else if (schema.contains("enum")) { + } + if (schema.contains("enum")) { std::vector enum_values; for (const auto & v : schema["enum"]) { enum_values.push_back(_generate_constant_rule(v)); } return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ") space"); - } else if ((schema_type.is_null() || schema_type == "object") + } + if ((schema_type.is_null() || schema_type == "object") && (schema.contains("properties") || (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) { std::unordered_set required; @@ -863,11 +873,12 @@ public: _build_object_rule( properties, required, name, schema.contains("additionalProperties") ? schema["additionalProperties"] : json())); - } else if ((schema_type.is_null() || schema_type == "object" || schema_type == "string") && schema.contains("allOf")) { + } + if ((schema_type.is_null() || schema_type == "object" || schema_type == "string") && schema.contains("allOf")) { std::unordered_set required; std::vector> properties; std::map enum_values; - std::string hybrid_name = name; + const std::string& hybrid_name = name; std::function add_component = [&](const json & comp_schema, bool is_required) { if (comp_schema.contains("$ref")) { add_component(_refs[comp_schema["$ref"]], is_required); @@ -890,9 +901,9 @@ public: // todo warning } }; - for (auto & t : schema["allOf"]) { + for (const auto & t : schema["allOf"]) { if (t.contains("anyOf")) { - for (auto & tt : t["anyOf"]) { + for (const auto & tt : t["anyOf"]) { add_component(tt, false); } } else { @@ -911,7 +922,8 @@ public: } } return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json())); - } else if ((schema_type.is_null() || schema_type == "array") && (schema.contains("items") || schema.contains("prefixItems"))) { + } + if ((schema_type.is_null() || schema_type == "array") && (schema.contains("items") || schema.contains("prefixItems"))) { json items = schema.contains("items") ? schema["items"] : schema["prefixItems"]; if (items.is_array()) { std::string rule = "\"[\" space "; @@ -923,27 +935,31 @@ public: } rule += " \"]\" space"; return _add_rule(rule_name, rule); - } else { - std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item"); - int min_items = schema.contains("minItems") ? schema["minItems"].get() : 0; - json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json(); - int max_items = max_items_json.is_number_integer() ? max_items_json.get() : std::numeric_limits::max(); - - return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space"); } - } else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) { + std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item"); + int min_items = schema.contains("minItems") ? schema["minItems"].get() : 0; + json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json(); + int max_items = max_items_json.is_number_integer() ? max_items_json.get() : std::numeric_limits::max(); + + return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space"); + } + if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) { return _visit_pattern(schema["pattern"], rule_name); - } else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) { + } + if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) { return _add_primitive(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid")); - } else if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) { + } + if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) { auto prim_name = schema_format + "-string"; return _add_rule(rule_name, _add_primitive(prim_name, STRING_FORMAT_RULES.at(prim_name))); - } else if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) { + } + if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) { std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char")); int min_len = schema.contains("minLength") ? schema["minLength"].get() : 0; int max_len = schema.contains("maxLength") ? schema["maxLength"].get() : std::numeric_limits::max(); return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space"); - } else if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) { + } + if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) { int64_t min_value = std::numeric_limits::min(); int64_t max_value = std::numeric_limits::max(); if (schema.contains("minimum")) { @@ -958,19 +974,24 @@ public: } std::stringstream out; out << "("; - _build_min_max_int(min_value, max_value, out); + build_min_max_int(min_value, max_value, out); out << ") space"; return _add_rule(rule_name, out.str()); - } else if (schema.empty() || schema_type == "object") { - return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object"))); - } else { - if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get()) == PRIMITIVE_RULES.end()) { - _errors.push_back("Unrecognized schema: " + schema.dump()); - return ""; - } - // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero - return _add_primitive(rule_name == "root" ? "root" : schema_type.get(), PRIMITIVE_RULES.at(schema_type.get())); } + if (schema.empty() || schema_type == "object") { + return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object"))); + } + if (schema_type.is_null() && schema.is_object()) { + // No type constraint and no recognized structural keywords (e.g. {"description": "..."}). + // Per JSON Schema semantics this is equivalent to {} and accepts any value. + return _add_rule(rule_name, _add_primitive("value", PRIMITIVE_RULES.at("value"))); + } + if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get()) == PRIMITIVE_RULES.end()) { + _errors.push_back("Unrecognized schema: " + schema.dump()); + return ""; + } + // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero + return _add_primitive(rule_name == "root" ? "root" : schema_type.get(), PRIMITIVE_RULES.at(schema_type.get())); } void check_errors() { @@ -985,7 +1006,7 @@ public: std::string format_grammar() { std::stringstream ss; for (const auto & kv : _rules) { - ss << kv.first << " ::= " << kv.second << std::endl; + ss << kv.first << " ::= " << kv.second << '\n'; } return ss.str(); } diff --git a/common/peg-parser.cpp b/common/peg-parser.cpp index f2fc84500f..a6d9a4c27c 100644 --- a/common/peg-parser.cpp +++ b/common/peg-parser.cpp @@ -1,14 +1,15 @@ -#include "common.h" #include "peg-parser.h" -#include "json-schema-to-grammar.h" -#include "unicode.h" -#include +#include "common.h" +#include "json-schema-to-grammar.h" +#include "log.h" +#include "unicode.h" #include #include #include #include +#include #include #include #include @@ -34,8 +35,7 @@ static bool is_hex_digit(const char c) { // This is used in common_peg_until_parser and to build a GBNF exclusion grammar struct trie { struct node { - size_t depth = 0; - std::map children; + std::map children; // Use uint32_t to store Unicode codepoints bool is_word; }; @@ -55,15 +55,22 @@ struct trie { size_t current = 0; // Start at root size_t pos = start_pos; + // LOG_DBG("%s: checking at pos %zu, sv='%s'\n", __func__, start_pos, std::string(sv).c_str()); + while (pos < sv.size()) { - auto it = nodes[current].children.find(sv[pos]); + auto result = common_parse_utf8_codepoint(sv, pos); + if (result.status != utf8_parse_result::SUCCESS) { + break; + } + + auto it = nodes[current].children.find(result.codepoint); if (it == nodes[current].children.end()) { // Can't continue matching return match_result{match_result::NO_MATCH}; } current = it->second; - pos++; + pos += result.bytes_consumed; // Check if we've matched a complete word if (nodes[current].is_word) { @@ -82,22 +89,22 @@ struct trie { } struct prefix_and_next { - std::string prefix; - std::string next_chars; + std::vector prefix; + std::vector next_chars; }; std::vector collect_prefix_and_next() { - std::string prefix; + std::vector prefix; std::vector result; collect_prefix_and_next(0, prefix, result); return result; } private: - void collect_prefix_and_next(size_t index, std::string & prefix, std::vector & out) { + void collect_prefix_and_next(size_t index, std::vector & prefix, std::vector & out) { if (!nodes[index].is_word) { if (!nodes[index].children.empty()) { - std::string chars; + std::vector chars; chars.reserve(nodes[index].children.size()); for (const auto & p : nodes[index].children) { chars.push_back(p.first); @@ -107,7 +114,7 @@ struct trie { } for (const auto & p : nodes[index].children) { - unsigned char ch = p.first; + uint32_t ch = p.first; auto child = p.second; prefix.push_back(ch); collect_prefix_and_next(child, prefix, out); @@ -123,11 +130,19 @@ struct trie { void insert(const std::string & word) { size_t current = 0; - for (unsigned char ch : word) { + size_t pos = 0; + while (pos < word.length()) { + auto result = common_parse_utf8_codepoint(word, pos); + if (result.status != utf8_parse_result::SUCCESS) { + break; + } + + uint32_t ch = result.codepoint; + pos += result.bytes_consumed; + auto it = nodes[current].children.find(ch); if (it == nodes[current].children.end()) { size_t child = create_node(); - nodes[child].depth = nodes[current].depth + 1; nodes[current].children[ch] = child; current = child; } else { @@ -286,6 +301,32 @@ struct parser_executor { parser_executor(const common_peg_arena & arena, common_peg_parse_context & ctx, size_t start) : arena(arena), ctx(ctx), start_pos(start) {} + std::string debug_indent() const { return std::string(ctx.parse_depth * 2, ' '); } + + std::string debug_input_snippet(size_t pos, size_t len = 60) const { + if (pos >= ctx.input.size()) { + return ""; + } + auto snippet = ctx.input.substr(pos, len); + // Escape newlines for display + std::string result; + for (char c : snippet) { + if (c == '\n') { + result += "\\n"; + } else if (c == '\r') { + result += "\\r"; + } else if (c == '\t') { + result += "\\t"; + } else { + result += c; + } + } + if (pos + len < ctx.input.size()) { + result += "..."; + } + return result; + } + common_peg_parse_result operator()(const common_peg_epsilon_parser & /* p */) const { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos); } @@ -308,7 +349,7 @@ struct parser_executor { auto pos = start_pos; for (auto i = 0u; i < p.literal.size(); ++i) { if (pos >= ctx.input.size()) { - if (!ctx.is_partial) { + if (!ctx.is_lenient()) { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos); } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos); @@ -323,12 +364,32 @@ struct parser_executor { } common_peg_parse_result operator()(const common_peg_sequence_parser & p) { + if (ctx.is_debug()) { + LOG_DBG("%sSEQ start at %zu '%s' (%zu children)\n", debug_indent().c_str(), start_pos, + debug_input_snippet(start_pos).c_str(), p.children.size()); + } + ctx.parse_depth++; + auto pos = start_pos; std::vector nodes; - for (const auto & child_id : p.children) { + for (size_t i = 0; i < p.children.size(); i++) { + const auto & child_id = p.children[i]; + if (ctx.is_debug()) { + fprintf(stderr, "%sSEQ child %zu: %s\n", debug_indent().c_str(), i, arena.dump(child_id).c_str()); + } auto result = arena.parse(child_id, ctx, pos); + + if (ctx.is_debug()) { + fprintf(stderr, "%sSEQ child %zu: %s at %zu->%zu\n", debug_indent().c_str(), i, + common_peg_parse_result_type_name(result.type), result.start, result.end); + } + if (result.fail()) { + ctx.parse_depth--; + if (ctx.is_debug()) { + fprintf(stderr, "%sSEQ -> FAIL\n", debug_indent().c_str()); + } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, result.end); } @@ -337,28 +398,65 @@ struct parser_executor { } if (result.need_more_input()) { + ctx.parse_depth--; + if (ctx.is_debug()) { + fprintf(stderr, "%sSEQ -> NEED_MORE\n", debug_indent().c_str()); + } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, result.end, std::move(nodes)); } pos = result.end; } + ctx.parse_depth--; + if (ctx.is_debug()) { + fprintf(stderr, "%sSEQ -> SUCCESS at %zu->%zu\n", debug_indent().c_str(), start_pos, pos); + } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos, std::move(nodes)); } common_peg_parse_result operator()(const common_peg_choice_parser & p) { + if (ctx.is_debug()) { + fprintf(stderr, "%sCHOICE start at %zu '%s' (%zu options)\n", debug_indent().c_str(), start_pos, + debug_input_snippet(start_pos).c_str(), p.children.size()); + } + ctx.parse_depth++; + auto pos = start_pos; - for (const auto & child_id : p.children) { + for (size_t i = 0; i < p.children.size(); i++) { + const auto & child_id = p.children[i]; + if (ctx.is_debug()) { + fprintf(stderr, "%sCHOICE option %zu: %s\n", debug_indent().c_str(), i, arena.dump(child_id).c_str()); + } auto result = arena.parse(child_id, ctx, pos); + if (ctx.is_debug()) { + fprintf(stderr, "%sCHOICE option %zu: %s\n", debug_indent().c_str(), i, + common_peg_parse_result_type_name(result.type)); + } if (!result.fail()) { + ctx.parse_depth--; + if (ctx.is_debug()) { + fprintf(stderr, "%sCHOICE -> %s (option %zu)\n", debug_indent().c_str(), + common_peg_parse_result_type_name(result.type), i); + } return result; } } + ctx.parse_depth--; + if (ctx.is_debug()) { + fprintf(stderr, "%sCHOICE -> FAIL (no options matched)\n", debug_indent().c_str()); + } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos); } common_peg_parse_result operator()(const common_peg_repetition_parser & p) { + if (ctx.is_debug()) { + fprintf(stderr, "%sREPEAT start at %zu '%s' (min=%d, max=%d)\n", debug_indent().c_str(), start_pos, + debug_input_snippet(start_pos).c_str(), p.min_count, p.max_count); + } + ctx.parse_depth++; + auto pos = start_pos; int match_count = 0; std::vector nodes; @@ -366,14 +464,26 @@ struct parser_executor { // Try to match up to max_count times (or unlimited if max_count is -1) while (p.max_count == -1 || match_count < p.max_count) { if (pos >= ctx.input.size()) { + if (ctx.is_debug()) { + fprintf(stderr, "%sREPEAT: at end of input, count=%d\n", debug_indent().c_str(), match_count); + } break; } auto result = arena.parse(p.child, ctx, pos); + if (ctx.is_debug()) { + fprintf(stderr, "%sREPEAT iter %d: %s at %zu->%zu, nodes=%zu\n", debug_indent().c_str(), match_count, + common_peg_parse_result_type_name(result.type), result.start, result.end, result.nodes.size()); + fprintf(stderr, "%sREPEAT CHILD: %s\n", debug_indent().c_str(), arena.dump(p.child).c_str()); + } + if (result.success()) { // Prevent infinite loop on empty matches if (result.end == pos) { + if (ctx.is_debug()) { + fprintf(stderr, "%s REPEAT: empty match, stopping\n", debug_indent().c_str()); + } break; } @@ -391,21 +501,43 @@ struct parser_executor { nodes.insert(nodes.end(), result.nodes.begin(), result.nodes.end()); } + ctx.parse_depth--; + if (ctx.is_debug()) { + fprintf(stderr, "%sREPEAT -> NEED_MORE (count=%d, nodes=%zu)\n", debug_indent().c_str(), + match_count, nodes.size()); + } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, result.end, std::move(nodes)); } // Child failed - stop trying + if (ctx.is_debug()) { + fprintf(stderr, "%sREPEAT: child failed, stopping\n", debug_indent().c_str()); + } break; } // Check if we got enough matches if (p.min_count > 0 && match_count < p.min_count) { - if (pos >= ctx.input.size() && ctx.is_partial) { + ctx.parse_depth--; + if (pos >= ctx.input.size() && ctx.is_lenient()) { + if (ctx.is_debug()) { + fprintf(stderr, "%sREPEAT -> NEED_MORE (not enough matches: %d < %d)\n", debug_indent().c_str(), + match_count, p.min_count); + } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos, std::move(nodes)); } + if (ctx.is_debug()) { + fprintf(stderr, "%sREPEAT -> FAIL (not enough matches: %d < %d)\n", debug_indent().c_str(), match_count, + p.min_count); + } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, pos); } + ctx.parse_depth--; + if (ctx.is_debug()) { + fprintf(stderr, "%sREPEAT -> SUCCESS (count=%d, nodes=%zu)\n", debug_indent().c_str(), match_count, + nodes.size()); + } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos, std::move(nodes)); } @@ -434,10 +566,10 @@ struct parser_executor { common_peg_parse_result operator()(const common_peg_any_parser & /* p */) const { // Parse a single UTF-8 codepoint (not just a single byte) - auto result = parse_utf8_codepoint(ctx.input, start_pos); + auto result = common_parse_utf8_codepoint(ctx.input, start_pos); if (result.status == utf8_parse_result::INCOMPLETE) { - if (!ctx.is_partial) { + if (!ctx.is_lenient()) { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos); } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos); @@ -468,7 +600,7 @@ struct parser_executor { // Try to match up to max_count times (or unlimited if max_count is -1) while (p.max_count == -1 || match_count < p.max_count) { - auto result = parse_utf8_codepoint(ctx.input, pos); + auto result = common_parse_utf8_codepoint(ctx.input, pos); if (result.status == utf8_parse_result::INCOMPLETE) { if (match_count >= p.min_count) { @@ -476,7 +608,7 @@ struct parser_executor { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); } // Not enough matches yet - if (!ctx.is_partial) { + if (!ctx.is_lenient()) { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos); } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos); @@ -517,7 +649,7 @@ struct parser_executor { // Check if we got enough matches if (match_count < p.min_count) { - if (pos >= ctx.input.size() && ctx.is_partial) { + if (pos >= ctx.input.size() && ctx.is_lenient()) { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos); } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, pos); @@ -526,31 +658,23 @@ struct parser_executor { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); } - static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos) { + static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos, const char delimiter) { ++pos; // consume '\' if (pos >= ctx.input.size()) { - if (!ctx.is_partial) { + if (!ctx.is_lenient()) { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start); } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos); } - switch (ctx.input[pos]) { - case '"': - case '\\': - case '/': - case 'b': - case 'f': - case 'n': - case 'r': - case 't': - ++pos; - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos); - case 'u': - return handle_unicode_escape(ctx, start, pos); - default: - // Invalid escape sequence - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start); + char c = ctx.input[pos]; + if (c == delimiter || c == '\\' || c == '/' || c == 'b' || c == 'f' || c == 'n' || c == 'r' || c == 't') { + ++pos; + return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos); + } else if (c == 'u') { + return handle_unicode_escape(ctx, start, pos); + } else { + return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start); } } @@ -558,7 +682,7 @@ struct parser_executor { ++pos; // consume 'u' for (int i = 0; i < 4; ++i) { if (pos >= ctx.input.size()) { - if (!ctx.is_partial) { + if (!ctx.is_lenient()) { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start); } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos); @@ -571,28 +695,28 @@ struct parser_executor { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos); } - common_peg_parse_result operator()(const common_peg_json_string_parser & /* p */) { + common_peg_parse_result operator()(const common_peg_string_parser & p) { auto pos = start_pos; // Parse string content (without quotes) while (pos < ctx.input.size()) { char c = ctx.input[pos]; - if (c == '"') { - // Found closing quote - success (don't consume it) + if (c == p.delimiter) { + // Found closing delimiter - success (don't consume it) return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); } if (c == '\\') { - auto result = handle_escape_sequence(ctx, start_pos, pos); + auto result = handle_escape_sequence(ctx, start_pos, pos, p.delimiter); if (!result.success()) { return result; } } else { - auto utf8_result = parse_utf8_codepoint(ctx.input, pos); + auto utf8_result = common_parse_utf8_codepoint(ctx.input, pos); if (utf8_result.status == utf8_parse_result::INCOMPLETE) { - if (!ctx.is_partial) { + if (!ctx.is_lenient()) { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos); } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos); @@ -607,7 +731,7 @@ struct parser_executor { } // Reached end without finding closing quote - if (!ctx.is_partial) { + if (!ctx.is_lenient()) { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, pos); } return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos); @@ -621,11 +745,11 @@ struct parser_executor { size_t last_valid_pos = start_pos; while (pos < ctx.input.size()) { - auto utf8_result = parse_utf8_codepoint(ctx.input, pos); + auto utf8_result = common_parse_utf8_codepoint(ctx.input, pos); if (utf8_result.status == utf8_parse_result::INCOMPLETE) { // Incomplete UTF-8 sequence - if (!ctx.is_partial) { + if (!ctx.is_lenient()) { // Input is complete but UTF-8 is incomplete = malformed return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos); } @@ -655,7 +779,7 @@ struct parser_executor { last_valid_pos = pos; } - if (last_valid_pos == ctx.input.size() && ctx.is_partial) { + if (last_valid_pos == ctx.input.size() && ctx.is_lenient()) { // Reached the end of a partial stream, there might still be more input that we need to consume. return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, last_valid_pos); } @@ -694,6 +818,9 @@ struct parser_executor { common_peg_parse_result operator()(const common_peg_tag_parser & p) { // Parse the child + if (ctx.is_debug()) { + fprintf(stderr, "%sTAG: %s\n", debug_indent().c_str(), p.tag.c_str()); + } auto result = arena.parse(p.child, ctx, start_pos); if (!result.fail()) { @@ -755,6 +882,31 @@ common_peg_parser_id common_peg_arena::resolve_ref(common_peg_parser_id id) { return id; } +static void bfs_node(common_peg_ast_arena &arena, std::ostringstream & oss, const common_peg_ast_node & node, int indent) { + for (int i = 0; i < indent; i++) { + oss << " "; + } + oss << "NODE " << node.id; + if (!node.rule.empty()) { + oss << " (rule " << node.rule << ")"; + } + if (!node.tag.empty()) { + oss << " (tag " << node.tag << ")"; + } + oss << " ['" << node.text << "']\n"; + for (const auto child : node.children) { + bfs_node(arena, oss, arena.get(child), indent + 1); + } +} + +std::string common_peg_ast_arena::dump() { + std::ostringstream oss; + for (auto & node : nodes_) { + bfs_node(*this, oss, node, 0); + } + return oss.str(); +} + void common_peg_arena::resolve_refs() { // Walk through all parsers and replace refs with their corresponding rule IDs for (auto & parser : parsers_) { @@ -785,7 +937,7 @@ void common_peg_arena::resolve_refs() { std::is_same_v || std::is_same_v || std::is_same_v || - std::is_same_v || + std::is_same_v || std::is_same_v || std::is_same_v || std::is_same_v) { @@ -803,9 +955,21 @@ void common_peg_arena::resolve_refs() { } std::string common_peg_arena::dump(common_peg_parser_id id) const { + std::unordered_set visited; + return dump_impl(id, visited); +} + +std::string common_peg_arena::dump_impl(common_peg_parser_id id, + std::unordered_set & visited) const { + // Check for cycles + if (visited.count(id)) { + return "[cycle]"; + } + visited.insert(id); + const auto & parser = parsers_.at(id); - return std::visit([this](const auto & p) -> std::string { + return std::visit([this, &visited](const auto & p) -> std::string { using T = std::decay_t; if constexpr (std::is_same_v) { @@ -819,24 +983,27 @@ std::string common_peg_arena::dump(common_peg_parser_id id) const { } else if constexpr (std::is_same_v) { std::vector parts; for (const auto & child : p.children) { - parts.push_back(dump(child)); + parts.push_back(dump_impl(child, visited)); } return "Sequence(" + string_join(parts, ", ") + ")"; } else if constexpr (std::is_same_v) { std::vector parts; for (const auto & child : p.children) { - parts.push_back(dump(child)); + parts.push_back(dump_impl(child, visited)); } return "Choice(" + string_join(parts, ", ") + ")"; } else if constexpr (std::is_same_v) { if (p.max_count == -1) { - return "Repetition(" + dump(p.child) + ", " + std::to_string(p.min_count) + ", unbounded)"; + return "Repetition(" + dump_impl(p.child, visited) + ", " + std::to_string(p.min_count) + + ", unbounded)"; } - return "Repetition(" + dump(p.child) + ", " + std::to_string(p.min_count) + ", " + std::to_string(p.max_count) + ")"; + return "Repetition(" + dump_impl(p.child, visited) + ", " + std::to_string(p.min_count) + ", " + std::to_string(p.max_count) + ")"; } else if constexpr (std::is_same_v) { - return "And(" + dump(p.child) + ")"; + return "And(" + dump_impl(p.child, visited) + ")"; } else if constexpr (std::is_same_v) { - return "Not(" + dump(p.child) + ")"; + return "Not(" + dump_impl(p.child, visited) + ")"; + } else if constexpr (std::is_same_v) { + return "Atomic(" + dump_impl(p.child, visited) + ")"; } else if constexpr (std::is_same_v) { return "Any"; } else if constexpr (std::is_same_v) { @@ -846,16 +1013,20 @@ std::string common_peg_arena::dump(common_peg_parser_id id) const { return "CharRepeat(" + p.pattern + ", " + std::to_string(p.min_count) + ", unbounded)"; } return "CharRepeat(" + p.pattern + ", " + std::to_string(p.min_count) + ", " + std::to_string(p.max_count) + ")"; - } else if constexpr (std::is_same_v) { - return "JsonString()"; + } else if constexpr (std::is_same_v) { + return "String(" + std::string(1, p.delimiter) + ")"; } else if constexpr (std::is_same_v) { return "Until(" + string_join(p.delimiters, " | ") + ")"; } else if constexpr (std::is_same_v) { - return "Schema(" + dump(p.child) + ", " + (p.schema ? p.schema->dump() : "null") + ")"; + return "Schema(" + dump_impl(p.child, visited) + ", " + (p.schema ? p.schema->dump() : "null") + ")"; } else if constexpr (std::is_same_v) { - return "Rule(" + p.name + ", " + dump(p.child) + ")"; + return "Rule(" + p.name + ", " + dump_impl(p.child, visited) + ")"; } else if constexpr (std::is_same_v) { return "Ref(" + p.name + ")"; + } else if constexpr (std::is_same_v) { + return "Tag(" + p.tag + ", " + dump(p.child) + ")"; + } else if constexpr (std::is_same_v) { + return "Atomic(" + dump(p.child) + ")"; } else { return "Unknown"; } @@ -1054,7 +1225,32 @@ common_peg_arena common_peg_parser_builder::build() { return std::move(arena_); } +// String primitives + +common_peg_parser common_peg_parser_builder::string_content(char delimiter) { + return wrap(arena_.add_parser(common_peg_string_parser{delimiter})); +} + +common_peg_parser common_peg_parser_builder::double_quoted_string() { + return rule("double-quoted-string", [this]() { + return sequence({literal("\""), string_content('"'), literal("\""), space()}); + }); +} + +common_peg_parser common_peg_parser_builder::single_quoted_string() { + return rule("single-quoted-string", [this]() { + return sequence({literal("'"), string_content('\''), literal("'"), space()}); + }); +} + +common_peg_parser common_peg_parser_builder::quoted_string() { + return rule("quoted-string", [this]() { + return choice({double_quoted_string(), single_quoted_string()}); + }); +} + // JSON parsers + common_peg_parser common_peg_parser_builder::json_number() { return rule("json-number", [this]() { auto digit1_9 = chars("[1-9]", 1, 1); @@ -1062,13 +1258,17 @@ common_peg_parser common_peg_parser_builder::json_number() { auto int_part = choice({literal("0"), sequence({digit1_9, chars("[0-9]", 0, -1)})}); auto frac = sequence({literal("."), digits}); auto exp = sequence({choice({literal("e"), literal("E")}), optional(chars("[+-]", 1, 1)), digits}); - return sequence({optional(literal("-")), int_part, optional(frac), optional(exp), space()}); + // Negative lookahead: only commit the number when the next character can't extend it. + // At EOF in partial mode, chars returns NEED_MORE → negate propagates NEED_MORE → number not committed. + // This prevents premature commits of partial numbers (e.g. "3" when "3.14" is incoming). + auto not_number_continuation = negate(chars("[0-9.eE+-]", 1, 1)); + return sequence({ optional(literal("-")), int_part, optional(frac), optional(exp), not_number_continuation, space() }); }); } common_peg_parser common_peg_parser_builder::json_string() { return rule("json-string", [this]() { - return sequence({literal("\""), json_string_content(), literal("\""), space()}); + return sequence({literal("\""), string_content('"'), literal("\""), space()}); }); } @@ -1130,8 +1330,81 @@ common_peg_parser common_peg_parser_builder::json() { }); } -common_peg_parser common_peg_parser_builder::json_string_content() { - return wrap(arena_.add_parser(common_peg_json_string_parser{})); +common_peg_parser common_peg_parser_builder::python_string() { + return rule("python-string", [this]() { + return choice({double_quoted_string(), single_quoted_string()}); + }); +} + +common_peg_parser common_peg_parser_builder::python_number() { + return json_number(); +} + +common_peg_parser common_peg_parser_builder::python_bool() { + return rule("python-bool", [this]() { + return sequence({ + choice({literal("True"), literal("False")}), + space() + }); + }); +} + +common_peg_parser common_peg_parser_builder::python_null() { + return rule("python-none", [this]() { + return sequence({literal("None"), space()}); + }); +} + +common_peg_parser common_peg_parser_builder::python_dict() { + return rule("python-dict", [this]() { + auto ws = space(); + auto member = sequence({python_string(), ws, literal(":"), ws, python_value()}); + auto members = sequence({member, zero_or_more(sequence({ws, literal(","), ws, member}))}); + return sequence({ + literal("{"), + ws, + choice({ + literal("}"), + sequence({members, ws, literal("}")}) + }), + ws + }); + }); +} + +common_peg_parser common_peg_parser_builder::python_array() { + return rule("python-array", [this]() { + auto ws = space(); + auto elements = sequence({python_value(), zero_or_more(sequence({literal(","), ws, python_value()}))}); + return sequence({ + literal("["), + ws, + choice({ + literal("]"), + sequence({elements, ws, literal("]")}) + }), + ws + }); + }); +} + +common_peg_parser common_peg_parser_builder::python_value() { + return rule("python-value", [this]() { + return choice({ + python_dict(), + python_array(), + python_string(), + python_number(), + python_bool(), + python_null() + }); + }); +} + +common_peg_parser common_peg_parser_builder::marker() { + auto sharp_bracket_parser = literal("<") + until(">") + literal(">"); + auto square_bracket_parser = literal("[") + until("]") + literal("]"); + return choice({ sharp_bracket_parser, square_bracket_parser }); } common_peg_parser common_peg_parser_builder::json_member(const std::string & key, const common_peg_parser & p) { @@ -1145,17 +1418,54 @@ common_peg_parser common_peg_parser_builder::json_member(const std::string & key }); } - -static std::string gbnf_escape_char_class(char c) { - switch (c) { - case '\n': return "\\n"; - case '\t': return "\\t"; - case '\r': return "\\r"; - case '\\': return "\\\\"; - case ']': return "\\]"; - case '[': return "\\["; - default: return std::string(1, c); +static std::string gbnf_escape_char_class(uint32_t c) { + if (c == '-' || c == ']' || c == '[' || c == '\\') { + return "\\" + std::string(1, (char) c); } + // Escape whitespace control characters + if (c == '\n') { + return "\\n"; + } + if (c == '\t') { + return "\\t"; + } + if (c == '\r') { + return "\\r"; + } + + // Printable ASCII + if (c >= 0x20 && c <= 0x7E) { + return std::string(1, (char) c); + } + + // Hex escape + char buf[16]; + const char * hex = "0123456789ABCDEF"; + + if (c <= 0xFF) { + buf[0] = '\\'; + buf[1] = 'x'; + buf[2] = hex[(c >> 4) & 0xF]; + buf[3] = hex[c & 0xF]; + buf[4] = '\0'; + } else if (c <= 0xFFFF) { + buf[0] = '\\'; + buf[1] = 'u'; + buf[2] = hex[(c >> 12) & 0xF]; + buf[3] = hex[(c >> 8) & 0xF]; + buf[4] = hex[(c >> 4) & 0xF]; + buf[5] = hex[c & 0xF]; + buf[6] = '\0'; + } else { + buf[0] = '\\'; + buf[1] = 'U'; + for (int i = 0; i < 8; i++) { + buf[2 + i] = hex[(c >> ((7 - i) * 4)) & 0xF]; + } + buf[10] = '\0'; + } + + return std::string(buf); } static std::string gbnf_excluding_pattern(const std::vector & strings) { @@ -1173,12 +1483,12 @@ static std::string gbnf_excluding_pattern(const std::vector & strin std::string cls; cls.reserve(chars.size()); - for (const auto & ch : chars) { + for (uint32_t ch : chars) { cls += gbnf_escape_char_class(ch); } if (!pre.empty()) { - pattern += gbnf_format_literal(pre) + " [^" + cls + "]"; + pattern += gbnf_format_literal(common_unicode_cpts_to_utf8(pre)) + " [^" + cls + "]"; } else { pattern += "[^" + cls + "]"; } @@ -1208,7 +1518,7 @@ static std::unordered_set collect_reachable_rules( std::is_same_v || std::is_same_v || std::is_same_v || - std::is_same_v) { + std::is_same_v) { // These parsers do not have any children } else if constexpr (std::is_same_v) { for (auto child : p.children) { @@ -1344,8 +1654,9 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo return result + "{" + std::to_string(p.min_count) + "}"; } return result + "{" + std::to_string(p.min_count) + "," + std::to_string(p.max_count) + "}"; - } else if constexpr (std::is_same_v) { - return R"(( [^"\\] | "\\" ( ["\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)"; + } else if constexpr (std::is_same_v) { + const std::string delim(1, p.delimiter); + return R"(( [^)" + delim + R"(\\] | "\\" ( [)" + delim + R"(\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)"; } else if constexpr (std::is_same_v) { if (p.delimiters.empty()) { return ".*"; @@ -1475,8 +1786,8 @@ static nlohmann::json serialize_parser_variant(const common_peg_parser_variant & {"min_count", p.min_count}, {"max_count", p.max_count} }; - } else if constexpr (std::is_same_v) { - return json{{"type", "json_string"}}; + } else if constexpr (std::is_same_v) { + return json{{"type", "string"}, {"delimiter", std::string(1, p.delimiter)}}; } else if constexpr (std::is_same_v) { return json{{"type", "until"}, {"delimiters", p.delimiters}}; } else if constexpr (std::is_same_v) { @@ -1603,8 +1914,15 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json } return parser; } - if (type == "json_string") { - return common_peg_json_string_parser{}; + if (type == "string") { + if (!j.contains("delimiter")) { + throw std::runtime_error("string parser missing delimiter field."); + } + std::string delimiter = j["delimiter"]; + if (delimiter.empty()) { + throw std::runtime_error("string parser delimiter is empty."); + } + return common_peg_string_parser{delimiter[0]}; } if (type == "until") { if (!j.contains("delimiters") || !j["delimiters"].is_array()) { diff --git a/common/peg-parser.h b/common/peg-parser.h index 1cd640365f..31cdf9ec2d 100644 --- a/common/peg-parser.h +++ b/common/peg-parser.h @@ -4,6 +4,7 @@ #include #include +#include #include #include #include @@ -111,6 +112,8 @@ class common_peg_ast_arena { void visit(common_peg_ast_id id, const common_peg_ast_visitor & visitor) const; void visit(const common_peg_parse_result & result, const common_peg_ast_visitor & visitor) const; + + std::string dump(); }; struct common_peg_parse_result { @@ -136,21 +139,43 @@ struct common_peg_parse_result { bool success() const { return type == COMMON_PEG_PARSE_RESULT_SUCCESS; } }; +enum common_peg_parse_flags { + COMMON_PEG_PARSE_FLAG_NONE = 0, + COMMON_PEG_PARSE_FLAG_LENIENT = 1 << 0, + COMMON_PEG_PARSE_FLAG_DEBUG = 1 << 1, +}; + +inline common_peg_parse_flags operator|(common_peg_parse_flags a, common_peg_parse_flags b) { + return static_cast(int(a) | int(b)); +} + +inline common_peg_parse_flags & operator|=(common_peg_parse_flags & a, common_peg_parse_flags b) { + return a = a | b; +} + +inline common_peg_parse_flags operator&(common_peg_parse_flags a, common_peg_parse_flags b) { + return static_cast(int(a) & int(b)); +} + +inline common_peg_parse_flags operator~(common_peg_parse_flags a) { + return static_cast(~int(a)); +} + struct common_peg_parse_context { std::string input; - bool is_partial; + common_peg_parse_flags flags; common_peg_ast_arena ast; int parse_depth; - common_peg_parse_context() - : is_partial(false), parse_depth(0) {} + common_peg_parse_context(common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_NONE) + : flags(flags), parse_depth(0) {} - common_peg_parse_context(const std::string & input) - : input(input), is_partial(false), parse_depth(0) {} + common_peg_parse_context(const std::string & input, common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_NONE) + : input(input), flags(flags), parse_depth(0) {} - common_peg_parse_context(const std::string & input, bool is_partial) - : input(input), is_partial(is_partial), parse_depth(0) {} + bool is_lenient() const { return flags & COMMON_PEG_PARSE_FLAG_LENIENT; } + bool is_debug() const { return flags & COMMON_PEG_PARSE_FLAG_DEBUG; } }; class common_peg_arena; @@ -206,7 +231,9 @@ struct common_peg_chars_parser { int max_count; // -1 for unbounded }; -struct common_peg_json_string_parser {}; +struct common_peg_string_parser { + char delimiter; +}; struct common_peg_until_parser { std::vector delimiters; @@ -254,7 +281,7 @@ using common_peg_parser_variant = std::variant< common_peg_any_parser, common_peg_space_parser, common_peg_chars_parser, - common_peg_json_string_parser, + common_peg_string_parser, common_peg_until_parser, common_peg_schema_parser, common_peg_rule_parser, @@ -299,6 +326,8 @@ class common_peg_arena { friend class common_peg_parser_builder; private: + std::string dump_impl(common_peg_parser_id id, std::unordered_set & visited) const; + common_peg_parser_id add_parser(common_peg_parser_variant parser); void add_rule(const std::string & name, common_peg_parser_id id); @@ -404,6 +433,18 @@ class common_peg_parser_builder { // S -> A{n} common_peg_parser repeat(const common_peg_parser & p, int n) { return repeat(p, n, n); } + // Matches a double-quoted string: '"' content '"' space + common_peg_parser double_quoted_string(); + + // Matches a single-quoted string: "'" content "'" space + common_peg_parser single_quoted_string(); + + // Matches a string that accepts both double-quoted and single-quoted styles. + common_peg_parser quoted_string(); + + // Matches string content without the surrounding delimiter. + common_peg_parser string_content(char delimiter); + // Creates a complete JSON parser supporting objects, arrays, strings, numbers, booleans, and null. // value -> object | array | string | number | true | false | null common_peg_parser json(); @@ -414,14 +455,24 @@ class common_peg_parser_builder { common_peg_parser json_bool(); common_peg_parser json_null(); - // Matches JSON string content without the surrounding quotes. - // Useful for extracting content within a JSON string. - common_peg_parser json_string_content(); - // Matches a JSON object member with a key and associated parser as the // value. common_peg_parser json_member(const std::string & key, const common_peg_parser & p); + // Creates a complete Python format parser supporting dicts, arrays, strings, numbers, booleans, and None. + // Differs from JSON: uses True/False/None, accepts both single and double-quoted strings. + // value -> dict | array | string | number | True | False | None + common_peg_parser python_value(); + common_peg_parser python_dict(); + common_peg_parser python_string(); + common_peg_parser python_array(); + common_peg_parser python_number(); + common_peg_parser python_bool(); + common_peg_parser python_null(); + + // A marker, i.e. text delimited by a pair of <> or [] + common_peg_parser marker(); + // Wraps a parser with JSON schema metadata for grammar generation. // Used internally to convert JSON schemas to GBNF grammar rules. common_peg_parser schema(const common_peg_parser & p, const std::string & name, const nlohmann::ordered_json & schema, bool raw = false); diff --git a/common/reasoning-budget.cpp b/common/reasoning-budget.cpp new file mode 100644 index 0000000000..a55e4f509d --- /dev/null +++ b/common/reasoning-budget.cpp @@ -0,0 +1,219 @@ +#include "reasoning-budget.h" +#include "common.h" +#include "unicode.h" + +#include "log.h" + +#include +#include +#include +#include + +struct token_matcher { + std::vector tokens; + size_t pos = 0; + + bool advance(llama_token token) { + if (tokens.empty()) { + return false; + } + + if (token == tokens[pos]) { + pos++; + if (pos >= tokens.size()) { + pos = 0; + return true; + } + } else { + pos = 0; + if (token == tokens[0]) { + pos = 1; + } + } + return false; + } + + void reset() { pos = 0; } +}; + +struct common_reasoning_budget_ctx { + const llama_vocab * vocab; + + token_matcher start_matcher; + token_matcher end_matcher; + std::vector forced_tokens; + + int32_t budget; // maximum tokens in reasoning block + int32_t remaining; // tokens remaining in budget + + common_reasoning_budget_state state; + + // for forcing + size_t force_pos; // next position in forced_tokens to force +}; + +static const char * common_reasoning_budget_name(const struct llama_sampler * /*smpl*/) { + return "reasoning-budget"; +} + +static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_token token) { + auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx; + + switch (ctx->state) { + case REASONING_BUDGET_IDLE: + { + if (ctx->start_matcher.advance(token)) { + ctx->state = REASONING_BUDGET_COUNTING; + ctx->remaining = ctx->budget; + LOG_INF("reasoning-budget: activated, budget=%d tokens\n", ctx->budget); + + if (ctx->remaining <= 0) { + ctx->state = REASONING_BUDGET_FORCING; + ctx->force_pos = 0; + LOG_INF("reasoning-budget: budget=0, forcing immediately\n"); + } + } + break; + } + case REASONING_BUDGET_COUNTING: + case REASONING_BUDGET_WAITING_UTF8: + { + if (ctx->end_matcher.advance(token)) { + ctx->state = REASONING_BUDGET_DONE; + LOG_INF("reasoning-budget: deactivated (natural end)\n"); + break; + } + + bool utf8_complete = true; + if (ctx->vocab != nullptr) { + const std::string piece = common_token_to_piece(ctx->vocab, token, false); + utf8_complete = common_utf8_is_complete(piece); + } + + if (ctx->state == REASONING_BUDGET_WAITING_UTF8) { + if (utf8_complete) { + ctx->state = REASONING_BUDGET_FORCING; + ctx->force_pos = 0; + ctx->end_matcher.reset(); + LOG_INF("reasoning-budget: UTF-8 complete, now forcing end sequence\n"); + } + } else if (ctx->state == REASONING_BUDGET_COUNTING) { + ctx->remaining--; + if (ctx->remaining <= 0) { + if (utf8_complete) { + ctx->state = REASONING_BUDGET_FORCING; + ctx->force_pos = 0; + ctx->end_matcher.reset(); + LOG_INF("reasoning-budget: budget exhausted, forcing end sequence\n"); + } else { + ctx->state = REASONING_BUDGET_WAITING_UTF8; + ctx->end_matcher.reset(); + LOG_INF("reasoning-budget: budget exhausted, waiting for UTF-8 completion\n"); + } + } + } + break; + } + case REASONING_BUDGET_FORCING: + // force_pos is advanced in apply(), not here. + // This ensures the first forced token isn't skipped when the sampler + // is initialized directly in FORCING state (e.g. COUNTING + budget=0) + break; + case REASONING_BUDGET_DONE: + break; + } +} + +static void common_reasoning_budget_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx; + + if (ctx->state != REASONING_BUDGET_FORCING) { + // passthrough — don't modify logits + return; + } + + if (ctx->force_pos >= ctx->forced_tokens.size()) { + return; + } + + const llama_token forced = ctx->forced_tokens[ctx->force_pos]; + + // set all logits to -inf except the forced token + for (size_t i = 0; i < cur_p->size; i++) { + if (cur_p->data[i].id != forced) { + cur_p->data[i].logit = -INFINITY; + } + } + + // advance to next forced token (done here rather than in accept so that + // the first forced token isn't skipped when starting in FORCING state) + ctx->force_pos++; + if (ctx->force_pos >= ctx->forced_tokens.size()) { + ctx->state = REASONING_BUDGET_DONE; + LOG_INF("reasoning-budget: forced sequence complete, done\n"); + } +} + +static void common_reasoning_budget_reset(struct llama_sampler * smpl) { + auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx; + ctx->state = REASONING_BUDGET_IDLE; + ctx->remaining = ctx->budget; + ctx->start_matcher.reset(); + ctx->end_matcher.reset(); + ctx->force_pos = 0; +} + +static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl) { + const auto * ctx = (const common_reasoning_budget_ctx *) smpl->ctx; + return common_reasoning_budget_init( + ctx->vocab, + ctx->start_matcher.tokens, + ctx->end_matcher.tokens, + ctx->forced_tokens, + ctx->budget, + ctx->state); +} + +static void common_reasoning_budget_free(struct llama_sampler * smpl) { + delete (common_reasoning_budget_ctx *) smpl->ctx; +} + +static struct llama_sampler_i common_reasoning_budget_i = { + /* .name = */ common_reasoning_budget_name, + /* .accept = */ common_reasoning_budget_accept, + /* .apply = */ common_reasoning_budget_apply, + /* .reset = */ common_reasoning_budget_reset, + /* .clone = */ common_reasoning_budget_clone, + /* .free = */ common_reasoning_budget_free, + /* .backend_init = */ nullptr, + /* .backend_accept = */ nullptr, + /* .backend_apply = */ nullptr, + /* .backend_set_input = */ nullptr, +}; + +struct llama_sampler * common_reasoning_budget_init( + const struct llama_vocab * vocab, + const std::vector & start_tokens, + const std::vector & end_tokens, + const std::vector & forced_tokens, + int32_t budget, + common_reasoning_budget_state initial_state) { + // promote COUNTING with budget <= 0 to FORCING + if (initial_state == REASONING_BUDGET_COUNTING && budget <= 0) { + initial_state = REASONING_BUDGET_FORCING; + } + + return llama_sampler_init( + /* .iface = */ &common_reasoning_budget_i, + /* .ctx = */ new common_reasoning_budget_ctx { + /* .vocab = */ vocab, + /* .start_matcher = */ { start_tokens, 0 }, + /* .end_matcher = */ { end_tokens, 0 }, + /* .forced_tokens = */ forced_tokens, + /* .budget = */ budget, + /* .remaining = */ budget, + /* .state = */ initial_state, + /* .force_pos = */ 0, + } + ); +} diff --git a/common/reasoning-budget.h b/common/reasoning-budget.h new file mode 100644 index 0000000000..08ad282481 --- /dev/null +++ b/common/reasoning-budget.h @@ -0,0 +1,41 @@ +#pragma once + +#include "llama.h" + +#include +#include + +enum common_reasoning_budget_state { + REASONING_BUDGET_IDLE, // waiting for start sequence + REASONING_BUDGET_COUNTING, // counting down tokens + REASONING_BUDGET_FORCING, // forcing budget message + end sequence + REASONING_BUDGET_WAITING_UTF8, // budget exhausted, waiting for UTF-8 completion + REASONING_BUDGET_DONE, // passthrough forever +}; + +// Creates a reasoning budget sampler that limits token generation inside a +// reasoning block (e.g. between and ). +// +// State machine: IDLE -> COUNTING -> WAITING_UTF8 -> FORCING -> DONE +// IDLE: passthrough, watching for start_tokens sequence +// COUNTING: counting down remaining tokens, watching for natural end_tokens +// WAITING_UTF8: budget exhausted, allowing tokens to complete a UTF-8 sequence +// FORCING: forces forced_tokens token-by-token (all other logits -> -inf) +// DONE: passthrough forever +// +// Parameters: +// vocab - vocabulary (used for UTF-8 boundary detection; can be nullptr) +// start_tokens - token sequence that activates counting +// end_tokens - token sequence for natural deactivation +// forced_tokens - token sequence forced when budget expires +// budget - max tokens allowed in the reasoning block +// initial_state - initial state of the sampler (e.g. IDLE or COUNTING) +// note: COUNTING with budget <= 0 is promoted to FORCING +// +struct llama_sampler * common_reasoning_budget_init( + const struct llama_vocab * vocab, + const std::vector & start_tokens, + const std::vector & end_tokens, + const std::vector & forced_tokens, + int32_t budget, + common_reasoning_budget_state initial_state); diff --git a/common/regex-partial.cpp b/common/regex-partial.cpp index e667a209e9..bd9034e931 100644 --- a/common/regex-partial.cpp +++ b/common/regex-partial.cpp @@ -102,7 +102,7 @@ std::string regex_to_reversed_partial_regex(const std::string & pattern) { auto is_star = *it == '*'; ++it; if (is_star) { - if (*it == '?') { + if (it != end && *it == '?') { ++it; } } diff --git a/common/sampling.cpp b/common/sampling.cpp index 11a1d48398..f849d4f61a 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -2,6 +2,7 @@ #include "common.h" #include "log.h" +#include "reasoning-budget.h" #include #include @@ -250,6 +251,17 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st } } + // reasoning budget sampler — added first so it can force tokens before other samplers + if (params.reasoning_budget_tokens >= 0 && !params.reasoning_budget_forced.empty()) { + samplers.push_back(common_reasoning_budget_init( + vocab, + params.reasoning_budget_start, + params.reasoning_budget_end, + params.reasoning_budget_forced, + params.reasoning_budget_tokens, + params.reasoning_budget_activate_immediately ? REASONING_BUDGET_COUNTING : REASONING_BUDGET_IDLE)); + } + if (params.has_logit_bias()) { samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data())); } diff --git a/common/unicode.cpp b/common/unicode.cpp index 56ab0f468e..f71fe56783 100644 --- a/common/unicode.cpp +++ b/common/unicode.cpp @@ -1,14 +1,20 @@ #include "unicode.h" +#include +#include +#include +#include +#include + // implementation adopted from src/unicode.cpp -size_t utf8_sequence_length(unsigned char first_byte) { +size_t common_utf8_sequence_length(unsigned char first_byte) { const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; uint8_t highbits = static_cast(first_byte) >> 4; return lookup[highbits]; } -utf8_parse_result parse_utf8_codepoint(std::string_view input, size_t offset) { +utf8_parse_result common_parse_utf8_codepoint(std::string_view input, size_t offset) { if (offset >= input.size()) { return utf8_parse_result(utf8_parse_result::INCOMPLETE); } @@ -62,3 +68,57 @@ utf8_parse_result parse_utf8_codepoint(std::string_view input, size_t offset) { // Invalid first byte return utf8_parse_result(utf8_parse_result::INVALID); } + +bool common_utf8_is_complete(const std::string & s) { + if (s.empty()) { + return true; + } + for (int i = 1; i <= std::min(4, (int)s.size()); i++) { + unsigned char c = s[s.size() - i]; + if ((c & 0xC0) != 0x80) { + int expected = (c >= 0xF0) ? 4 : (c >= 0xE0) ? 3 : (c >= 0xC0) ? 2 : 1; + return i >= expected; + } + } + return false; +} + +std::string common_unicode_cpts_to_utf8(const std::vector & cps) { + std::string result; + for (size_t i = 0; i < cps.size(); ++i) { + result.append(common_unicode_cpt_to_utf8(cps[i])); + } + return result; +} + +std::string common_unicode_cpt_to_utf8(uint32_t cpt) { + std::string result; + + if (/* 0x00 <= cpt && */ cpt <= 0x7f) { + result.push_back(cpt); + return result; + } + if (0x80 <= cpt && cpt <= 0x7ff) { + result.push_back(0xc0 | ((cpt >> 6) & 0x1f)); + result.push_back(0x80 | (cpt & 0x3f)); + return result; + } + if (0x800 <= cpt && cpt <= 0xffff) { + result.push_back(0xe0 | ((cpt >> 12) & 0x0f)); + result.push_back(0x80 | ((cpt >> 6) & 0x3f)); + result.push_back(0x80 | (cpt & 0x3f)); + return result; + } + if (0x10000 <= cpt && cpt <= 0x10ffff) { + result.push_back(0xf0 | ((cpt >> 18) & 0x07)); + result.push_back(0x80 | ((cpt >> 12) & 0x3f)); + result.push_back(0x80 | ((cpt >> 6) & 0x3f)); + result.push_back(0x80 | (cpt & 0x3f)); + return result; + } + + throw std::invalid_argument("invalid codepoint"); +} + + + diff --git a/common/unicode.h b/common/unicode.h index 9d9e8e1227..9b32fa19d6 100644 --- a/common/unicode.h +++ b/common/unicode.h @@ -2,6 +2,8 @@ #include #include +#include +#include // UTF-8 parsing utilities for streaming-aware unicode support @@ -16,7 +18,13 @@ struct utf8_parse_result { // Determine the expected length of a UTF-8 sequence from its first byte // Returns 0 for invalid first bytes -size_t utf8_sequence_length(unsigned char first_byte); +size_t common_utf8_sequence_length(unsigned char first_byte); + +// Check if a string ends with a complete UTF-8 sequence. +bool common_utf8_is_complete(const std::string & s); // Parse a single UTF-8 codepoint from input -utf8_parse_result parse_utf8_codepoint(std::string_view input, size_t offset); +utf8_parse_result common_parse_utf8_codepoint(std::string_view input, size_t offset); + +std::string common_unicode_cpts_to_utf8(const std::vector & cps); +std::string common_unicode_cpt_to_utf8(uint32_t cpt); diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index aa2639c423..2fa1a93cea 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -117,7 +117,8 @@ class ModelBase: split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None, disable_mistral_community_chat_template: bool = False, - sentence_transformers_dense_modules: bool = False, target_model_dir: Path | None = None): + sentence_transformers_dense_modules: bool = False, target_model_dir: Path | None = None, + fuse_gate_up_exps: bool = False): if type(self) is ModelBase or \ type(self) is TextModel or \ type(self) is MmprojModel: @@ -137,11 +138,15 @@ class ModelBase: self.remote_hf_model_id = remote_hf_model_id self.sentence_transformers_dense_modules = sentence_transformers_dense_modules self.target_model_dir = target_model_dir + self.fuse_gate_up_exps = fuse_gate_up_exps + self._gate_exp_buffer: dict[int, Tensor] = {} + self._up_exp_buffer: dict[int, Tensor] = {} self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id) self.metadata_override = metadata_override self.model_name = model_name self.dir_model_card = dir_model # overridden in convert_lora_to_gguf.py + self._is_nvfp4 = False # Apply heuristics to figure out typical tensor encoding based on first tensor's dtype # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie. @@ -269,6 +274,10 @@ class ModelBase: return tensors def dequant_model(self): + # If all quantized tensors were already handled (e.g. pure NVFP4), skip + if self._is_nvfp4 and not any(k.endswith((".weight_scale", ".weight_scale_inv")) for k in self.model_tensors): + return + tensors_to_remove: list[str] = [] new_tensors: dict[str, Callable[[], Tensor]] = {} @@ -468,7 +477,20 @@ class ModelBase: tensors_to_remove.append(base_name + "_zero_point") else: raise NotImplementedError(f"Quant format {quant_format!r} for method {quant_method!r} is not yet supported") - else: + elif quant_method == "modelopt": + # Mixed-precision ModelOpt models: NVFP4 tensors are handled by + # _generate_nvfp4_tensors; FP8 tensors have 1D weight_scale and + # are dequantized here. input_scale tensors are unused. + for name in self.model_tensors.keys(): + if name.endswith(".weight_scale"): + weight_name = name.removesuffix("_scale") + w = self.model_tensors[weight_name] + s = self.model_tensors[name] + self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), None) + tensors_to_remove.append(name) + if name.endswith((".input_scale", ".k_scale", ".v_scale")): + tensors_to_remove.append(name) + elif quant_method is not None: raise NotImplementedError(f"Quant method is not yet supported: {quant_method!r}") for name in tensors_to_remove: @@ -514,8 +536,32 @@ class ModelBase: raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses") def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: - del bid # unused - return [(self.map_tensor_name(name), data_torch)] + + new_name = self.map_tensor_name(name) + + # Handle gate/up expert tensor fusion if enabled + if self.fuse_gate_up_exps and bid is not None: + if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid): + self._gate_exp_buffer[bid] = data_torch + elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid): + self._up_exp_buffer[bid] = data_torch + + # Check if both gate and up are buffered for this layer + if bid in self._gate_exp_buffer and bid in self._up_exp_buffer: + gate_data = self._gate_exp_buffer.pop(bid) + up_data = self._up_exp_buffer.pop(bid) + # gate/up shape: (n_expert, n_ff, n_embd), concatenate to (n_expert, n_ff*2, n_embd) + fused_data = torch.cat([gate_data, up_data], dim=1) + fused_name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_UP_EXP, bid) + logger.info(f"Fused gate_exps and up_exps for layer {bid}") + return [(fused_name, fused_data)] + + # If we buffered a gate/up tensor, wait for the other + if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid) or \ + self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid): + return [] + + return [(new_name, data_torch)] def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: del name, new_name, bid, n_dims # unused @@ -526,7 +572,164 @@ class ModelBase: def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: return () + @staticmethod + def _nvfp4_pack(weight: Tensor, scale: Tensor) -> tuple[np.ndarray, list[int]]: + """Repack NVFP4 ModelOpt tensors into ggml super-block layout. + Preserves original E4M3 scale bits as UE4M3 (strip sign bit). + The per-tensor scale2 factor is stored as a separate tensor and applied at inference time via ggml_mul(). + Returns (raw_data, logical_shape).""" + + out_features = weight.shape[0] + n_blocks = scale.shape[1] + + # Unpack ModelOpt nibble-packed weights + w = weight.reshape(out_features, n_blocks, 8) + vals = torch.stack([w & 0x0F, w >> 4], dim=-1).reshape(out_features, n_blocks, 16) + + # Preserve original E4M3 scale bits as UE4M3 (strip sign bit) + d_ue = scale.view(torch.uint8).numpy().reshape(out_features, n_blocks) & 0x7F + qs = (vals[:, :, :8] | (vals[:, :, 8:] << 4)).to(torch.uint8).numpy() + + # Pack into super-blocks: [4 UE4M3 scales, 32 qs bytes] = 36 bytes per 64 elements + n_super = n_blocks // 4 + d_grouped = d_ue.reshape(out_features, n_super, 4) + qs_grouped = qs.reshape(out_features, n_super, 4, 8).reshape(out_features, n_super, 32) + raw = np.concatenate([d_grouped, qs_grouped], axis=-1).reshape(out_features, n_super * 36) + return raw, [out_features, n_super * 64] + + @staticmethod + def _nvfp4_scale2_is_trivial(scale2: Tensor) -> bool: + return scale2.numel() <= 1 and abs(float(scale2.float().sum()) - 1.0) < 1e-6 + + def _repack_nvfp4(self, new_name: str, weight: Tensor, scale: Tensor, scale2: Tensor): + raw, shape = self._nvfp4_pack(weight, scale) + logger.info(f"Repacked {new_name} with shape {shape} and quantization NVFP4") + self.gguf_writer.add_tensor(new_name, raw, raw_dtype=gguf.GGMLQuantizationType.NVFP4) + + # Emit per-tensor scale2 as a separate F32 tensor when non-trivial + if not self._nvfp4_scale2_is_trivial(scale2): + scale2_f32 = scale2.float().numpy().flatten() + scale_name = new_name.replace(".weight", ".scale") + logger.info(f" + {scale_name} (per-tensor NVFP4 scale2, shape [{scale2_f32.size}])") + self.gguf_writer.add_tensor(scale_name, scale2_f32) + + def _generate_nvfp4_tensors(self): + # Per-layer expert merging to avoid holding all experts in memory + expert_blocks: dict[tuple[int, str], list[tuple[int, np.ndarray]]] = {} + expert_scales: dict[tuple[int, str], list[tuple[int, float]]] = {} + expert_shapes: dict[tuple[int, str], list[int]] = {} + n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0 + consumed: list[str] = [] + + for name in list(self.model_tensors.keys()): + if not name.endswith(".weight"): + continue + scale_name = name.replace(".weight", ".weight_scale") + scale2_name = name.replace(".weight", ".weight_scale_2") + if scale_name not in self.model_tensors: + continue + # Force eager materialization of lazy tensors + weight = LazyTorchTensor.to_eager(self.model_tensors[name]()) + scale = LazyTorchTensor.to_eager(self.model_tensors[scale_name]()) + + # Skip non-NVFP4 tensors (e.g. FP8 with per-channel 1D scales) + if scale.ndim < 2: + continue + + scale2 = LazyTorchTensor.to_eager(self.model_tensors.get(scale2_name, lambda: torch.tensor(1.0))()) + + # Mark tensors for removal from model_tensors (already written to gguf) + consumed.extend([name, scale_name]) + if scale2_name in self.model_tensors: + consumed.append(scale2_name) + + # Check if this is a per-expert tensor + m = re.search(r'\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight$', name) + if m: + expert_id = int(m.group(1)) + proj_type = m.group(2) + bid_m = re.search(r'\.layers\.(\d+)\.', name) + bid = int(bid_m.group(1)) if bid_m else 0 + key = (bid, proj_type) + + raw, shape = self._nvfp4_pack(weight, scale) + + if key not in expert_blocks: + expert_blocks[key] = [] + expert_scales[key] = [] + expert_shapes[key] = shape + expert_blocks[key].append((expert_id, raw.copy())) + # Collect per-expert scale2 (scalar per expert) + expert_scales[key].append((expert_id, float(scale2.float().sum()))) + + # Flush when all experts for this (layer, proj) are collected + if n_experts > 0 and len(expert_blocks[key]) >= n_experts: + self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_shapes, bid, proj_type) + else: + new_name = self.map_tensor_name(name) + self._repack_nvfp4(new_name, weight, scale, scale2) + + # Flush any remaining experts (fallback if n_experts was unknown) + for (bid, proj_type) in list(expert_blocks.keys()): + self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_shapes, bid, proj_type) + + # Remove consumed tensors so get_tensors/modify_tensors won't see them + for name in consumed: + self.model_tensors.pop(name, None) + + # Remove unused auxiliary tensors (input_scale, k_scale, v_scale) + for name in list(self.model_tensors.keys()): + if name.endswith((".input_scale", ".k_scale", ".v_scale")): + del self.model_tensors[name] + + def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_shapes, bid, proj_type): + experts = expert_blocks.pop(key) + scales = expert_scales.pop(key) + shape = expert_shapes.pop(key) + + experts.sort(key=lambda x: x[0]) + merged = np.stack([e[1] for e in experts], axis=0) + merged_name = f"model.layers.{bid}.mlp.experts.{proj_type}.weight" + new_name = self.map_tensor_name(merged_name) + logger.info(f"Repacked {new_name} with shape [{len(experts)}, {shape[0]}, {shape[1]}] and quantization NVFP4") + self.gguf_writer.add_tensor(new_name, merged, raw_dtype=gguf.GGMLQuantizationType.NVFP4) + + # Emit per-expert scale2 tensor if any expert has non-trivial scale2 + scales.sort(key=lambda x: x[0]) + scale_vals = np.array([s[1] for s in scales], dtype=np.float32) + if not np.allclose(scale_vals, 1.0, atol=1e-6): + scale_name = new_name.replace(".weight", ".scale") + logger.info(f" + {scale_name} (per-expert NVFP4 scale2, shape [{len(scales)}])") + self.gguf_writer.add_tensor(scale_name, scale_vals) + + del experts, merged + def prepare_tensors(self): + # detect NVFP4 quantization (ModelOpt format) + quant_algo = (self.hparams.get("quantization_config") or {}).get("quant_algo") + quant_layers = (self.hparams.get("quantization_config") or {}).get("quantized_layers") or {} + quant_config_file = self.dir_model / "hf_quant_config.json" + + if (not quant_algo or not quant_layers) and quant_config_file.is_file(): + with open(quant_config_file, "r", encoding="utf-8") as f: + quant_config = json.load(f).get("quantization") or {} + quant_algo = quant_config.get("quant_algo", quant_algo) + quant_layers = quant_config.get("quantized_layers", quant_layers) or {} + + # Some models use per-tensor quant_algo (e.g. "MIXED_PRECISION" with + # per-layer NVFP4/FP8) instead of a single global "NVFP4" value. + if quant_algo != "NVFP4": + if any(v.get("quant_algo") == "NVFP4" for v in quant_layers.values() if isinstance(v, dict)): + quant_algo = "NVFP4" + + self._is_nvfp4 = quant_algo == "NVFP4" + + # NVFP4 weights are repacked and written directly to gguf_writer. + # This must run before dequant_model so NVFP4 tensors are removed + # from model_tensors, leaving only non-NVFP4 (e.g. FP8) for dequant. + if self._is_nvfp4: + self._generate_nvfp4_tensors() + self.dequant_model() # Handle empty tensor_map for models with block_count=0 (like MobileNetV5) @@ -1150,6 +1353,9 @@ class TextModel(ModelBase): if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de res = "jina-v2-de" + if chkhsh == "a023e9fdc5a11f034d3ef515b92350e56fb2af1f66c6b6811a4444ea9bf8763d": + # ref: https://huggingface.co/jinaai/jina-embeddings-v5-text-nano + res = "jina-v5-nano" if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d": # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct res = "smaug-bpe" @@ -2029,6 +2235,8 @@ class GPTNeoXModel(TextModel): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + assert n_head is not None + assert n_embed is not None if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name): # Map bloom-style qkv_linear to gpt-style qkv_linear @@ -2066,6 +2274,8 @@ class BloomModel(TextModel): def set_gguf_parameters(self): n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + assert n_head is not None + assert n_embed is not None self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) self.gguf_writer.add_embedding_length(n_embed) self.gguf_writer.add_feed_forward_length(4 * n_embed) @@ -2078,6 +2288,8 @@ class BloomModel(TextModel): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + assert n_head is not None + assert n_embed is not None name = re.sub(r'transformer\.', '', name) @@ -3825,6 +4037,7 @@ class LLaDAModel(TextModel): if (rope_dim := hparams.get("head_dim")) is None: n_heads = hparams.get("num_attention_heads", hparams.get("n_heads")) + assert n_heads is not None rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads self.gguf_writer.add_rope_dimension_count(rope_dim) @@ -3856,6 +4069,7 @@ class LLaDAModel(TextModel): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads")) + assert n_head is not None n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads")) if self.undo_permute: @@ -4140,7 +4354,7 @@ class Qwen2VLVisionModel(MmprojModel): # split Conv3D into Conv2Ds c1, c2, kt, kh, kw = data_torch.shape del c1, c2, kh, kw # unused - assert kt == 2, "Current implmentation only support temporal_patch_size of 2" + assert kt == 2, "Current implementation only support temporal_patch_size of 2" yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight" , data_torch[:, :, 0, ...]) yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...]) else: @@ -4412,6 +4626,14 @@ class Qwen2MoeModel(TextModel): # process the experts separately name = name.replace("language_model.", "") # InternVL + # NVFP4 expert weights are handled in _generate_nvfp4_tensors + if self._is_nvfp4 and "experts" in name: + if name.endswith((".weight", ".weight_scale", ".weight_scale_2", ".input_scale")): + if name.endswith(".weight") and name.replace(".weight", ".weight_scale") in self.model_tensors: + return + if not name.endswith(".weight"): + return + # handle aggregated expert tensors # GGUF stores dimensions reversed from PyTorch, so: # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A} @@ -4499,15 +4721,31 @@ class Qwen3Model(Qwen2Model): hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False) self.origin_hf_arch = hparams.get('architectures', [None])[0] - # a bit hacky, but currently the only way to detect if this is a rerank model - # ref: https://huggingface.co/Qwen/Qwen3-Reranker-0.6B + if self._is_qwen3_reranker(): + self._find_rerank_config() + + def _is_qwen3_reranker(self) -> bool: readme_path = self.dir_model / "README.md" readme_text = "" if readme_path.exists(): with readme_path.open("r", encoding="utf-8") as f: readme_text = f.read() - if "# Qwen3-Reranker" in readme_text: - self._find_rerank_config() + + name_hints = [ + str(self.dir_model.name), + str(self.hparams.get("_name_or_path", "")), + str(self.hparams.get("model_type", "")), + str(self.origin_hf_arch or ""), + ] + name_hints = [hint.lower() for hint in name_hints if hint] + + if "# qwen3-reranker" in readme_text.lower() or "# qwen3-vl-reranker" in readme_text.lower(): + return True + + if any("qwen3-reranker" in hint or "qwen3-vl-reranker" in hint for hint in name_hints): + return True + + return "sequenceclassification" in (self.origin_hf_arch or "").lower() def set_vocab(self): # deal with intern-s1-mini @@ -4951,12 +5189,12 @@ class _LinearAttentionVReorderBase(Qwen3NextModel): yield from super().modify_tensors(data_torch, name, bid) -@ModelBase.register("Qwen3_5ForConditionalGeneration") +@ModelBase.register("Qwen3_5ForConditionalGeneration", "Qwen3_5ForCausalLM") class Qwen3_5TextModel(_LinearAttentionVReorderBase): model_arch = gguf.MODEL_ARCH.QWEN35 -@ModelBase.register("Qwen3_5MoeForConditionalGeneration") +@ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM") class Qwen3_5MoeTextModel(_LinearAttentionVReorderBase): model_arch = gguf.MODEL_ARCH.QWEN35MOE @@ -5010,7 +5248,7 @@ class Phi2Model(TextModel): self.gguf_writer.add_add_bos_token(False) -@ModelBase.register("Phi3ForCausalLM") +@ModelBase.register("Phi3ForCausalLM", "Phi4ForCausalLMV") class Phi3MiniModel(TextModel): model_arch = gguf.MODEL_ARCH.PHI3 @@ -5185,6 +5423,129 @@ class Phi3MiniModel(TextModel): yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32)) yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name.startswith(("model.vision_tower.", "vision_tower.", "model.mm_projector.", "mm_projector.")): + return + + yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("Phi4ForCausalLMV") +class Phi4VisionMmprojModel(MmprojModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + assert self.hparams_vision is not None + + self.vision_total_layers = int(self.find_vparam(self.n_block_keys)) + if self.vision_total_layers < 2: + raise ValueError( + f"Phi-4 vision mmproj conversion requires at least 2 vision layers, got {self.vision_total_layers}" + ) + + # Phi-4 uses SigLIP2 hidden_states[-2], so export one fewer encoder block and + # drop post-layernorm/head weights. This makes the GGUF runtime output match + # the feature map consumed by the patched siglip.cpp Phi-4 projector path. + self.vision_export_layers = self.vision_total_layers - 1 + self.vision_last_layer_idx = self.vision_total_layers - 1 + + for key in self.n_block_keys: + if key in self.hparams_vision: + self.hparams_vision[key] = self.vision_export_layers + break + + self.block_count = self.vision_export_layers + self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count) + + patch_size = self.preprocessor_config.get("patch_size") + if patch_size is None: + raise KeyError("Phi-4 vision mmproj conversion requires patch_size in preprocessor_config.json") + + self.hparams_vision["patch_size"] = patch_size + + pos_emb_name = next( + ( + name for name in self.model_tensors + if name.endswith("vision_model.embeddings.position_embedding.weight") + ), + None, + ) + if pos_emb_name is None: + raise KeyError("Phi-4 vision mmproj conversion could not find position_embedding.weight") + + pos_emb_shape = self.model_tensors[pos_emb_name]().shape + base_grid_tokens = int(pos_emb_shape[0]) + grid_side = math.isqrt(base_grid_tokens) + if grid_side * grid_side != base_grid_tokens: + raise ValueError(f"Unexpected Phi-4 position embedding shape: {tuple(pos_emb_shape)}") + + self.hparams_vision["image_size"] = grid_side * patch_size + + min_num_patches = self.preprocessor_config.get("min_num_patches", self.global_config.get("min_num_patches")) + max_num_patches = self.preprocessor_config.get("max_num_patches", self.global_config.get("max_num_patches")) + if min_num_patches is None or max_num_patches is None: + raise KeyError("Phi-4 vision mmproj conversion requires min_num_patches and max_num_patches") + + self.min_pixels = int(min_num_patches) * patch_size * patch_size + self.max_pixels = int(max_num_patches) * patch_size * patch_size + + def set_gguf_parameters(self): + super().set_gguf_parameters() + assert self.hparams_vision is not None + + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PHI4) + self.gguf_writer.add_vision_min_pixels(self.min_pixels) + self.gguf_writer.add_vision_max_pixels(self.max_pixels) + self.gguf_writer.add_vision_use_gelu(True) + self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name.startswith(("model.vision_tower.vision_tower.", "vision_tower.")): + if ".vision_model.head." in name: + return + + new_name = name.replace("model.vision_tower.vision_tower.", "vision_tower.") + + if ".vision_model.post_layernorm." in new_name: + return + + if bid is not None and bid == self.vision_last_layer_idx: + return + + if new_name.endswith("vision_model.embeddings.patch_embedding.weight"): + assert self.hparams_vision is not None + if data_torch.ndim != 2: + raise ValueError(f"Unexpected Phi-4 patch embedding shape: {tuple(data_torch.shape)}") + + patch_area = self.hparams_vision["patch_size"] ** 2 + in_features = data_torch.shape[1] + if in_features % patch_area != 0: + raise ValueError( + f"Phi-4 patch embedding input dim {in_features} is not divisible by patch area {patch_area}" + ) + + num_channels = in_features // patch_area + patch_size = self.hparams_vision["patch_size"] + data_torch = data_torch.view(data_torch.shape[0], patch_size, patch_size, num_channels) + data_torch = data_torch.permute(0, 3, 1, 2) + + yield from super().modify_tensors(data_torch, new_name, bid) + return + + if name.startswith(("model.mm_projector.", "mm_projector.")): + local_name = name + local_name = local_name.replace("model.mm_projector.", "") + local_name = local_name.replace("mm_projector.", "") + + if not (local_name.startswith("0.") or local_name.startswith("2.")): + return + + suffix = ".bias" if local_name.endswith(".bias") else ".weight" + mm_idx = int(local_name.split(".", maxsplit=1)[0]) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_idx, suffix=suffix), data_torch) + return + + return + @ModelBase.register("PhiMoEForCausalLM") class PhiMoeModel(Phi3MiniModel): @@ -5513,7 +5874,7 @@ class KimiLinearModel(TextModel): # Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv linear_attn_config = self.hparams["linear_attn_config"] # n_head == 0 for KDA layers, n_head > 0 for MLA layers - # full_attention_layers list will be used to distingush layer type + # full_attention_layers list will be used to distinguish layer type _num_kv_heads = list() _full_attn_layers = linear_attn_config["full_attn_layers"] for il in range(self.hparams["num_hidden_layers"]): @@ -6264,6 +6625,32 @@ class NeoBert(BertModel): yield from super().modify_tensors(data_torch, name, bid) +@ModelBase.register("EuroBertModel", "JinaEmbeddingsV5Model") +class EuroBertModel(TextModel): + model_arch = gguf.MODEL_ARCH.EUROBERT + + def set_vocab(self): + self.gguf_writer.add_add_bos_token(False) + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + # EuroBert is bidirectional (encoder) + self.gguf_writer.add_causal_attention(False) + + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + self._try_set_pooling_type() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # Strip "model." prefix from tensor names + if name.startswith("model."): + name = name[6:] + + yield from super().modify_tensors(data_torch, name, bid) + + @ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification") class XLMRobertaModel(BertModel): model_arch = gguf.MODEL_ARCH.BERT @@ -6588,7 +6975,7 @@ class Gemma3VisionModel(MmprojModel): super().set_gguf_parameters() hparams = self.hparams self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GEMMA3) - # default values below are taken from HF tranformers code + # default values below are taken from HF transformers code self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6)) self.gguf_writer.add_vision_use_gelu(True) # calculate proj_scale_factor (used by tinygemma3 test model) @@ -7180,7 +7567,7 @@ class Rwkv7Model(TextModel): if bid == 0 and "time_mix_a" in new_name: # dummy v0/v1/v2 on first layer - # easist way to make llama happy + # easiest way to make llama happy yield (new_name.replace("time_mix_a", "time_mix_v"), data_torch) yield (new_name, data_torch) @@ -9284,7 +9671,9 @@ class ChatGLMModel(TextModel): def set_gguf_parameters(self): n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + assert n_embed is not None n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + assert n_head is not None n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head)) self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) self.gguf_writer.add_embedding_length(n_embed) @@ -9679,7 +10068,7 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel): # NOTE: Explicitly include hparam prefix prefix for d_model to # disambiguate with top-level head_dim # NOTE 2: If needed for future models, this can be isolated in a method - # to separate the prefix setting and teh keys used + # to separate the prefix setting and the keys used self.d_model = self.find_hparam([f"{self.hparam_prefixes[0]}_head_dim", "hidden_size", "d_model"]) self.n_group = self.find_hparam(["n_groups", "num_groups"]) self.d_inner = self.find_hparam(["expand", "num_heads"]) * self.d_model @@ -9810,23 +10199,38 @@ class NemotronHModel(GraniteHybridModel): # M: Mamba2, *: Attention, -: MLP # MoE: # M: Mamba2, *: Attention, E: Expert - hybrid_override_pattern = self.hparams["hybrid_override_pattern"] - self._ssm_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "M"] - self._mlp_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == ("E" if self.is_moe else "-")] + pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type") + if pattern is None: + self._ssm_layers = [] + self._mlp_layers = [] + elif isinstance(pattern, str): + self._ssm_layers = [i for i, val in enumerate(pattern) if val == "M"] + self._mlp_layers = [i for i, val in enumerate(pattern) if val == ("E" if self.is_moe else "-")] + else: + self._ssm_layers = [i for i, val in enumerate(pattern) if val == "mamba"] + self._mlp_layers = [i for i, val in enumerate(pattern) if val == "moe"] def get_attn_layers(self): - hybrid_override_pattern = self.hparams["hybrid_override_pattern"] - assert len(hybrid_override_pattern) == self.block_count, "Mismatch between hybrid override and num_hidden_layers!" - return [i for i, val in enumerate(hybrid_override_pattern) if val == "*"] + pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type") + if pattern is None: + return [] + assert len(pattern) == self.block_count, f"Mismatch between pattern ({len(pattern)}) and block_count ({self.block_count})!" + if isinstance(pattern, str): + return [i for i, val in enumerate(pattern) if val == "*"] + + return [i for i, val in enumerate(pattern) if val == "attention"] def set_gguf_parameters(self): super().set_gguf_parameters() - self.gguf_writer.add_key_length(self.head_dim) - self.gguf_writer.add_value_length(self.head_dim) + head_dim = self.head_dim + if head_dim is None: + raise ValueError("Could not find the attention head dim in config") + self.gguf_writer.add_key_length(head_dim) + self.gguf_writer.add_value_length(head_dim) # Set feed_forward_length - # NOTE: This will trigger an override warning. This is preferrable to + # NOTE: This will trigger an override warning. This is preferable to # duplicating all the parent logic if not self.is_moe: n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"]) @@ -9851,6 +10255,9 @@ class NemotronHModel(GraniteHybridModel): if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None: self.gguf_writer.add_expert_used_count(n_experts_used) + if (latent_size := self.hparams.get("moe_latent_size")) is not None: + self.gguf_writer.add_moe_latent_size(latent_size) + def set_vocab(self): super().set_vocab() @@ -9870,6 +10277,13 @@ class NemotronHModel(GraniteHybridModel): name = name[len("language_model."):] if self.is_moe and bid is not None: + # Skip Multi-Token Prediction (MTP) tensors. These are used for + # for speculative decoding but we don't include them in this model + # conversion. See https://github.com/ggml-org/llama.cpp/pull/18886 + if name.startswith("mtp."): + logger.info(f"gguf: Skipping MTP (Speculative) layer: {name}") + return + if name.endswith("mixer.gate.e_score_correction_bias"): new_name = name.replace("e_score_correction_bias", "e_score_correction.bias") yield from ModelBase.modify_tensors(self, data_torch, new_name, bid) @@ -12057,6 +12471,11 @@ def parse_args() -> argparse.Namespace: "Default these modules are not included.") ) + parser.add_argument( + "--fuse-gate-up-exps", action="store_true", + help="Fuse gate_exps and up_exps tensors into a single gate_up_exps tensor for MoE models.", + ) + args = parser.parse_args() if not args.print_supported_models and args.model is None: parser.error("the following arguments are required: model") @@ -12195,7 +12614,8 @@ def main() -> None: small_first_shard=args.no_tensor_first_split, remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template, sentence_transformers_dense_modules=args.sentence_transformers_dense_modules, - target_model_dir=Path(args.target_model_dir) if args.target_model_dir else None + target_model_dir=Path(args.target_model_dir) if args.target_model_dir else None, + fuse_gate_up_exps=args.fuse_gate_up_exps ) if args.vocab_only: diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 53a73759ec..b31ddcca77 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -107,6 +107,7 @@ models = [ {"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! {"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, + {"name": "jina-v5-nano", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano", }, {"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", }, {"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", }, {"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", }, diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py index b0adde8a8b..871ce82422 100755 --- a/convert_lora_to_gguf.py +++ b/convert_lora_to_gguf.py @@ -128,6 +128,12 @@ class LoraTorchTensor: assert dim is None return self.shape + def contiguous(self) -> LoraTorchTensor: + return LoraTorchTensor( + self._lora_A.contiguous(), + self._lora_B.contiguous(), + ) + def reshape(self, *shape: int | tuple[int, ...]) -> LoraTorchTensor: if isinstance(shape[0], tuple): new_shape: tuple[int, ...] = shape[0] diff --git a/docs/autoparser.md b/docs/autoparser.md new file mode 100644 index 0000000000..686b2c249b --- /dev/null +++ b/docs/autoparser.md @@ -0,0 +1,525 @@ +# Auto-Parser Architecture + +The auto-parser automatically analyzes chat templates to determine how to parse model outputs, including content, reasoning, and tool calls. + +## Overview + +The unified auto-parser uses a pure differential, compositional approach (inspired by the `git diff` algorithm) to analyze chat templates: + +**Core Philosophy**: + +- **Minimize Hardcoded Patterns**: All markers extracted through template comparison (the only heuristic is JSON detection to distinguish `JSON_NATIVE` from tag-based formats) +- **Compositional Architecture**: Separate analyzer structs for reasoning, content, and tools — each responsible for its own analysis and parser construction + +**Analysis + Parser Building in Two Steps**: + +1. `autoparser::autoparser tmpl_analysis(tmpl)` — runs all differential comparisons and populates the analysis structs +2. `autoparser::peg_generator::generate_parser(tmpl, params, tmpl_analysis)` — uses the analysis to build a PEG parser and optional GBNF grammar + +## Data Structures + +All structs are defined in [common/chat-auto-parser.h](common/chat-auto-parser.h). + +### Top-Level: `autoparser` (main analyzer and generator) + +[common/chat-auto-parser.h:367-388](common/chat-auto-parser.h#L367-L388) — top-level analysis result aggregating `jinja_caps`, `reasoning`, `content`, and `tools` sub-analyses, plus `preserved_tokens` (union of all non-empty markers). + +### `analyze_reasoning` + +[common/chat-auto-parser.h:254-274](common/chat-auto-parser.h#L254-L274) — reasoning analysis result: `mode` enum, `start` marker (e.g. ``), and `end` marker (e.g. ``). + +### `analyze_content` + +[common/chat-auto-parser.h:280-295](common/chat-auto-parser.h#L280-L295) — content analysis result: `mode` enum, `start`/`end` markers, and `requires_nonnull_content` flag. + +### `analyze_tools` and its sub-structs + +- [common/chat-auto-parser.h:176-194](common/chat-auto-parser.h#L176-L194) — `tool_format_analysis`: `mode` enum, `section_start/end`, `per_call_start/end`, JSON field names (`function_field`, `name_field`, `args_field`, `id_field`, `gen_id_field`), and format flags (`fun_name_is_key`, `tools_array_wrapped`, `uses_python_dicts`) +- [common/chat-auto-parser.h:196-200](common/chat-auto-parser.h#L196-L200) — `tool_function_analysis`: `name_prefix`, `name_suffix`, `close` markers around function names +- [common/chat-auto-parser.h:202-210](common/chat-auto-parser.h#L202-L210) — `tool_arguments_analysis`: `start/end` container markers, `name_prefix/suffix`, `value_prefix/suffix`, `separator` +- [common/chat-auto-parser.h:212-217](common/chat-auto-parser.h#L212-L217) — `tool_id_analysis`: `pos` enum, `prefix`/`suffix` markers around call ID values +- [common/chat-auto-parser.h:301-361](common/chat-auto-parser.h#L301-L361) — `analyze_tools`: aggregates the four sub-structs above + +### Enums + +**`reasoning_mode`**: How the template handles reasoning/thinking blocks. + +| Value | Description | +|-----------------|-----------------------------------------------------------------------------------| +| `NONE` | No reasoning markers detected | +| `TAG_BASED` | Standard tag-based: `...` | +| `DELIMITER` | Delimiter-based: reasoning ends at a delimiter (e.g., `[BEGIN FINAL RESPONSE]`) | +| `FORCED_OPEN` | Template ends with open reasoning tag when `enable_thinking=true` | +| `FORCED_CLOSED` | `enable_thinking=false` emits both tags; `enable_thinking=true` emits only start | +| `TOOLS_ONLY` | Reasoning only appears in tool call responses, not plain content | + +**`content_mode`**: How the template wraps assistant content. + +| Value | Description | +|--------------------------|----------------------------------------------------------------| +| `PLAIN` | No content markers | +| `ALWAYS_WRAPPED` | Content always wrapped: `...` | +| `WRAPPED_WITH_REASONING` | Content wrapped only when reasoning is present | + +**`tool_format`**: Classification of tool call structure. + +| Value | Description | +|------------------|------------------------------------------------------------------| +| `NONE` | No tool support detected | +| `JSON_NATIVE` | Pure JSON: `{"name": "X", "arguments": {...}}` | +| `TAG_WITH_JSON` | Tag-based with JSON args: `{...}` | +| `TAG_WITH_TAGGED`| Tag-based with tagged args: `value` | + +**`call_id_position`**: Where call IDs appear in tag-based formats. + +| Value | Description | +|--------------------------|----------------------------------------------| +| `NONE` | No call ID support detected | +| `PRE_FUNC_NAME` | Before function name | +| `BETWEEN_FUNC_AND_ARGS` | Between function name and arguments | +| `POST_ARGS` | After arguments | + +## Tool Calling Formats + +### JSON_NATIVE + +**Structure**: The entire tool call (function name, arguments, values) is in JSON format. Optional enclosing tags around the section. + +**Detection**: Function name appears inside a JSON structure (quotes preceded by `{` or `:`). + +**Examples**: + +Standard OpenAI-style: + +```json + +{"name": "get_weather", "arguments": {"location": "Paris", "unit": "celsius"}} + +``` + +Mistral Nemo with array wrapper: + +```json +[TOOL_CALLS] +[{"name": "calculate", "arguments": {"expr": "2+2"}}] +``` + +Function name as JSON key (Apertus style): + +```json +{"get_weather": {"location": "Paris"}} +``` + +--- + +### TAG_WITH_JSON + +**Structure**: Function name is outside JSON, in tag attributes or XML-style tags. Arguments are a JSON object. + +**Detection**: Function name not in JSON, but argument names appear in JSON context. + +**Examples**: + +Functionary v3.1: + +```xml +{"location": "Paris", "unit": "celsius"} +``` + +MiniMax: + +```xml + +calculate +{"expr": "2+2"} + +``` + +--- + +### TAG_WITH_TAGGED + +**Structure**: Both function name and argument names are in XML-style tags. String values are unquoted; non-string values are JSON-formatted. + +**Detection**: Neither function name nor argument names appear in a JSON context. + +**Examples**: + +Qwen/Hermes XML format: + +```xml + +Paris +celsius + +``` + +Mixed types: + +```xml + +2+2 +2 +{"round": true} + +``` + +String values (`Paris`, `celsius`, `2+2`) are unquoted; `options` (object type) is JSON-formatted. + +--- + +## Analysis Flow + +```text +autoparser::autoparser(tmpl) + | + |-- Phase 1: analyze_reasoning(tmpl, jinja_caps.supports_tool_calls) + | |-- R1: compare_reasoning_presence() — with/without reasoning_content field + | |-- R2: compare_thinking_enabled() — enable_thinking=false vs true + | '-- R3: compare_reasoning_scope() — reasoning+content vs reasoning+tools + | (only if supports_tool_calls) + | + |-- Phase 2: analyze_content(tmpl, reasoning) + | '-- C1: compares content-only vs tools output and content-only vs reasoning output + | + |-- Phase 3: analyze_tools(tmpl, jinja_caps, reasoning) + | (skipped entirely if !jinja_caps.supports_tool_calls) + | | + | |-- T1: analyze_tool_calls() — no tools vs with tools; classifies format + | | |-- JSON path → analyze_tool_call_format_json_native() + | | '-- tag path → analyze_tool_call_format_non_json() + | | + | (if format != NONE and format != JSON_NATIVE:) + | | + | |-- T2: check_per_call_markers() — 1 call vs 2 calls; moves section→per-call if needed + | | (only if supports_parallel_tool_calls) + | | + | |-- T3: extract_function_markers() — func_alpha vs func_beta; extracts name prefix/suffix/close + | | + | |-- T4: analyze_arguments() — (TAG_WITH_TAGGED only) + | | |-- A1: extract_argument_name_markers() — arg_name_A vs arg_name_B + | | '-- A2: extract_argument_value_markers() — value "XXXX" vs "YYYY" + | | + | |-- T5: extract_argument_separator() — 1 arg vs 2 args; finds separator between args + | | + | |-- T6: extract_args_markers() — 0 args vs 1 arg; finds args container markers + | | + | '-- T7: extract_call_id_markers() — call_id "call00001" vs "call99999" + | + '-- collect_preserved_tokens() — union of all non-empty markers + | + '-- apply workarounds() — post-hoc patches for edge-case templates + | + v +autoparser (analysis result) + | + v +autoparser::peg_generator::generate_parser(tmpl, inputs, analysis) + |-- analysis.build_parser(inputs) — builds PEG parser arena + | |-- reasoning.build_parser(ctx) — reasoning parser (mode-dependent) + | |-- content.build_parser(ctx) — content parser (mode-dependent) + | '-- tools.build_parser(ctx) — tool parser (dispatches by tool_format) + | |-- build_tool_parser_json_native() + | |-- build_tool_parser_tag_json() + | '-- build_tool_parser_tag_tagged() + | + |-- Build GBNF grammar (if tools present and trigger_marker non-empty) + '-- Set grammar_triggers from section_start or per_call_start + | + v +common_chat_params (prompt, parser, grammar, triggers, preserved_tokens) +``` + +## Entry Point + +The auto-parser is invoked in [common/chat.cpp:1280-1310](common/chat.cpp#L1280-L1310) in `common_chat_templates_apply_jinja`. A few specialized templates are handled first (Ministral/Magistral Large 3, GPT-OSS with `<|channel|>`, Functionary v3.2 with `>>>all`), then the auto-parser handles everything else via `autoparser::autoparser` + `peg_generator::generate_parser`. + +## Algorithm Details + +### Core Mechanism: Differential Comparison + +All analysis phases use the same factorized comparison function declared in [common/chat-auto-parser-helpers.h:68](common/chat-auto-parser-helpers.h#L68): + +```cpp +compare_variants(tmpl, params_A, params_modifier) +``` + +This creates variant B by applying a modifier lambda to a copy of `params_A`, renders both through the template, and computes a `diff_split` ([common/chat-auto-parser.h:28-37](common/chat-auto-parser.h#L28-L37)): + +- `prefix` — common prefix between A and B +- `suffix` — common suffix between A and B +- `left` — unique to variant A +- `right` — unique to variant B + +The diff is computed via `calculate_diff_split()`, which finds the longest-common-prefix and longest-common-suffix, then iteratively moves incomplete `<...>` or `[...]` markers from the prefix/suffix into left/right until stable (tag boundary fixing). + +Text is segmentized into markers and non-marker fragments using `segmentize_markers()`, which splits on `<...>` and `[...]` boundaries. + +### Phase 1: Reasoning Analysis + +**R1 — `compare_reasoning_presence()`**: Compares assistant message with vs without a `reasoning_content` field. + +- Searches `diff.right` (output with reasoning) for the reasoning content needle +- Uses PEG parsers to find surrounding markers: + - If both pre/post markers found in `diff.right` → `TAG_BASED` (both tags visible in diff = no forced close) + - If both found but post marker only in the full output B → `FORCED_CLOSED` + - If only post marker found → `DELIMITER` +- Sets `reasoning.start` and `reasoning.end` + +**R2 — `compare_thinking_enabled()`**: Compares `enable_thinking=false` vs `true` with a generation prompt. + +- Detects `FORCED_OPEN`: `enable_thinking=true` adds a non-empty marker at the end of the prompt (where model will start generating) — sets `reasoning.start`, mode = `FORCED_OPEN` +- Detects `FORCED_CLOSED`: `enable_thinking=false` produces both start+end markers; `enable_thinking=true` produces only start marker +- Handles the reverse case: if both start and end are still empty, looks for a single-segment diff on each side to extract both markers + +**R3 — `compare_reasoning_scope()`**: Compares assistant message with reasoning+text-content vs reasoning+tool-calls. + +- Only runs if `jinja_caps.supports_tool_calls` +- Detects `TOOLS_ONLY`: reasoning content present in B (with tools) but not in A (with text content) +- Extracts reasoning markers from the tool call output using PEG parsers + +### Phase 2: Content Analysis + +**C1**: Two comparisons in the `analyze_content` constructor: + +- Comparison 1: content-only output vs tool-call output → `diff_tools` +- Comparison 2: content-only output vs reasoning+empty-content output → `diff_reasoning` + +Classification logic: + +- `PLAIN`: `diff_tools.left` equals the response string (content is the entire diff, no wrapper) +- `ALWAYS_WRAPPED`: markers found surrounding the content text in `pure_content` → extracts `start`/`end` + +### Phase 3: Tool Call Analysis + +**T1 — `analyze_tool_calls()`**: Compares no-tools vs with-tools output. + +- Extracts the tool call section as `diff.right` +- Calls `analyze_tool_call_format()` which first strips reasoning markers from the haystack, then: + - Calls `in_json_haystack()` for both function name and argument name needles + - `in_json_haystack()` uses a PEG parser to check whether the needle appears in a JSON context (preceded by `{` or `:` with surrounding quotes) + - If function name is in JSON → `JSON_NATIVE` → `analyze_tool_call_format_json_native()` + - If function name not in JSON, arg name is in JSON → `TAG_WITH_JSON` + - If neither in JSON → `TAG_WITH_TAGGED` + - `analyze_tool_call_format_json_native()`: parses the JSON object, matches field values to needles to populate `name_field`, `args_field`, `id_field`, `gen_id_field`; detects `tools_array_wrapped`; extracts `section_start`/`section_end` + - `analyze_tool_call_format_non_json()`: uses PEG parsers on the haystack to find up to two opening markers (section + per-call) then up to two closing markers + +**T2 — `check_per_call_markers()`**: Compares 1 call vs 2 calls. + +- Computes a secondary diff of the second call portion vs the common suffix +- If the second call content starts with `section_start` → the section marker is actually per-call → moves `section_start/end` to `per_call_start/end` and clears the section markers + +**T3 — `extract_function_markers()`**: Compares function name `FUN_FIRST` vs `FUN_SECOND` (two different named functions). + +- Finds where the function name appears in `diff.left` +- Extracts `function.name_prefix` from the common prefix up to the function marker, and `function.name_suffix` from after the name up to the next marker +- Extends `name_suffix` into `diff.suffix` (to the first marker for TAG_WITH_TAGGED; to the first `{` or `[` for TAG_WITH_JSON) +- Extracts `function.close` from after the last argument value up to the per-call/section end marker + +**T4 — `analyze_arguments()`** (TAG_WITH_TAGGED only): + +- **A1 `extract_argument_name_markers()`**: Compares `arg_name_A` vs `arg_name_B` (two different argument names). + - Finds shared surrounding structure → `arguments.name_prefix`, `arguments.name_suffix` +- **A2 `extract_argument_value_markers()`**: Compares argument value `"XXXX"` vs `"YYYY"` (same arg, different value). + - Finds markers surrounding the value → `arguments.value_prefix`, `arguments.value_suffix` + +**T5 — `extract_argument_separator()`**: Compares 1 argument vs 2 arguments (same function). + +- Uses `until_common_prefix(diff.right, ARG_FIRST, ARG_SECOND)` to find what separates the two argument blocks + +**T6 — `extract_args_markers()`**: Compares 0 arguments vs 1 argument. + +- Uses `until_common_prefix()` and `after_common_suffix()` with the empty and single-arg JSON strings as anchors to find container markers (`arguments.start`, `arguments.end`) + +**T7 — `extract_call_id_markers()`**: Compares call IDs `"call00001"` vs `"call99999"`. + +- Determines whether function name appears in `diff.prefix` or `diff.suffix` to classify position: + - Function name in prefix only → `BETWEEN_FUNC_AND_ARGS` or `POST_ARGS` (further distinguished by where `{` appears) + - Function name in suffix only → `PRE_FUNC_NAME` +- Extracts `call_id.prefix` and `call_id.suffix` markers around the call ID value +- Clears `per_call_end` if it incorrectly incorporated the call ID suffix + +### Workarounds + +A workaround array in `common/chat-diff-analyzer.cpp` applies post-hoc patches after analysis. Each workaround is a lambda that inspects the template source and overrides analysis results. Current workarounds: + +1. **Old Qwen/DeepSeek thinking templates** — source contains `content.split('')`: sets `reasoning.mode = FORCED_OPEN` with ``/`` markers if no reasoning was detected +2. **Granite 3.3** — source contains specific "Write your thoughts" text: forces `TAG_BASED` reasoning with ``/`` and `WRAPPED_WITH_REASONING` content with ``/`` +3. **Cohere Command R+** — source contains `<|CHATBOT_TOKEN|>`: sets `ALWAYS_WRAPPED` content mode if no content start is already set +4. **Functionary 3.1** — source contains `set has_code_interpreter`: forces `PLAIN` content, specific `per_call_start/end`, clears preserved tokens to only keep Functionary-specific markers +5. **DeepSeek-R1-Distill-Qwen** — source contains `tool▁calls▁begin` markers: overrides tool section/per-call markers with the correct Unicode block characters + +### Parser Building + +Each analyzer struct (`analyze_reasoning`, `analyze_content`, `analyze_tools`) implements `build_parser(parser_build_context&)`. They share a `parser_build_context` that carries the PEG builder, inference inputs, the pre-built reasoning parser, and a pointer to the content analyzer. + +#### Reasoning Parser (`analyze_reasoning::build_parser`) + +| Mode | Parser | +|-----------------------------------|---------------------------------------------------------------------| +| Not extracting reasoning | `eps()` | +| `FORCED_OPEN` or `FORCED_CLOSED` | `reasoning(until(end)) + end` — opening tag was in the prompt | +| `TAG_BASED` or `TOOLS_ONLY` | `optional(start + reasoning(until(end)) + end)` | +| `DELIMITER` | `optional(reasoning(until(end)) + end)` — no start marker | + +#### Content Parser (`analyze_content::build_parser`) + +| Condition | Parser | +|----------------------------------------|---------------------------------------------------------------------------------| +| `json_schema` present | `reasoning + space() + content(schema(json(), "response-format", ...)) + end()` | +| Tools present | Dispatches to `analyze_tools::build_parser()` | +| `ALWAYS_WRAPPED` with reasoning | `reasoning + start + content(until(end)) + end + end()` | +| `ALWAYS_WRAPPED` without reasoning | `content(until(start)) + start + content(until(end)) + end + end()` | +| Default (PLAIN) | `reasoning + content(rest()) + end()` | + +#### Tool Parsers (`analyze_tools::build_parser`) + +Dispatches by `format.mode`: + +**`build_tool_parser_json_native()`**: Calls `p.standard_json_tools()` which internally dispatches to: + +- `build_json_tools_function_is_key()` — function name is the JSON key: `{"get_weather": {...}}` +- `build_json_tools_nested_keys()` — nested: `{"function": {"name": "X", "arguments": {...}}}` +- `build_json_tools_flat_keys()` — flat: `{"name": "X", "arguments": {...}}` + +Handles content wrappers, array wrapping (`tools_array_wrapped`), parallel calls, and `parameter_order`. + +**`build_tool_parser_tag_json()`**: For each tool function: + +```text +tool_open(name_prefix + tool_name(literal(name)) + name_suffix) + + call_id_section + + tool_args(schema(json(), tool_schema)) + [+ function.close if non-empty] +``` + +Wrapped in per-call markers (with optional parallel call repetition) then optionally in section markers. + +**`build_tool_parser_tag_tagged()`**: For each tool function, builds one parser per argument: + +- String types: `tool_arg_string_value(schema(until(value_suffix), ...))` +- JSON types: `tool_arg_json_value(schema(json(), ...))` +- Required args are plain; optional args wrapped in `optional()` +- Arguments joined with `space()` between consecutive parsers + +For closing: uses `function.close` if present; otherwise uses `peek(per_call_end)` to avoid premature close during partial streaming; falls back to `tool_close(space())` to trigger mapper callbacks. + +All three tool parsers return: + +```text +reasoning + optional(content(until(trigger_marker))) + tool_calls + end() +``` + +### Python Dict Format + +When `format.uses_python_dicts` is true (detected when single-quoted strings appear in JSON argument context), `build_parser()` pre-registers a `json-string` rule that accepts both single-quoted and double-quoted strings. This is done before any `p.json()` call so all JSON parsing inherits the flexible rule. + +## Mapper + +`common_chat_peg_mapper` maps PEG parse results (AST nodes) into `common_chat_msg` structures. Key design: + +- **Buffered arguments**: Before `tool_name` is known, argument text goes to `args_buffer`; once the name is set, the buffer is flushed to `current_tool->arguments` +- **`args_target()`**: Returns a reference to whichever destination is currently active (buffer or tool args), eliminating branching +- **`closing_quote_pending`**: Tracks whether a closing `"` needs to be appended when a string argument value is finalized (for schema-declared string types in tagged format) +- **Quote normalization**: Python-style quotes (`'key': 'value'`) are converted to JSON (`"key": "value"`) +- **Brace auto-closing**: At tool close, unclosed `{` braces are closed automatically + +## Files + +| File | Purpose | +|-------------------------------------------|----------------------------------------------------------------------| +| `common/chat-auto-parser.h` | All analysis structs, enums, `autoparser`, `peg_generator`, `templates_params` | +| `common/chat-auto-parser-generator.cpp` | Parser generator: `generate_parser()` and `build_parser()` methods | +| `common/chat-diff-analyzer.cpp` | Differential analysis implementation and workarounds | +| `common/chat-auto-parser-helpers.h/cpp` | `calculate_diff_split()`, `segmentize_markers()`, | +| | `compare_variants()`, string helpers | +| `common/chat-peg-parser.h/cpp` | `common_chat_peg_builder`, `common_chat_peg_mapper`, and helpers | +| `common/chat.cpp` | Entry point: `common_chat_templates_apply_jinja()` | +| `tools/parser/debug-template-parser.cpp` | Debug tool for template analysis | +| `tools/parser/template-analysis.cpp` | Template analysis tool | + +## Testing & Debugging + +### Debug Tools + +**Template Debugger**: `tools/parser/debug-template-parser.cpp` + +- Usage: `./bin/llama-debug-template-parser path/to/template.jinja` +- Shows detected format, markers, generated parser, and GBNF grammar + +**Template Analysis**: `tools/parser/template-analysis.cpp` + +- Usage: `./bin/llama-template-analysis path/to/template.jinja` + +**Debug Logging**: Enable with `LLAMA_LOG_VERBOSITY=2` + +- Shows detailed analysis steps, pattern extraction results, and generated parser structure + +**PEG Test Builder**: Fluent API for creating test cases — see [tests/test-chat.cpp:947-1043](tests/test-chat.cpp#L947-L1043). Example usage: + +```cpp +auto tst = peg_tester("models/templates/Template.jinja"); +tst.test("input text") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({tool_json}) + .parallel_tool_calls(true) + .enable_thinking(true) + .expect(expected_message) + .run(); +``` + +### Tested Templates + +The following templates have active tests in `tests/test-chat.cpp`: + +| Template | Format | Notes | +| -------- | ------ | ----- | +| Ministral-3-14B-Reasoning | Reasoning | `[THINK]...[/THINK]` tags (specialized handler) | +| NVIDIA-Nemotron-3-Nano-30B | TAG_WITH_TAGGED | Reasoning + tools | +| CohereForAI Command-R7B | JSON_NATIVE | `<\|START_THINKING\|>`/`<\|START_RESPONSE\|>` markers | +| Google Gemma 2 2B | Content only | No tool support | +| Qwen-QwQ-32B | Reasoning | Forced-open thinking | +| NousResearch Hermes 2 Pro | JSON_NATIVE | `` wrapper | +| IBM Granite 3.3 | JSON_NATIVE | `` + `` | +| ByteDance Seed-OSS | TAG_WITH_TAGGED | Custom `` and `` tags | +| Qwen3-Coder | TAG_WITH_TAGGED | XML-style tool format | +| DeepSeek V3.1 | JSON_NATIVE | Forced thinking mode | +| GLM-4.6 | TAG_WITH_TAGGED | `name\n......` format | +| GLM-4.7-Flash | TAG_WITH_TAGGED | Updated GLM format | +| Kimi-K2-Thinking | JSON_NATIVE | Reasoning + JSON tools | +| Apertus-8B-Instruct | JSON_NATIVE | Function name as JSON key | +| MiniMax-M2 | TAG_WITH_JSON | XML invoke with JSON args | +| NVIDIA-Nemotron-Nano-v2 | JSON_NATIVE | `` wrapper (nested) | +| CohereForAI Command-R Plus | JSON_NATIVE | Markdown code block format | +| Mistral-Nemo-Instruct-2407 | JSON_NATIVE | `[TOOL_CALLS]` wrapper with ID field | +| Functionary v3.1 | TAG_WITH_JSON | `` format | +| Functionary v3.2 | Specialized | `>>>` recipient delimiter (dedicated handler) | +| Fireworks Firefunction v2 | TAG_WITH_JSON | Fireworks tool format | +| DeepSeek R1 Distill (Llama/Qwen) | Reasoning | Forced-open thinking | +| llama-cpp-deepseek-r1 | Reasoning | Forced-open thinking | +| Kimi-K2 / Kimi-K2-Instruct | JSON_NATIVE | JSON tools with special markers | +| Llama 3.1/3.2/3.3 | JSON_NATIVE | Standard Llama tool format | +| OpenAI GPT-OSS | Specialized | Channel-based (dedicated handler) | +| Apriel 1.5 | JSON_NATIVE | `` wrapper with JSON array | +| Apriel 1.6 Thinker | Reasoning | Implicit reasoning start | +| Mistral Small 3.2 | JSON_NATIVE | `[TOOL_CALLS]func[ARGS]{...}` with call ID | +| Devstral | JSON_NATIVE | `[TOOL_CALLS]func[ARGS]{...}` without call ID | +| StepFun 3.5 Flash | TAG_WITH_TAGGED | `` format | + +## Adding Support for New Templates + +To support a new template format: + +1. **If it follows standard patterns** — The auto-parser should detect it automatically. Run `llama-debug-template-parser` to verify markers are correctly extracted. +2. **If differential analysis extracts incorrect markers** — Add a workaround lambda to the `workarounds` vector in `common/chat-diff-analyzer.cpp`. Inspect the template source for a unique identifying substring. +3. **If it needs fundamentally different handling** — Add a dedicated handler function in `chat.cpp` before the auto-parser block (as done for GPT-OSS, Functionary v3.2, and Ministral). + +## Edge Cases and Quirks + +1. **Forced Thinking**: When `enable_thinking=true` and the model prompt ends with an open reasoning tag (e.g., ``), the parser enters forced thinking mode and immediately expects reasoning content without waiting for a start marker. +2. **Per-Call vs Per-Section Markers**: Some templates wrap each tool call individually (`per_call_start/end`); others wrap the entire section (`section_start/end`). T2 (`check_per_call_markers()`) disambiguates by checking if the second call in a two-call output starts with the section marker. +3. **Python Dict Format**: The Seed template family uses single-quoted JSON (`'key': 'value'`). The `uses_python_dicts` flag causes the PEG builder to register a flexible `json-string` rule accepting both quote styles before any JSON rules are built. +4. **Tag Boundary Fixing**: `calculate_diff_split()` iteratively adjusts prefix/suffix boundaries to avoid splitting `` or `[marker]` tokens, ensuring clean extraction. +5. **Call ID Side Effects**: When a call ID is detected, `per_call_end` may have been incorrectly set to include the call ID suffix. T7 clears `per_call_end` in this case. +6. **Tool Analysis Gating**: `analyze_tools` is only constructed (and all tool analysis phases run) when `jinja_caps.supports_tool_calls` is true. Within tool analysis, `check_per_call_markers()` (T2) only runs if `jinja_caps.supports_parallel_tool_calls`. +7. **`analyze_arguments()` Gating**: Within tool analysis, A1 and A2 (argument name/value marker extraction) only run for `TAG_WITH_TAGGED` format. `extract_argument_separator()` and `extract_args_markers()` run for all non-`JSON_NATIVE` formats. diff --git a/docs/backend/CANN.md b/docs/backend/CANN.md index 23b6a62763..51adaaf95f 100755 --- a/docs/backend/CANN.md +++ b/docs/backend/CANN.md @@ -20,7 +20,7 @@ **Llama.cpp + CANN** -The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the ability of AscendC and ACLNN which are intergrated to CANN Toolkit and kernels to using Ascend NPU directly. +The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the ability of AscendC and ACLNN which are integrated to CANN Toolkit and kernels to using Ascend NPU directly. ## News @@ -210,7 +210,7 @@ docker run --name llamacpp --device /dev/davinci0 --device /dev/davinci_manager # and install driver. sudo sh Ascend-hdk-910b-npu-firmware_x.x.x.x.X.run --full ``` - If the following messaage appers, firmware is installed successfully. + If the following message appears, firmware is installed successfully. ```sh Firmware package installed successfully! ``` diff --git a/docs/backend/OPENVINO.md b/docs/backend/OPENVINO.md new file mode 100644 index 0000000000..2c57dc1a59 --- /dev/null +++ b/docs/backend/OPENVINO.md @@ -0,0 +1,343 @@ +# OpenVINO Backend for llama.cpp +[OpenVINO](https://docs.openvino.ai/) is an open-source toolkit for optimizing and deploying high-performance AI inference, specifically designed for Intel hardware, including CPUs, GPUs, and NPUs, in the cloud, on-premises, and on the edge. +This document describes the [OpenVINO backend for llama.cpp](../../src/ggml-openvino), which enables hardware-accelerated inference on **Intel® CPUs, GPUs, and NPUs** while remaining compatible with the existing **GGUF model ecosystem**. The backend translates GGML compute graphs into OpenVINO graphs and leverages graph compilation, kernel fusion, and device-specific optimizations to improve inference performance on supported Intel hardware. + +The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a translation layer for core GGML operations. The OpenVINO backend replaces the standard GGML graph execution path with Intel's OpenVINO inference engine. This approach allows the same GGUF model file to run on Intel CPUs, Intel GPUs (integrated and discrete), and Intel NPUs without changes to the model or the rest of the llama.cpp stack. When a `ggml_cgraph` is dispatched to OpenVINO backend, it: + +- Walks the GGML graph and identifies inputs, outputs, weights, and KV cache tensors. +- Translates the GGML operations into an `ov::Model` using OpenVINO's frontend API. +- Compiles and caches the model for the target device. +- Binds GGML tensor memory to OpenVINO inference tensors and runs inference. + +## Supported Devices + +OpenVINO backend supports the following hardware: + +- Intel CPUs +- Intel GPUs (integrated and discrete) +- Intel NPUs + +Although OpenVINO supports a wide range of [Intel hardware](https://docs.openvino.ai/2026/about-openvino/release-notes-openvino/system-requirements.html), the llama.cpp OpenVINO backend has been validated specifically on AI PCs such as the Intel® Core™ Ultra Series 1 and Series 2. + +## Supported Model Precisions + +- `FP16` +- `BF16` (on Intel Xeon) +- `Q8_0` +- `Q4_0` +- `Q4_1` +- `Q4_K` +- `Q4_K_M` +- `Q5_K` (converted to Q8_0_C at runtime) +- `Q6_K` (converted to Q8_0_C at runtime) + +> [!NOTE] +> Accuracy validation and performance optimizations for quantized models are a work in progress. + +## Quantization Support Details + +### CPU and GPU + +- **`Q4_0`, `Q4_1`, `Q4_K_M`, `Q6_K` models are supported** +- `Q5_K` and `Q6_K` tensors are converted to `Q8_0_C` + +### NPU + +- **Primary supported quantization scheme is `Q4_0`** +- `Q6_K` tensors are requantized to `Q4_0_128` in general. For embedding weights, `Q6_K` tensors are requantized to `Q8_0_C` except for the token embedding matrix which is dequantized to fp16 + +### Additional Notes + +- Both `Q4_0` and `Q4_1` models use `Q6_K` for the token embedding tensor and the final matmul weight tensor (often the same tensor) +- `Q4_0` models may produce some `Q4_1` tensors if an imatrix is provided during quantization using `llama-quantize` +- `Q4_K_M` models may include both `Q6_K` and `Q5_K` tensors (observed in Phi-3) + +## Validated Models + +The following models have been validated for functionality on Intel® Core™ Ultra Series 1 and Series 2: + +- [Llama-3.2-1B-Instruct-GGUF](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/) +- [Llama-3.1-8B-Instruct](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF) +- [microsoft/Phi-3-mini-4k-instruct-gguf](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) +- [Qwen/Qwen2.5-1.5B-Instruct-GGUF](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF) +- [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B-GGUF) +- [openbmb/MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-S-1B-sft-gguf) +- [tencent/Hunyuan-7B-Instruct](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF) +- [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF) +- [bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF) + +## Build Instructions + +### Prerequisites + +- Linux or Windows system with Intel hardware (CPU, GPU, or NPU) +- **For Intel GPU or NPU Usage**: Install the appropriate hardware drivers for your Intel GPU or NPU. For detailed instructions, see: [Additional Configurations for Hardware Acceleration](https://docs.openvino.ai/2025/get-started/install-openvino/configurations.html). + +- **Linux:** + - Git, CMake, and Ninja software tools are needed for building. + ```bash + sudo apt-get update + sudo apt-get install -y build-essential libcurl4-openssl-dev libtbb12 cmake ninja-build python3-pip curl wget tar + ``` + - OpenCL + ```bash + sudo apt install ocl-icd-opencl-dev opencl-headers opencl-clhpp-headers intel-opencl-icd + ``` + +- **Windows:** + - Download and install [Microsoft Visual Studio 2022 Build Tools](https://aka.ms/vs/17/release/vs_BuildTools.exe). During installation, select the **"Desktop development with C++"** workload. + + - Install required tools: + ```powershell + # Windows PowerShell + winget install Git.Git + winget install GNU.Wget + winget install Ninja-build.Ninja + ``` + + - Install **OpenCL** using **vcpkg**: + ```powershell + # Windows PowerShell + cd C:\ + git clone https://github.com/microsoft/vcpkg + cd vcpkg + .\bootstrap-vcpkg.bat + .\vcpkg install opencl + # Optional but recommended: Integrate vcpkg with Visual Studio / CMake: + .\vcpkg integrate install + ``` + +### 1. Install OpenVINO Runtime + +- Follow the guide to install OpenVINO Runtime from an archive file: [Linux](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-archive-linux.html) | [Windows](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-archive-windows.html) + +- **Linux:** + +
+ 📦 Click to expand OpenVINO installation from an archive file on Ubuntu +
+ + ```bash + wget https://raw.githubusercontent.com/ravi9/misc-scripts/main/openvino/ov-archive-install/install-openvino-from-archive.sh + chmod +x install-openvino-from-archive.sh + ./install-openvino-from-archive.sh + ``` + + Verify OpenVINO is initialized properly: + ```bash + echo $OpenVINO_DIR + ``` +
+ + +### 2. Build llama.cpp with OpenVINO Backend + +Clone the OpenVINO-enabled llama.cpp fork and build it: + +```bash +git clone https://github.com/ggml-org/llama.cpp +cd llama.cpp +``` + +- **Linux:** + ```bash + source /opt/intel/openvino/setupvars.sh + cmake -B build/ReleaseOV -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_OPENVINO=ON + cmake --build build/ReleaseOV --parallel + ``` + +- **Windows:** + ```cmd + # x64 Native Tools Command Prompt for VS 2022 + "C:\Program Files (x86)\Intel\openvino_2026.0\setupvars.bat" + cmake -B build\ReleaseOV -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_OPENVINO=ON -DLLAMA_CURL=OFF -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake + cmake --build build\ReleaseOV --parallel + ``` +> [!NOTE] +> Use `x64 Native Tools Command Prompt` for Windows build. After building, you could use either `cmd` or `PowerShell` to run the OpenVINO backend. + +### 3. Download Sample Model + +Download models for testing: + +```bash +# Linux +mkdir -p ~/models/ +wget https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf \ + -O ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf + +# Windows PowerShell +mkdir C:\models +Invoke-WebRequest -Uri https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf -OutFile C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf + +# Windows Command Line +mkdir C:\models +curl -L https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf -o C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf +``` + +### 4. Run Inference with OpenVINO Backend + +When using the OpenVINO backend, the first inference token may have slightly higher latency due to on-the-fly conversion to the OpenVINO graph. Subsequent tokens and runs will be faster. + +```bash +# If device is unset or unavailable, defaults to CPU. +# If the system has multiple GPUs, use GPU.0 or GPU.1 to explicitly target a specific GPU. + +# Linux +export GGML_OPENVINO_DEVICE=GPU +# To run llama-simple: +./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -n 50 "The story of AI is " +# To run in chat mode: +./build/ReleaseOV/bin/llama-cli -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf + +# Windows Command Line +set GGML_OPENVINO_DEVICE=GPU +# Windows PowerShell +$env:GGML_OPENVINO_DEVICE = "GPU" + +# To run llama-simple +build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -n 50 "The story of AI is " +# To run in chat mode: +build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" + +``` +> [!NOTE] +> On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html) for more details. + + +### Docker Build + +You can build and run llama.cpp with OpenVINO backend using Docker. + +```bash +# Build the base runtime image with compiled shared libraries and minimal dependencies. +docker build -t llama-openvino:base -f .devops/openvino.Dockerfile . + +# Build the complete image with all binaries, Python tools, gguf-py library, and model conversion utilities. +docker build --target=full -t llama-openvino:full -f .devops/openvino.Dockerfile . + +# Build a minimal CLI-only image containing just the llama-cli executable. +docker build --target=light -t llama-openvino:light -f .devops/openvino.Dockerfile . + +# Builds a server-only image with llama-server executable, health check endpoint, and REST API support. +docker build --target=server -t llama-openvino:server -f .devops/openvino.Dockerfile . + +# If you are behind a proxy: +docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --target=light -t llama-openvino:light -f .devops/openvino.Dockerfile . +``` + +Run llama.cpp with OpenVINO backend Docker container. +Save sample models in `~/models` as [shown above](#3-download-sample-model). It will be mounted to the container in the examples below. + +```bash +# Run Docker container +docker run --rm -it -v ~/models:/models llama-openvino:light --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf + +# With Intel GPU access (iGPU or dGPU) +docker run --rm -it -v ~/models:/models \ +--device=/dev/dri --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \ +llama-openvino:light --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf + +# With Intel NPU access +docker run --rm -it --env GGML_OPENVINO_DEVICE=NPU -v ~/models:/models \ +--device=/dev/accel --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \ +llama-openvino:light --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf +``` + +Run Llama.cpp Server with OpenVINO Backend: +```bash +# Run the Server Docker container +docker run --rm -it -p 8080:8080 -v ~/models:/models llama-openvino:server --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf + +# In a NEW terminal, test the server with curl + +# If you are behind a proxy, make sure to set NO_PROXY to avoid proxy for localhost +export NO_PROXY=localhost,127.0.0.1 + +# Test health endpoint +curl -f http://localhost:8080/health + +# Test with a simple prompt +curl -X POST "http://localhost:8080/v1/chat/completions" -H "Content-Type: application/json" \ + -d '{"messages":[{"role":"user","content":"Write a poem about OpenVINO"}],"max_tokens":100}' | jq . +``` + +## Runtime Configuration + +The OpenVINO backend can be configured using the following environment variables at runtime to control device selection, caching, debugging, and profiling behavior. + +### Configuration Options + +| Variable | Default | Description | +|-----------------------------------|------------|-------------------------------------------------------------------------------------------------------------| +| `GGML_OPENVINO_DEVICE` | `CPU` | Specify the target device (CPU, GPU, NPU). On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html). When set to **NPU**, static compilation mode is enabled for optimal performance. | +| `GGML_OPENVINO_CACHE_DIR` | `not set` | Directory for OpenVINO model caching (recommended: `/tmp/ov_cache`). Enables model caching when set. **Not supported on NPU devices.** | +| `GGML_OPENVINO_PREFILL_CHUNK_SIZE`| `256` | Token chunk size for **NPU** prefill. | +| `GGML_OPENVINO_STATEFUL_EXECUTION`| `0` | Enable stateful KV cache on for better performance. Recommended on CPU, GPU. | +| `GGML_OPENVINO_PROFILING` | `0` | Enable execution-time profiling. | +| `GGML_OPENVINO_DUMP_CGRAPH` | `0` | Dump the GGML compute graph to `cgraph_ov.txt`. | +| `GGML_OPENVINO_DUMP_IR` | `0` | Serialize OpenVINO IR files with timestamps. | +| `GGML_OPENVINO_DEBUG_INPUT` | `0` | Enable input debugging and print input tensor info. | +| `GGML_OPENVINO_DEBUG_OUTPUT` | `0` | Enable output debugging and print output tensor info. | +| `GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS` | `0` | Print tensor address map once. | + +> [!NOTE] +>`GGML_OPENVINO_STATEFUL_EXECUTION` is an **Experimental** feature to allow stateful execution for managing the KV cache internally inside the OpenVINO model, improving performance on CPUs and GPUs. Stateful execution is not effective on NPUs, and not all models currently support this feature. This feature is experimental and has been validated only with the llama-simple, llama-cli, llama-bench, and llama-run applications and is recommended to enable for the best performance. Other applications, such as llama-server and llama-perplexity, are not yet supported. + +### Example Usage + +#### GPU Inference with Profiling + +```bash +# If the system has multiple GPUs, use GPU.0 or GPU.1 to explicitly target a specific GPU. + +# Linux +export GGML_OPENVINO_CACHE_DIR=/tmp/ov_cache +export GGML_OPENVINO_PROFILING=1 +export GGML_OPENVINO_DEVICE=GPU + +./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -n 50 "The story of AI is " + +# Windows Command Line +set GGML_OPENVINO_CACHE_DIR=C:\tmp\ov_cache +set GGML_OPENVINO_PROFILING=1 +set GGML_OPENVINO_DEVICE=GPU + +# Windows PowerShell +$env:GGML_OPENVINO_CACHE_DIR = "C:\tmp\ov_cache" +$env:GGML_OPENVINO_PROFILING = "1" +$env:GGML_OPENVINO_DEVICE = "GPU" + +build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -n 50 "The story of AI is " + +``` + +#### llama-bench + +```bash +# -fa 1 is required when running llama-bench with the OpenVINO backend. +GGML_OPENVINO_DEVICE=GPU ./llama-bench -fa 1 +``` + +### NPU Notes + +- Model caching is not yet supported +- Does not support llama-server -np > 1 (multiple parallel sequences) +- Only supports llama-perplexity -b 512 or smaller + +## Llama.cpp Tools + +The following tools work with the OpenVINO backend on CPU, GPU, NPU: +- llama-simple +- llama-run +- llama-cli +- llama-server +- llama-bench +- llama-perplexity + +## Work in Progress + +- Performance and memory optimizations +- Accuracy validation +- Broader quantization coverage +- Support for additional model architectures diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index 07c68be5cb..7fb78eae37 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -9,6 +9,7 @@ - [Linux](#linux) - [Windows](#windows) - [Environment Variable](#environment-variable) +- [Design Rule](#design-rule) - [Known Issue](#known-issues) - [Q&A](#qa) - [TODO](#todo) @@ -41,6 +42,9 @@ The following releases are verified and recommended: ## News +- 2026.03 + - Support Flash-Attention: less memory usage, performance impact depends on LLM. + - 2026.02 - Remove support for Nvidia & AMD GPU, because the oneAPI plugin for Nvidia & AMD GPU is unavailable: download/installation channels are out of work. User can't build up the software for Nvidia & AMD GPU. @@ -378,17 +382,27 @@ use 1 SYCL GPUs: [0] with Max compute units:512 ## Windows -### I. Setup Environment - -1. Install GPU driver +### Install GPU driver Intel GPU drivers instructions guide and download page can be found here: [Get Intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). -2. Install Visual Studio +### Option 1: download the binary package directly + +Download the binary package for Windows from: https://github.com/ggml-org/llama.cpp/releases. + +Extract the package to local folder, run the llama tools directly. Refer to [Run the inference](#iii-run-the-inference-1). + +Note, the package includes the SYCL running time and all depended dll files, no need to install oneAPI package and activte them. + +### Option 2: build locally from the source code. + +#### I. Setup environment + +1. Install Visual Studio If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/). -3. Install Intel® oneAPI Base toolkit +2. Install Intel® oneAPI Base toolkit SYCL backend depends on: - Intel® oneAPI DPC++/C++ compiler/running-time. @@ -439,25 +453,25 @@ Output (example): [ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] ``` -4. Install build tools +3. Install build tools a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer) b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/) -### II. Build llama.cpp +#### II. Build llama.cpp You could download the release package for Windows directly, which including binary files and depended oneAPI dll files. Choose one of following methods to build from source code. -#### 1. Script +##### Option 1: Script ```sh .\examples\sycl\win-build-sycl.bat ``` -#### 2. CMake +##### Option 2: CMake On the oneAPI command line window, step into the llama.cpp main directory and run the following: @@ -486,7 +500,7 @@ cmake --preset x64-windows-sycl-debug cmake --build build-x64-windows-sycl-debug -j --target llama-completion ``` -#### 3. Visual Studio +##### Option 3: Visual Studio You have two options to use Visual Studio to build llama.cpp: - As CMake Project using CMake presets. @@ -496,7 +510,7 @@ You have two options to use Visual Studio to build llama.cpp: All following commands are executed in PowerShell. -##### - Open as a CMake Project +###### - Open as a CMake Project You can use Visual Studio to open the `llama.cpp` folder directly as a CMake project. Before compiling, select one of the SYCL CMake presets: @@ -511,7 +525,7 @@ You can use Visual Studio to open the `llama.cpp` folder directly as a CMake pro cmake --build build --config Release -j --target llama-completion ``` -##### - Generating a Visual Studio Solution +###### - Generating a Visual Studio Solution You can use Visual Studio solution to build and work on llama.cpp on Windows. You need to convert the CMake Project into a `.sln` file. @@ -599,7 +613,7 @@ found 2 SYCL devices: ``` -#### Choose level-zero devices +##### Choose level-zero devices |Chosen Device ID|Setting| |-|-| @@ -607,7 +621,7 @@ found 2 SYCL devices: |1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`| |0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"` or `set ONEAPI_DEVICE_SELECTOR="level_zero:*"`| -#### Execute +##### Execute Choose one of following methods to run. @@ -665,7 +679,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512 ## Environment Variable -#### Build +### Build | Name | Value | Function | |--------------------|---------------------------------------|---------------------------------------------| @@ -680,23 +694,50 @@ use 1 SYCL GPUs: [0] with Max compute units:512 1. FP32 or FP16 have different performance impact to LLM. Recommended to test them for better prompt processing performance on your models. You need to rebuild the code after change `GGML_SYCL_F16=OFF/ON`. -#### Runtime +### Runtime | Name | Value | Function | |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------| | GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG | +| GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.| | GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for intel devices older than Gen 10) | | GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. | | GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. | | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer | | UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.| +## Design Rule +- Open to all contributors. + +- All code change should be useful to user: + - Fix bug. + - Add new function. + - Improve the performance/usage. + - Make code be easy to maintain. + - ... + +- Don't accept the codes of following cases: + - Break legacy function. + - Reduce the performance of legacy case in default. + - Not completed work/the functionality cannot be demonstrated. + +- Encourage to use environment variable to control features to be opened/closed. + - User can evaluate the feature without rebuild the code. + - Recommend the best features to user by setting them be opened as default. + +- Design the code based on the published official releases of oneAPI packages: compiler, library, driver, OS kernel. + +- Developers need to maintain the code they submit. ## Known Issues - `Split-mode:[row]` is not supported. +- Missed the AOT (Ahead-of-Time) in buiding. + - Good: build quickly, smaller size of binary file. + - Bad: The startup is slow (JIT) in first time, but subsequent performance is unaffected. + ## Q&A - Error: `error while loading shared libraries: libsycl.so: cannot open shared object file: No such file or directory`. @@ -708,7 +749,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512 - Remove **build** folder or try a clean-build. -- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux. +- I can **not** see `[ext_oneapi_level_zero:gpu]` after installing the GPU driver on Linux. Please double-check with `sudo sycl-ls`. @@ -746,7 +787,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512 ``` ### **GitHub contribution**: -Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay. +Please add the `[SYCL]` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay. ## TODO diff --git a/docs/backend/VirtGPU.md b/docs/backend/VirtGPU.md index c81468da13..72f376dea7 100644 --- a/docs/backend/VirtGPU.md +++ b/docs/backend/VirtGPU.md @@ -152,7 +152,9 @@ Commands and data are serialized using a custom binary protocol with: - **VM-specific**: Only works in virtual machines with virtio-gpu support - **Host dependency**: Requires properly configured host-side backend - **Latency**: Small overhead from VM escaping for each operation - +- **Shared-memory size**: with the `libkrun` hypervisor, the RAM + VRAM + addressable memory is limited to 64 GB. So the maximum GPU memory + will be `64GB - RAM`, regardless of the hardware VRAM size. * This work is pending upstream changes in the VirglRenderer project. diff --git a/docs/backend/VirtGPU/development.md b/docs/backend/VirtGPU/development.md index ca2e47772a..c5c85dfb49 100644 --- a/docs/backend/VirtGPU/development.md +++ b/docs/backend/VirtGPU/development.md @@ -55,7 +55,8 @@ LLAMA_MAC_BUILD=$PWD/build/ggml-virtgpu-backend cmake -S . -B $LLAMA_MAC_BUILD \ -DGGML_NATIVE=OFF \ -DLLAMA_CURL=ON \ - -DGGML_REMOTINGBACKEND=ONLY \ + -DGGML_VIRTGPU=ON \ + -DGGML_VIRTGPU_BACKEND=ONLY \ -DGGML_METAL=ON TARGETS="ggml-metal" @@ -71,6 +72,7 @@ cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $EXTRA_TARGETS ```bash # Build virglrenderer with APIR support mkdir virglrenderer +cd virglrenderer git clone https://gitlab.freedesktop.org/kpouget/virglrenderer -b main-macos src cd src @@ -95,7 +97,7 @@ mkdir llama.cpp git clone https://github.com/ggml-org/llama.cpp.git src cd src -LLAMA_LINUX_BUILD=$PWD//build-virtgpu +LLAMA_LINUX_BUILD=$PWD/build-virtgpu cmake -S . -B $LLAMA_LINUX_BUILD \ -DGGML_VIRTGPU=ON diff --git a/docs/backend/ZenDNN.md b/docs/backend/ZenDNN.md index b57fd97b69..3b1f8242ff 100644 --- a/docs/backend/ZenDNN.md +++ b/docs/backend/ZenDNN.md @@ -22,7 +22,7 @@ **Llama.cpp + ZenDNN** -The llama.cpp ZenDNN backend leverages AMD's optimized matrix multiplication primitives to accelerate inference on AMD CPUs. It utilizes ZenDNN's **LowOHA (Low Overhead Hardware Accelerated)** MatMul operator for efficient GEMM operations with minimal execution overhead, built-in weight caching, and direct access to backend libraries (AOCL BLIS, LibXSMM, OneDNN). +The llama.cpp ZenDNN backend leverages AMD's optimized matrix multiplication primitives to accelerate inference on AMD CPUs. It utilizes ZenDNN's **LowOHA (Low Overhead Hardware Accelerated)** MatMul operator for efficient GEMM operations with minimal execution overhead, built-in weight caching, and direct access to backend libraries (AOCL DLP, LibXSMM, OneDNN). For more information about ZenDNN, visit: https://www.amd.com/en/developer/zendnn.html @@ -32,7 +32,7 @@ For more information about ZenDNN, visit: https://www.amd.com/en/developer/zendn |:-------:|:-------:|:----------------------------------------------:| | Linux | Support | Ubuntu 20.04, 22.04, 24.04 | -For the latest list of supported operating systems, see the [ZenDNN Supported OS](https://github.com/amd/ZenDNN/blob/zendnnl/README.md#15-supported-os). +For the latest list of supported operating systems, see the [ZenDNN Supported OS](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/README.md#15-supported-os). ## Hardware @@ -44,9 +44,9 @@ ZenDNN is optimized for AMD EPYC™ processors and AMD Ryzen™ processors based | CPU Family | Status | Notes | |:-----------------------------:|:-------:|:----------------------------------:| -| AMD EPYC™ 9005 Series (Turin)| Support | 5th Gen - Zen 5 architecture | -| AMD EPYC™ 9004 Series (Genoa)| Support | 4th Gen - Zen 4 architecture | -| AMD EPYC™ 7003 Series (Milan)| Support | 3rd Gen - Zen 3 architecture | +| AMD EPYC™ 9005 Series (Turin) | Support | 5th Gen - Zen 5 architecture | +| AMD EPYC™ 9004 Series (Genoa) | Support | 4th Gen - Zen 4 architecture | +| AMD EPYC™ 7003 Series (Milan) | Support | 3rd Gen - Zen 3 architecture | | AMD Ryzen™ AI MAX (Strix Halo)| Support | High-performance mobile processors | *Notes:* @@ -61,7 +61,7 @@ The ZenDNN backend currently accelerates **matrix multiplication (MUL_MAT)** ope | Operation | Status | Notes | |:-------------|:-------:|:----------------------------------------------:| -| MUL_MAT | ✓ | Accelerated via ZenDNN LowOHA MatMul | +| MUL_MAT | Support | Accelerated via ZenDNN LowOHA MatMul | *Note:* Since only MUL_MAT is accelerated, models will benefit most from ZenDNN when matrix multiplications dominate the computational workload (which is typical for transformer-based LLMs). @@ -104,7 +104,6 @@ If you want to build ZenDNN yourself or use a specific version: # Clone ZenDNN repository git clone https://github.com/amd/ZenDNN.git cd ZenDNN -git checkout zendnnl # Build and install (requires CMake >= 3.25) mkdir build && cd build @@ -114,7 +113,7 @@ cmake --build . --target all Default installation path: `ZenDNN/build/install` -**For detailed build instructions**, refer to the [ZenDNN README](https://github.com/amd/ZenDNN/blob/zendnnl/README.md). +**For detailed build instructions**, refer to the [ZenDNN README](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/README.md). **Step 2: Build llama.cpp with custom ZenDNN path** @@ -146,8 +145,7 @@ Run llama.cpp server with ZenDNN acceleration: ```sh # Set optimal configuration -export OMP_NUM_THREADS=64 # Adjust to your CPU core count -export ZENDNNL_MATMUL_ALGO=2 # Blocked AOCL BLIS for best performance +export ZENDNNL_MATMUL_ALGO=1 # Blocked AOCL DLP algo for best performance # Start server ./build/bin/llama-server \ @@ -160,62 +158,26 @@ export ZENDNNL_MATMUL_ALGO=2 # Blocked AOCL BLIS for best performance Access the server at `http://localhost:8080`. **Performance tips**: -- Set `OMP_NUM_THREADS` to match your physical core count -- Use `ZENDNNL_MATMUL_ALGO=2` for optimal performance +- Use `ZENDNNL_MATMUL_ALGO=1` for optimal performance - For NUMA systems: `numactl --cpunodebind=0 --membind=0 ./build/bin/llama-server ...` ## Environment Variable -### Build Time +For environment variables related to ZenDNN, refer to the [ZenDNN Environment Variables Documentation](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/docs/runtime_env.md). -| Name | Value | Function | -|--------------------|---------------------------------------|---------------------------------------------| -| GGML_ZENDNN | ON/OFF | Enable ZenDNN backend support | -| ZENDNN_ROOT | Path to ZenDNN installation | Set ZenDNN installation directory | -| GGML_OPENMP | ON/OFF (recommended: ON) | Enable OpenMP for multi-threading | +### Performance Optimization -### Runtime - -| Name | Value | Function | -|-------------------------|--------------------------|-------------------------------------------------------------------| -| OMP_NUM_THREADS | Number (e.g., 64) | Set number of OpenMP threads (recommended: physical core count) | -| ZENDNNL_MATMUL_ALGO | 0-5 | Select MatMul backend algorithm (see Performance Optimization) | -| ZENDNNL_PROFILE_LOG_LEVEL | 0-4 | Profiling log level (0=disabled, 4=verbose) | -| ZENDNNL_ENABLE_PROFILER | 0 or 1 | Enable detailed profiling (1=enabled) | -| ZENDNNL_API_LOG_LEVEL | 0-4 | API log level (0=disabled, 4=verbose) | - -**Example**: +ZenDNN's LowOHA MatMul supports multiple backend algorithms. For **best performance**, use the **Blocked AOCL DLP** algorithm: ```sh -export OMP_NUM_THREADS=64 -export ZENDNNL_MATMUL_ALGO=2 # Use Blocked AOCL BLIS for best performance -./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Test" -n 100 +export ZENDNNL_MATMUL_ALGO=1 # Blocked AOCL DLP algo (recommended) ``` -## Performance Optimization - -### MatMul Algorithm Selection - -ZenDNN's LowOHA MatMul supports multiple backend algorithms. For **best performance**, use the **Blocked AOCL BLIS** algorithm: - -```sh -export ZENDNNL_MATMUL_ALGO=2 # Blocked AOCL BLIS (recommended) -``` - -**Available algorithms**: - -| Value | Algorithm | Description | -|:-----:|:-----------------------|:----------------------------------------------| -| 0 | Dynamic Dispatch | Automatic backend selection (default) | -| 1 | AOCL BLIS | AOCL BLIS backend | -| 2 | AOCL BLIS Blocked | **Blocked AOCL BLIS (recommended)** | -| 3 | OneDNN | OneDNN backend | -| 4 | OneDNN Blocked | Blocked OneDNN | -| 5 | LibXSMM | LibXSMM backend | +For more details on available algorithms, see the [ZenDNN MatMul Algorithm Documentation](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/docs/runtime_env.md#algorithm-details). ### Profiling and Debugging -For detailed profiling and logging options, refer to the [ZenDNN Logging Documentation](https://github.com/amd/ZenDNN/blob/zendnnl/docs/logging.md). +For detailed profiling and logging options, refer to the [ZenDNN Logging Documentation](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/docs/logging.md). ## Known Issues @@ -245,10 +207,9 @@ A: Currently, ZenDNN primarily supports FP32 and BF16 data types. Quantized mode A: Ensure: 1. You're using an AMD EPYC or Ryzen processor (Zen 2 or newer) -2. `OMP_NUM_THREADS` is set appropriately (physical core count) -3. `ZENDNNL_MATMUL_ALGO=2` is set for best performance (Blocked AOCL BLIS) -4. You're using a sufficiently large model (small models may not benefit as much) -5. Enable profiling to verify ZenDNN MatMul is being called +2. `ZENDNNL_MATMUL_ALGO=1` is set for best performance (Blocked AOCL DLP) +3. You're using a sufficiently large model (small models may not benefit as much) +4. Enable profiling to verify ZenDNN MatMul is being called ### **GitHub Contribution**: Please add the **[ZenDNN]** prefix/tag in issues/PRs titles to help the ZenDNN-team check/address them without delay. diff --git a/docs/backend/snapdragon/README.md b/docs/backend/snapdragon/README.md index 2c3f88e91a..0783555ce8 100644 --- a/docs/backend/snapdragon/README.md +++ b/docs/backend/snapdragon/README.md @@ -116,7 +116,7 @@ Llama-3.2-1B-Instruct-Q4_0.gguf: 1 file pushed, 0 skipped. 38.3 MB/s (773025920 ### Windows All artifacts are already installed in the `pkg-snapdragon` folder. -To run, adapt below instructions to use Powershell scrits in `scripts/snapdragon/windows`. +To run, adapt below instructions to use Powershell scripts in `scripts/snapdragon/windows`. ## How to Run diff --git a/docs/backend/snapdragon/windows.md b/docs/backend/snapdragon/windows.md index e9346ccadf..6307e1b69f 100644 --- a/docs/backend/snapdragon/windows.md +++ b/docs/backend/snapdragon/windows.md @@ -144,7 +144,7 @@ Once the build is complete HTP ops libraries will be installed like this -a---- 1/22/2026 6:01 PM 4139 libggml-htp.cat ``` -The .cat file, the signature and proper certicate installation can be verified with +The .cat file, the signature and proper certificate installation can be verified with ``` > signtool.exe verify /v /pa .\pkg-snapdragon\lib\libggml-htp.cat diff --git a/docs/build.md b/docs/build.md index fd447424c7..508245d536 100644 --- a/docs/build.md +++ b/docs/build.md @@ -13,6 +13,21 @@ cd llama.cpp The following sections describe how to build with different backends and options. +* [CPU Build](#cpu-build) +* [BLAS Build](#blas-build) +* [Metal Build](#metal-build) +* [SYCL](#sycl) +* [CUDA](#cuda) +* [MUSA](#musa) +* [HIP](#hip) +* [Vulkan](#vulkan) +* [CANN](#cann) +* [Arm® KleidiAI™](#arm-kleidiai) +* [OpenCL](#opencl) +* [Android](#android-1) +* [OpenVINO](#openvino) +* [Notes about GPU-accelerated backends](#notes-about-gpu-accelerated-backends) + ## CPU Build Build llama.cpp using `CMake`: @@ -108,7 +123,7 @@ Building through oneAPI compilers will make avx_vnni instruction set available f - Using oneAPI docker image: If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above. -Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. +Check [Optimizing and Running LLaMA2 on Intel® CPU](https://builders.intel.com/solutionslibrary/optimizing-and-running-llama2-on-intel-cpu) for more information. ### Other BLAS libraries @@ -254,6 +269,14 @@ The environment variable [`CUDA_SCALE_LAUNCH_QUEUES`](https://docs.nvidia.com/cu Consider setting `CUDA_SCALE_LAUNCH_QUEUES=4x`, which increases the CUDA command buffer to 4 times its default size. This optimization is particularly beneficial for **Multi-GPU setups with pipeline parallelism**, where it significantly improves prompt processing throughput by allowing more operations to be enqueued across GPUs. +#### GGML_CUDA_FORCE_CUBLAS_COMPUTE_32F + +Use `GGML_CUDA_FORCE_CUBLAS_COMPUTE_32F` environment variable to use FP32 compute type on all GPUs in FP16 cuBLAS for preventing possible numerical overflows in exchange for slower prompt processing (small impact on RTX PRO/Datacenter products and significant on GeForce products). + +#### GGML_CUDA_FORCE_CUBLAS_COMPUTE_16F + +Use `GGML_CUDA_FORCE_CUBLAS_COMPUTE_16F` environment variable to force use FP16 compute type (instead of default FP32) in FP16 cuBLAS for V100, CDNA and RDNA4. + ### Unified Memory The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`. @@ -265,7 +288,7 @@ The following compilation options are also available to tweak performance: | Option | Legal values | Default | Description | |-------------------------------|------------------------|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, CDNA and RDNA3+). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. | -| GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models. There may be issues with numerical overflows (except for CDNA and RDNA4) and memory use will be higher. Prompt processing may become faster on recent datacenter GPUs (the custom kernels were tuned primarily for RTX 3000/4000). | +| GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models. There may be issues with numerical overflows (except for V100, CDNA and RDNA4 which use FP32 compute type by default) and memory use will be higher. Prompt processing may become faster on recent datacenter GPUs (the custom kernels were tuned primarily for RTX 3000/4000). | | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | | GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | @@ -595,11 +618,17 @@ You can verify that KleidiAI is being used by running ```bash ./build/bin/llama-cli -m PATH_TO_MODEL -p "What is a car?" ``` -If KleidiAI is enabled, the ouput will contain a line similar to: +If KleidiAI is enabled, the output will contain a line similar to: ``` load_tensors: CPU_KLEIDIAI model buffer size = 3474.00 MiB ``` -KleidiAI's microkernels implement optimized tensor operations using Arm CPU features such as dotprod, int8mm and SME. llama.cpp selects the most efficient kernel based on runtime CPU feature detection. However, on platforms that support SME, you must manually enable SME microkernels by setting the environment variable `GGML_KLEIDIAI_SME=1`. +KleidiAI’s microkernels implement optimized tensor operations using Arm CPU features such as dotprod, int8mm, SVE, and SME. Llama.cpp selects the most efficient kernels at runtime based on detected CPU capabilities. +On CPUs that support SME, SME microkernels are enabled automatically using runtime detection. +The environment variable GGML_KLEIDIAI_SME can be used to control SME behavior: +- Not set: enable SME automatically if supported and detected. +- 0: disable SME. +- > 0: enable SME and assume available SME units (override auto detection). +If SME is not supported by the CPU, SME microkernels are always disabled. Depending on your build target, other higher priority backends may be enabled by default. To ensure the CPU backend is used, you must disable the higher priority backends either at compile time, e.g. -DGGML_METAL=OFF, or during run-time using the command line option `--device none`. @@ -699,7 +728,7 @@ To read documentation for how to build on Android, [click here](./android.md) ## WebGPU [In Progress] -The WebGPU backend relies on [Dawn](https://dawn.googlesource.com/dawn). Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/quickstart-cmake.md) to install Dawn locally so that llama.cpp can find it using CMake. The currrent implementation is up-to-date with Dawn commit `bed1a61`. +The WebGPU backend relies on [Dawn](https://dawn.googlesource.com/dawn). Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/quickstart-cmake.md) to install Dawn locally so that llama.cpp can find it using CMake. The current implementation is up-to-date with Dawn commit `bed1a61`. In the llama.cpp directory, build with CMake: @@ -718,6 +747,14 @@ Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/m To read documentation for how to build on IBM Z & LinuxONE, [click here](./build-s390x.md) +## OpenVINO + +[OpenVINO](https://docs.openvino.ai/) is an open-source toolkit for optimizing and deploying high-performance AI inference, specifically designed for Intel hardware (CPUs, GPUs, and NPUs). + +For build instructions and usage examples, refer to [OPENVINO.md](backend/OPENVINO.md). + + +--- ## Notes about GPU-accelerated backends The GPU may still be used to accelerate some parts of the computation even when using the `-ngl 0` option. You can fully disable GPU acceleration by using `--device none`. diff --git a/docs/development/parsing.md b/docs/development/parsing.md index dbb989bf08..a41057db2b 100644 --- a/docs/development/parsing.md +++ b/docs/development/parsing.md @@ -22,7 +22,7 @@ Below is a contrived example demonstrating how to use the PEG parser to parse output from a model that emits arguments as JSON. ```cpp -auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) { +auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { // Build a choice of all available tools auto tool_choice = p.choice(); for (const auto & tool : tools) { @@ -212,7 +212,7 @@ mapper.from_ast(ctx.ast, result); ### Native -The `common_chat_peg_native_builder` builds a `native` parser suitable for +The `common_chat_peg_builder` builds a `native` parser suitable for models that emit tool arguments as a direct JSON object. - **`reasoning(p)`** - Tag node for `reasoning_content` @@ -225,7 +225,7 @@ models that emit tool arguments as a direct JSON object. - **`tool_args(p)`** - Tag the tool arguments ```cpp -build_chat_peg_native_parser([&](common_chat_peg_native_parser & p) { +build_chat_peg_parser([&](common_chat_peg_builder & p) { auto get_weather_tool = p.tool(p.sequence({ p.tool_open(p.literal("{")), p.json_member("name", "\"" + p.tool_name(p.literal("get_weather")) + "\""), @@ -246,7 +246,7 @@ build_chat_peg_native_parser([&](common_chat_peg_native_parser & p) { ### Constructed -The `common_chat_peg_constructed_builder` builds a `constructed` parser +The `common_chat_peg_builder` builds a `constructed` parser suitable for models that emit tool arguments as separate entities, such as XML tags. @@ -264,7 +264,7 @@ tags. - **`tool_arg_json_value(p)`** - Tag JSON value for the argument ```cpp -build_chat_peg_constructed_parser([&](common_chat_peg_constructed_builder & p) { +build_chat_peg_parser([&](common_chat_peg_builder & p) { auto location_arg = p.tool_arg( p.tool_arg_open(""), p.tool_arg_string_value(p.until("")), diff --git a/docs/multimodal/MobileVLM.md b/docs/multimodal/MobileVLM.md index 3bfab9f3d2..6c17dbf902 100644 --- a/docs/multimodal/MobileVLM.md +++ b/docs/multimodal/MobileVLM.md @@ -281,7 +281,7 @@ llama_print_timings: total time = 5990.25 ms / 202 tokens Just the same as above. -**ouput** +**output** ```sh encode_image_with_clip: image embedding created: 144 tokens @@ -305,7 +305,7 @@ llama_print_timings: total time = 15513.95 ms / 412 tokens ## Run on Intel(R) Core(TM) Ultra7 115H ### operation system Windows11 -### comiple +### compile ```sh make -j32 ``` diff --git a/docs/ops.md b/docs/ops.md index 296c0ba1d4..00bdcd5d2b 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -15,7 +15,7 @@ Legend: | Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN | |-----------|------|------|------|------|------|------|------|------|------|------|------| | ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | | ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | @@ -23,30 +23,31 @@ Legend: | ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | | CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | -| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | +| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | +| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | | CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | | CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | | CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | -| DIAG | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| DIAG | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | -| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | +| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | -| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | | FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | +| GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -54,7 +55,7 @@ Legend: | GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | -| GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | +| GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | | GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -63,7 +64,7 @@ Legend: | IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | L2_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | -| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | +| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | | MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | @@ -75,34 +76,34 @@ Legend: | OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 | | PAD | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | | PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | -| POOL_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | -| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ | +| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ROPE | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | -| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | -| SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | +| SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | | SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | -| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | +| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | -| SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | -| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | +| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -116,5 +117,5 @@ Legend: | TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | -| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | -| XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | +| XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | diff --git a/docs/ops/CPU.csv b/docs/ops/CPU.csv index fef3bbce70..3838502e16 100644 --- a/docs/ops/CPU.csv +++ b/docs/ops/CPU.csv @@ -878,6 +878,54 @@ "CPU","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=0,p1=1","support","1","yes","CPU" "CPU","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=0","support","1","yes","CPU" "CPU","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=0","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=1","support","1","yes","CPU" +"CPU","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=1","support","1","yes","CPU" 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"CPU","TRUNC","type=f16,ne=[10,2,2,2]","support","1","yes","CPU" "CPU","SQR","type=f16,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","SQR","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","SQRT","type=f16,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","SQRT","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","LOG","type=f16,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","LOG","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","SIN","type=f16,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","SIN","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","COS","type=f16,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","COS","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","CPU" +"CPU","CLAMP","type=f16,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","1","yes","CPU" "CPU","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","CPU" +"CPU","LEAKY_RELU","type=f16,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","1","yes","CPU" "CPU","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","CPU" "CPU","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","CPU" @@ -8693,12 +8978,19 @@ "CPU","ROUND","type=f32,ne=[10,2,2,2]","support","1","yes","CPU" "CPU","TRUNC","type=f32,ne=[10,2,2,2]","support","1","yes","CPU" "CPU","SQR","type=f32,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","SQR","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","SQRT","type=f32,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","SQRT","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","LOG","type=f32,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","LOG","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","SIN","type=f32,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","SIN","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","COS","type=f32,ne=[7,1,5,3]","support","1","yes","CPU" +"CPU","COS","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","CPU" +"CPU","CLAMP","type=f32,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","1","yes","CPU" "CPU","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","CPU" +"CPU","LEAKY_RELU","type=f32,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","1","yes","CPU" "CPU","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","CPU" "CPU","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU" "CPU","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","CPU" @@ -8917,6 +9209,11 @@ "CPU","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","1","yes","CPU" "CPU","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","CPU" "CPU","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","CPU" +"CPU","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","CPU" +"CPU","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","CPU" +"CPU","SOFT_MAX","type=f32,ne=[200000,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","CPU" +"CPU","SOFT_MAX","type=f32,ne=[200000,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","CPU" +"CPU","SOFT_MAX","type=f32,ne=[643251,3,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","CPU" "CPU","SOFT_MAX_BACK","type=f32,ne=[16,16,1,1],scale=1.000000,max_bias=0.000000","support","1","yes","CPU" "CPU","SOFT_MAX_BACK","type=f32,ne=[15,15,1,1],scale=1.000000,max_bias=0.000000","support","1","yes","CPU" "CPU","SOFT_MAX_BACK","type=f32,ne=[16,16,2,3],scale=1.000000,max_bias=0.000000","support","1","yes","CPU" @@ -8969,6 +9266,7 @@ "CPU","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" @@ -8978,6 +9276,7 @@ "CPU","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" @@ -8988,11 +9287,13 @@ "CPU","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" @@ -9002,6 +9303,7 @@ "CPU","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" @@ -9012,11 +9314,13 @@ "CPU","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" @@ -9026,6 +9330,7 @@ "CPU","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" @@ -9036,11 +9341,13 @@ "CPU","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" @@ -9050,6 +9357,7 @@ "CPU","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" @@ -9060,6 +9368,7 @@ "CPU","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" @@ -9185,6 +9494,7 @@ "CPU","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" @@ -9194,6 +9504,7 @@ "CPU","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" @@ -9204,11 +9515,13 @@ "CPU","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" @@ -9218,6 +9531,7 @@ "CPU","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" @@ -9228,11 +9542,13 @@ "CPU","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" @@ -9242,6 +9558,7 @@ "CPU","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" @@ -9252,11 +9569,13 @@ "CPU","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" @@ -9266,6 +9585,7 @@ "CPU","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" @@ -9276,6 +9596,7 @@ "CPU","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" +"CPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","CPU" @@ -9398,25 +9719,65 @@ "CPU","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","CPU" "CPU","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","CPU" +"CPU","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11140,11 +10636,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11175,16 +10671,16 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11200,11 +10696,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11235,16 +10731,16 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11320,11 +10816,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11340,11 +10836,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11360,11 +10856,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11380,11 +10876,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11400,11 +10896,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11420,11 +10916,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11440,11 +10936,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11460,11 +10956,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11480,11 +10976,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11515,16 +11011,16 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11540,11 +11036,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11575,16 +11071,16 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11600,11 +11096,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11660,11 +11156,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11695,16 +11191,16 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11720,11 +11216,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11740,11 +11236,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11780,11 +11276,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11800,11 +11296,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11820,11 +11316,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11840,11 +11336,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11860,11 +11356,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11880,11 +11376,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11900,11 +11396,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11920,11 +11416,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11940,11 +11436,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11960,11 +11456,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -11980,11 +11476,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12000,11 +11496,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12020,11 +11516,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12040,11 +11536,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12060,11 +11556,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12080,11 +11576,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12100,11 +11596,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12120,11 +11616,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12140,11 +11636,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12160,11 +11656,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12180,11 +11676,11 @@ "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12200,11 +11696,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12235,16 +11731,16 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12260,11 +11756,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12320,11 +11816,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12340,11 +11836,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12360,11 +11856,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12380,11 +11876,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12435,16 +11931,16 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12460,11 +11956,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12495,16 +11991,16 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12520,11 +12016,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" 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"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12560,11 +12056,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12580,11 +12076,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12600,11 +12096,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12620,11 +12116,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" 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-"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12640,11 +12136,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12660,11 +12156,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12680,11 +12176,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" -"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" +"CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" @@ -12700,11 +12196,11 @@ "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU" "CPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","CPU" 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+"CPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=32,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","CPU" +"CPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","1","yes","CPU" +"CPU","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=1","support","1","yes","CPU" +"CPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","1","yes","CPU" diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index c1622cc6f0..13ac447cc1 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -33,14 +33,14 @@ "SYCL0","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" -"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" -"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" +"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" "SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" -"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" -"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" -"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" -"SYCL0","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" +"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" +"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" +"SYCL0","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" "SYCL0","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" "SYCL0","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" @@ -5057,12 +5057,18 @@ "SYCL0","DUP","type=f16,ne=[10,10,5,1],permute=[1,0,2,3]","support","1","yes","SYCL" "SYCL0","DUP","type=i16,ne=[10,8,3,1],permute=[0,2,1,3]","support","1","yes","SYCL" "SYCL0","DUP","type=i16,ne=[10,8,3,1],permute=[1,2,0,3]","support","1","yes","SYCL" -"SYCL0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1","support","1","yes","SYCL" -"SYCL0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2","support","1","yes","SYCL" -"SYCL0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3","support","1","yes","SYCL" -"SYCL0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=1","support","0","no","SYCL" -"SYCL0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2","support","0","no","SYCL" -"SYCL0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3","support","0","no","SYCL" +"SYCL0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1,inplace=0","support","1","yes","SYCL" +"SYCL0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1,inplace=1","support","1","yes","SYCL" +"SYCL0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2,inplace=0","support","1","yes","SYCL" +"SYCL0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2,inplace=1","support","1","yes","SYCL" +"SYCL0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3,inplace=0","support","1","yes","SYCL" +"SYCL0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3,inplace=1","support","1","yes","SYCL" +"SYCL0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=1,inplace=0","support","0","no","SYCL" +"SYCL0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=1,inplace=1","support","0","no","SYCL" +"SYCL0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2,inplace=0","support","0","no","SYCL" +"SYCL0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2,inplace=1","support","0","no","SYCL" +"SYCL0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3,inplace=0","support","0","no","SYCL" +"SYCL0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3,inplace=1","support","0","no","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","SYCL" @@ -5508,230 +5514,366 @@ "SYCL0","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","0","no","SYCL" "SYCL0","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","0","no","SYCL" "SYCL0","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","0","no","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","ADD","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","SUB","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" -"SYCL0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" +"SYCL0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" 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+"SYCL0","DIV","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","ADD","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","SUB","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" +"SYCL0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","SYCL" "SYCL0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","SYCL" @@ -5744,77 +5886,103 @@ "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000000","support","1","yes","SYCL" -"SYCL0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000000,v=0","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000000,v=1","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000000","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000000","support","1","yes","SYCL" -"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3]","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000000,v=0","support","1","yes","SYCL" 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"SYCL0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000001","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000001","support","1","yes","SYCL" -"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3]","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000001,v=0","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000001,v=1","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","SYCL" "SYCL0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000100","support","1","yes","SYCL" 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"SYCL0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","SYCL" "SYCL0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.100000","support","1","yes","SYCL" -"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3]","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=0","support","1","yes","SYCL" +"SYCL0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=1","support","1","yes","SYCL" "SYCL0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[3,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[66,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[66,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[3,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[6,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[3,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[66,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[66,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[3,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[6,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[3,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[66,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[66,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[67,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[67,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[4,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[67,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[67,1536,4,1],ne_b=[4,1536,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[67,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[67,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[9,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[18,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[9,1024,4,1],ne_b=[9,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[72,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[72,1024,4,1],ne_b=[9,1024,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[9,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[18,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[9,1536,4,1],ne_b=[9,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[72,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[72,1536,4,1],ne_b=[9,1536,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[9,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[18,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[72,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" +"SYCL0","SSM_CONV","type=f32,ne_a=[72,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","SYCL" "SYCL0","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","SYCL" @@ -6698,6 +6866,7 @@ "SYCL0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq3_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=6,n=4096,k=5120,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" @@ -8760,32 +8929,39 @@ "SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","SYCL" "SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","SYCL" "SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","SYCL" -"SYCL0","SQR","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL" -"SYCL0","SQRT","type=f16,ne=[10,3,3,2]","support","1","yes","SYCL" -"SYCL0","LOG","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL" -"SYCL0","SIN","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" -"SYCL0","COS","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" -"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" +"SYCL0","SQR","type=f16,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","SQRT","type=f16,ne=[10,3,3,2]","support","0","no","SYCL" +"SYCL0","LOG","type=f16,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","SIN","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" +"SYCL0","COS","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" +"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","0","no","SYCL" "SYCL0","LEAKY_RELU","type=f16,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL" -"SYCL0","FLOOR","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" "SYCL0","CEIL","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" -"SYCL0","ROUND","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" -"SYCL0","TRUNC","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL" -"SYCL0","SQR","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" -"SYCL0","SQRT","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" -"SYCL0","LOG","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" -"SYCL0","SIN","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" -"SYCL0","COS","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" -"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" +"SYCL0","TRUNC","type=f16,ne=[10,2,2,2]","support","0","no","SYCL" +"SYCL0","SQR","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" +"SYCL0","SQR","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL" +"SYCL0","SQRT","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" +"SYCL0","SQRT","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL" +"SYCL0","LOG","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" +"SYCL0","LOG","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL" +"SYCL0","SIN","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" +"SYCL0","SIN","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL" +"SYCL0","COS","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" +"SYCL0","COS","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL" +"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","SYCL" +"SYCL0","CLAMP","type=f16,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","0","no","SYCL" "SYCL0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL" -"SYCL0","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" -"SYCL0","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" +"SYCL0","LEAKY_RELU","type=f16,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" +"SYCL0","FLOOR","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL" "SYCL0","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" "SYCL0","CEIL","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" -"SYCL0","ROUND","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" -"SYCL0","ROUND","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" -"SYCL0","TRUNC","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" -"SYCL0","TRUNC","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" +"SYCL0","ROUND","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL" +"SYCL0","TRUNC","type=f16,ne=[7,1,5,3]","support","0","no","SYCL" +"SYCL0","TRUNC","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL" "SYCL0","SQR","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","SQRT","type=f32,ne=[10,3,3,2]","support","1","yes","SYCL" "SYCL0","LOG","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" @@ -8798,12 +8974,19 @@ "SYCL0","ROUND","type=f32,ne=[10,2,2,2]","support","1","yes","SYCL" "SYCL0","TRUNC","type=f32,ne=[10,2,2,2]","support","1","yes","SYCL" "SYCL0","SQR","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","SQR","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","SQRT","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","SQRT","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","LOG","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","LOG","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","SIN","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","SIN","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","COS","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","COS","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" +"SYCL0","CLAMP","type=f32,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL" +"SYCL0","LEAKY_RELU","type=f32,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" "SYCL0","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" @@ -9303,254 +9486,294 @@ "SYCL0","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" -"SYCL0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","0","no","SYCL" 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+"SYCL0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" 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+"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","SYCL" "SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" +"SYCL0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","SYCL" "SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","SYCL" "SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","SYCL" "SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","SYCL" @@ -10005,30 +10228,41 @@ "SYCL0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic|align_corners","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic|align_corners","support","0","no","SYCL" "SYCL0","SUM","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" -"SYCL0","SUM_ROWS","type=f32,ne=[10,5,4,3],permute=0,slice=0","support","1","yes","SYCL" "SYCL0","SUM","type=f32,ne=[11,5,6,3],permute=[0,2,1,3]","support","0","no","SYCL" "SYCL0","SUM","type=f32,ne=[11,5,6,3],permute=[0,3,2,1]","support","0","no","SYCL" "SYCL0","SUM","type=f32,ne=[11,5,6,3],permute=[0,1,3,2]","support","0","no","SYCL" +"SYCL0","MEAN","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[33,1,1,1]","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[33,256,1,1]","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[32769,1,1,1]","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[32,1,1,1]","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[32,256,1,1]","support","1","yes","SYCL" +"SYCL0","MEAN","type=f32,ne=[32768,1,1,1]","support","1","yes","SYCL" +"SYCL0","SUM","type=f32,ne=[33,1,1,1]","support","1","yes","SYCL" +"SYCL0","SUM","type=f32,ne=[33,1024,1,1]","support","1","yes","SYCL" +"SYCL0","SUM","type=f32,ne=[33,256,1,1]","support","1","yes","SYCL" +"SYCL0","SUM","type=f32,ne=[33,256,1,1],permute=[1,0,2,3]","support","0","no","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[10,5,4,3],permute=0,slice=0","support","1","yes","SYCL" "SYCL0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=1,slice=0","support","0","no","SYCL" "SYCL0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=0,slice=1","support","0","no","SYCL" "SYCL0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=1,slice=1","support","0","no","SYCL" -"SYCL0","MEAN","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" -"SYCL0","SUM","type=f32,ne=[33,1,1,1]","support","1","yes","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[16,5,6,3],permute=1,slice=0","support","0","no","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[16,5,6,3],permute=0,slice=1","support","0","no","SYCL" +"SYCL0","SUM_ROWS","type=f32,ne=[16,5,6,3],permute=1,slice=1","support","0","no","SYCL" "SYCL0","SUM_ROWS","type=f32,ne=[33,1,1,1],permute=0,slice=0","support","1","yes","SYCL" -"SYCL0","MEAN","type=f32,ne=[33,1,1,1]","support","1","yes","SYCL" -"SYCL0","SUM","type=f32,ne=[33,1024,1,1]","support","1","yes","SYCL" "SYCL0","SUM_ROWS","type=f32,ne=[33,1024,1,1],permute=0,slice=0","support","1","yes","SYCL" -"SYCL0","SUM","type=f32,ne=[33,256,1,1]","support","1","yes","SYCL" -"SYCL0","SUM","type=f32,ne=[33,256,1,1],permute=[1,0,2,3]","support","0","no","SYCL" "SYCL0","SUM_ROWS","type=f32,ne=[33,256,1,1],permute=0,slice=0","support","1","yes","SYCL" -"SYCL0","MEAN","type=f32,ne=[33,256,1,1]","support","1","yes","SYCL" -"SYCL0","MEAN","type=f32,ne=[32769,1,1,1]","support","1","yes","SYCL" "SYCL0","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","SYCL" "SYCL0","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","SYCL" -"SYCL0","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","SYCL" +"SYCL0","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1],stride_dim=-1","support","1","yes","SYCL" +"SYCL0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[256,16,2,3],stride_dim=-1","support","1","yes","SYCL" +"SYCL0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[128,16,2,3],stride_dim=-1","support","1","yes","SYCL" +"SYCL0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[256,16,2,3],stride_dim=1","support","0","no","SYCL" +"SYCL0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[128,16,2,3],stride_dim=2","support","0","no","SYCL" +"SYCL0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[64,16,2,3],stride_dim=3","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","0","no","SYCL" -"SYCL0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","1","yes","SYCL" +"SYCL0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,tfrm=0,circular=0","support","1","yes","SYCL" "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","1","yes","SYCL" "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","SYCL" "SYCL0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","SYCL" @@ -10087,8817 +10321,3287 @@ "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,1],ne_rhs=[32,128,4,1]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[200,64,4,4]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[384,64,4,4]","support","0","no","SYCL" -"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","1","yes","SYCL" -"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","1","yes","SYCL" -"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","0","no","SYCL" -"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","0","no","SYCL" -"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","0","no","SYCL" -"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","0","no","SYCL" -"SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","SYCL" -"SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" 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-"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","SYCL" -"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","SYCL" +"SYCL0","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","SYCL" "SYCL0","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=32,head_size=128,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=1,kda=1","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=16,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=0","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=0","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=1,kda=0","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=16,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=32,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=1","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","1","yes","SYCL" +"SYCL0","GATED_DELTA_NET","type=f32,head_count=4,head_size=16,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","1","yes","SYCL" diff --git a/docs/ops/Vulkan.csv b/docs/ops/Vulkan.csv index d284fcb0f8..e693154968 100644 --- a/docs/ops/Vulkan.csv +++ b/docs/ops/Vulkan.csv @@ -1,16 +1,16 @@ "backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name" "Vulkan0","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" -"Vulkan0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" -"Vulkan0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" +"Vulkan0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" +"Vulkan0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","STEP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","TANH","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","TANH","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" -"Vulkan0","ELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" -"Vulkan0","ELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" +"Vulkan0","ELU","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" +"Vulkan0","ELU","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","RELU","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","RELU","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","SIGMOID","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" @@ -85,16 +85,16 @@ "Vulkan0","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","Vulkan" "Vulkan0","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" -"Vulkan0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" -"Vulkan0","SGN","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" +"Vulkan0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" +"Vulkan0","SGN","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","NEG","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","NEG","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","STEP","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","STEP","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","TANH","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","TANH","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" -"Vulkan0","ELU","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" -"Vulkan0","ELU","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" +"Vulkan0","ELU","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" +"Vulkan0","ELU","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","RELU","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","RELU","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","SIGMOID","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" @@ -394,10 +394,10 @@ "Vulkan0","GET_ROWS","type=iq4_xs,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","Vulkan" "Vulkan0","GET_ROWS","type=iq4_xs,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","Vulkan" "Vulkan0","GET_ROWS","type=iq4_xs,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","Vulkan" -"Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","Vulkan" -"Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","Vulkan" -"Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","Vulkan" -"Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","Vulkan" +"Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","Vulkan" "Vulkan0","GET_ROWS_BACK","type=f32,n=1,m=8,r=2,b=1,v=0","support","0","no","Vulkan" "Vulkan0","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=0","support","0","no","Vulkan" "Vulkan0","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=1","support","0","no","Vulkan" @@ -878,6 +878,54 @@ "Vulkan0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=0,p1=1","support","1","yes","Vulkan" "Vulkan0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=0","support","1","yes","Vulkan" "Vulkan0","POOL_2D","pool_type=max,type_input=f32,ne_input=[10,10,3,1],k0=3,k1=3,s0=2,s1=2,p0=1,p1=1","support","1","yes","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=avg,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=1,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=1,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=1,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=1,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=0","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[10,3,2,1],k0=3,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[11,1,3,2],k0=3,s0=2,p0=1","support","0","no","Vulkan" +"Vulkan0","POOL_1D","pool_type=max,type_input=f32,ne_input=[128,2,1,3],k0=3,s0=2,p0=1","support","0","no","Vulkan" "Vulkan0","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","Vulkan" "Vulkan0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","Vulkan" "Vulkan0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","Vulkan" @@ -965,6 +1013,7 @@ "Vulkan0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,1,2560],ne_kernel=[3,3,1,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","Vulkan" "Vulkan0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","Vulkan" "Vulkan0","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","Vulkan" +"Vulkan0","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","Vulkan" "Vulkan0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","1","yes","Vulkan" "Vulkan0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","1","yes","Vulkan" "Vulkan0","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","1","yes","Vulkan" @@ -4964,6 +5013,7 @@ "Vulkan0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","Vulkan" +"Vulkan0","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","Vulkan" "Vulkan0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","Vulkan" "Vulkan0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","Vulkan" "Vulkan0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","Vulkan" @@ -5007,12 +5057,18 @@ "Vulkan0","DUP","type=f16,ne=[10,10,5,1],permute=[1,0,2,3]","support","1","yes","Vulkan" "Vulkan0","DUP","type=i16,ne=[10,8,3,1],permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","DUP","type=i16,ne=[10,8,3,1],permute=[1,2,0,3]","support","1","yes","Vulkan" -"Vulkan0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1","support","0","no","Vulkan" -"Vulkan0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2","support","0","no","Vulkan" -"Vulkan0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3","support","0","no","Vulkan" -"Vulkan0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=1","support","0","no","Vulkan" -"Vulkan0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2","support","0","no","Vulkan" -"Vulkan0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3","support","0","no","Vulkan" +"Vulkan0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2,inplace=1","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3,inplace=1","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2,inplace=1","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3,inplace=1","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","Vulkan" @@ -5458,230 +5514,366 @@ "Vulkan0","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","Vulkan" "Vulkan0","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","Vulkan" "Vulkan0","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","ADD","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","SUB","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" -"Vulkan0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" 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+"Vulkan0","DIV","type=f32,ne=[1,1,4,320],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[1,1,640,1],nr=[32,32,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","ADD","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","SUB","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" +"Vulkan0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1,perm1=0","support","1","yes","Vulkan" "Vulkan0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" "Vulkan0","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","Vulkan" @@ -5694,45 +5886,103 @@ "Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","0","no","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000000","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000000,v=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000000,v=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000","support","1","yes","Vulkan" +"Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000000","support","0","no","Vulkan" +"Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000000","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000000,v=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000000,v=1","support","1","yes","Vulkan" "Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=0","support","1","yes","Vulkan" "Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","0","no","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000001,v=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000001,v=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000001","support","1","yes","Vulkan" +"Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000001,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000001","support","0","no","Vulkan" +"Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","Vulkan" +"Vulkan0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000001","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000001,v=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000001,v=1","support","1","yes","Vulkan" "Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","Vulkan" "Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","0","no","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000100","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000100,v=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000100,v=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000100","support","1","yes","Vulkan" +"Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000100","support","0","no","Vulkan" +"Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","Vulkan" +"Vulkan0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000100","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000100,v=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000100,v=1","support","1","yes","Vulkan" "Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","Vulkan" "Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","0","no","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.100000","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.100000,v=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.100000,v=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000","support","1","yes","Vulkan" +"Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000","support","0","no","Vulkan" +"Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.100000","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[3,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[66,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[66,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[3,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[6,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[3,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[66,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[66,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[3,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[6,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[3,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[66,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[66,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[67,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[67,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[4,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[67,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[67,1536,4,1],ne_b=[4,1536,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[67,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[67,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[9,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[18,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[9,1024,4,1],ne_b=[9,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[72,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[72,1024,4,1],ne_b=[9,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[9,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[18,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[9,1536,4,1],ne_b=[9,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[72,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[72,1536,4,1],ne_b=[9,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[9,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[18,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[72,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[72,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","Vulkan" "Vulkan0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","1","yes","Vulkan" "Vulkan0","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","1","yes","Vulkan" @@ -6591,7 +6841,36 @@ "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=193,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=67,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=64,n=77,k=77,bs=[12,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=2,n=1,k=3,bs=[128,1024],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=2,n=3,k=4,bs=[128,1024],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=2,n=1,k=3,bs=[131072,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=2,n=1,k=3,bs=[131072,1],nr=[1,1],per=[0,1,2,3],k_v=64,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=576,n=512,k=576,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=2048,k=8192,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=bf16,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q6_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq1_m,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_nl,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq3_s,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=6,n=4096,k=5120,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" @@ -8666,12 +8945,19 @@ "Vulkan0","ROUND","type=f16,ne=[10,2,2,2]","support","1","yes","Vulkan" "Vulkan0","TRUNC","type=f16,ne=[10,2,2,2]","support","1","yes","Vulkan" "Vulkan0","SQR","type=f16,ne=[7,1,5,3]","support","0","no","Vulkan" +"Vulkan0","SQR","type=f16,ne=[1024,1024,1,1]","support","0","no","Vulkan" "Vulkan0","SQRT","type=f16,ne=[7,1,5,3]","support","0","no","Vulkan" +"Vulkan0","SQRT","type=f16,ne=[1024,1024,1,1]","support","0","no","Vulkan" "Vulkan0","LOG","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","LOG","type=f16,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","SIN","type=f16,ne=[7,1,5,3]","support","0","no","Vulkan" +"Vulkan0","SIN","type=f16,ne=[1024,1024,1,1]","support","0","no","Vulkan" "Vulkan0","COS","type=f16,ne=[7,1,5,3]","support","0","no","Vulkan" +"Vulkan0","COS","type=f16,ne=[1024,1024,1,1]","support","0","no","Vulkan" "Vulkan0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","Vulkan" +"Vulkan0","CLAMP","type=f16,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","0","no","Vulkan" "Vulkan0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","0","no","Vulkan" +"Vulkan0","LEAKY_RELU","type=f16,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","0","no","Vulkan" "Vulkan0","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" "Vulkan0","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" @@ -8692,12 +8978,19 @@ "Vulkan0","ROUND","type=f32,ne=[10,2,2,2]","support","1","yes","Vulkan" "Vulkan0","TRUNC","type=f32,ne=[10,2,2,2]","support","1","yes","Vulkan" "Vulkan0","SQR","type=f32,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","SQR","type=f32,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","SQRT","type=f32,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","SQRT","type=f32,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","LOG","type=f32,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","LOG","type=f32,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","SIN","type=f32,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","SIN","type=f32,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","COS","type=f32,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","COS","type=f32,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","Vulkan" +"Vulkan0","CLAMP","type=f32,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","1","yes","Vulkan" "Vulkan0","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","Vulkan" +"Vulkan0","LEAKY_RELU","type=f32,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","1","yes","Vulkan" "Vulkan0","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","Vulkan" "Vulkan0","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","Vulkan" @@ -8916,6 +9209,11 @@ "Vulkan0","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","1","yes","Vulkan" "Vulkan0","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Vulkan" "Vulkan0","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SOFT_MAX","type=f32,ne=[200000,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SOFT_MAX","type=f32,ne=[200000,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","SOFT_MAX","type=f32,ne=[643251,3,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","Vulkan" "Vulkan0","SOFT_MAX_BACK","type=f32,ne=[16,16,1,1],scale=1.000000,max_bias=0.000000","support","1","yes","Vulkan" "Vulkan0","SOFT_MAX_BACK","type=f32,ne=[15,15,1,1],scale=1.000000,max_bias=0.000000","support","1","yes","Vulkan" "Vulkan0","SOFT_MAX_BACK","type=f32,ne=[16,16,2,3],scale=1.000000,max_bias=0.000000","support","1","yes","Vulkan" @@ -8968,6 +9266,7 @@ "Vulkan0","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" @@ -8977,6 +9276,7 @@ "Vulkan0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" @@ -8987,11 +9287,13 @@ "Vulkan0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" @@ -9001,6 +9303,7 @@ "Vulkan0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" @@ -9011,11 +9314,13 @@ "Vulkan0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" @@ -9025,6 +9330,7 @@ "Vulkan0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" @@ -9035,11 +9341,13 @@ "Vulkan0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" @@ -9049,6 +9357,7 @@ "Vulkan0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" @@ -9059,6 +9368,7 @@ "Vulkan0","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" @@ -9184,6 +9494,7 @@ "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" @@ -9193,6 +9504,7 @@ "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" @@ -9203,11 +9515,13 @@ "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" @@ -9217,6 +9531,7 @@ "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" @@ -9227,11 +9542,13 @@ "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" @@ -9241,6 +9558,7 @@ "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" @@ -9251,11 +9569,13 @@ "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" @@ -9265,6 +9585,7 @@ "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" @@ -9275,6 +9596,7 @@ "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" +"Vulkan0","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","Vulkan" @@ -9397,25 +9719,65 @@ "Vulkan0","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f32,ne_a=[128,32,2,3],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" +"Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" @@ -9542,357 +9904,369 @@ "Vulkan0","ARGSORT","type=f32,ne=[2048,2,1,3],order=1","support","1","yes","Vulkan" "Vulkan0","ARGSORT","type=f32,ne=[2049,2,1,3],order=1","support","1","yes","Vulkan" "Vulkan0","ARGSORT","type=f32,ne=[2,8,8192,1],order=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[12,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[13,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[13,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[15,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[15,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[15,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[19,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[19,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[19,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[19,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[16,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[27,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[16,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[27,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[16,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[27,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[16,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[27,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[16,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[27,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[32,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[43,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[32,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[43,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[32,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[43,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[32,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[43,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[32,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[43,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[64,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[75,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[64,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[75,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[64,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[75,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[64,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[75,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[64,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[75,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[128,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[139,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[128,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[139,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[128,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[139,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[128,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[139,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[128,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[139,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[128,1,1,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[139,1,2,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[256,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[267,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[256,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[267,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[256,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[267,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[256,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[267,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[256,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[267,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[256,1,1,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[267,1,2,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[512,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[523,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[512,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[523,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[512,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[523,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[512,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[523,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[512,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[523,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[512,1,1,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[523,1,2,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[512,1,1,1],k=500","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[523,1,2,1],k=500","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1024,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1035,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1024,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1035,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1024,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1035,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1024,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1035,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1024,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1035,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1024,1,1,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1035,1,2,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1024,1,1,1],k=500","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1035,1,2,1],k=500","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1024,1,1,1],k=1023","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[1035,1,2,1],k=1023","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2048,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2059,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2048,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2059,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2048,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2059,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2048,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2059,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2048,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2059,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2048,1,1,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2059,1,2,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2048,1,1,1],k=500","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2059,1,2,1],k=500","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2048,1,1,1],k=1023","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[2059,1,2,1],k=1023","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4096,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4107,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4096,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4107,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4096,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4107,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4096,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4107,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4096,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4107,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4096,1,1,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4107,1,2,1],k=100","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4096,1,1,1],k=500","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4107,1,2,1],k=500","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4096,1,1,1],k=1023","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[4107,1,2,1],k=1023","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8192,1,1,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8203,1,2,1],k=1","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8192,1,1,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8203,1,2,1],k=2","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8192,1,1,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8203,1,2,1],k=3","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8192,1,1,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8203,1,2,1],k=7","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8192,1,1,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8203,1,2,1],k=15","support","1","yes","Vulkan" -"Vulkan0","TOP_K","type=f32,ne=[8192,1,1,1],k=100","support","1","yes","Vulkan" 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-"Vulkan0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear","support","1","yes","Vulkan" "Vulkan0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=0","support","1","yes","Vulkan" "Vulkan0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","1","yes","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=none","support","1","yes","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic,flags=none","support","1","yes","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=0","support","0","no","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=1","support","0","no","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","0","no","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","0","no","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","1","yes","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","1","yes","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","1","yes","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=align_corners","support","1","yes","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic,flags=align_corners","support","1","yes","Vulkan" -"Vulkan0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic,flags=align_corners","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=0","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bilinear|antialias,transpose=1","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|antialias","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear|antialias","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear|align_corners","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear|align_corners","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear|align_corners","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic|align_corners","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bicubic|align_corners","support","1","yes","Vulkan" +"Vulkan0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bicubic|align_corners","support","1","yes","Vulkan" "Vulkan0","SUM","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" -"Vulkan0","SUM_ROWS","type=f32,ne=[10,5,4,3],permute=0,slice=0","support","1","yes","Vulkan" "Vulkan0","SUM","type=f32,ne=[11,5,6,3],permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","SUM","type=f32,ne=[11,5,6,3],permute=[0,3,2,1]","support","1","yes","Vulkan" "Vulkan0","SUM","type=f32,ne=[11,5,6,3],permute=[0,1,3,2]","support","1","yes","Vulkan" +"Vulkan0","MEAN","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" +"Vulkan0","MEAN","type=f32,ne=[33,1,1,1]","support","1","yes","Vulkan" +"Vulkan0","MEAN","type=f32,ne=[33,256,1,1]","support","1","yes","Vulkan" +"Vulkan0","MEAN","type=f32,ne=[32769,1,1,1]","support","1","yes","Vulkan" +"Vulkan0","MEAN","type=f32,ne=[32,1,1,1]","support","1","yes","Vulkan" +"Vulkan0","MEAN","type=f32,ne=[32,256,1,1]","support","1","yes","Vulkan" +"Vulkan0","MEAN","type=f32,ne=[32768,1,1,1]","support","1","yes","Vulkan" +"Vulkan0","SUM","type=f32,ne=[33,1,1,1]","support","1","yes","Vulkan" +"Vulkan0","SUM","type=f32,ne=[33,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SUM","type=f32,ne=[33,256,1,1]","support","1","yes","Vulkan" +"Vulkan0","SUM","type=f32,ne=[33,256,1,1],permute=[1,0,2,3]","support","0","no","Vulkan" +"Vulkan0","SUM_ROWS","type=f32,ne=[10,5,4,3],permute=0,slice=0","support","1","yes","Vulkan" "Vulkan0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=1,slice=0","support","1","yes","Vulkan" "Vulkan0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=0,slice=1","support","1","yes","Vulkan" "Vulkan0","SUM_ROWS","type=f32,ne=[11,5,6,3],permute=1,slice=1","support","1","yes","Vulkan" -"Vulkan0","MEAN","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" -"Vulkan0","SUM","type=f32,ne=[33,1,1,1]","support","1","yes","Vulkan" +"Vulkan0","SUM_ROWS","type=f32,ne=[16,5,6,3],permute=1,slice=0","support","1","yes","Vulkan" +"Vulkan0","SUM_ROWS","type=f32,ne=[16,5,6,3],permute=0,slice=1","support","1","yes","Vulkan" +"Vulkan0","SUM_ROWS","type=f32,ne=[16,5,6,3],permute=1,slice=1","support","1","yes","Vulkan" "Vulkan0","SUM_ROWS","type=f32,ne=[33,1,1,1],permute=0,slice=0","support","1","yes","Vulkan" -"Vulkan0","MEAN","type=f32,ne=[33,1,1,1]","support","1","yes","Vulkan" -"Vulkan0","SUM","type=f32,ne=[33,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","SUM_ROWS","type=f32,ne=[33,1024,1,1],permute=0,slice=0","support","1","yes","Vulkan" -"Vulkan0","SUM","type=f32,ne=[33,256,1,1]","support","1","yes","Vulkan" -"Vulkan0","SUM","type=f32,ne=[33,256,1,1],permute=[1,0,2,3]","support","0","no","Vulkan" "Vulkan0","SUM_ROWS","type=f32,ne=[33,256,1,1],permute=0,slice=0","support","1","yes","Vulkan" -"Vulkan0","MEAN","type=f32,ne=[33,256,1,1]","support","1","yes","Vulkan" -"Vulkan0","MEAN","type=f32,ne=[32769,1,1,1]","support","1","yes","Vulkan" "Vulkan0","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","Vulkan" "Vulkan0","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","Vulkan" -"Vulkan0","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","Vulkan" -"Vulkan0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","Vulkan" -"Vulkan0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","1","yes","Vulkan" +"Vulkan0","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1],stride_dim=-1","support","1","yes","Vulkan" +"Vulkan0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[256,16,2,3],stride_dim=-1","support","1","yes","Vulkan" +"Vulkan0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[128,16,2,3],stride_dim=-1","support","1","yes","Vulkan" +"Vulkan0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[256,16,2,3],stride_dim=1","support","1","yes","Vulkan" +"Vulkan0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[128,16,2,3],stride_dim=2","support","1","yes","Vulkan" +"Vulkan0","ACC","type=f32,ne_a=[256,17,2,3],ne_b=[64,16,2,3],stride_dim=3","support","1","yes","Vulkan" +"Vulkan0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","Vulkan" +"Vulkan0","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","1","yes","Vulkan" +"Vulkan0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,tfrm=0,circular=0","support","1","yes","Vulkan" "Vulkan0","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","Vulkan" "Vulkan0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","0","no","Vulkan" "Vulkan0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","Vulkan" @@ -9903,6 +10277,7 @@ "Vulkan0","CUMSUM","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" "Vulkan0","CUMSUM","type=f32,ne=[127,5,4,3]","support","1","yes","Vulkan" "Vulkan0","CUMSUM","type=f32,ne=[128,5,4,3]","support","1","yes","Vulkan" +"Vulkan0","CUMSUM","type=f32,ne=[128,128,4,4]","support","1","yes","Vulkan" "Vulkan0","CUMSUM","type=f32,ne=[255,5,4,3]","support","1","yes","Vulkan" "Vulkan0","CUMSUM","type=f32,ne=[256,5,4,3]","support","1","yes","Vulkan" "Vulkan0","CUMSUM","type=f32,ne=[511,5,4,3]","support","1","yes","Vulkan" @@ -9913,7 +10288,8 @@ "Vulkan0","CUMSUM","type=f32,ne=[2048,5,4,3]","support","1","yes","Vulkan" "Vulkan0","CUMSUM","type=f32,ne=[242004,1,1,1]","support","1","yes","Vulkan" "Vulkan0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","1","yes","Vulkan" -"Vulkan0","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","Vulkan" +"Vulkan0","CUMSUM","type=f32,ne=[20481,4,1,1]","support","1","yes","Vulkan" +"Vulkan0","XIELU","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" "Vulkan0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","1","yes","Vulkan" "Vulkan0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","1","yes","Vulkan" "Vulkan0","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","1","yes","Vulkan" @@ -9922,1137 +10298,269 @@ "Vulkan0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","1","yes","Vulkan" "Vulkan0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","1","yes","Vulkan" "Vulkan0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","1","yes","Vulkan" +"Vulkan0","DIAG","type=f32,ne=[10,1,4,3]","support","1","yes","Vulkan" +"Vulkan0","DIAG","type=f32,ne=[79,1,19,13]","support","1","yes","Vulkan" +"Vulkan0","DIAG","type=f32,ne=[256,1,8,16]","support","1","yes","Vulkan" "Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","1","yes","Vulkan" "Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","1","yes","Vulkan" "Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","1","yes","Vulkan" "Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","1","yes","Vulkan" "Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","1","yes","Vulkan" "Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","1","yes","Vulkan" -"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","Vulkan" -"Vulkan0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","Vulkan" -"Vulkan0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","1","yes","Vulkan" -"Vulkan0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","1","yes","Vulkan" -"Vulkan0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[64,64,2,2]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[79,79,5,3],ne_rhs=[417,79,5,3]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,2],ne_rhs=[32,128,4,2]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[80,80,2,8]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[79,80,2,8]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[81,80,2,8]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[80,80,8,8]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[79,80,8,8]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[81,80,8,8]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[84,84,4,4],ne_rhs=[32,84,4,4]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[95,95,8,8],ne_rhs=[40,95,8,8]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[32,128,4,4]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[128,128,3,4],ne_rhs=[32,128,3,4]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,1],ne_rhs=[32,128,4,1]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[200,64,4,4]","support","1","yes","Vulkan" +"Vulkan0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[384,64,4,4]","support","1","yes","Vulkan" +"Vulkan0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=0,circular=0","support","1","yes","Vulkan" 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-"Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11068,11 +10576,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11103,16 +10611,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11128,11 +10636,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11163,16 +10671,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11188,11 +10696,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11223,16 +10731,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11248,11 +10756,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11283,16 +10791,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11308,11 +10816,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11328,11 +10836,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11348,11 +10856,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11368,11 +10876,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11388,11 +10896,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11408,11 +10916,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11428,11 +10936,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11448,11 +10956,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11468,11 +10976,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11503,16 +11011,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11528,11 +11036,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11563,16 +11071,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11588,11 +11096,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11623,16 +11131,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11648,11 +11156,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11683,16 +11191,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11708,11 +11216,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11728,11 +11236,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11748,11 +11256,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11768,11 +11276,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11788,11 +11296,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11808,11 +11316,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11828,11 +11336,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11848,11 +11356,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11868,11 +11376,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11888,11 +11396,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11908,11 +11416,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11928,11 +11436,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11948,11 +11456,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11968,11 +11476,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -11988,11 +11496,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12008,11 +11516,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12028,11 +11536,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12048,11 +11556,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12068,11 +11576,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12088,11 +11596,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12108,11 +11616,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12128,11 +11636,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12148,11 +11656,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12168,11 +11676,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12188,11 +11696,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12223,16 +11731,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12248,11 +11756,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12283,16 +11791,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12308,11 +11816,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12328,11 +11836,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12348,11 +11856,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12368,11 +11876,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12388,11 +11896,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12423,16 +11931,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12448,11 +11956,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12483,16 +11991,16 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12508,11 +12016,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12528,11 +12036,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12548,11 +12056,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12568,11 +12076,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12588,11 +12096,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12608,11 +12116,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12628,11 +12136,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12648,11 +12156,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12668,11 +12176,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12688,11 +12196,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12708,11 +12216,11 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" @@ -12728,6014 +12236,1371 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=35,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" 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-"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" 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-"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" 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-"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" 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-"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" 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-"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" 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"Vulkan0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","Vulkan" "Vulkan0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","Vulkan" "Vulkan0","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" "Vulkan0","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=128,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=16,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=1,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=32,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","0","no","Vulkan" diff --git a/docs/ops/WebGPU.csv b/docs/ops/WebGPU.csv index e2ed3e2cfa..b7761b9dd3 100644 --- a/docs/ops/WebGPU.csv +++ b/docs/ops/WebGPU.csv @@ -5023,20 +5023,20 @@ "WebGPU: WebGPU","ARGMAX","type=f32,ne=[1024,12,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","ARGMAX","type=f32,ne=[2000,10,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","ARGMAX","type=f32,ne=[5438,3,1,1]","support","1","yes","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[2,1,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,2,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,2,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,1],nr=[2,1,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,1],nr=[1,1,1,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[2,1,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,2,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,2,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,3],nr=[2,1,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,3],nr=[1,1,1,2]","support","0","no","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,2,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,2,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,2,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,2,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,1],v=0","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[2,1,1,1],v=0","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,2,1,1],v=0","support","0","no","WebGPU" @@ -9535,38 +9535,38 @@ "WebGPU: WebGPU","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","0","no","WebGPU" -"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","0","no","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","WebGPU" "WebGPU: WebGPU","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","WebGPU" diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index a8c19a6aba..d2b2e336e7 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -5,6 +5,7 @@ #include "sampling.h" #include +#include #include #include #include @@ -16,6 +17,8 @@ static void print_usage(int, char ** argv) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; params.prompt = "Hello my name is"; diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 767198aafa..702bc74bee 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -5,14 +5,16 @@ #include "common.h" #include "log.h" +#include +#include +#include +#include +#include +#include +#include +#include #include #include -#include -#include -#include -#include -#include -#include #include #include #include @@ -874,6 +876,8 @@ static std::string basename(const std::string &path) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_init(); struct train_params params = get_default_train_params(); diff --git a/examples/debug/README.md b/examples/debug/README.md index 28e00c9342..2ea716eb54 100644 --- a/examples/debug/README.md +++ b/examples/debug/README.md @@ -2,7 +2,7 @@ This is a utility intended to help debug a model by registering a callback that logs GGML operations and tensor data. It can also store the generated logits or -embeddings as well as the prompt and token ids for comparision with the original +embeddings as well as the prompt and token ids for comparison with the original model. ### Usage diff --git a/examples/deprecation-warning/deprecation-warning.cpp b/examples/deprecation-warning/deprecation-warning.cpp index 11f5147328..0cde17f6e9 100644 --- a/examples/deprecation-warning/deprecation-warning.cpp +++ b/examples/deprecation-warning/deprecation-warning.cpp @@ -1,11 +1,14 @@ // Warns users that this filename was deprecated, and provides a link for more information. +#include #include #include #include // Main int main(int argc, char** argv) { + std::setlocale(LC_NUMERIC, "C"); + std::string filename = "main"; if (argc >= 1) { filename = argv[0]; diff --git a/examples/diffusion/README.md b/examples/diffusion/README.md index f71d241319..b394200214 100644 --- a/examples/diffusion/README.md +++ b/examples/diffusion/README.md @@ -43,12 +43,12 @@ Choose one of the following scheduling methods: - `-b`: Batch size ### Examples -#### Dream architechture: +#### Dream architecture: ``` llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual ``` -#### LLaDA architechture: +#### LLaDA architecture: ``` llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual ``` diff --git a/examples/diffusion/diffusion-cli.cpp b/examples/diffusion/diffusion-cli.cpp index d50f754092..d38bfe7f82 100644 --- a/examples/diffusion/diffusion-cli.cpp +++ b/examples/diffusion/diffusion-cli.cpp @@ -7,6 +7,7 @@ #include #include +#include #include #include #include @@ -538,6 +539,8 @@ static std::string format_input_text(const std::string & prompt, const std::stri } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + ggml_time_init(); common_params params; diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index d8eaaa2691..33ef2a7521 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -3,6 +3,7 @@ #include "log.h" #include "llama.h" +#include #include #include @@ -94,6 +95,8 @@ static void print_raw_embeddings(const float * emb, } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) { diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index bd58734979..17d162d95d 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -4,6 +4,8 @@ #include "log.h" #include "llama.h" #include "llama-cpp.h" + +#include #include #include @@ -29,6 +31,8 @@ static bool run(llama_context * ctx, const common_params & params) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + base_callback_data cb_data; common_params params; diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp index 0aa33e8245..7ba7d79f72 100644 --- a/examples/gen-docs/gen-docs.cpp +++ b/examples/gen-docs/gen-docs.cpp @@ -1,6 +1,7 @@ #include "arg.h" #include "common.h" +#include #include #include #include @@ -100,6 +101,8 @@ static void write_help(std::ostringstream & ss, const md_file & md) { } int main(int, char **) { + std::setlocale(LC_NUMERIC, "C"); + for (const auto & md : md_files) { std::ifstream infile(md.fname); if (!infile.is_open()) { diff --git a/examples/gguf-hash/gguf-hash.cpp b/examples/gguf-hash/gguf-hash.cpp index 9523ec122f..331de301ff 100644 --- a/examples/gguf-hash/gguf-hash.cpp +++ b/examples/gguf-hash/gguf-hash.cpp @@ -1,13 +1,14 @@ #include "ggml.h" #include "gguf.h" -#include /* abort() */ +#include +#include #include #include -#include -#include -#include +#include /* abort() */ #include +#include +#include #include #include @@ -626,6 +627,8 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) { } int main(int argc, const char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + hash_params params; manifest_check_params manifest_check; hash_params_parse(argc, argv, params); diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index 499cfacc92..79ad38711e 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -1,6 +1,7 @@ #include "ggml.h" #include "gguf.h" +#include #include #include #include @@ -240,6 +241,8 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + if (argc < 3) { printf("usage: %s data.gguf r|w [n]\n", argv[0]); printf("r: read data.gguf file\n"); diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py index 9fc90a3c98..018ba49b24 100755 --- a/examples/json_schema_to_grammar.py +++ b/examples/json_schema_to_grammar.py @@ -633,7 +633,7 @@ class SchemaConverter: return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=None)) elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema): - items = schema.get('items') or schema['prefixItems'] + items = schema.get('items', schema.get('prefixItems')) if isinstance(items, list): return self._add_rule( rule_name, @@ -689,6 +689,11 @@ class SchemaConverter: elif (schema_type == 'object') or (len(schema) == 0): return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['object'])) + elif schema_type is None and isinstance(schema, dict): + # No type constraint and no recognized structural keywords (e.g. {"description": "..."}). + # Per JSON Schema semantics this is equivalent to {} and accepts any value. + return self._add_rule(rule_name, self._add_primitive('value', PRIMITIVE_RULES['value'])) + else: assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}' # TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero diff --git a/examples/llama.vim b/examples/llama.vim index 736802d365..23a281fc33 100644 --- a/examples/llama.vim +++ b/examples/llama.vim @@ -52,8 +52,8 @@ highlight llama_hl_info guifg=#77ff2f ctermfg=119 " n_prefix: number of lines before the cursor location to include in the local prefix " n_suffix: number of lines after the cursor location to include in the local suffix " n_predict: max number of tokens to predict -" t_max_prompt_ms: max alloted time for the prompt processing (TODO: not yet supported) -" t_max_predict_ms: max alloted time for the prediction +" t_max_prompt_ms: max allotted time for the prompt processing (TODO: not yet supported) +" t_max_predict_ms: max allotted time for the prediction " show_info: show extra info about the inference (0 - disabled, 1 - statusline, 2 - inline) " auto_fim: trigger FIM completion automatically on cursor movement " max_line_suffix: do not auto-trigger FIM completion if there are more than this number of characters to the right of the cursor diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index aa6efa62b3..d5fde081c5 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -4,10 +4,11 @@ #include "log.h" #include "llama.h" +#include +#include #include #include #include -#include struct ngram_data { bool active = false; @@ -38,6 +39,8 @@ struct ngram_container { }; int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp index f7b6ea1b19..439e3f726e 100644 --- a/examples/lookup/lookup-create.cpp +++ b/examples/lookup/lookup-create.cpp @@ -3,10 +3,13 @@ #include "ngram-cache.h" #include "llama.h" +#include #include #include int main(int argc, char ** argv){ + std::setlocale(LC_NUMERIC, "C"); + common_params params; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) { diff --git a/examples/lookup/lookup-merge.cpp b/examples/lookup/lookup-merge.cpp index 6871c0f5fd..ee3c7249cf 100644 --- a/examples/lookup/lookup-merge.cpp +++ b/examples/lookup/lookup-merge.cpp @@ -3,6 +3,7 @@ #include "common.h" #include "ngram-cache.h" +#include #include #include #include @@ -17,6 +18,8 @@ static void print_usage(char* argv0) { } int main(int argc, char ** argv){ + std::setlocale(LC_NUMERIC, "C"); + if (argc < 3) { print_usage(argv[0]); exit(1); diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index ae28b2e6e8..c3158281c7 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -5,14 +5,17 @@ #include "llama.h" #include "ggml.h" +#include +#include #include #include -#include #include #include #include int main(int argc, char ** argv){ + std::setlocale(LC_NUMERIC, "C"); + common_params params; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) { diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index c7552ddde1..bd216035c0 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -6,6 +6,7 @@ #include "log.h" #include "llama.h" +#include #include #include #include @@ -13,6 +14,8 @@ #include int main(int argc, char ** argv){ + std::setlocale(LC_NUMERIC, "C"); + common_params params; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) { diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile index 342de63bd0..9356aaf854 100644 --- a/examples/model-conversion/Makefile +++ b/examples/model-conversion/Makefile @@ -77,7 +77,10 @@ causal-verify-embeddings: causal-run-original-embeddings causal-run-converted-em @./scripts/causal/compare-embeddings-logits.sh causal-inspect-original-model: - @./scripts/utils/inspect-org-model.py + @./scripts/utils/inspect-org-model.py --list-all -s + +causal-list-original-model-tensors: + @./scripts/utils/inspect-org-model.py --list-all-short -s causal-inspect-converted-model: @./scripts/utils/inspect-converted-model.sh @@ -153,7 +156,7 @@ embedding-verify-logits-st: embedding-run-original-model-st embedding-run-conver embedding-inspect-original-model: $(call validate_embedding_model_path,embedding-inspect-original-model) - @EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" ./scripts/utils/inspect-org-model.py -m ${EMBEDDING_MODEL_PATH} + @EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" ./scripts/utils/inspect-org-model.py -m ${EMBEDDING_MODEL_PATH} --list-all -s embedding-inspect-converted-model: @CONVERTED_EMBEDDING_MODEL="$(CONVERTED_EMBEDDING_MODEL)" ./scripts/utils/inspect-converted-model.sh ${CONVERTED_EMBEDDING_MODEL} diff --git a/examples/model-conversion/README.md b/examples/model-conversion/README.md index 637870a5c1..c43e642fee 100644 --- a/examples/model-conversion/README.md +++ b/examples/model-conversion/README.md @@ -69,7 +69,7 @@ Command line arguments take precedence over environment variables when both are In cases where the transformer implementation for the model has not been released yet it is possible to set the environment variable `UNRELEASED_MODEL_NAME` which -will then cause the transformer implementation to be loaded explicitely and not +will then cause the transformer implementation to be loaded explicitly and not use AutoModelForCausalLM: ``` export UNRELEASED_MODEL_NAME=SomeNewModel @@ -120,7 +120,7 @@ The converted model can be inspected using the following command: (venv) $ make causal-run-converted-model ``` -### Model logits verfication +### Model logits verification The following target will run the original model and the converted model and compare the logits: ```console @@ -235,7 +235,7 @@ new model the model can be converted to GGUF format using the following command: (venv) $ make embedding-run-converted-model ``` -### Model logits verfication +### Model logits verification The following target will run the original model and the converted model (which was done manually in the previous steps) and compare the logits: ```console @@ -335,7 +335,7 @@ $ make perplexity-run-full QUANTIZED_MODEL=~/path/to/quantized/model-Qxx.gguf LO ## HuggingFace utilities The following targets are useful for creating collections and model repositories -on Hugging Face in the the ggml-org. These can be used when preparing a relase +on Hugging Face in the the ggml-org. These can be used when preparing a release to script the process for new model releases. For the following targets a `HF_TOKEN` environment variable is required. @@ -347,7 +347,7 @@ For the following targets a `HF_TOKEN` environment variable is required. > $ unset HF_TOKEN ### Create a new Hugging Face Model (model repository) -This will create a new model repsository on Hugging Face with the specified +This will create a new model repository on Hugging Face with the specified model name. ```console (venv) $ make hf-create-model MODEL_NAME='TestModel' NAMESPACE="danbev" ORIGINAL_BASE_MODEL="some-base-model" diff --git a/examples/model-conversion/scripts/utils/inspect-org-model.py b/examples/model-conversion/scripts/utils/inspect-org-model.py index bc6f45a5fb..5c3674af71 100755 --- a/examples/model-conversion/scripts/utils/inspect-org-model.py +++ b/examples/model-conversion/scripts/utils/inspect-org-model.py @@ -1,67 +1,290 @@ #!/usr/bin/env python3 import argparse -import os import json +import os +import re +import struct +import sys +from pathlib import Path +from typing import Optional from safetensors import safe_open -from collections import defaultdict -parser = argparse.ArgumentParser(description='Process model with specified path') -parser.add_argument('--model-path', '-m', help='Path to the model') -args = parser.parse_args() -model_path = os.environ.get('MODEL_PATH', args.model_path) -if model_path is None: - parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable") +MODEL_SAFETENSORS_FILE = "model.safetensors" +MODEL_SAFETENSORS_INDEX = "model.safetensors.index.json" -# Check if there's an index file (multi-file model) -index_path = os.path.join(model_path, "model.safetensors.index.json") -single_file_path = os.path.join(model_path, "model.safetensors") +DTYPE_SIZES = { + "F64": 8, "I64": 8, "U64": 8, + "F32": 4, "I32": 4, "U32": 4, + "F16": 2, "BF16": 2, "I16": 2, "U16": 2, + "I8": 1, "U8": 1, "BOOL": 1, + "F8_E4M3": 1, "F8_E5M2": 1, +} -if os.path.exists(index_path): - # Multi-file model - print("Multi-file model detected") +SIZE_UNITS = ['B', 'KB', 'MB', 'GB', 'TB'] - with open(index_path, 'r') as f: - index_data = json.load(f) - # Get the weight map (tensor_name -> file_name) - weight_map = index_data.get("weight_map", {}) +def get_weight_map(model_path: Path) -> Optional[dict[str, str]]: + index_file = model_path / MODEL_SAFETENSORS_INDEX - # Group tensors by file for efficient processing - file_tensors = defaultdict(list) - for tensor_name, file_name in weight_map.items(): - file_tensors[file_name].append(tensor_name) + if index_file.exists(): + with open(index_file, 'r') as f: + index = json.load(f) + return index.get("weight_map", {}) - print("Tensors in model:") + return None - # Process each shard file - for file_name, tensor_names in file_tensors.items(): - file_path = os.path.join(model_path, file_name) - print(f"\n--- From {file_name} ---") - with safe_open(file_path, framework="pt") as f: - for tensor_name in sorted(tensor_names): - tensor = f.get_tensor(tensor_name) - print(f"- {tensor_name} : shape = {tensor.shape}, dtype = {tensor.dtype}") +def get_all_tensor_names(model_path: Path) -> list[str]: + weight_map = get_weight_map(model_path) -elif os.path.exists(single_file_path): - # Single file model (original behavior) - print("Single-file model detected") + if weight_map is not None: + return list(weight_map.keys()) - with safe_open(single_file_path, framework="pt") as f: - keys = f.keys() - print("Tensors in model:") - for key in sorted(keys): - tensor = f.get_tensor(key) - print(f"- {key} : shape = {tensor.shape}, dtype = {tensor.dtype}") + single_file = model_path / MODEL_SAFETENSORS_FILE + if single_file.exists(): + try: + with safe_open(single_file, framework="pt", device="cpu") as f: + return list(f.keys()) + except Exception as e: + print(f"Error reading {single_file}: {e}") + sys.exit(1) -else: - print(f"Error: Neither 'model.safetensors.index.json' nor 'model.safetensors' found in {model_path}") - print("Available files:") - if os.path.exists(model_path): - for item in sorted(os.listdir(model_path)): - print(f" {item}") + print(f"Error: No safetensors files found in {model_path}") + sys.exit(1) + + +def find_tensor_file(model_path: Path, tensor_name: str) -> Optional[str]: + weight_map = get_weight_map(model_path) + + if weight_map is not None: + return weight_map.get(tensor_name) + + single_file = model_path / MODEL_SAFETENSORS_FILE + if single_file.exists(): + return single_file.name + + return None + + +def read_safetensors_header(file_path: Path) -> dict: + with open(file_path, 'rb') as f: + header_size = struct.unpack(' int: + offsets = tensor_meta.get("data_offsets") + if offsets and len(offsets) == 2: + return offsets[1] - offsets[0] + n_elements = 1 + for d in tensor_meta.get("shape", []): + n_elements *= d + return n_elements * DTYPE_SIZES.get(tensor_meta.get("dtype", "F32"), 4) + + +def format_size(size_bytes: int) -> str: + val = float(size_bytes) + for unit in SIZE_UNITS[:-1]: + if val < 1024.0: + return f"{val:.2f} {unit}" + val /= 1024.0 + return f"{val:.2f} {SIZE_UNITS[-1]}" + + +def get_all_tensor_metadata(model_path: Path) -> dict[str, dict]: + weight_map = get_weight_map(model_path) + + if weight_map is not None: + file_to_tensors: dict[str, list[str]] = {} + for tensor_name, file_name in weight_map.items(): + file_to_tensors.setdefault(file_name, []).append(tensor_name) + + all_metadata: dict[str, dict] = {} + for file_name, tensor_names in file_to_tensors.items(): + try: + header = read_safetensors_header(model_path / file_name) + for tensor_name in tensor_names: + if tensor_name in header: + all_metadata[tensor_name] = header[tensor_name] + except Exception as e: + print(f"Warning: Could not read header from {file_name}: {e}", file=sys.stderr) + return all_metadata + + single_file = model_path / MODEL_SAFETENSORS_FILE + if single_file.exists(): + try: + header = read_safetensors_header(single_file) + return {k: v for k, v in header.items() if k != "__metadata__"} + except Exception as e: + print(f"Error reading {single_file}: {e}") + sys.exit(1) + + print(f"Error: No safetensors files found in {model_path}") + sys.exit(1) + + +def normalize_tensor_name(tensor_name: str) -> str: + normalized = re.sub(r'\.\d+\.', '.#.', tensor_name) + normalized = re.sub(r'\.\d+$', '.#', normalized) + return normalized + + +def list_all_tensors( + model_path: Path, + short: bool = False, + show_sizes: bool = False, +): + tensor_names = get_all_tensor_names(model_path) + + metadata: Optional[dict[str, dict]] = None + if show_sizes: + metadata = get_all_tensor_metadata(model_path) + + total_bytes = 0 + + if short: + seen: dict[str, str] = {} + for tensor_name in sorted(tensor_names): + normalized = normalize_tensor_name(tensor_name) + if normalized not in seen: + seen[normalized] = tensor_name + display_pairs = list(sorted(seen.items())) + name_width = max((len(n) for n, _ in display_pairs), default=0) + for normalized, first_name in display_pairs: + if metadata and first_name in metadata: + m = metadata[first_name] + size = get_tensor_size_bytes(m) + total_bytes += size + print(f"{normalized:{name_width}} {m.get('dtype', '?'):6s} {str(m.get('shape', '')):30s} {format_size(size)}") + else: + print(normalized) else: - print(f" Directory {model_path} does not exist") - exit(1) + name_width = max((len(n) for n in tensor_names), default=0) + for tensor_name in sorted(tensor_names): + if metadata and tensor_name in metadata: + m = metadata[tensor_name] + size = get_tensor_size_bytes(m) + total_bytes += size + print(f"{tensor_name:{name_width}} {m.get('dtype', '?'):6s} {str(m.get('shape', '')):30s} {format_size(size)}") + else: + print(tensor_name) + + if show_sizes: + print(f"\nTotal: {format_size(total_bytes)}") + + +def print_tensor_info(model_path: Path, tensor_name: str, num_values: Optional[int] = None): + tensor_file = find_tensor_file(model_path, tensor_name) + + if tensor_file is None: + print(f"Error: Could not find tensor '{tensor_name}' in model index") + print(f"Model path: {model_path}") + sys.exit(1) + + file_path = model_path / tensor_file + + try: + header = read_safetensors_header(file_path) + tensor_meta = header.get(tensor_name, {}) + dtype_str = tensor_meta.get("dtype") + + with safe_open(file_path, framework="pt", device="cpu") as f: + if tensor_name in f.keys(): + tensor_slice = f.get_slice(tensor_name) + shape = tensor_slice.get_shape() + print(f"Tensor: {tensor_name}") + print(f"File: {tensor_file}") + print(f"Shape: {shape}") + if dtype_str: + print(f"Dtype: {dtype_str}") + if tensor_meta: + print(f"Size: {format_size(get_tensor_size_bytes(tensor_meta))}") + if num_values is not None: + tensor = f.get_tensor(tensor_name) + if not dtype_str: + print(f"Dtype: {tensor.dtype}") + flat = tensor.flatten() + n = min(num_values, flat.numel()) + print(f"Values: {flat[:n].tolist()}") + else: + print(f"Error: Tensor '{tensor_name}' not found in {tensor_file}") + sys.exit(1) + + except FileNotFoundError: + print(f"Error: The file '{file_path}' was not found.") + sys.exit(1) + except Exception as e: + print(f"An error occurred: {e}") + sys.exit(1) + + +def main(): + parser = argparse.ArgumentParser( + description="Print tensor information from a safetensors model" + ) + parser.add_argument( + "tensor_name", + nargs="?", + help="Name of the tensor to inspect" + ) + parser.add_argument( + "-m", "--model-path", + type=Path, + help="Path to the model directory (default: MODEL_PATH environment variable)" + ) + parser.add_argument( + "-l", "--list-all-short", + action="store_true", + help="List unique tensor patterns (layer numbers replaced with #)" + ) + parser.add_argument( + "-la", "--list-all", + action="store_true", + help="List all tensor names with actual layer numbers" + ) + parser.add_argument( + "-n", "--num-values", + nargs="?", + const=10, + default=None, + type=int, + metavar="N", + help="Print the first N values of the tensor flattened (default: 10 if flag is given without a number)" + ) + parser.add_argument( + "-s", "--sizes", + action="store_true", + help="Show dtype, shape, and size for each tensor when listing" + ) + + args = parser.parse_args() + + model_path = args.model_path + if model_path is None: + model_path_str = os.environ.get("MODEL_PATH") + if model_path_str is None: + print("Error: --model-path not provided and MODEL_PATH environment variable not set") + sys.exit(1) + model_path = Path(model_path_str) + + if not model_path.exists(): + print(f"Error: Model path does not exist: {model_path}") + sys.exit(1) + + if not model_path.is_dir(): + print(f"Error: Model path is not a directory: {model_path}") + sys.exit(1) + + if args.list_all_short or args.list_all: + list_all_tensors(model_path, short=args.list_all_short, show_sizes=args.sizes) + else: + if args.tensor_name is None: + print("Error: tensor_name is required when not using --list-all-short or --list-all") + sys.exit(1) + print_tensor_info(model_path, args.tensor_name, args.num_values) + + +if __name__ == "__main__": + main() diff --git a/examples/model-conversion/scripts/utils/tensor-info.py b/examples/model-conversion/scripts/utils/tensor-info.py deleted file mode 100755 index 1bb9e0564c..0000000000 --- a/examples/model-conversion/scripts/utils/tensor-info.py +++ /dev/null @@ -1,174 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import json -import os -import re -import sys -from pathlib import Path -from typing import Optional -from safetensors import safe_open - - -MODEL_SAFETENSORS_FILE = "model.safetensors" -MODEL_SAFETENSORS_INDEX = "model.safetensors.index.json" - - -def get_weight_map(model_path: Path) -> Optional[dict[str, str]]: - index_file = model_path / MODEL_SAFETENSORS_INDEX - - if index_file.exists(): - with open(index_file, 'r') as f: - index = json.load(f) - return index.get("weight_map", {}) - - return None - - -def get_all_tensor_names(model_path: Path) -> list[str]: - weight_map = get_weight_map(model_path) - - if weight_map is not None: - return list(weight_map.keys()) - - single_file = model_path / MODEL_SAFETENSORS_FILE - if single_file.exists(): - try: - with safe_open(single_file, framework="pt", device="cpu") as f: - return list(f.keys()) - except Exception as e: - print(f"Error reading {single_file}: {e}") - sys.exit(1) - - print(f"Error: No safetensors files found in {model_path}") - sys.exit(1) - - -def find_tensor_file(model_path: Path, tensor_name: str) -> Optional[str]: - weight_map = get_weight_map(model_path) - - if weight_map is not None: - return weight_map.get(tensor_name) - - single_file = model_path / MODEL_SAFETENSORS_FILE - if single_file.exists(): - return single_file.name - - return None - - -def normalize_tensor_name(tensor_name: str) -> str: - normalized = re.sub(r'\.\d+\.', '.#.', tensor_name) - normalized = re.sub(r'\.\d+$', '.#', normalized) - return normalized - - -def list_all_tensors(model_path: Path, unique: bool = False): - tensor_names = get_all_tensor_names(model_path) - - if unique: - seen = set() - for tensor_name in sorted(tensor_names): - normalized = normalize_tensor_name(tensor_name) - if normalized not in seen: - seen.add(normalized) - print(normalized) - else: - for tensor_name in sorted(tensor_names): - print(tensor_name) - - -def print_tensor_info(model_path: Path, tensor_name: str, num_values: Optional[int] = None): - tensor_file = find_tensor_file(model_path, tensor_name) - - if tensor_file is None: - print(f"Error: Could not find tensor '{tensor_name}' in model index") - print(f"Model path: {model_path}") - sys.exit(1) - - file_path = model_path / tensor_file - - try: - with safe_open(file_path, framework="pt", device="cpu") as f: - if tensor_name in f.keys(): - tensor_slice = f.get_slice(tensor_name) - shape = tensor_slice.get_shape() - print(f"Tensor: {tensor_name}") - print(f"File: {tensor_file}") - print(f"Shape: {shape}") - if num_values is not None: - tensor = f.get_tensor(tensor_name) - print(f"Dtype: {tensor.dtype}") - flat = tensor.flatten() - n = min(num_values, flat.numel()) - print(f"Values: {flat[:n].tolist()}") - else: - print(f"Error: Tensor '{tensor_name}' not found in {tensor_file}") - sys.exit(1) - - except FileNotFoundError: - print(f"Error: The file '{file_path}' was not found.") - sys.exit(1) - except Exception as e: - print(f"An error occurred: {e}") - sys.exit(1) - - -def main(): - parser = argparse.ArgumentParser( - description="Print tensor information from a safetensors model" - ) - parser.add_argument( - "tensor_name", - nargs="?", # optional (if --list is used for example) - help="Name of the tensor to inspect" - ) - parser.add_argument( - "-m", "--model-path", - type=Path, - help="Path to the model directory (default: MODEL_PATH environment variable)" - ) - parser.add_argument( - "-l", "--list", - action="store_true", - help="List unique tensor patterns in the model (layer numbers replaced with #)" - ) - parser.add_argument( - "-n", "--num-values", - nargs="?", - const=10, - default=None, - type=int, - metavar="N", - help="Print the first N values of the tensor flattened (default: 10 if flag is given without a number)" - ) - - args = parser.parse_args() - - model_path = args.model_path - if model_path is None: - model_path_str = os.environ.get("MODEL_PATH") - if model_path_str is None: - print("Error: --model-path not provided and MODEL_PATH environment variable not set") - sys.exit(1) - model_path = Path(model_path_str) - - if not model_path.exists(): - print(f"Error: Model path does not exist: {model_path}") - sys.exit(1) - - if not model_path.is_dir(): - print(f"Error: Model path is not a directory: {model_path}") - sys.exit(1) - - if args.list: - list_all_tensors(model_path, unique=True) - else: - if args.tensor_name is None: - print("Error: tensor_name is required when not using --list") - sys.exit(1) - print_tensor_info(model_path, args.tensor_name, args.num_values) - - -if __name__ == "__main__": - main() diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index c92173ae29..1700ceefbf 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -7,12 +7,13 @@ #include "log.h" #include "llama.h" +#include +#include #include #include #include #include #include -#include // trim whitespace from the beginning and end of a string static std::string trim(const std::string & str) { @@ -153,6 +154,8 @@ static std::vector split_string(const std::string& input, char deli } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + srand(1234); common_params params; diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index 8a4faa383b..665191047a 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -3,6 +3,7 @@ #include "log.h" #include "llama.h" +#include #include #include #include @@ -16,6 +17,8 @@ static void print_usage(int, char ** argv) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; params.n_junk = 250; diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 3f2afd4346..9e05fc2233 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -4,6 +4,7 @@ #include "llama.h" #include +#include #include #include // TODO: remove me @@ -112,6 +113,8 @@ static void batch_process(llama_context * ctx, llama_batch & batch, float * outp } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) { diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 5e35dcd603..174c8c7585 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -2,11 +2,14 @@ #include "common.h" #include "llama.h" +#include #include #include int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; params.prompt = "The quick brown fox"; diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp index 57195df331..97e9dc9842 100644 --- a/examples/simple-chat/simple-chat.cpp +++ b/examples/simple-chat/simple-chat.cpp @@ -1,4 +1,5 @@ #include "llama.h" +#include #include #include #include @@ -12,6 +13,8 @@ static void print_usage(int, char ** argv) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + std::string model_path; int ngl = 99; int n_ctx = 2048; diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index d09771d104..9f0a25d713 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -1,4 +1,5 @@ #include "llama.h" +#include #include #include #include @@ -11,6 +12,8 @@ static void print_usage(int, char ** argv) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + // path to the model gguf file std::string model_path; // prompt to generate text from diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp index 336144d3b5..661d92c97a 100644 --- a/examples/speculative-simple/speculative-simple.cpp +++ b/examples/speculative-simple/speculative-simple.cpp @@ -6,12 +6,15 @@ #include "llama.h" #include "chat.h" +#include #include #include #include #include int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) { diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 3e5cf5f46b..250c5b7c62 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -5,6 +5,7 @@ #include "llama.h" #include +#include #include #include #include @@ -30,6 +31,8 @@ struct seq_draft { }; int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; // needed to get candidate probs even for temp <= 0.0 diff --git a/examples/sycl/README.md b/examples/sycl/README.md index 8819d87f56..29143dd617 100644 --- a/examples/sycl/README.md +++ b/examples/sycl/README.md @@ -6,11 +6,11 @@ This example program provides the tools for llama.cpp for SYCL on Intel GPU. |Tool Name| Function|Status| |-|-|-| -|llama-ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, ect.|Support| +|llama-ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, etc.|Support| ### llama-ls-sycl-device -List all SYCL devices with ID, compute capability, max work group size, ect. +List all SYCL devices with ID, compute capability, max work group size, etc. 1. Build the llama.cpp for SYCL for the specified target *(using GGML_SYCL_TARGET)*. diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp index 74a8b7fd81..3bdc405982 100644 --- a/examples/sycl/ls-sycl-device.cpp +++ b/examples/sycl/ls-sycl-device.cpp @@ -6,8 +6,10 @@ #include "ggml-sycl.h" +#include int main() { + std::setlocale(LC_NUMERIC, "C"); ggml_backend_sycl_print_sycl_devices(); return 0; } diff --git a/examples/training/finetune.cpp b/examples/training/finetune.cpp index c82de8d35d..e20f89488f 100644 --- a/examples/training/finetune.cpp +++ b/examples/training/finetune.cpp @@ -3,6 +3,7 @@ #include "log.h" #include "llama.h" +#include #include #include #include @@ -14,6 +15,8 @@ #endif int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; params.escape = false; diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 4323afe57b..44e58a5276 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -248,12 +248,14 @@ set (GGML_SYCL_TARGET "INTEL" CACHE STRING set (GGML_SYCL_DEVICE_ARCH "" CACHE STRING "ggml: sycl device architecture") +option(GGML_OPENVINO "ggml: use OPENVINO" OFF) + option(GGML_OPENCL "ggml: use OpenCL" OFF) option(GGML_OPENCL_PROFILING "ggml: use OpenCL profiling (increases overhead)" OFF) option(GGML_OPENCL_EMBED_KERNELS "ggml: embed kernels" ON) option(GGML_OPENCL_USE_ADRENO_KERNELS "ggml: use optimized kernels for Adreno" ON) set (GGML_OPENCL_TARGET_VERSION "300" CACHE STRING - "gmml: OpenCL API version to target") + "ggml: OpenCL API version to target") option(GGML_HEXAGON "ggml: enable Hexagon backend" OFF) set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml: quantize group size (32, 64, or 128)") @@ -327,6 +329,7 @@ set(GGML_PUBLIC_HEADERS include/ggml-vulkan.h include/ggml-webgpu.h include/ggml-zendnn.h + include/ggml-openvino.h include/gguf.h) set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index a9d1778641..9fd3f7f32a 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -259,7 +259,7 @@ extern "C" { Example usage: // operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be assigned - // preferrably to run on the same backend as the buffer + // preferably to run on the same backend as the buffer ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false, true); diff --git a/ggml/include/ggml-openvino.h b/ggml/include/ggml-openvino.h new file mode 100644 index 0000000000..c43beb07b6 --- /dev/null +++ b/ggml/include/ggml-openvino.h @@ -0,0 +1,37 @@ +#pragma once + +#include "ggml-backend.h" + +#include + +#ifdef __cplusplus +extern "C" { +#endif + +#define GGML_OPENVINO_NAME "OPENVINO" + +// backend API +GGML_BACKEND_API ggml_backend_t ggml_backend_openvino_init(int device); + +GGML_BACKEND_API bool ggml_backend_is_openvino(ggml_backend_t backend); + +GGML_BACKEND_API bool ggml_backend_buffer_is_openvino(ggml_backend_buffer_t buffer); + +GGML_BACKEND_API bool ggml_backend_buft_is_openvino(ggml_backend_buffer_type_t buft); + +GGML_BACKEND_API bool ggml_backend_buft_is_openvino_host(ggml_backend_buffer_type_t buft); + +GGML_BACKEND_API size_t ggml_backend_openvino_buffer_get_ctx_id(ggml_backend_buffer_t buffer); + +// device buffer +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_openvino_buffer_type(int device); + +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_openvino_host_buffer_type(int device); + +GGML_BACKEND_API int ggml_backend_openvino_get_device_count(void); + +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_openvino_reg(void); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/include/ggml-opt.h b/ggml/include/ggml-opt.h index 4703a05afe..1c2ed79b77 100644 --- a/ggml/include/ggml-opt.h +++ b/ggml/include/ggml-opt.h @@ -138,7 +138,7 @@ extern "C" { GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params); GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx); - // set gradients to zero, initilize loss, and optionally reset the optimizer + // set gradients to zero, initialize loss, and optionally reset the optimizer GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer); GGML_API bool ggml_opt_static_graphs(ggml_opt_context_t opt_ctx); // whether the graphs are allocated_statically diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index df1ad2a516..1c11495b66 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -8,7 +8,12 @@ extern "C" { #define RPC_PROTO_MAJOR_VERSION 3 #define RPC_PROTO_MINOR_VERSION 6 -#define RPC_PROTO_PATCH_VERSION 0 +#define RPC_PROTO_PATCH_VERSION 1 + +#ifdef __cplusplus +static_assert(GGML_OP_COUNT == 96, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION"); +#endif + #define GGML_RPC_MAX_SERVERS 16 // backend API diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 77af0e7fb6..25f9601e9b 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -427,7 +427,8 @@ extern "C" { // GGML_TYPE_IQ4_NL_4_8 = 37, // GGML_TYPE_IQ4_NL_8_8 = 38, GGML_TYPE_MXFP4 = 39, // MXFP4 (1 block) - GGML_TYPE_COUNT = 40, + GGML_TYPE_NVFP4 = 40, // NVFP4 (4 blocks, E4M3 scale) + GGML_TYPE_COUNT = 41, }; // precision @@ -463,6 +464,7 @@ extern "C" { GGML_FTYPE_MOSTLY_IQ1_M = 23, // except 1d tensors GGML_FTYPE_MOSTLY_BF16 = 24, // except 1d tensors GGML_FTYPE_MOSTLY_MXFP4 = 25, // except 1d tensors + GGML_FTYPE_MOSTLY_NVFP4 = 26, // except 1d tensors }; // available tensor operations: @@ -556,6 +558,7 @@ extern "C" { GGML_OP_GATED_LINEAR_ATTN, GGML_OP_RWKV_WKV7, GGML_OP_SOLVE_TRI, + GGML_OP_GATED_DELTA_NET, GGML_OP_UNARY, @@ -730,10 +733,6 @@ extern "C" { GGML_API size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row - GGML_DEPRECATED( - GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float - "use ggml_row_size() instead"); - GGML_API const char * ggml_type_name(enum ggml_type type); GGML_API const char * ggml_op_name (enum ggml_op op); GGML_API const char * ggml_op_symbol(enum ggml_op op); @@ -2467,6 +2466,17 @@ extern "C" { bool lower, bool uni); + // TODO: add ggml_gated_delta_net_set_bcast() to be able to configure Q, K broadcast type: tiled vs interleaved [TAG_GGML_GDN_BCAST] + // ref: https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306 + GGML_API struct ggml_tensor * ggml_gated_delta_net( + struct ggml_context * ctx, + struct ggml_tensor * q, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * g, + struct ggml_tensor * beta, + struct ggml_tensor * state); + // custom operators typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata); @@ -2579,7 +2589,7 @@ extern "C" { struct ggml_tensor * grad, struct ggml_tensor * sgd_params); // alpha, weight decay - // build forward mutiple tensors and select one of them for computing + // build forward multiple tensors and select one of them for computing // this is useful for creating graphs that have constant topology but compute different things based on the input // ref: https://github.com/ggml-org/llama.cpp/pull/18550 // diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 265023733e..78853304d9 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -460,6 +460,7 @@ ggml_add_backend(zDNN) ggml_add_backend(OpenCL) ggml_add_backend(Hexagon) ggml_add_backend(ZenDNN) +ggml_add_backend(OPENVINO) foreach (target ggml-base ggml) target_include_directories(${target} PUBLIC $ $) diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp index 311fa5fe36..0587109212 100644 --- a/ggml/src/ggml-backend-reg.cpp +++ b/ggml/src/ggml-backend-reg.cpp @@ -82,6 +82,10 @@ #include "ggml-zendnn.h" #endif +#ifdef GGML_USE_OPENVINO +#include "ggml-openvino.h" +#endif + namespace fs = std::filesystem; static std::string path_str(const fs::path & path) { @@ -154,6 +158,9 @@ struct ggml_backend_registry { #ifdef GGML_USE_RPC register_backend(ggml_backend_rpc_reg()); #endif +#ifdef GGML_USE_OPENVINO + register_backend(ggml_backend_openvino_reg()); +#endif #ifdef GGML_USE_CPU register_backend(ggml_backend_cpu_reg()); #endif @@ -557,6 +564,7 @@ void ggml_backend_load_all_from_path(const char * dir_path) { ggml_backend_load_best("opencl", silent, dir_path); ggml_backend_load_best("hexagon", silent, dir_path); ggml_backend_load_best("musa", silent, dir_path); + ggml_backend_load_best("openvino", silent, dir_path); ggml_backend_load_best("cpu", silent, dir_path); // check the environment variable GGML_BACKEND_PATH to load an out-of-tree backend const char * backend_path = std::getenv("GGML_BACKEND_PATH"); diff --git a/ggml/src/ggml-blas/ggml-blas.cpp b/ggml/src/ggml-blas/ggml-blas.cpp index 2e9ddf2240..5de64b816f 100644 --- a/ggml/src/ggml-blas/ggml-blas.cpp +++ b/ggml/src/ggml-blas/ggml-blas.cpp @@ -339,8 +339,8 @@ static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t } static void ggml_backend_blas_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - // TODO - *free = 0; + // no memory to report + *free = 0; *total = 0; GGML_UNUSED(dev); diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h index 93ab7ea446..92cf739e7a 100644 --- a/ggml/src/ggml-common.h +++ b/ggml/src/ggml-common.h @@ -102,6 +102,9 @@ typedef sycl::half2 ggml_half2; #define QI_MXFP4 (QK_MXFP4 / (4 * QR_MXFP4)) #define QR_MXFP4 2 +#define QI_NVFP4 (QK_NVFP4 / (4 * QR_NVFP4)) +#define QR_NVFP4 2 + #define QI5_0 (QK5_0 / (4 * QR5_0)) #define QR5_0 2 @@ -194,6 +197,14 @@ typedef struct { } block_mxfp4; static_assert(sizeof(block_mxfp4) == sizeof(uint8_t) + QK_MXFP4/2, "wrong mxfp4 block size/padding"); +#define QK_NVFP4 64 +#define QK_NVFP4_SUB 16 // sub-block size for per-group scales +typedef struct { + uint8_t d[QK_NVFP4/QK_NVFP4_SUB]; // UE4M3 scales (4 bytes, one per 16-element sub-block) + uint8_t qs[QK_NVFP4/2]; // packed 4-bit E2M1 values (32 bytes) +} block_nvfp4; +static_assert(sizeof(block_nvfp4) == sizeof(uint8_t)*(QK_NVFP4/QK_NVFP4_SUB) + QK_NVFP4/2, "wrong nvfp4 block size/padding"); + #define QK5_0 32 typedef struct { ggml_half d; // delta diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 3dc948e4d8..6ca3176a2f 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -566,9 +566,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name) # Fetch KleidiAI sources: include(FetchContent) - set(KLEIDIAI_COMMIT_TAG "v1.16.0") + set(KLEIDIAI_COMMIT_TAG "v1.22.0") set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz") - set(KLEIDIAI_ARCHIVE_MD5 "0a9e9008adb6031f9e8cf70dff4a3321") + set(KLEIDIAI_ARCHIVE_MD5 "54049037570ab0ee0a0d126b2ba5ece1") if (POLICY CMP0135) cmake_policy(SET CMP0135 NEW) @@ -608,6 +608,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name) ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/ ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/ ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/ + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_f16p_qsi4c32p/ ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/) set(ARCH_FLAGS_TEMP "${ARCH_FLAGS}") @@ -648,7 +649,6 @@ function(ggml_add_cpu_backend_variant_impl tag_name) if (NOT SME_ENABLED MATCHES -1) list(APPEND GGML_KLEIDIAI_SOURCES - ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa_asm.S @@ -656,10 +656,13 @@ function(ggml_add_cpu_backend_variant_impl tag_name) ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot_asm.S ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa_asm.S + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_f16p_qsi4c32p/kai_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_f16p_qsi4c32p/kai_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa_asm.S ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_pack_bf16p2vlx2_f32_sme.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_pack_f16pmrx2_f32_neon.c ${KLEIDIAI_SRC}/kai/kai_common_sme_asm.S) - set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}+sve+sve2") + set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}+sve+sve2+sme2+fp16") endif() if (NOT SVE_ENABLED MATCHES -1) diff --git a/ggml/src/ggml-cpu/amx/amx.cpp b/ggml/src/ggml-cpu/amx/amx.cpp index 895a571375..9baf3e025e 100644 --- a/ggml/src/ggml-cpu/amx/amx.cpp +++ b/ggml/src/ggml-cpu/amx/amx.cpp @@ -141,27 +141,50 @@ static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_typ namespace ggml::cpu::amx { class extra_buffer_type : ggml::cpu::extra_buffer_type { bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override { - // handle only 2d gemm for now - auto is_contiguous_2d = [](const struct ggml_tensor * t) { - return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1; - }; - - if (op->op == GGML_OP_MUL_MAT && is_contiguous_2d(op->src[0]) && // src0 must be contiguous - is_contiguous_2d(op->src[1]) && // src1 must be contiguous - op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_amx_buffer_type() && - op->src[0]->ne[0] % (TILE_K * 2 * 32) == 0 && // TODO: not sure if correct (https://github.com/ggml-org/llama.cpp/pull/16315) - op->ne[0] % (TILE_N * 2) == 0 && // out_features is 32x - (qtype_has_amx_kernels(op->src[0]->type) || (op->src[0]->type == GGML_TYPE_F16))) { - // src1 must be host buffer - if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) { - return false; - } - // src1 must be float32 - if (op->src[1]->type == GGML_TYPE_F32) { - return true; - } + if (op->op != GGML_OP_MUL_MAT) { + return false; } - return false; + auto * src0 = op->src[0]; + auto * src1 = op->src[1]; + + if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) { + return false; + } + if (!src0->buffer || src0->buffer->buft != ggml_backend_amx_buffer_type()) { + return false; + } + if (src1->buffer && !ggml_backend_buft_is_host(src1->buffer->buft)) { + return false; + } + if (op->ne[0] % (TILE_N * 2)) { + return false; + } + int alignment; + switch (src0->type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q8_0: + alignment = TILE_K; + break; + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ4_XS: + alignment = 256; // QK_K + break; + case GGML_TYPE_F16: + alignment = 16; + break; + default: + return false; + } + if (src0->ne[0] % alignment) { + return false; + } + if (src1->type != GGML_TYPE_F32) { + return false; + } + return true; } ggml::cpu::tensor_traits * get_tensor_traits(const struct ggml_tensor * op) override { diff --git a/ggml/src/ggml-cpu/amx/common.h b/ggml/src/ggml-cpu/amx/common.h index f392e89851..26a6ec1a2d 100644 --- a/ggml/src/ggml-cpu/amx/common.h +++ b/ggml/src/ggml-cpu/amx/common.h @@ -9,6 +9,8 @@ #if defined(GGML_USE_OPENMP) #include +#else +#include #endif #define TILE_M 16 @@ -56,18 +58,40 @@ inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) { } template -inline void parallel_for(int n, const func_t& f) { +inline void parallel_for(int n, const func_t & f) { + if (n <= 0) { + return; + } #if defined(GGML_USE_OPENMP) -#pragma omp parallel -{ - int nth = omp_get_num_threads(); - int ith = omp_get_thread_num(); - int tbegin, tend; - balance211(n, nth, ith, tbegin, tend); - f(tbegin, tend); -} + #pragma omp parallel + { + int nth = omp_get_num_threads(); + int ith = omp_get_thread_num(); + int tbegin, tend; + balance211(n, nth, ith, tbegin, tend); + f(tbegin, tend); + } #else - f(0, n); + int nth = std::thread::hardware_concurrency(); + if (nth <= 1) { + f(0, n); + return; + } + if (nth > n) { + nth = n; + } + std::vector threads; + threads.reserve(nth); + for (int ith = 0; ith < nth; ++ith) { + threads.emplace_back([&f, n, ith, nth] { + int tbegin, tend; + balance211(n, nth, ith, tbegin, tend); + f(tbegin, tend); + }); + } + for (auto & t : threads) { + t.join(); + } #endif } diff --git a/ggml/src/ggml-cpu/amx/mmq.cpp b/ggml/src/ggml-cpu/amx/mmq.cpp index 47c61b8816..93a6d397f7 100644 --- a/ggml/src/ggml-cpu/amx/mmq.cpp +++ b/ggml/src/ggml-cpu/amx/mmq.cpp @@ -1,4 +1,3 @@ - #if defined(__GNUC__) #pragma GCC diagnostic ignored "-Wpedantic" #pragma GCC diagnostic ignored "-Wunused-local-typedefs" @@ -196,41 +195,33 @@ struct tile_config_t{ // will be needed. // // Here another commonly used pattern 1-3-3 is skipped, as it is mostly used when m <=16; -// and the sinlge batch gemm (m=1) has a special fast path with `avx512-vnni`. +// and the single batch gemm (m=1) has a special fast path with `avx512-vnni`. // // ref: https://www.intel.com/content/www/us/en/developer/articles/code-sample/ // advanced-matrix-extensions-intrinsics-functions.html // -#define TC_CONFIG_TILE(i, r, cb) tc.rows[i] = r; tc.colsb[i] = cb -void ggml_tile_config_init(void) { - static thread_local bool is_first_time = true; +inline void ggml_tile_config_init(void) { + static thread_local bool done = false; - if (!is_first_time) { + if (done) { return; } - static thread_local tile_config_t tc; - tile_config_t current_tc; - _tile_storeconfig(¤t_tc); + alignas(64) tile_config_t tc = {}; + tc.palette_id = 1; + tc.start_row = 0; + tc.rows[0] = 8; tc.colsb[0] = 64; + tc.rows[1] = 8; tc.colsb[1] = 64; + tc.rows[2] = 16; tc.colsb[2] = 32; + tc.rows[3] = 16; tc.colsb[3] = 32; + tc.rows[4] = 16; tc.colsb[4] = 64; + tc.rows[5] = 16; tc.colsb[5] = 64; + tc.rows[6] = 16; tc.colsb[6] = 64; + tc.rows[7] = 16; tc.colsb[7] = 64; - // load only when config changes - if (tc.palette_id == 0 || (memcmp(¤t_tc.colsb, &tc.colsb, sizeof(uint16_t) * 8) != 0 && - memcmp(¤t_tc.rows, &tc.rows, sizeof(uint8_t) * 8) != 0)) { - tc.palette_id = 1; - tc.start_row = 0; - TC_CONFIG_TILE(TMM0, 8, 64); - TC_CONFIG_TILE(TMM1, 8, 64); - TC_CONFIG_TILE(TMM2, 16, 32); - TC_CONFIG_TILE(TMM3, 16, 32); - TC_CONFIG_TILE(TMM4, 16, 64); - TC_CONFIG_TILE(TMM5, 16, 64); - TC_CONFIG_TILE(TMM6, 16, 64); - TC_CONFIG_TILE(TMM7, 16, 64); - _tile_loadconfig(&tc); - } - - is_first_time = false; + _tile_loadconfig(&tc); + done = true; } // we need an extra 16 * 4B (TILE_N * int32_t) for each NB/KB block for compensation. @@ -268,33 +259,6 @@ int get_row_size(int K) { return row_size; } -// vectorized dtype conversion -inline float FP16_TO_FP32(ggml_half val) { - __m256i v = _mm256_setr_epi16( - val, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0); - __m512 o = _mm512_cvtph_ps(v); - return _mm512_cvtss_f32(o); -} - -inline __m512 FP16_TO_FP32_VEC(ggml_half val) { - __m256i v = _mm256_set1_epi16(val); - return _mm512_cvtph_ps(v); -} - -// horizontal reduce -inline float _mm512_reduce_max_ps(const __m512 x) { - __m512 v = x; - __m512 v1 = _mm512_shuffle_f32x4(v, v, 0x4E); - v = _mm512_max_ps(v, v1); - v1 = _mm512_shuffle_f32x4(v, v, 0xB1); - v = _mm512_max_ps(v, v1); - v1 = _mm512_shuffle_ps(v, v, 0x4E); - v = _mm512_max_ps(v, v1); - v1 = _mm512_shuffle_ps(v, v, 0xB1); - v = _mm512_max_ps(v, v1); - return _mm512_cvtss_f32(v); -} - // transpose utils #define SHUFFLE_EPI32(a, b, mask) \ _mm256_castps_si256(_mm256_shuffle_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b), mask)) @@ -1370,9 +1334,9 @@ struct tinygemm_kernel_avx #define LAUNCH_TINYGEMM_KERNEL_AVX(MB_SIZE, NB_SIZE) \ tinygemm_kernel_avx::apply( \ - K, (const float *)src1->data + mb_start * K, \ - (const type *)src0->data + nb_start * K, \ - (float *)dst->data + mb_start * ldc + nb_start, ldc); + K, (const float *)src1->data + src1_offset + mb_start * K, \ + (const type *)src0->data + src0_offset + nb_start * K, \ + (float *)dst->data + dst_offset + mb_start * ldc + nb_start, ldc) // re-organize in the format {NB, KB, TILE_SIZE}: @@ -1415,8 +1379,8 @@ struct tinygemm_kernel_vnni::apply( \ - KB, (const char *)wdata + 0 * row_size_A, \ - (const char *)src0->data + PACKED_INDEX(nb * kTilesN, 0, KB, TILE_SIZE), \ - (float *) dst->data + 0 * N + nb_start, ldc) +#define LAUNCH_TINYGEMM_KERNEL_VNNI(NB_SIZE) \ + tinygemm_kernel_vnni::apply( \ + KB, wdata_batch, \ + (const char *)src0->data + src0_offset + PACKED_INDEX(nb * kTilesN, 0, KB, TILE_SIZE), \ + (float *) dst->data + dst_offset + nb_start, ldc) template ::value, int>::type = 0> @@ -2079,7 +2043,7 @@ void tinygemm_kernel_amx(int M, int N, int KB, const void * RESTRICT _A, const v _tile_stored(TMM5, Tile5(C_pre), TILE_N * sizeof(int32_t)); if (need_unpack) { - unpack_B(Tile1, B_blk0); + unpack_B(Tile1, B_blk1); _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK); } else { _tile_loadd(TMM1, B_blk1, TILE_N * VNNI_BLK); @@ -2336,6 +2300,13 @@ void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * d }); } +// ne2 is passed explicitly to help compiler optimize repeated calls +inline int64_t ggml_batch_offset(const ggml_tensor * t, int64_t batch_idx, int64_t ne2) { + const int64_t i2 = batch_idx % ne2; + const int64_t i3 = batch_idx / ne2; + return i3 * t->nb[3] + i2 * t->nb[2]; +} + size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst) { struct ggml_tensor * src0 = dst->src[0]; @@ -2348,12 +2319,13 @@ size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst) { const int M = dst->ne[1]; const int K = src0->ne[0]; + const int64_t n_batch = dst->ne[2] * dst->ne[3]; size_t desired_wsize = 0; GGML_DISPATCH_QTYPES(TYPE, [&] { const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); - desired_wsize = M * row_size_A; + desired_wsize = n_batch * M * row_size_A; }); return desired_wsize; @@ -2365,7 +2337,7 @@ size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst) { // src1: input in shape of {M, K}, float32 // dst: output in shape of {M, N}, float32 // -// the function performs: dst = src1 @ src0.T +// the function performs: dst = src1 @ src0.T for each batch // void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_tensor * dst) { struct ggml_tensor * src0 = dst->src[0]; @@ -2382,17 +2354,26 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te const int K = src0->ne[0]; const int ldc = dst->nb[1] / dst->nb[0]; + const int64_t ne2 = dst->ne[2]; + const int64_t n_batch = ne2 * dst->ne[3]; + if (is_floating_type) { constexpr int BLOCK_M = 4; constexpr int BLOCK_N = 6; const int MB = div_up(M, BLOCK_M); const int NB = div_up(N, BLOCK_N); - parallel_for_ggml(params, MB * NB, [&](int begin, int end) { + parallel_for_ggml(params, n_batch * MB * NB, [&](int begin, int end) { GGML_DISPATCH_FLOATING_TYPES(TYPE, [&] { for (int i = begin; i < end; ++i) { - int mb = i / NB; - int nb = i % NB; + int batch_idx = i / (MB * NB); + int remaining = i % (MB * NB); + int mb = remaining / NB; + int nb = remaining % NB; + + int64_t src0_offset = ggml_batch_offset(src0, batch_idx, ne2); + int64_t src1_offset = ggml_batch_offset(src1, batch_idx, ne2); + int64_t dst_offset = ggml_batch_offset(dst, batch_idx, ne2); int mb_start = mb * BLOCK_M; int mb_size = std::min(BLOCK_M, M - mb_start); @@ -2424,10 +2405,10 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te void * wdata = params->wdata; //TODO: performance improvement: merge quant A - if (params->ith == 0) { + // if (params->ith == 0) { GGML_DISPATCH_QTYPES(TYPE, [&] { const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); - const size_t desired_wsize = M * row_size_A; + const size_t desired_wsize = n_batch * M * row_size_A; if (params->wsize < desired_wsize) { GGML_ABORT("insufficient work space size"); } @@ -2436,12 +2417,19 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size); - const float * A_data = static_cast(src1->data); - for (int m = 0; m < M; ++m) { - from_float(A_data + m * K, (char *)wdata + m * row_size_A, K); - } + parallel_for_ggml(params, n_batch, [&](int begin, int end) { + for (int batch_idx = begin; batch_idx < end; ++batch_idx) { + int64_t src1_offset = ggml_batch_offset(src1, batch_idx, ne2); + const float * A_data = (const float *)((const char *)src1->data + src1_offset); + char * wdata_batch = (char *)wdata + batch_idx * M * row_size_A; + + for (int m = 0; m < M; ++m) { + from_float(A_data + m * K, wdata_batch + m * row_size_A, K); + } + } + }); }); - } + // } ggml_barrier(params->threadpool); @@ -2451,13 +2439,19 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te constexpr int BLOCK_N = TILE_N * kTilesN; const int NB = div_up(N, BLOCK_N); - parallel_for_ggml(params, NB, [&](int begin, int end) { + parallel_for_ggml(params, n_batch * NB, [&](int begin, int end) { GGML_DISPATCH_QTYPES(TYPE, [&] { const int KB = K / blck_size; const int TILE_SIZE = get_tile_size(); const int row_size_A = KB * sizeof(vec_dot_type); for (int i = begin; i < end; ++i) { - int nb = i; + int batch_idx = i / NB; + int nb = i % NB; + + int64_t src0_offset = ggml_batch_offset(src0, batch_idx, ne2); + int64_t dst_offset = ggml_batch_offset(dst, batch_idx, ne2); + const char * wdata_batch = (const char *)wdata + batch_idx * row_size_A; + int nb_start = nb * BLOCK_N; int nb_size = std::min(BLOCK_N, N - nb_start); // 32, 64, 96 @@ -2481,7 +2475,7 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te const int MB = div_up(M, BLOCK_M); const int NB = div_up(N, BLOCK_N); - parallel_for_ggml(params, MB * NB, [&](int begin, int end) { + parallel_for_ggml(params, n_batch * MB * NB, [&](int begin, int end) { // init tile config for each thread ggml_tile_config_init(); @@ -2491,8 +2485,14 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te const int row_size_A = KB * sizeof(vec_dot_type); for (int i = begin; i < end; ++i) { - int mb = i / NB; - int nb = i % NB; + int batch_idx = i / (MB * NB); + int remaining = i % (MB * NB); + int mb = remaining / NB; + int nb = remaining % NB; + + int64_t src0_offset = ggml_batch_offset(src0, batch_idx, ne2); + int64_t dst_offset = ggml_batch_offset(dst, batch_idx, ne2); + const char * wdata_batch = (const char *)wdata + batch_idx * M * row_size_A; int mb_start = mb * BLOCK_M; int mb_size = std::min(BLOCK_M, M - mb_start); @@ -2501,9 +2501,9 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te tinygemm_kernel_amx( mb_size, nb_size, KB, - (const char *)wdata + mb_start * row_size_A, - (const char *)src0->data + PACKED_INDEX(nb * 2, 0, KB, TILE_SIZE), - (float *) dst->data + mb_start * N + nb_start, ldc); + wdata_batch + mb_start * row_size_A, + (const char *)src0->data + src0_offset + PACKED_INDEX(nb * 2, 0, KB, TILE_SIZE), + (float *) dst->data + dst_offset + mb_start * N + nb_start, ldc); } }); }); diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h index 55526e6fb3..41da829315 100644 --- a/ggml/src/ggml-cpu/arch-fallback.h +++ b/ggml/src/ggml-cpu/arch-fallback.h @@ -15,6 +15,7 @@ #define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1 #define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0 #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0 +#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K @@ -42,11 +43,14 @@ #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_q5_K_8x4_q8_K_generic ggml_gemv_q5_K_8x4_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K #define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0 +#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0 +#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 @@ -55,11 +59,14 @@ #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_q5_K_8x4_q8_K_generic ggml_gemm_q5_K_8x4_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K #define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K -#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K +#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0 +#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0 +#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0 #elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64) @@ -67,35 +74,44 @@ #define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0 +#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0 +#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64) +// quants.c +#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0 // repack.cpp #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 #define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K +#define ggml_gemv_q5_K_8x4_q8_K_generic ggml_gemv_q5_K_8x4_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K #define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 +#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K +#define ggml_gemm_q5_K_8x4_q8_K_generic ggml_gemm_q5_K_8x4_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K #define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 +#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0 #elif defined(__POWERPC__) || defined(__powerpc__) // ref: https://github.com/ggml-org/llama.cpp/pull/14146#issuecomment-2972561679 // quants.c #define quantize_row_q8_K_generic quantize_row_q8_K +#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K @@ -110,11 +126,14 @@ #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_q5_K_8x4_q8_K_generic ggml_gemv_q5_K_8x4_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K #define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0 +#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0 +#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 @@ -123,11 +142,14 @@ #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_q5_K_8x4_q8_K_generic ggml_gemm_q5_K_8x4_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K #define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0 +#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0 +#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0 #elif defined(__loongarch64) @@ -137,6 +159,7 @@ #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0 +#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0 // repack.cpp #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 @@ -148,11 +171,14 @@ #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_q5_K_8x4_q8_K_generic ggml_gemv_q5_K_8x4_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K #define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0 +#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0 +#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 @@ -161,25 +187,23 @@ #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_q5_K_8x4_q8_K_generic ggml_gemm_q5_K_8x4_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K #define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0 +#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0 +#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0 #elif defined(__riscv) // quants.c -#define quantize_row_q8_K_generic quantize_row_q8_K -#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K -#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K -#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K -#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0 -#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K -#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0 +#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0 // repack.cpp +#define ggml_quantize_mat_q8_0_4x1_generic ggml_quantize_mat_q8_0_4x1 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 -#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 +#define ggml_quantize_mat_q8_K_4x1_generic ggml_quantize_mat_q8_K_4x1 #define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 @@ -187,11 +211,14 @@ #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_q5_K_8x4_q8_K_generic ggml_gemv_q5_K_8x4_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K #define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0 +#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0 +#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 @@ -199,16 +226,20 @@ #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_q5_K_8x4_q8_K_generic ggml_gemm_q5_K_8x4_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K #define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0 +#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0 +#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0 #elif defined(__s390x__) // quants.c #define quantize_row_q8_K_generic quantize_row_q8_K +#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K @@ -230,11 +261,14 @@ #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_q5_K_8x4_q8_K_generic ggml_gemv_q5_K_8x4_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K #define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0 +#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0 +#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 @@ -243,11 +277,14 @@ #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_q5_K_8x4_q8_K_generic ggml_gemm_q5_K_8x4_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K #define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0 +#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0 +#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0 #elif defined(__wasm__) @@ -265,6 +302,7 @@ #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0 #define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0 +#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0 // repack.cpp #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 @@ -276,11 +314,14 @@ #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_q5_K_8x4_q8_K_generic ggml_gemv_q5_K_8x4_q8_K #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K #define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0 +#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0 +#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 @@ -289,11 +330,14 @@ #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_q5_K_8x4_q8_K_generic ggml_gemm_q5_K_8x4_q8_K #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K #define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0 +#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0 +#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0 #endif diff --git a/ggml/src/ggml-cpu/arch/arm/quants.c b/ggml/src/ggml-cpu/arch/arm/quants.c index b390ab61c7..c1856201b3 100644 --- a/ggml/src/ggml-cpu/arch/arm/quants.c +++ b/ggml/src/ggml-cpu/arch/arm/quants.c @@ -650,6 +650,90 @@ void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo *s = sumf; } +void ggml_vec_dot_nvfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK_NVFP4 == 0); + + const block_nvfp4 * GGML_RESTRICT x = vx; + const block_q8_0 * GGML_RESTRICT y = vy; + + // Each NVFP4 super-block (64 elements) spans 2 q8_0 blocks + const int nb = n / QK_NVFP4; + + float sumf = 0; + +#if defined __ARM_NEON + const int8x16_t values = vld1q_s8(kvalues_mxfp4); + const uint8x16_t m4b = vdupq_n_u8(0x0f); + float32x4_t acc = vdupq_n_f32(0.0f); + + for (int ib = 0; ib < nb; ++ib) { + const uint8x16_t q4bits_0 = vld1q_u8(x[ib].qs); + const uint8x16_t q4bits_1 = vld1q_u8(x[ib].qs + 16); + + const int8x16_t q4_lo_0 = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits_0, m4b)); + const int8x16_t q4_hi_0 = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits_0, 4)); + const int8x16_t q4_lo_1 = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits_1, m4b)); + const int8x16_t q4_hi_1 = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits_1, 4)); + + const int8x16_t q8_0a = vld1q_s8(y[2*ib].qs); + const int8x16_t q8_0b = vld1q_s8(y[2*ib].qs + 16); + const int8x16_t q8_lo_0 = vcombine_s8(vget_low_s8(q8_0a), vget_low_s8(q8_0b)); + const int8x16_t q8_hi_0 = vcombine_s8(vget_high_s8(q8_0a), vget_high_s8(q8_0b)); + + const int8x16_t q8_1a = vld1q_s8(y[2*ib+1].qs); + const int8x16_t q8_1b = vld1q_s8(y[2*ib+1].qs + 16); + const int8x16_t q8_lo_1 = vcombine_s8(vget_low_s8(q8_1a), vget_low_s8(q8_1b)); + const int8x16_t q8_hi_1 = vcombine_s8(vget_high_s8(q8_1a), vget_high_s8(q8_1b)); + + const int32x4_t p0 = vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), q4_lo_0, q8_lo_0), + ggml_vdotq_s32(vdupq_n_s32(0), q4_hi_0, q8_hi_0)); + const int32x4_t p1 = vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), q4_lo_1, q8_lo_1), + ggml_vdotq_s32(vdupq_n_s32(0), q4_hi_1, q8_hi_1)); + + const int32x4_t sums = vpaddq_s32(p0, p1); + + // Decode 4 UE4M3 scales to f32 and multiply with q8 scales + const float dy0 = GGML_CPU_FP16_TO_FP32(y[2*ib].d); + const float dy1 = GGML_CPU_FP16_TO_FP32(y[2*ib+1].d); + const float32x4_t nvsc = { + ggml_ue4m3_to_fp32(x[ib].d[0]), + ggml_ue4m3_to_fp32(x[ib].d[1]), + ggml_ue4m3_to_fp32(x[ib].d[2]), + ggml_ue4m3_to_fp32(x[ib].d[3]) + }; + const float32x4_t scales = vmulq_f32(nvsc, (float32x4_t){dy0, dy0, dy1, dy1}); + + acc = vfmaq_f32(acc, vcvtq_f32_s32(sums), scales); + } + sumf = vaddvq_f32(acc); +#else + for (int ib = 0; ib < nb; ++ib) { + for (int si = 0; si < 4; ++si) { + const float d = ggml_ue4m3_to_fp32(x[ib].d[si]); + const int q8b = si / 2; + const int q8o = (si % 2) * QK_NVFP4_SUB; + const float dy = GGML_CPU_FP16_TO_FP32(y[2*ib + q8b].d); + + int sumi_lo = 0, sumi_hi = 0; + for (int j = 0; j < QK_NVFP4_SUB/2; ++j) { + const uint8_t qv = x[ib].qs[si*(QK_NVFP4_SUB/2) + j]; + sumi_lo += y[2*ib + q8b].qs[q8o + j + 0] * kvalues_mxfp4[qv & 0xf]; + sumi_hi += y[2*ib + q8b].qs[q8o + j + QK_NVFP4_SUB/2] * kvalues_mxfp4[qv >> 4]; + } + sumf += dy * d * (sumi_lo + sumi_hi); + } + } +#endif + *s = sumf; +} + void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { const int qk = QK8_0; const int nb = n / qk; @@ -968,7 +1052,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi const int vector_length = ggml_cpu_get_sve_cnt()*8; - //VLA Implemenation for SVE + //VLA Implementation for SVE switch (vector_length) { case 128: { diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp index 3a3b32efb2..80ff5ce549 100644 --- a/ggml/src/ggml-cpu/arch/arm/repack.cpp +++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp @@ -498,6 +498,81 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const ggml_gemv_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc); } +void ggml_gemv_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + const int8x16_t kvalues = vld1q_s8(kvalues_mxfp4); + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + float * res_ptr = s; + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_mxfp4x4 * b_ptr = (const block_mxfp4x4 *) vx + (x * nb); + + float32x4_t sumf = vdupq_n_f32(0); + for (int l = 0; l < nb; l++) { + uint8x16_t b_0 = vld1q_u8(b_ptr[l].qs + 0); + uint8x16_t b_1 = vld1q_u8(b_ptr[l].qs + 16); + uint8x16_t b_2 = vld1q_u8(b_ptr[l].qs + 32); + uint8x16_t b_3 = vld1q_u8(b_ptr[l].qs + 48); + + int8x16_t b_0_hi = vqtbl1q_s8(kvalues, b_0 >> 4); + int8x16_t b_0_lo = vqtbl1q_s8(kvalues, b_0 & 0x0F); + int8x16_t b_1_hi = vqtbl1q_s8(kvalues, b_1 >> 4); + int8x16_t b_1_lo = vqtbl1q_s8(kvalues, b_1 & 0x0F); + int8x16_t b_2_hi = vqtbl1q_s8(kvalues, b_2 >> 4); + int8x16_t b_2_lo = vqtbl1q_s8(kvalues, b_2 & 0x0F); + int8x16_t b_3_hi = vqtbl1q_s8(kvalues, b_3 >> 4); + int8x16_t b_3_lo = vqtbl1q_s8(kvalues, b_3 & 0x0F); + + int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 0); + int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16); + + int32x4_t sumi = vdupq_n_s32(0); + sumi = vdotq_laneq_s32(sumi, b_0_lo, a_0, 0); + sumi = vdotq_laneq_s32(sumi, b_0_hi, a_1, 0); + sumi = vdotq_laneq_s32(sumi, b_1_lo, a_0, 1); + sumi = vdotq_laneq_s32(sumi, b_1_hi, a_1, 1); + sumi = vdotq_laneq_s32(sumi, b_2_lo, a_0, 2); + sumi = vdotq_laneq_s32(sumi, b_2_hi, a_1, 2); + sumi = vdotq_laneq_s32(sumi, b_3_lo, a_0, 3); + sumi = vdotq_laneq_s32(sumi, b_3_hi, a_1, 3); + + float32x4_t a_d = vcvt_f32_f16(vld1_dup_f16((const float16_t *)&a_ptr[l].d)); + float32x4_t b_d = { + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[0]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[1]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[2]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[3]), + }; + float32x4_t d = a_d * b_d; + + sumf = vmlaq_f32(sumf, d, vcvtq_f32_s32(sumi)); + } + + vst1q_f32(res_ptr + x * 4, sumf); + } + return; +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + ggml_gemv_mxfp4_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + void ggml_gemv_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { constexpr int qk = QK_K; const int nb = n / qk; @@ -706,7 +781,7 @@ void ggml_gemv_q4_K_8x8_q8_K(int n, const uint8_t * q4_base = q4_ptr[b].qs + sb * QK_K; - // Load the 64 quants from q8K duplicated to use vecdots with the interelaved columns + // Load the 64 quants from q8K duplicated to use vecdots with the interleaved columns // but still need the qs to use the low and hi bits from q4 const int8_t * q8_base = q8_ptr[b].qs + sb * 64; int8x16_t q8_qs[8]; @@ -785,6 +860,165 @@ void ggml_gemv_q4_K_8x8_q8_K(int n, ggml_gemv_q4_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc); } +void ggml_gemv_q5_K_8x4_q8_K(int n, + float * GGML_RESTRICT s, + size_t bs, + const void * GGML_RESTRICT vx, + const void * GGML_RESTRICT vy, + int nr, + int nc) { + constexpr int qk = QK_K; + const int nb = n / qk; + + constexpr int ncols_interleaved = 8; + constexpr int blocklen = 4; + + assert(n % qk == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + constexpr int col_groups = ncols_interleaved / 4; // 0123 and 4567 + const uint8x16_t m4b = vdupq_n_u8(0x0f); + const uint8x16_t mone = vdupq_n_u8(1); + const uint8x16_t mtwo = vdupq_n_u8(2); + + // 1x8 tile = 2 x 4 + float32x4_t acc_f32[col_groups]; + + const block_q8_K * GGML_RESTRICT q8_ptr = (const block_q8_K *) vy; + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q5_Kx8 * GGML_RESTRICT q5_ptr = (const block_q5_Kx8 *) vx + (x * nb); + + for (int i = 0; i < col_groups; i++) { + acc_f32[i] = vdupq_n_f32(0); + } + + for (int b = 0; b < nb; b++) { + float32x4_t q5_d_0 = vcvt_f32_f16(vld1_f16((const __fp16 *) q5_ptr[b].d)); // d0 d1 d2 d3 + float32x4_t q5_d_1 = vcvt_f32_f16(vld1_f16((const __fp16 *) q5_ptr[b].d + 4)); // d4 d5 d6 d7 + float32x4_t q8_d = vdupq_n_f32(q8_ptr[b].d); + float32x4_t sb_scale_0123 = vmulq_f32(q5_d_0, q8_d); + float32x4_t sb_scale_4567 = vmulq_f32(q5_d_1, q8_d); + float32x4_t q5_dmin_0 = vcvt_f32_f16(vld1_f16((const __fp16 *) q5_ptr[b].dmin)); // dmin 0..3 + float32x4_t q5_dmin_1 = vcvt_f32_f16(vld1_f16((const __fp16 *) q5_ptr[b].dmin + 4)); // dmin 4..7 + float32x4_t sb_min_0123 = vmulq_f32(q5_dmin_0, q8_d); + float32x4_t sb_min_4567 = vmulq_f32(q5_dmin_1, q8_d); + + // interleaved bias_acc: [0]->r0 0123, [1]->r0 4567 + int32x4_t bias_acc[2] = { vdupq_n_s32(0), vdupq_n_s32(0) }; + int32x4_t acc_lo[col_groups]; + int32x4_t acc_hi[col_groups]; + + // Each bsum is 16 elements, pairwise add leaves us with the 8 bsums of the entire block + const int16x8_t bsums = vpaddq_s16(vld1q_s16(q8_ptr[b].bsums), vld1q_s16(q8_ptr[b].bsums + 8)); + int16_t bsums_arr[8]; + vst1q_s16(bsums_arr, bsums); + + uint8x16_t qh[col_groups][8]; + for (int c = 0; c < col_groups; c++) { + for (int i = 0; i < 8; i++) { + qh[c][i] = vld1q_u8(q5_ptr[b].qh + i * 32 + 16 * c); + } + } + + for (int sb = 0; sb < QK_K / 64; sb++) { + for (int i = 0; i < col_groups; i++) { + acc_lo[i] = vdupq_n_s32(0); + acc_hi[i] = vdupq_n_s32(0); + } + // Need scales for the low and high nibbles + // 2 * 12 = 24 bytes per subblock, 4 sbs -> 4 * 24 = 96 bytes total + int16x8_t q5sb_mins[2]; + int16x8_t q5sb_scales[2]; + for (int i = 0; i < 2; i++) { + int8_t aux_q5sb[8]; + const int offset = sb * 24 + i * 12; + decode_q_Kx8_6bit_scales(&q5_ptr[b].scales[offset], &q5sb_mins[i], aux_q5sb); + q5sb_scales[i] = vmovl_s8(vld1_s8(aux_q5sb)); + } + + int8x16_t q8_qs[4]; + for (int i = 0; i < 4; i++) { + q8_qs[i] = vld1q_s8(q8_ptr[b].qs + sb * 64 + i * 16); + } + + for (int c = 0; c < col_groups; c++) { + uint8x16_t q5_cols[8]; + uint8x16_t hbit_lo[8]; + uint8x16_t hbit_hi[8]; + int8x16_t q5_lo[8]; + int8x16_t q5_hi[8]; + + for (int i = 0; i < 8; i++) { + q5_cols[i] = vld1q_u8(q5_ptr[b].qs + sb * QK_K + i * 32 + 16 * c); + hbit_lo[i] = vandq_u8(qh[c][i], mone); + hbit_hi[i] = vshlq_n_u8(vandq_u8(qh[c][i], mtwo), 3); + qh[c][i] = vshrq_n_u8(qh[c][i], 2); + q5_lo[i] = vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q5_cols[i], m4b), hbit_lo[i], 4)); + q5_hi[i] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5_cols[i], 4), hbit_hi[i])); + } + + acc_lo[c] = vdotq_laneq_s32(acc_lo[c], q5_lo[0], q8_qs[0], 0); + acc_lo[c] = vdotq_laneq_s32(acc_lo[c], q5_lo[1], q8_qs[0], 1); + acc_lo[c] = vdotq_laneq_s32(acc_lo[c], q5_lo[2], q8_qs[0], 2); + acc_lo[c] = vdotq_laneq_s32(acc_lo[c], q5_lo[3], q8_qs[0], 3); + acc_lo[c] = vdotq_laneq_s32(acc_lo[c], q5_lo[4], q8_qs[1], 0); + acc_lo[c] = vdotq_laneq_s32(acc_lo[c], q5_lo[5], q8_qs[1], 1); + acc_lo[c] = vdotq_laneq_s32(acc_lo[c], q5_lo[6], q8_qs[1], 2); + acc_lo[c] = vdotq_laneq_s32(acc_lo[c], q5_lo[7], q8_qs[1], 3); + + acc_hi[c] = vdotq_laneq_s32(acc_hi[c], q5_hi[0], q8_qs[2], 0); + acc_hi[c] = vdotq_laneq_s32(acc_hi[c], q5_hi[1], q8_qs[2], 1); + acc_hi[c] = vdotq_laneq_s32(acc_hi[c], q5_hi[2], q8_qs[2], 2); + acc_hi[c] = vdotq_laneq_s32(acc_hi[c], q5_hi[3], q8_qs[2], 3); + acc_hi[c] = vdotq_laneq_s32(acc_hi[c], q5_hi[4], q8_qs[3], 0); + acc_hi[c] = vdotq_laneq_s32(acc_hi[c], q5_hi[5], q8_qs[3], 1); + acc_hi[c] = vdotq_laneq_s32(acc_hi[c], q5_hi[6], q8_qs[3], 2); + acc_hi[c] = vdotq_laneq_s32(acc_hi[c], q5_hi[7], q8_qs[3], 3); + } + + // Scales + // row c0123 blk0 and blk1 + const int16x4_t sc_0123_lo = vget_low_s16(q5sb_scales[0]); + const int16x4_t sc_0123_hi = vget_low_s16(q5sb_scales[1]); + const float32x4_t sumf_0123 = vcvtq_f32_s32(vaddq_s32(vmulq_s32(vmovl_s16(sc_0123_lo), acc_lo[0]), + vmulq_s32(vmovl_s16(sc_0123_hi), acc_hi[0]))); + acc_f32[0] = vfmaq_f32(acc_f32[0], sb_scale_0123, sumf_0123); + // row c4567 blk0 and blk1 + const int16x4_t sc_4567_lo = vget_high_s16(q5sb_scales[0]); + const int16x4_t sc_4567_hi = vget_high_s16(q5sb_scales[1]); + const float32x4_t sumf_4567 = vcvtq_f32_s32(vaddq_s32(vmulq_s32(vmovl_s16(sc_4567_lo), acc_lo[1]), + vmulq_s32(vmovl_s16(sc_4567_hi), acc_hi[1]))); + acc_f32[1] = vfmaq_f32(acc_f32[1], sb_scale_4567, sumf_4567); + + // Bias Correction + const int16x4_t bsums_vec_lo = vdup_n_s16(bsums_arr[2 * sb + 0]); + const int16x4_t bsums_vec_hi = vdup_n_s16(bsums_arr[2 * sb + 1]); + + bias_acc[0] = vmlal_s16(bias_acc[0], bsums_vec_lo, vget_low_s16(q5sb_mins[0])); + bias_acc[0] = vmlal_s16(bias_acc[0], bsums_vec_hi, vget_low_s16(q5sb_mins[1])); + bias_acc[1] = vmlal_s16(bias_acc[1], bsums_vec_lo, vget_high_s16(q5sb_mins[0])); + bias_acc[1] = vmlal_s16(bias_acc[1], bsums_vec_hi, vget_high_s16(q5sb_mins[1])); + } // for sb + + acc_f32[0] = vmlsq_f32(acc_f32[0], vcvtq_f32_s32(bias_acc[0]), sb_min_0123); + acc_f32[1] = vmlsq_f32(acc_f32[1], vcvtq_f32_s32(bias_acc[1]), sb_min_4567); + } // for b + + int base = x * ncols_interleaved; + vst1q_f32(s + base, acc_f32[0]); + vst1q_f32(s + base + 4, acc_f32[1]); + } // for x + return; +#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + ggml_gemv_q5_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc); +} + void ggml_gemv_q5_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, @@ -3005,6 +3239,87 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const ggml_gemm_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc); } +void ggml_gemm_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + const int8x16_t kvalues = vld1q_s8(kvalues_mxfp4); + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_mxfp4x4 * b_ptr = (const block_mxfp4x4 *) vx + (x * nb); + + float32x4_t sumf[4]; + for (int m = 0; m < 4; m++) { + sumf[m] = vdupq_n_f32(0); + } + + for (int l = 0; l < nb; l++) { + float32x4_t a_d = vcvt_f32_f16(vld1_f16((const float16_t *)a_ptr[l].d)); + float32x4_t b_d = { + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[0]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[1]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[2]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[3]), + }; + + int32x4_t sumi_0 = vdupq_n_s32(0); + int32x4_t sumi_1 = vdupq_n_s32(0); + int32x4_t sumi_2 = vdupq_n_s32(0); + int32x4_t sumi_3 = vdupq_n_s32(0); + + for (int k = 0; k < 4; k++) { + int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 16 * k + 0); + int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16 * k + 64); + + uint8x16_t b = vld1q_u8(b_ptr[l].qs + 16 * k); + int8x16_t b_hi = vqtbl1q_s8(kvalues, b >> 4); + int8x16_t b_lo = vqtbl1q_s8(kvalues, b & 0xF); + + sumi_0 = vdotq_laneq_s32(sumi_0, b_lo, a_0, 0); + sumi_1 = vdotq_laneq_s32(sumi_1, b_lo, a_0, 1); + sumi_2 = vdotq_laneq_s32(sumi_2, b_lo, a_0, 2); + sumi_3 = vdotq_laneq_s32(sumi_3, b_lo, a_0, 3); + sumi_0 = vdotq_laneq_s32(sumi_0, b_hi, a_1, 0); + sumi_1 = vdotq_laneq_s32(sumi_1, b_hi, a_1, 1); + sumi_2 = vdotq_laneq_s32(sumi_2, b_hi, a_1, 2); + sumi_3 = vdotq_laneq_s32(sumi_3, b_hi, a_1, 3); + } + + sumf[0] = vmlaq_f32(sumf[0], vmulq_laneq_f32(b_d, a_d, 0), vcvtq_f32_s32(sumi_0)); + sumf[1] = vmlaq_f32(sumf[1], vmulq_laneq_f32(b_d, a_d, 1), vcvtq_f32_s32(sumi_1)); + sumf[2] = vmlaq_f32(sumf[2], vmulq_laneq_f32(b_d, a_d, 2), vcvtq_f32_s32(sumi_2)); + sumf[3] = vmlaq_f32(sumf[3], vmulq_laneq_f32(b_d, a_d, 3), vcvtq_f32_s32(sumi_3)); + } + + for (int m = 0; m < 4; m++) { + vst1q_f32(s + (y * 4 + m) * bs + x * 4, sumf[m]); + } + } + } + return; +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + ggml_gemm_mxfp4_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + void ggml_gemm_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { constexpr int qk = QK_K; const int nb = n / qk; @@ -3205,6 +3520,235 @@ void ggml_gemm_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo ggml_gemm_q4_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc); } +void ggml_gemm_q5_K_8x4_q8_K(int n, + float * GGML_RESTRICT s, + size_t bs, + const void * GGML_RESTRICT vx, + const void * GGML_RESTRICT vy, + int nr, + int nc) { + constexpr int qk = QK_K; + const int nb = n / qk; + + constexpr int ncols_interleaved = 8; + constexpr int blocklen = 4; + + assert(n % qk == 0); + assert(nr % 4 == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + constexpr int q8_k_blocklen = 4; + constexpr int acc_size = 2 * 4; // 2 row pairs, 4 col pairs + constexpr int col_groups = ncols_interleaved / 4; + const uint8x16_t m4b = vdupq_n_u8(0x0f); + const uint8x16_t mone = vdupq_n_u8(1); + const uint8x16_t mtwo = vdupq_n_u8(2); + + // 8 accumulators: 2 row pairs, 4 col pairs + float32x4_t acc_f32[acc_size]; + + for (int y = 0; y < nr / q8_k_blocklen; y++) { + const block_q8_Kx4 * GGML_RESTRICT q8_ptr = (const block_q8_Kx4 *) vy + (y * nb); + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q5_Kx8 * GGML_RESTRICT q5_ptr = (const block_q5_Kx8 *) vx + (x * nb); + + for (int i = 0; i < acc_size; i++) { + acc_f32[i] = vdupq_n_f32(0); + } + + for (int b = 0; b < nb; b++) { + // d5 0 1 2 3, 4 5 6 7 + float32x4_t q5_d_0123 = vcvt_f32_f16(vld1_f16((const __fp16 *) q5_ptr[b].d)); + float32x4_t q5_d_4567 = vcvt_f32_f16(vld1_f16((const __fp16 *) q5_ptr[b].d + 4)); + // d8 0 1 2 3 + float32x4_t q8_d_0123 = vld1q_f32(q8_ptr[b].d); + // mins + float32x4_t q5_dmin_0123 = vcvt_f32_f16(vld1_f16((const __fp16 *) q5_ptr[b].dmin)); + float32x4_t q5_dmin_4567 = vcvt_f32_f16(vld1_f16((const __fp16 *) q5_ptr[b].dmin + 4)); + + // Precomputation of scales and mins + float32x4_t sbd_scale_0123[q8_k_blocklen]; + float32x4_t sbd_scale_4567[q8_k_blocklen]; + float32x4_t sbd_min_0123[q8_k_blocklen]; + float32x4_t sbd_min_4567[q8_k_blocklen]; + + sbd_scale_0123[0] = vmulq_laneq_f32(q5_d_0123, q8_d_0123, 0); + sbd_scale_4567[0] = vmulq_laneq_f32(q5_d_4567, q8_d_0123, 0); + sbd_min_0123[0] = vmulq_laneq_f32(q5_dmin_0123, q8_d_0123, 0); + sbd_min_4567[0] = vmulq_laneq_f32(q5_dmin_4567, q8_d_0123, 0); + + sbd_scale_0123[1] = vmulq_laneq_f32(q5_d_0123, q8_d_0123, 1); + sbd_scale_4567[1] = vmulq_laneq_f32(q5_d_4567, q8_d_0123, 1); + sbd_min_0123[1] = vmulq_laneq_f32(q5_dmin_0123, q8_d_0123, 1); + sbd_min_4567[1] = vmulq_laneq_f32(q5_dmin_4567, q8_d_0123, 1); + + sbd_scale_0123[2] = vmulq_laneq_f32(q5_d_0123, q8_d_0123, 2); + sbd_scale_4567[2] = vmulq_laneq_f32(q5_d_4567, q8_d_0123, 2); + sbd_min_0123[2] = vmulq_laneq_f32(q5_dmin_0123, q8_d_0123, 2); + sbd_min_4567[2] = vmulq_laneq_f32(q5_dmin_4567, q8_d_0123, 2); + + sbd_scale_0123[3] = vmulq_laneq_f32(q5_d_0123, q8_d_0123, 3); + sbd_scale_4567[3] = vmulq_laneq_f32(q5_d_4567, q8_d_0123, 3); + sbd_min_0123[3] = vmulq_laneq_f32(q5_dmin_0123, q8_d_0123, 3); + sbd_min_4567[3] = vmulq_laneq_f32(q5_dmin_4567, q8_d_0123, 3); + + // Precomputation of bsums, each vpaddq calcs all the bsums for each row + const int16x8_t bsums[q8_k_blocklen] = { + vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 0), vld1q_s16(q8_ptr[b].bsums + 16 * 0 + 8)), + vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 1), vld1q_s16(q8_ptr[b].bsums + 16 * 1 + 8)), + vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 2), vld1q_s16(q8_ptr[b].bsums + 16 * 2 + 8)), + vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 3), vld1q_s16(q8_ptr[b].bsums + 16 * 3 + 8)), + }; + int16_t bsums_arr[QK_K / 64][8]; + for (int q8_row = 0; q8_row < 4; q8_row++) { + vst1q_s16(bsums_arr[q8_row], bsums[q8_row]); + } + + // interleaved bias_acc: [0]->r0 0123, [1]->r1 0123, .., [4]->r0 4567, [5]->r1 4567 .. + int32x4_t bias_acc[acc_size]; + for (int i = 0; i < acc_size; i++) { + bias_acc[i] = vdupq_n_s32(0); + } + + uint8x16_t qh[col_groups][8]; + for (int c = 0; c < col_groups; c++) { + for (int i = 0; i < 8; i++) { + qh[c][i] = vld1q_u8(q5_ptr[b].qh + i * 32 + 16 * c); + } + } + + for (int sb = 0; sb < QK_K / 64; sb++) { + // Int accumulators for qs vecdot (4 row * 2 col quartets) + int32x4_t acc_lo[acc_size]; + int32x4_t acc_hi[acc_size]; + for (int i = 0; i < acc_size; i++) { + acc_lo[i] = vdupq_n_s32(0); + acc_hi[i] = vdupq_n_s32(0); + } + // Need scales for the low and high nibbles + // 2 * 12 = 24 bytes per subblock, 4 sbs -> 4 * 24 = 96 bytes total + int16x8_t q5sb_scales[2]; + int16x8_t q5sb_mins[2]; + for (int i = 0; i < 2; i++) { + int8_t aux_q5sb[8]; + const int offset = sb * 24 + i * 12; + decode_q_Kx8_6bit_scales(&q5_ptr[b].scales[offset], &q5sb_mins[i], aux_q5sb); + q5sb_scales[i] = vmovl_s8(vld1_s8(aux_q5sb)); + } + + constexpr int reads_per_sb = 8; // 8 * 16 bytes each => 32 qs * 4 rows + for (int k = 0; k < reads_per_sb; k++) { + const int8x16_t q8_blk0 = vld1q_s8(q8_ptr[b].qs + sb * 256 + 16 * k); + const int8x16_t q8_blk1 = vld1q_s8(q8_ptr[b].qs + sb * 256 + 16 * k + 128); + + // 0..3 & 32..35 + const uint8x16_t q5_0123 = vld1q_u8(q5_ptr[b].qs + sb * QK_K + 32 * k); + const uint8x16_t q5_4567 = vld1q_u8(q5_ptr[b].qs + sb * QK_K + 32 * k + 16); + + // NOTE: This is the only difference with q4_K + const uint8x16_t hbit_lo_0123 = vandq_u8(qh[0][k], mone); + const uint8x16_t hbit_hi_0123 = vshlq_n_u8(vandq_u8(qh[0][k], mtwo), 3); + qh[0][k] = vshrq_n_u8(qh[0][k], 2); + const uint8x16_t hbit_lo_4567 = vandq_u8(qh[1][k], mone); + const uint8x16_t hbit_hi_4567 = vshlq_n_u8(vandq_u8(qh[1][k], mtwo), 3); + qh[1][k] = vshrq_n_u8(qh[1][k], 2); + // From here, same as q4_K + + const int8x16_t q5_0123_lo = + vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q5_0123, m4b), hbit_lo_0123, 4)); + const int8x16_t q5_0123_hi = + vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5_0123, 4), hbit_hi_0123)); + + acc_lo[0] = vdotq_laneq_s32(acc_lo[0], q5_0123_lo, q8_blk0, 0); // 0..3 r0 c0123 + acc_lo[1] = vdotq_laneq_s32(acc_lo[1], q5_0123_lo, q8_blk0, 1); // 0..3 r1 c0123 + acc_lo[2] = vdotq_laneq_s32(acc_lo[2], q5_0123_lo, q8_blk0, 2); // 0..3 r2 c0123 + acc_lo[3] = vdotq_laneq_s32(acc_lo[3], q5_0123_lo, q8_blk0, 3); // 0..3 r3 c0123 + + acc_hi[0] = vdotq_laneq_s32(acc_hi[0], q5_0123_hi, q8_blk1, 0); // 32..35 r0 c0123 + acc_hi[1] = vdotq_laneq_s32(acc_hi[1], q5_0123_hi, q8_blk1, 1); // 32..35 r1 c0123 + acc_hi[2] = vdotq_laneq_s32(acc_hi[2], q5_0123_hi, q8_blk1, 2); // 32..35 r2 c0123 + acc_hi[3] = vdotq_laneq_s32(acc_hi[3], q5_0123_hi, q8_blk1, 3); // 32..35 r3 c0123 + + const int8x16_t q5_4567_lo = + vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q5_4567, m4b), hbit_lo_4567, 4)); + const int8x16_t q5_4567_hi = + vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5_4567, 4), hbit_hi_4567)); + + acc_lo[4] = vdotq_laneq_s32(acc_lo[4], q5_4567_lo, q8_blk0, 0); // 0..3 r0 c4567 + acc_lo[5] = vdotq_laneq_s32(acc_lo[5], q5_4567_lo, q8_blk0, 1); // 0..3 r1 c4567 + acc_lo[6] = vdotq_laneq_s32(acc_lo[6], q5_4567_lo, q8_blk0, 2); // 0..3 r2 c4567 + acc_lo[7] = vdotq_laneq_s32(acc_lo[7], q5_4567_lo, q8_blk0, 3); // 0..3 r3 c4567 + + acc_hi[4] = vdotq_laneq_s32(acc_hi[4], q5_4567_hi, q8_blk1, 0); // 32..35 r0 c4567 + acc_hi[5] = vdotq_laneq_s32(acc_hi[5], q5_4567_hi, q8_blk1, 1); // 32..35 r1 c4567 + acc_hi[6] = vdotq_laneq_s32(acc_hi[6], q5_4567_hi, q8_blk1, 2); // 32..35 r2 c4567 + acc_hi[7] = vdotq_laneq_s32(acc_hi[7], q5_4567_hi, q8_blk1, 3); // 32..35 r3 c4567 + } + + // Scale and bias application + // acc is stored interleaved to match output layout + const int16x4_t sc_0123_lo = vget_low_s16(q5sb_scales[0]); + const int16x4_t sc_4567_lo = vget_high_s16(q5sb_scales[0]); + const int16x4_t sc_0123_hi = vget_low_s16(q5sb_scales[1]); + const int16x4_t sc_4567_hi = vget_high_s16(q5sb_scales[1]); + for (int row = 0; row < q8_k_blocklen; row++) { + // Bias correction + // row c0123 blk0 and blk1 + const float32x4_t sumf_0123 = + vcvtq_f32_s32(vaddq_s32(vmulq_s32(vmovl_s16(sc_0123_lo), acc_lo[row]), + vmulq_s32(vmovl_s16(sc_0123_hi), acc_hi[row]))); + acc_f32[2 * row] = vfmaq_f32(acc_f32[2 * row], sbd_scale_0123[row], sumf_0123); + + // row c4567 blk0 and blk1 + const float32x4_t sumf_4567 = + vcvtq_f32_s32(vaddq_s32(vmulq_s32(vmovl_s16(sc_4567_lo), acc_lo[row + 4]), + vmulq_s32(vmovl_s16(sc_4567_hi), acc_hi[row + 4]))); + acc_f32[2 * row + 1] = vfmaq_f32(acc_f32[2 * row + 1], sbd_scale_4567[row], sumf_4567); + + // Bias + const int16x4_t bsums_vec_lo = vdup_n_s16(bsums_arr[sb][row * 2]); + const int16x4_t bsums_vec_hi = vdup_n_s16(bsums_arr[sb][row * 2 + 1]); + + // row c0123 blk0 and blk1 + bias_acc[2 * row] = vmlal_s16(bias_acc[2 * row], bsums_vec_lo, vget_low_s16(q5sb_mins[0])); + bias_acc[2 * row] = vmlal_s16(bias_acc[2 * row], bsums_vec_hi, vget_low_s16(q5sb_mins[1])); + + // row c4567 blk0 and blk1 + bias_acc[2 * row + 1] = + vmlal_s16(bias_acc[2 * row + 1], bsums_vec_lo, vget_high_s16(q5sb_mins[0])); + bias_acc[2 * row + 1] = + vmlal_s16(bias_acc[2 * row + 1], bsums_vec_hi, vget_high_s16(q5sb_mins[1])); + } + } // for sb + + for (int row = 0; row < q8_k_blocklen; row++) { + acc_f32[2 * row] = vmlsq_f32(acc_f32[2 * row], vcvtq_f32_s32(bias_acc[2 * row]), sbd_min_0123[row]); + acc_f32[2 * row + 1] = + vmlsq_f32(acc_f32[2 * row + 1], vcvtq_f32_s32(bias_acc[2 * row + 1]), sbd_min_4567[row]); + } + } // for b + + for (int i = 0; i < q8_k_blocklen; i++) { + int row = y * q8_k_blocklen + i; + for (int j = 0; j < 2; j++) { + int col = x * ncols_interleaved + j * 4; + int offset = row * bs + col; + vst1q_f32(s + offset, acc_f32[2 * i + j]); + } + } + } // for x + } // for y + return; +#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + ggml_gemm_q5_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc); +} + void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, @@ -3252,7 +3796,7 @@ void ggml_gemm_q4_K_8x8_q8_K(int n, for (int b = 0; b < nb; b++) { // bsums pairs belongs to the same q8_k subblock - // 64 elemnts loaded and made sum of 0-7 and 8-15 sum || 16-23 and 24 - 31 sum + // 64 elements loaded and made sum of 0-7 and 8-15 sum || 16-23 and 24 - 31 sum const int16x8_t bsums[4]{ vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 0), vld1q_s16(q8_ptr[b].bsums + 16 * 0 + 8)), vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 1), vld1q_s16(q8_ptr[b].bsums + 16 * 1 + 8)), diff --git a/ggml/src/ggml-cpu/arch/riscv/quants.c b/ggml/src/ggml-cpu/arch/riscv/quants.c index bf9f4df118..826055dd9a 100644 --- a/ggml/src/ggml-cpu/arch/riscv/quants.c +++ b/ggml/src/ggml-cpu/arch/riscv/quants.c @@ -113,6 +113,104 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i #endif } +void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) { + assert(k % QK_K == 0); + block_q8_K * y_blocks = (block_q8_K *)y; + size_t nb = k / QK_K; + +#if defined(__riscv_v_intrinsic) + const size_t vlmax_f32m8 = __riscv_vsetvlmax_e32m8(); + + for (size_t i = 0; i < nb; i++) { + const float* x_block = x + i * QK_K; + block_q8_K* y_block = &y_blocks[i]; + + // 1. Calculate Min/Max + vfloat32m8_t max_v = __riscv_vfmv_v_f_f32m8(-__builtin_inff(), vlmax_f32m8); + vfloat32m8_t min_v = __riscv_vfmv_v_f_f32m8(__builtin_inff(), vlmax_f32m8); + + size_t rem = QK_K; + size_t offset = 0; + while (rem > 0) { + size_t vl = __riscv_vsetvl_e32m8(rem); + vfloat32m8_t v_curr = __riscv_vle32_v_f32m8(x_block + offset, vl); + max_v = __riscv_vfmax_vv_f32m8(max_v, v_curr, vl); + min_v = __riscv_vfmin_vv_f32m8(min_v, v_curr, vl); + rem -= vl; + offset += vl; + } + + vfloat32m1_t v_init_max = __riscv_vfmv_s_f_f32m1(-__builtin_inff(), 1); + vfloat32m1_t v_init_min = __riscv_vfmv_s_f_f32m1(__builtin_inff(), 1); + + vfloat32m1_t v_scalar_max = __riscv_vfredmax_vs_f32m8_f32m1(max_v, v_init_max, vlmax_f32m8); + vfloat32m1_t v_scalar_min = __riscv_vfredmin_vs_f32m8_f32m1(min_v, v_init_min, vlmax_f32m8); + + float max_val = __riscv_vfmv_f_s_f32m1_f32(v_scalar_max); + float min_val = __riscv_vfmv_f_s_f32m1_f32(v_scalar_min); + + float amax = fabsf(max_val) > fabsf(min_val) ? fabsf(max_val) : fabsf(min_val); + + if (amax == 0.0f) { + y_block->d = 0.0f; + memset(y_block->qs, 0, QK_K); + memset(y_block->bsums, 0, sizeof(y_block->bsums)); + continue; + } + + const float iscale = -127.f / (fabsf(max_val) > fabsf(min_val) ? max_val : min_val); + y_block->d = 1.0f / iscale; + + // 2. Quantize and Calculate Sums + offset = 0; + rem = QK_K; + vint16m1_t v_zero_sum = __riscv_vmv_v_x_i16m1(0, 1); + + while (rem > 0) { + size_t vl = __riscv_vsetvl_e32m8(rem); + vfloat32m8_t v_f = __riscv_vle32_v_f32m8(x_block + offset, vl); + + v_f = __riscv_vfmul_vf_f32m8(v_f, iscale, vl); + + vint32m8_t v_i32 = __riscv_vfcvt_x_f_v_i32m8_rm(v_f, __RISCV_FRM_RNE, vl); + vint16m4_t v_i16 = __riscv_vnclip_wx_i16m4(v_i32, 0, __RISCV_VXRM_RNE, vl); + vint8m2_t v_q = __riscv_vnclip_wx_i8m2(v_i16, 0, __RISCV_VXRM_RNE, vl); + + __riscv_vse8_v_i8m2(y_block->qs + offset, v_q, vl); + + // first iteration clear + + int sum_idx; + vint8m1_t chunk_m1; + vint16m1_t v_sum; + sum_idx = offset / 16; + chunk_m1 = __riscv_vget_v_i8m2_i8m1(v_q, 0); + v_sum = __riscv_vwredsum_vs_i8m1_i16m1(chunk_m1, v_zero_sum, 16); + y_block->bsums[sum_idx] = (int16_t)__riscv_vmv_x_s_i16m1_i16(v_sum); + + // remaining iterations + vint8m2_t slid_q = v_q; + for (size_t k = 16; k < vl; k += 16) { + slid_q = __riscv_vslidedown_vx_i8m2(slid_q, 16, vl); + + sum_idx = (offset + k) / 16; + chunk_m1 = __riscv_vget_v_i8m2_i8m1(slid_q, 0); + + v_sum = __riscv_vwredsum_vs_i8m1_i16m1(chunk_m1, v_zero_sum, 16); + y_block->bsums[sum_idx] =(int16_t)__riscv_vmv_x_s_i16m1_i16(v_sum); + } + + rem -= vl; + offset += vl; + } + } +#else + GGML_UNUSED(nb); + // scalar + quantize_row_q8_K_ref(x, y, k); +#endif +} + //===================================== Dot products ================================= void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { @@ -1954,544 +2052,6 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi #endif } -static const uint8_t sign_gather_indices_arr[64] = { - 0,0,0,0,0,0,0,0, 1,1,1,1,1,1,1,1, 2,2,2,2,2,2,2,2, 3,3,3,3,3,3,3,3, - 4,4,4,4,4,4,4,4, 5,5,5,5,5,5,5,5, 6,6,6,6,6,6,6,6, 7,7,7,7,7,7,7,7 -}; - -static const uint8_t sign_bit_masks_arr[64] = { - 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, - 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128 -}; - -static void ggml_vec_dot_iq2_s_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { - assert(n % QK_K == 0); - UNUSED(nrc); UNUSED(bx); UNUSED(by); UNUSED(bs); - - const block_iq2_s * GGML_RESTRICT x = vx; - const block_q8_K * GGML_RESTRICT y = vy; - - const int nb = n / QK_K; - const uint64_t * grid64 = (const uint64_t *)iq2s_grid; - - // --- Pre-load Constants --- - uint16_t gather_qh_arr[8] = {0, 0, 0, 0, 1, 1, 1, 1}; - vuint16mf2_t v_gather_qh = __riscv_vle16_v_u16mf2(gather_qh_arr, 8); - uint16_t shift_qh_arr[8] = {11, 9, 7, 5, 11, 9, 7, 5}; - vuint16mf2_t v_shift_qh = __riscv_vle16_v_u16mf2(shift_qh_arr, 8); - - // Constants for sign extraction - vuint8m2_t v_sign_gather_indices = __riscv_vle8_v_u8m2(sign_gather_indices_arr, 64); - vuint8m2_t v_sign_masks = __riscv_vle8_v_u8m2(sign_bit_masks_arr, 64); - - float sumf = 0.0f; - - for (int i = 0; i < nb; ++i) { - const float combined_scale = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d; - - const uint8_t * GGML_RESTRICT qs = x[i].qs; - const uint8_t * GGML_RESTRICT qh = x[i].qh; - const uint8_t * GGML_RESTRICT scales = x[i].scales; - const int8_t * GGML_RESTRICT q8 = y[i].qs; - - const uint8_t * signs_ptr = qs + 32; - - float sum_block = 0.0f; - - for (int ib = 0; ib < 4; ++ib) { - // Combine low + high bits - vuint8mf4_t v_qs_u8 = __riscv_vle8_v_u8mf4(qs, 8); - qs += 8; - uint16_t qh_val; - memcpy(&qh_val, qh, 2); - qh += 2; - vuint8mf8_t v_qh_raw = __riscv_vle8_v_u8mf8((const uint8_t*)&qh_val, 2); - vuint16mf4_t v_qh_u16 = __riscv_vwcvtu_x_x_v_u16mf4(v_qh_raw, 2); - vuint16mf2_t v_qh_u16_ext = __riscv_vlmul_ext_v_u16mf4_u16mf2(v_qh_u16); - vuint16mf2_t v_qh_expanded = __riscv_vrgather_vv_u16mf2(v_qh_u16_ext, v_gather_qh, 8); - v_qh_expanded = __riscv_vsll_vv_u16mf2(v_qh_expanded, v_shift_qh, 8); - - // Mask: We want bits 11-12. 0x1800 = 0001 1000 0000 0000 - v_qh_expanded = __riscv_vand_vx_u16mf2(v_qh_expanded, 0x1800, 8); - vuint16mf2_t v_qs_u16 = __riscv_vwcvtu_x_x_v_u16mf2(v_qs_u8, 8); - - // Multiply by 8 to get byte offset, instead of element offset - v_qs_u16 = __riscv_vsll_vx_u16mf2(v_qs_u16, 3, 8); - vuint16mf2_t v_grid_offsets = __riscv_vor_vv_u16mf2(v_qs_u16, v_qh_expanded, 8); - - // Lookup Grid using Byte Offsets - vuint64m2_t v_grid_vals = __riscv_vluxei16_v_u64m2(grid64, v_grid_offsets, 8); - - vuint8m2_t v_grid_u8 = __riscv_vreinterpret_v_u64m2_u8m2(v_grid_vals); - vint8m2_t v_grid_i8 = __riscv_vreinterpret_v_u8m2_i8m2(v_grid_u8); - - // Load signs and generate sign mask - vuint8mf4_t v_signs_raw = __riscv_vle8_v_u8mf4(signs_ptr, 8); - signs_ptr += 8; - - vuint8m2_t v_signs_source = __riscv_vlmul_ext_v_u8mf4_u8m2(v_signs_raw); - vuint8m2_t v_signs_bcast = __riscv_vrgather_vv_u8m2(v_signs_source, v_sign_gather_indices, 64); - - vuint8m2_t v_sign_bits = __riscv_vand_vv_u8m2(v_signs_bcast, v_sign_masks, 64); - vbool4_t m_negative = __riscv_vmsne_vx_u8m2_b4(v_sign_bits, 0, 64); - - vint8m2_t v_q8 = __riscv_vle8_v_i8m2(q8, 64); - q8 += 64; - - vint8m2_t v_q8_signed = __riscv_vrsub_vx_i8m2_mu(m_negative, v_q8, v_q8, 0, 64); - vint16m4_t v_dot = __riscv_vwmul_vv_i16m4(v_grid_i8, v_q8_signed, 64); - - vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, 1); - - int32_t s0 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( - __riscv_vget_v_i16m4_i16m1(v_dot, 0), v_zero, 16)); - int32_t s1 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( - __riscv_vget_v_i16m4_i16m1(v_dot, 1), v_zero, 16)); - int32_t s2 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( - __riscv_vget_v_i16m4_i16m1(v_dot, 2), v_zero, 16)); - int32_t s3 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( - __riscv_vget_v_i16m4_i16m1(v_dot, 3), v_zero, 16)); - - uint8_t sc0 = scales[0]; - uint8_t sc1 = scales[1]; - scales += 2; - - sum_block += s0 * (2 * (sc0 & 0xF) + 1); - sum_block += s1 * (2 * (sc0 >> 4) + 1); - sum_block += s2 * (2 * (sc1 & 0xF) + 1); - sum_block += s3 * (2 * (sc1 >> 4) + 1); - } - sumf += sum_block * combined_scale; - } - *s = 0.125f * sumf; -} - -static void ggml_vec_dot_iq2_s_q8_K_vl128(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { - assert(n % QK_K == 0); - UNUSED(nrc); UNUSED(bx); UNUSED(by); UNUSED(bs); - - const block_iq2_s * GGML_RESTRICT x = vx; - const block_q8_K * GGML_RESTRICT y = vy; - - const int nb = n / QK_K; - const uint64_t * grid64 = (const uint64_t *)iq2s_grid; - - // Pre-load Constants - vuint8m2_t v_ids = __riscv_vid_v_u8m2(32); - vuint8m2_t v_sign_gather_indices = __riscv_vsrl_vx_u8m2(v_ids, 3, 32); - vuint8m2_t v_ones = __riscv_vmv_v_x_u8m2(1, 32); - vuint8m2_t v_shift_amts = __riscv_vand_vx_u8m2(v_ids, 7, 32); - vuint8m2_t v_sign_masks = __riscv_vsll_vv_u8m2(v_ones, v_shift_amts, 32); - uint16_t shift_qh_arr[4] = {11, 9, 7, 5}; - vuint16mf2_t v_shift_qh = __riscv_vle16_v_u16mf2(shift_qh_arr, 4); - - float sumf = 0.0f; - - for (int i = 0; i < nb; ++i) { - const float combined_scale = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d; - - const uint8_t * GGML_RESTRICT qs = x[i].qs; - const uint8_t * GGML_RESTRICT qh = x[i].qh; - const uint8_t * GGML_RESTRICT scales = x[i].scales; - const int8_t * GGML_RESTRICT q8 = y[i].qs; - - const uint8_t * signs_ptr = qs + 32; - float sum_block = 0.0f; - - for (int ib = 0; ib < 8; ++ib) { - - // Load Low Bits [4 bytes] - vuint8mf4_t v_qs_u8 = __riscv_vle8_v_u8mf4(qs, 4); - qs += 4; - - // Load 1 byte. It contains bits for 4 mini-blocks. - uint8_t qh_val = *qh++; - - // Combine Low + High bits of 10bit indices - vuint8mf4_t v_qh_raw = __riscv_vmv_v_x_u8mf4(qh_val, 4); - vuint16mf2_t v_qh_u16 = __riscv_vwcvtu_x_x_v_u16mf2(v_qh_raw, 4); - vuint16mf2_t v_qh_mf2 = __riscv_vsll_vv_u16mf2(v_qh_u16, v_shift_qh, 4); - v_qh_mf2 = __riscv_vand_vx_u16mf2(v_qh_mf2, 0x1800, 4); - vuint16mf2_t v_qs_u16_mf2 = __riscv_vwcvtu_x_x_v_u16mf2(v_qs_u8, 4); - vuint16mf2_t v_qs_u16 = __riscv_vsll_vx_u16mf2(v_qs_u16_mf2, 3, 4); - vuint16mf2_t v_grid_offsets = __riscv_vor_vv_u16mf2(v_qs_u16, v_qh_mf2, 4); - - // Lookup Grid - vint8m2_t v_grid_i8 = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u64m2_u8m2(__riscv_vluxei16_v_u64m2(grid64, v_grid_offsets, 4))); - - vuint8mf4_t v_signs_raw = __riscv_vle8_v_u8mf4(signs_ptr, 4); - signs_ptr += 4; - vuint8m2_t v_signs_source = __riscv_vlmul_ext_v_u8mf4_u8m2(v_signs_raw); - vuint8m2_t v_signs_bcast = __riscv_vrgather_vv_u8m2(v_signs_source, v_sign_gather_indices, 32); - - // generating sign mask - vuint8m2_t v_sign_bits = __riscv_vand_vv_u8m2(v_signs_bcast, v_sign_masks, 32); - vbool4_t m_negative = __riscv_vmsne_vx_u8m2_b4(v_sign_bits, 0, 32); - - vint8m2_t v_q8 = __riscv_vle8_v_i8m2(q8, 32); - q8 += 32; - - // apply signs - vint8m2_t v_q8_signed = __riscv_vrsub_vx_i8m2_mu(m_negative,v_q8, v_q8, 0, 32); - vint16m4_t v_dot = __riscv_vwmul_vv_i16m4(v_grid_i8, v_q8_signed, 32); - - // Reduction - vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, 1); - - // Reduce 0-15 (First Half) - int32_t s0 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m2_i32m1( - __riscv_vget_v_i16m4_i16m2(v_dot, 0), v_zero, 16)); - - // Reduce 16-31 (Second Half) - int32_t s1 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m2_i32m1( - __riscv_vget_v_i16m4_i16m2(v_dot, 1), v_zero, 16)); - - // Apply sub Scales - uint8_t sc = *scales++; - - sum_block += s0 * (2 * (sc & 0xF) + 1); - sum_block += s1 * (2 * (sc >> 4) + 1); - } - sumf += sum_block * combined_scale; - } - *s = 0.125f * sumf; -} - -void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { -#if defined __riscv_v_intrinsic - switch (__riscv_vlenb() * 8) { - case 128: - ggml_vec_dot_iq2_s_q8_K_vl128(n, s, bs, vx, bx, vy, by, nrc); - break; - case 256: - ggml_vec_dot_iq2_s_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); - break; - default: - ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); - break; - } -#else - ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); -#endif -} - -static void ggml_vec_dot_iq3_s_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { - assert(n % QK_K == 0); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq3_s * GGML_RESTRICT x = vx; - const block_q8_K * GGML_RESTRICT y = vy; - - const int nb = n / QK_K; - - const uint64_t * grid64 = (const uint64_t *)iq3s_grid; - - // --- Pre-load Constants --- - const uint16_t qh_bit_shifts_arr[16] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 - }; - vuint8m2_t v_sign_gather_indices = __riscv_vle8_v_u8m2(sign_gather_indices_arr, 64); - vuint8m2_t v_sign_masks = __riscv_vle8_v_u8m2(sign_bit_masks_arr, 64); - vuint16m1_t v_qh_shifts = __riscv_vle16_v_u16m1(qh_bit_shifts_arr, 16); - - float sumf = 0.0f; - - for (int i = 0; i < nb; ++i) { - const float d = GGML_CPU_FP16_TO_FP32(x[i].d); - const float combined_scale = d * y[i].d; - - const uint8_t * GGML_RESTRICT qs = x[i].qs; - const uint8_t * GGML_RESTRICT qh = x[i].qh; - const uint8_t * GGML_RESTRICT scales = x[i].scales; - const uint8_t * GGML_RESTRICT signs = x[i].signs; - const int8_t * GGML_RESTRICT q8 = y[i].qs; - - float sum_block = 0.0f; - - // Loop: Process 64 weights (16 mini-blocks of 4) per iteration - for (int ib = 0; ib < 4; ++ib) { - - vuint8mf2_t v_qs_u8 = __riscv_vle8_v_u8mf2(qs, 16); - qs += 16; - - uint16_t qh_val; - memcpy(&qh_val, qh, 2); - qh += 2; - - vuint16m1_t v_qh_val = __riscv_vmv_v_x_u16m1(qh_val, 16); - // Extract bits: (qh >> i) & 1 - v_qh_val = __riscv_vsrl_vv_u16m1(v_qh_val, v_qh_shifts, 16); - v_qh_val = __riscv_vand_vx_u16m1(v_qh_val, 1, 16); - - vuint16m1_t v_qs_u16 = __riscv_vwcvtu_x_x_v_u16m1(v_qs_u8, 16); - v_qs_u16 = __riscv_vsll_vx_u16m1(v_qs_u16, 2, 16); - v_qh_val = __riscv_vsll_vx_u16m1(v_qh_val, 10, 16); - vuint16m1_t v_grid_offsets = __riscv_vor_vv_u16m1(v_qs_u16, v_qh_val, 16); - - // Grid value is 4xuint8 - vuint32m2_t v_grid_packed = __riscv_vluxei16_v_u32m2((const uint32_t *)grid64, v_grid_offsets, 16); - vuint8m2_t v_grid_u8 = __riscv_vreinterpret_v_u32m2_u8m2(v_grid_packed); - vuint8mf4_t v_signs_raw = __riscv_vle8_v_u8mf4(signs, 8); - signs += 8; - - // Generate sign mask - vuint8m2_t v_signs_source = __riscv_vlmul_ext_v_u8mf4_u8m2(v_signs_raw); - vuint8m2_t v_signs_bcast = __riscv_vrgather_vv_u8m2(v_signs_source, v_sign_gather_indices, 64); - vuint8m2_t v_sign_bits = __riscv_vand_vv_u8m2(v_signs_bcast, v_sign_masks, 64); - vbool4_t m_negative = __riscv_vmsne_vx_u8m2_b4(v_sign_bits, 0, 64); - - vint8m2_t v_q8 = __riscv_vle8_v_i8m2(q8, 64); - q8 += 64; - - // Apply Signs - vint8m2_t v_q8_signed = __riscv_vrsub_vx_i8m2_mu(m_negative, v_q8, v_q8, 0, 64); - vint16m4_t v_dot = __riscv_vwmulsu_vv_i16m4(v_q8_signed, v_grid_u8, 64); - - // Reduction - vint16m2_t v_dot_lo = __riscv_vget_v_i16m4_i16m2(v_dot, 0); - vint16m2_t v_dot_hi = __riscv_vget_v_i16m4_i16m2(v_dot, 1); - vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, 1); - - int32_t s_lo = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m2_i32m1(v_dot_lo, v_zero, 32)); - int32_t s_hi = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m2_i32m1(v_dot_hi, v_zero, 32)); - - // Apply sub-scales - uint8_t sc_byte = *scales++; - int sc_lo = (sc_byte & 0xF) * 2 + 1; - int sc_hi = (sc_byte >> 4) * 2 + 1; - - sum_block += s_lo * sc_lo + s_hi * sc_hi; - } - sumf += sum_block * combined_scale; - } - *s = 0.125f * sumf; -} - -void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { -#if defined __riscv_v_intrinsic - switch (__riscv_vlenb() * 8) { - case 256: - ggml_vec_dot_iq3_s_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); - break; - default: - ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); - break; - } -#else - ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); -#endif -} - -static void ggml_vec_dot_tq1_0_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_tq1_0 * GGML_RESTRICT x = vx; - const block_q8_K * GGML_RESTRICT y = vy; - - const int nb = n / QK_K; - - float sumf = 0.0f; - uint8_t pow[16] = {1, 1, 1, 1, 3, 3, 3, 3, 9, 9, 9, 9, 27, 27, 27, 27}; - - for (int i = 0; i < nb; i++) { - // First loop. - vint32m4_t suml1; - { - const int vl = 32; - vuint8m1_t tq = __riscv_vle8_v_u8m1(x[i].qs, vl); - - vuint16m2_t tq0 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(tq, 3, vl), 8, vl); - vuint16m2_t tq1 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(__riscv_vmul_vx_u8m1(tq, 3, vl), 3, vl), 8, vl); - vuint16m2_t tq2 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(__riscv_vmul_vx_u8m1(tq, 9, vl), 3, vl), 8, vl); - vuint16m2_t tq3 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(__riscv_vmul_vx_u8m1(tq, 27, vl), 3, vl), 8, vl); - vuint16m2_t tq4 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(__riscv_vmul_vx_u8m1(tq, 81, vl), 3, vl), 8, vl); - - vint16m2_t q80 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 0, vl), vl); - vint16m2_t q81 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 32, vl), vl); - vint16m2_t q82 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 64, vl), vl); - vint16m2_t q83 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 96, vl), vl); - vint16m2_t q84 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 128, vl), vl); - - vint16m2_t sum0 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq0, 1, vl)), q80, vl); - vint16m2_t sum1 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq1, 1, vl)), q81, vl); - vint16m2_t sum2 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq2, 1, vl)), q82, vl); - vint16m2_t sum3 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq3, 1, vl)), q83, vl); - vint16m2_t sum4 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq4, 1, vl)), q84, vl); - - vint32m4_t sumi0 = __riscv_vwadd_vv_i32m4(sum0, sum1, vl); - vint32m4_t sumi1 = __riscv_vwadd_vv_i32m4(sum2, sum3, vl); - suml1 = __riscv_vadd_vv_i32m4(__riscv_vwcvt_x_x_v_i32m4(sum4, vl), __riscv_vadd_vv_i32m4(sumi0, sumi1, vl), vl); - } - - // Second loop. - vint32m2_t suml2; - { - const int vl = 16; - vuint8mf2_t tq = __riscv_vle8_v_u8mf2(x[i].qs + 32, vl); - - vuint16m1_t tq0 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(tq, 3 * 1, vl), 8, vl); - vuint16m1_t tq1 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vx_u8mf2(tq, 3, vl), 3, vl), 8, vl); - vuint16m1_t tq2 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vx_u8mf2(tq, 9, vl), 3, vl), 8, vl); - vuint16m1_t tq3 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vx_u8mf2(tq, 27, vl), 3, vl), 8, vl); - vuint16m1_t tq4 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vx_u8mf2(tq, 81, vl), 3, vl), 8, vl); - - vint16m1_t q80 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 160, vl), vl); - vint16m1_t q81 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 176, vl), vl); - vint16m1_t q82 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 192, vl), vl); - vint16m1_t q83 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 208, vl), vl); - vint16m1_t q84 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 224, vl), vl); - - vint16m1_t sum0 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq0, 1, vl)), q80, vl); - vint16m1_t sum1 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq1, 1, vl)), q81, vl); - vint16m1_t sum2 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq2, 1, vl)), q82, vl); - vint16m1_t sum3 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq3, 1, vl)), q83, vl); - vint16m1_t sum4 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq4, 1, vl)), q84, vl); - - vint32m2_t sumi0 = __riscv_vwadd_vv_i32m2(sum0, sum1, vl); - vint32m2_t sumi1 = __riscv_vwadd_vv_i32m2(sum2, sum3, vl); - suml2 = __riscv_vadd_vv_i32m2(__riscv_vwcvt_x_x_v_i32m2(sum4, vl), __riscv_vadd_vv_i32m2(sumi0, sumi1, vl), vl); - } - - // Third loop. - vint32m2_t suml3; - { - const int vl = 16; - - uint32_t qh; - memcpy(&qh, &x[i].qh[0], 4); - // Prevent fusion with vmv. - __asm__ __volatile__("" : "+r"(qh)); - vuint8mf2_t tq = __riscv_vreinterpret_v_u32mf2_u8mf2(__riscv_vmv_v_x_u32mf2(qh, vl / 4)); - - vuint8mf2_t p = __riscv_vle8_v_u8mf2(pow, vl); - - vuint16m1_t tq0 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vv_u8mf2(tq, p, vl), 3, vl), 8, vl); - - vint16m1_t q80 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 240, vl), vl); - - vint16m1_t sum0 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq0, 1, vl)), q80, vl); - suml3 = __riscv_vwcvt_x_x_v_i32m2(sum0, vl); - } - - vint32m2_t sumb = __riscv_vadd_vv_i32m2(__riscv_vget_v_i32m4_i32m2(suml1, 0), __riscv_vget_v_i32m4_i32m2(suml1, 1), 16); - sumb = __riscv_vadd_vv_i32m2(sumb, suml2, 16); - sumb = __riscv_vadd_vv_i32m2(sumb, suml3, 16); - - vint32m1_t sum = __riscv_vredsum_vs_i32m2_i32m1(sumb, __riscv_vmv_v_x_i32m1(0, 1), 16); - sumf += __riscv_vmv_x_s_i32m1_i32(sum) * y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d); - } - - *s = sumf; -} - -void ggml_vec_dot_tq1_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { -#if defined __riscv_v_intrinsic - switch (__riscv_vlenb() * 8) { - case 256: - ggml_vec_dot_tq1_0_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); - break; - default: - ggml_vec_dot_tq1_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); - break; - } -#else - ggml_vec_dot_tq1_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); -#endif -} - -static void ggml_vec_dot_tq2_0_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_tq2_0 * GGML_RESTRICT x = vx; - const block_q8_K * GGML_RESTRICT y = vy; - - const int nb = n / QK_K; - - float sumf = 0.0f; - for (int i = 0; i < nb; ++i) { - int32_t sumi = 0; - - for (size_t j = 0; j < sizeof(x[0].qs); j += 32) { - const int8_t * py0 = &y[i].qs[j * 4 + 0 * 32]; - const int8_t * py1 = &y[i].qs[j * 4 + 1 * 32]; - const int8_t * py2 = &y[i].qs[j * 4 + 2 * 32]; - const int8_t * py3 = &y[i].qs[j * 4 + 3 * 32]; - const uint8_t* px = &x[i].qs[j]; - - size_t vlmax_16m2 = __riscv_vsetvl_e16m2(32); - vint16m2_t vacc16 = __riscv_vmv_v_x_i16m2(0, vlmax_16m2); - - size_t vl = __riscv_vsetvl_e8m1(32); - - vuint8m1_t vx_u8 = __riscv_vle8_v_u8m1(px, vl); - - vint8m1_t vy0 = __riscv_vle8_v_i8m1(py0 , vl); - vint8m1_t vy1 = __riscv_vle8_v_i8m1(py1, vl); - vint8m1_t vy2 = __riscv_vle8_v_i8m1(py2, vl); - vint8m1_t vy3 = __riscv_vle8_v_i8m1(py3, vl); - - // l=0 (bits 1:0) - vuint8m1_t t0 = __riscv_vand_vx_u8m1(vx_u8, 0x03, vl); - vint8m1_t vq0 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(t0), 1, vl); - - // l=1 (bits 3:2) - vuint8m1_t t1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vx_u8, 2, vl), 0x03, vl); - vint8m1_t vq1 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(t1), 1, vl); - - // l=2 (bits 5:4) - vuint8m1_t t2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vx_u8, 4, vl), 0x03, vl); - vint8m1_t vq2 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(t2), 1, vl); - - // l=3 (bits 7:6) - vuint8m1_t t3 = __riscv_vsrl_vx_u8m1(vx_u8, 6, vl); // No final AND needed as vsrl shifts in zeros - vint8m1_t vq3 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(t3), 1, vl); - - // 4. Multiply and accumulate - vacc16 = __riscv_vwmacc_vv_i16m2(vacc16, vq0, vy0, vl); - vacc16 = __riscv_vwmacc_vv_i16m2(vacc16, vq1, vy1, vl); - vacc16 = __riscv_vwmacc_vv_i16m2(vacc16, vq2, vy2, vl); - vacc16 = __riscv_vwmacc_vv_i16m2(vacc16, vq3, vy3, vl); - - vlmax_16m2 = __riscv_vsetvl_e16m2(32); - vint32m1_t vzero32 = __riscv_vmv_v_x_i32m1(0, 1); - vint32m1_t vred32 = __riscv_vwredsum_vs_i16m2_i32m1(vacc16, vzero32, vlmax_16m2); - - sumi += __riscv_vmv_x_s_i32m1_i32(vred32); - } - const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d); - sumf += (float)sumi * d; - } - - *s = sumf; -} - -void ggml_vec_dot_tq2_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { -#if defined __riscv_v_intrinsic - switch (__riscv_vlenb() * 8) { - case 256: - ggml_vec_dot_tq2_0_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); - break; - default: - ggml_vec_dot_tq2_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); - break; - } -#else - ggml_vec_dot_tq2_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); -#endif -} - static void ggml_vec_dot_iq1_s_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -2724,3 +2284,1326 @@ void ggml_vec_dot_iq1_m_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo ggml_vec_dot_iq1_m_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); #endif } + +static const uint8_t sign_gather_indices_arr[64] = { + 0,0,0,0,0,0,0,0, 1,1,1,1,1,1,1,1, 2,2,2,2,2,2,2,2, 3,3,3,3,3,3,3,3, + 4,4,4,4,4,4,4,4, 5,5,5,5,5,5,5,5, 6,6,6,6,6,6,6,6, 7,7,7,7,7,7,7,7 +}; + +static const uint8_t sign_bit_masks_arr[64] = { + 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, + 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128, 1,2,4,8,16,32,64,128 +}; + + +static void ggml_vec_dot_iq2_s_q8_K_vl128(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + UNUSED(nrc); UNUSED(bx); UNUSED(by); UNUSED(bs); + + const block_iq2_s * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + + const int nb = n / QK_K; + const uint64_t * grid64 = (const uint64_t *)iq2s_grid; + + // Pre-load Constants + vuint8m2_t v_ids = __riscv_vid_v_u8m2(32); + vuint8m2_t v_sign_gather_indices = __riscv_vsrl_vx_u8m2(v_ids, 3, 32); + vuint8m2_t v_ones = __riscv_vmv_v_x_u8m2(1, 32); + vuint8m2_t v_shift_amts = __riscv_vand_vx_u8m2(v_ids, 7, 32); + vuint8m2_t v_sign_masks = __riscv_vsll_vv_u8m2(v_ones, v_shift_amts, 32); + uint16_t shift_qh_arr[4] = {11, 9, 7, 5}; + vuint16mf2_t v_shift_qh = __riscv_vle16_v_u16mf2(shift_qh_arr, 4); + + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + const float combined_scale = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * GGML_RESTRICT qs = x[i].qs; + const uint8_t * GGML_RESTRICT qh = x[i].qh; + const uint8_t * GGML_RESTRICT scales = x[i].scales; + const int8_t * GGML_RESTRICT q8 = y[i].qs; + + const uint8_t * signs_ptr = qs + 32; + float sum_block = 0.0f; + + for (int ib = 0; ib < 8; ++ib) { + + // Load Low Bits [4 bytes] + vuint8mf4_t v_qs_u8 = __riscv_vle8_v_u8mf4(qs, 4); + qs += 4; + + // Load 1 byte. It contains bits for 4 mini-blocks. + uint8_t qh_val = *qh++; + + // Combine Low + High bits of 10bit indices + vuint8mf4_t v_qh_raw = __riscv_vmv_v_x_u8mf4(qh_val, 4); + vuint16mf2_t v_qh_u16 = __riscv_vwcvtu_x_x_v_u16mf2(v_qh_raw, 4); + vuint16mf2_t v_qh_mf2 = __riscv_vsll_vv_u16mf2(v_qh_u16, v_shift_qh, 4); + v_qh_mf2 = __riscv_vand_vx_u16mf2(v_qh_mf2, 0x1800, 4); + vuint16mf2_t v_qs_u16_mf2 = __riscv_vwcvtu_x_x_v_u16mf2(v_qs_u8, 4); + vuint16mf2_t v_qs_u16 = __riscv_vsll_vx_u16mf2(v_qs_u16_mf2, 3, 4); + vuint16mf2_t v_grid_offsets = __riscv_vor_vv_u16mf2(v_qs_u16, v_qh_mf2, 4); + + // Lookup Grid + vint8m2_t v_grid_i8 = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u64m2_u8m2(__riscv_vluxei16_v_u64m2(grid64, v_grid_offsets, 4))); + + vuint8mf4_t v_signs_raw = __riscv_vle8_v_u8mf4(signs_ptr, 4); + signs_ptr += 4; + vuint8m2_t v_signs_source = __riscv_vlmul_ext_v_u8mf4_u8m2(v_signs_raw); + vuint8m2_t v_signs_bcast = __riscv_vrgather_vv_u8m2(v_signs_source, v_sign_gather_indices, 32); + + // generating sign mask + vuint8m2_t v_sign_bits = __riscv_vand_vv_u8m2(v_signs_bcast, v_sign_masks, 32); + vbool4_t m_negative = __riscv_vmsne_vx_u8m2_b4(v_sign_bits, 0, 32); + + vint8m2_t v_q8 = __riscv_vle8_v_i8m2(q8, 32); + q8 += 32; + + // apply signs + vint8m2_t v_q8_signed = __riscv_vrsub_vx_i8m2_mu(m_negative,v_q8, v_q8, 0, 32); + vint16m4_t v_dot = __riscv_vwmul_vv_i16m4(v_grid_i8, v_q8_signed, 32); + + // Reduction + vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, 1); + + // Reduce 0-15 (First Half) + int32_t s0 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m2_i32m1( + __riscv_vget_v_i16m4_i16m2(v_dot, 0), v_zero, 16)); + + // Reduce 16-31 (Second Half) + int32_t s1 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m2_i32m1( + __riscv_vget_v_i16m4_i16m2(v_dot, 1), v_zero, 16)); + + // Apply sub Scales + uint8_t sc = *scales++; + + sum_block += s0 * (2 * (sc & 0xF) + 1); + sum_block += s1 * (2 * (sc >> 4) + 1); + } + sumf += sum_block * combined_scale; + } + *s = 0.125f * sumf; +} + +static void ggml_vec_dot_iq2_s_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + UNUSED(nrc); UNUSED(bx); UNUSED(by); UNUSED(bs); + + const block_iq2_s * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + + const int nb = n / QK_K; + const uint64_t * grid64 = (const uint64_t *)iq2s_grid; + + // --- Pre-load Constants --- + uint16_t gather_qh_arr[8] = {0, 0, 0, 0, 1, 1, 1, 1}; + vuint16mf2_t v_gather_qh = __riscv_vle16_v_u16mf2(gather_qh_arr, 8); + uint16_t shift_qh_arr[8] = {11, 9, 7, 5, 11, 9, 7, 5}; + vuint16mf2_t v_shift_qh = __riscv_vle16_v_u16mf2(shift_qh_arr, 8); + + // Constants for sign extraction + vuint8m2_t v_sign_gather_indices = __riscv_vle8_v_u8m2(sign_gather_indices_arr, 64); + vuint8m2_t v_sign_masks = __riscv_vle8_v_u8m2(sign_bit_masks_arr, 64); + + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + const float combined_scale = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * GGML_RESTRICT qs = x[i].qs; + const uint8_t * GGML_RESTRICT qh = x[i].qh; + const uint8_t * GGML_RESTRICT scales = x[i].scales; + const int8_t * GGML_RESTRICT q8 = y[i].qs; + + const uint8_t * signs_ptr = qs + 32; + + float sum_block = 0.0f; + + for (int ib = 0; ib < 4; ++ib) { + // Combine low + high bits + vuint8mf4_t v_qs_u8 = __riscv_vle8_v_u8mf4(qs, 8); + qs += 8; + uint16_t qh_val; + memcpy(&qh_val, qh, 2); + qh += 2; + vuint8mf8_t v_qh_raw = __riscv_vle8_v_u8mf8((const uint8_t*)&qh_val, 2); + vuint16mf4_t v_qh_u16 = __riscv_vwcvtu_x_x_v_u16mf4(v_qh_raw, 2); + vuint16mf2_t v_qh_u16_ext = __riscv_vlmul_ext_v_u16mf4_u16mf2(v_qh_u16); + vuint16mf2_t v_qh_expanded = __riscv_vrgather_vv_u16mf2(v_qh_u16_ext, v_gather_qh, 8); + v_qh_expanded = __riscv_vsll_vv_u16mf2(v_qh_expanded, v_shift_qh, 8); + + // Mask: We want bits 11-12. 0x1800 = 0001 1000 0000 0000 + v_qh_expanded = __riscv_vand_vx_u16mf2(v_qh_expanded, 0x1800, 8); + vuint16mf2_t v_qs_u16 = __riscv_vwcvtu_x_x_v_u16mf2(v_qs_u8, 8); + + // Multiply by 8 to get byte offset, instead of element offset + v_qs_u16 = __riscv_vsll_vx_u16mf2(v_qs_u16, 3, 8); + vuint16mf2_t v_grid_offsets = __riscv_vor_vv_u16mf2(v_qs_u16, v_qh_expanded, 8); + + // Lookup Grid using Byte Offsets + vuint64m2_t v_grid_vals = __riscv_vluxei16_v_u64m2(grid64, v_grid_offsets, 8); + + vuint8m2_t v_grid_u8 = __riscv_vreinterpret_v_u64m2_u8m2(v_grid_vals); + vint8m2_t v_grid_i8 = __riscv_vreinterpret_v_u8m2_i8m2(v_grid_u8); + + // Load signs and generate sign mask + vuint8mf4_t v_signs_raw = __riscv_vle8_v_u8mf4(signs_ptr, 8); + signs_ptr += 8; + + vuint8m2_t v_signs_source = __riscv_vlmul_ext_v_u8mf4_u8m2(v_signs_raw); + vuint8m2_t v_signs_bcast = __riscv_vrgather_vv_u8m2(v_signs_source, v_sign_gather_indices, 64); + + vuint8m2_t v_sign_bits = __riscv_vand_vv_u8m2(v_signs_bcast, v_sign_masks, 64); + vbool4_t m_negative = __riscv_vmsne_vx_u8m2_b4(v_sign_bits, 0, 64); + + vint8m2_t v_q8 = __riscv_vle8_v_i8m2(q8, 64); + q8 += 64; + + vint8m2_t v_q8_signed = __riscv_vrsub_vx_i8m2_mu(m_negative, v_q8, v_q8, 0, 64); + vint16m4_t v_dot = __riscv_vwmul_vv_i16m4(v_grid_i8, v_q8_signed, 64); + + vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, 1); + + int32_t s0 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( + __riscv_vget_v_i16m4_i16m1(v_dot, 0), v_zero, 16)); + int32_t s1 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( + __riscv_vget_v_i16m4_i16m1(v_dot, 1), v_zero, 16)); + int32_t s2 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( + __riscv_vget_v_i16m4_i16m1(v_dot, 2), v_zero, 16)); + int32_t s3 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( + __riscv_vget_v_i16m4_i16m1(v_dot, 3), v_zero, 16)); + + uint8_t sc0 = scales[0]; + uint8_t sc1 = scales[1]; + scales += 2; + + sum_block += s0 * (2 * (sc0 & 0xF) + 1); + sum_block += s1 * (2 * (sc0 >> 4) + 1); + sum_block += s2 * (2 * (sc1 & 0xF) + 1); + sum_block += s3 * (2 * (sc1 >> 4) + 1); + } + sumf += sum_block * combined_scale; + } + *s = 0.125f * sumf; +} + +void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 128: + ggml_vec_dot_iq2_s_q8_K_vl128(n, s, bs, vx, bx, vy, by, nrc); + break; + case 256: + ggml_vec_dot_iq2_s_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +#if defined(__riscv_v) +static const int8_t keven_signs_q2xs[1024] = { + 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, + 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, + 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, -1, + 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, + 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1, + 1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, + 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, + 1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, + 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, + 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, 1, + 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, + 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, + 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, 1, + 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, + 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, + 1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, + 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, + 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, + 1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, + 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, + 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, + 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, -1, + 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, + 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, + 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, + 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, -1, + 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, + 1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, + 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, + 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, + 1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, + 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, +}; +#endif + +static void ggml_vec_dot_iq2_xs_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_xs * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + + const int nb = n / QK_K; + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + const uint64_t * grid64 = (const uint64_t *)iq2xs_grid; + + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * GGML_RESTRICT qs = x[i].qs; + const int8_t * GGML_RESTRICT q8 = y[i].qs; + const uint8_t * GGML_RESTRICT scales = x[i].scales; + + int32_t sum_int = 0; + + // Loop over 4 subblocks of 64 elements (QK_K = 256) + for (int ib64 = 0; ib64 < QK_K / 64; ++ib64) { + // Load 8 uint16 indices (controls 64 values) + vuint16mf2_t v_qs = __riscv_vle16_v_u16mf2(qs, 8); + qs += 8; + + // Extract indices for grid (low 9 bits) and signs (high 7 bits) + // Multiply by 8 (<< 3) for byte offsets into the uint64 tables + vuint16mf2_t vidx_grid = __riscv_vsll_vx_u16mf2(__riscv_vand_vx_u16mf2(v_qs, 511, 8), 3, 8); + vuint16mf2_t vidx_sign = __riscv_vsll_vx_u16mf2(__riscv_vsrl_vx_u16mf2(v_qs, 9, 8), 3, 8); + + vuint64m2_t vq2_64 = __riscv_vluxei16_v_u64m2(grid64, vidx_grid, 8); + vuint64m2_t vs2_64 = __riscv_vluxei16_v_u64m2(signs64, vidx_sign, 8); + + vint8m2_t q2u = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u64m2_u8m2(vq2_64)); + vint8m2_t q2s = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u64m2_u8m2(vs2_64)); + + vint8m2_t q2_final = __riscv_vmul_vv_i8m2(q2u, q2s, 64); + + vint8m2_t q8v = __riscv_vle8_v_i8m2(q8, 64); + q8 += 64; + + vint16m4_t prod = __riscv_vwmul_vv_i16m4(q2_final, q8v, 64); + + vint32m1_t zero_vec = __riscv_vmv_v_x_i32m1(0, 1); + + int32_t sum0 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( + __riscv_vget_v_i16m4_i16m1(prod, 0), zero_vec, 16)); + int32_t sum1 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( + __riscv_vget_v_i16m4_i16m1(prod, 1), zero_vec, 16)); + int32_t sum2 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( + __riscv_vget_v_i16m4_i16m1(prod, 2), zero_vec, 16)); + int32_t sum3 = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m1_i32m1( + __riscv_vget_v_i16m4_i16m1(prod, 3), zero_vec, 16)); + + const uint8_t scale_byte_1 = scales[0]; + const uint8_t scale_byte_2 = scales[1]; + scales += 2; + + sum_int += sum0 * ((scale_byte_1 & 0x0F) * 2 + 1); + sum_int += sum1 * ((scale_byte_1 >> 4) * 2 + 1); + sum_int += sum2 * ((scale_byte_2 & 0x0F) * 2 + 1); + sum_int += sum3 * ((scale_byte_2 >> 4) * 2 + 1); + } + + sumf += d * sum_int; + } + *s = 0.125f * sumf; +} + +void ggml_vec_dot_iq2_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 256: + ggml_vec_dot_iq2_xs_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_iq2_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + ggml_vec_dot_iq2_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +static void ggml_vec_dot_iq2_xxs_q8_K_vl128(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_xxs * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + + const int nb = n / QK_K; + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + const uint64_t * grid64 = (const uint64_t *)iq2xxs_grid; + + uint32_t shift_constants[4] = {0, 7, 14, 21}; + vuint32m1_t v_shifts = __riscv_vle32_v_u32m1(shift_constants, 4); + + float sumf = 0.0f; + for (int i = 0; i < nb; ++i) { + const float combined_scale = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * GGML_RESTRICT q2_ptr = (const uint8_t *) x[i].qs; + const int8_t * GGML_RESTRICT q8 = y[i].qs; + + float sum = 0.0f; + + #pragma GCC unroll 1 + for (int ib32 = 0; ib32 < QK_K / 32; ib32 += 2) { + vint8m2_t q8_1 = __riscv_vle8_v_i8m2(q8, 32); q8 += 32; + vint8m2_t q8_2 = __riscv_vle8_v_i8m2(q8, 32); q8 += 32; + + vuint8mf4_t v_raw_q2_1 = __riscv_vle8_v_u8mf4(q2_ptr, 4); + vuint8mf4_t v_raw_q2_2 = __riscv_vle8_v_u8mf4(q2_ptr + 8, 4); + + vuint16mf2_t vidx_q2_1 = __riscv_vwcvtu_x_x_v_u16mf2(v_raw_q2_1, 4); + vuint16mf2_t vidx_q2_2 = __riscv_vwcvtu_x_x_v_u16mf2(v_raw_q2_2, 4); + + vidx_q2_1 = __riscv_vsll_vx_u16mf2(vidx_q2_1, 3, 4); + vidx_q2_2 = __riscv_vsll_vx_u16mf2(vidx_q2_2, 3, 4); + + uint32_t s_packed_1, s_packed_2; + memcpy(&s_packed_1, q2_ptr + 4, 4); + memcpy(&s_packed_2, q2_ptr + 12, 4); + + vuint32m1_t v_s_1 = __riscv_vmv_v_x_u32m1(s_packed_1, 4); + vuint32m1_t v_s_2 = __riscv_vmv_v_x_u32m1(s_packed_2, 4); + v_s_1 = __riscv_vsrl_vv_u32m1(v_s_1, v_shifts, 4); + v_s_2 = __riscv_vsrl_vv_u32m1(v_s_2, v_shifts, 4); + + v_s_1 = __riscv_vand_vx_u32m1(v_s_1, 127, 4); + v_s_2 = __riscv_vand_vx_u32m1(v_s_2, 127, 4); + + vuint16mf2_t vidx_s2_1 = __riscv_vsll_vx_u16mf2(__riscv_vncvt_x_x_w_u16mf2(v_s_1, 4), 3, 4); + vuint16mf2_t vidx_s2_2 = __riscv_vsll_vx_u16mf2(__riscv_vncvt_x_x_w_u16mf2(v_s_2, 4), 3, 4); + + vuint64m2_t vq2_64_1 = __riscv_vluxei16_v_u64m2(grid64, vidx_q2_1, 4); + vuint64m2_t vq2_64_2 = __riscv_vluxei16_v_u64m2(grid64, vidx_q2_2, 4); + + vint8m2_t q2_1 = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u64m2_u8m2(vq2_64_1)); + vint8m2_t q2_2 = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u64m2_u8m2(vq2_64_2)); + + vuint64m2_t vs2_64_1 = __riscv_vluxei16_v_u64m2(signs64, vidx_s2_1, 4); + vuint64m2_t vs2_64_2 = __riscv_vluxei16_v_u64m2(signs64, vidx_s2_2, 4); + vint8m2_t s2_1 = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u64m2_u8m2(vs2_64_1)); + vint8m2_t s2_2 = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u64m2_u8m2(vs2_64_2)); + + vint8m2_t q8s_1 = __riscv_vmul_vv_i8m2(q8_1, s2_1, 32); + vint8m2_t q8s_2 = __riscv_vmul_vv_i8m2(q8_2, s2_2, 32); + + vint16m4_t dot1 = __riscv_vwmul_vv_i16m4(q8s_1, q2_1, 32); + vint16m4_t dot2 = __riscv_vwmul_vv_i16m4(q8s_2, q2_2, 32); + + vint32m1_t zero_vec = __riscv_vmv_v_x_i32m1(0, 1); + vint32m1_t sumv1 = __riscv_vwredsum_vs_i16m4_i32m1(dot1, zero_vec, 32); + vint32m1_t sumv2 = __riscv_vwredsum_vs_i16m4_i32m1(dot2, zero_vec, 32); + + int32_t scalar_sum1 = __riscv_vmv_x_s_i32m1_i32(sumv1); + int32_t scalar_sum2 = __riscv_vmv_x_s_i32m1_i32(sumv2); + + int16_t scale1 = 2 * ((s_packed_1 >> 28) & 0xF) + 1; + int16_t scale2 = 2 * ((s_packed_2 >> 28) & 0xF) + 1; + + sum += scalar_sum1 * scale1 + scalar_sum2 * scale2; + q2_ptr += 16; + } + sumf += sum * combined_scale; + } + *s = 0.125f * sumf; +} + +static void ggml_vec_dot_iq2_xxs_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_xxs * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + + const int nb = n / QK_K; + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + const uint64_t * grid64 = (const uint64_t *)iq2xxs_grid; + + uint32_t shift_constants[4] = {0, 7, 14, 21}; + vuint32mf2_t v_shifts = __riscv_vle32_v_u32mf2(shift_constants, 4); + + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + const float combined_scale = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * GGML_RESTRICT q2_ptr = (const uint8_t *) x[i].qs; + const int8_t * GGML_RESTRICT q8 = y[i].qs; + + float sum = 0.0f; + + for (int ib32 = 0; ib32 < QK_K / 32; ib32 += 2) { + vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8, 32); q8 += 32; + vint8m1_t q8_2 = __riscv_vle8_v_i8m1(q8, 32); q8 += 32; + + vuint8mf8_t v_raw_q2_1 = __riscv_vle8_v_u8mf8(q2_ptr, 4); + vuint8mf8_t v_raw_q2_2 = __riscv_vle8_v_u8mf8(q2_ptr + 8, 4); + + vuint16mf4_t vidx_q2_1 = __riscv_vwcvtu_x_x_v_u16mf4(v_raw_q2_1, 4); + vuint16mf4_t vidx_q2_2 = __riscv_vwcvtu_x_x_v_u16mf4(v_raw_q2_2, 4); + + vidx_q2_1 = __riscv_vsll_vx_u16mf4(vidx_q2_1, 3, 4); + vidx_q2_2 = __riscv_vsll_vx_u16mf4(vidx_q2_2, 3, 4); + + uint32_t s_packed_1, s_packed_2; + memcpy(&s_packed_1, q2_ptr + 4, 4); + memcpy(&s_packed_2, q2_ptr + 12, 4); + + vuint32mf2_t v_s_1 = __riscv_vmv_v_x_u32mf2(s_packed_1, 4); + vuint32mf2_t v_s_2 = __riscv_vmv_v_x_u32mf2(s_packed_2, 4); + + v_s_1 = __riscv_vsrl_vv_u32mf2(v_s_1, v_shifts, 4); + v_s_2 = __riscv_vsrl_vv_u32mf2(v_s_2, v_shifts, 4); + + v_s_1 = __riscv_vand_vx_u32mf2(v_s_1, 127, 4); + v_s_2 = __riscv_vand_vx_u32mf2(v_s_2, 127, 4); + + // Narrow u32 -> u16 (vncvt) and Scale by 8 to get byte offsets + vuint16mf4_t vidx_s2_1 = __riscv_vsll_vx_u16mf4(__riscv_vncvt_x_x_w_u16mf4(v_s_1, 4), 3, 4); + vuint16mf4_t vidx_s2_2 = __riscv_vsll_vx_u16mf4(__riscv_vncvt_x_x_w_u16mf4(v_s_2, 4), 3, 4); + + // Load q2 values from lookup grid + vuint64m1_t vq2_64_1 = __riscv_vluxei16_v_u64m1(grid64, vidx_q2_1, 4); + vuint64m1_t vq2_64_2 = __riscv_vluxei16_v_u64m1(grid64, vidx_q2_2, 4); + vint8m1_t q2_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vreinterpret_v_u64m1_u8m1(vq2_64_1)); + vint8m1_t q2_2 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vreinterpret_v_u64m1_u8m1(vq2_64_2)); + + // Load sign values + vuint64m1_t vs2_64_1 = __riscv_vluxei16_v_u64m1(signs64, vidx_s2_1, 4); + vuint64m1_t vs2_64_2 = __riscv_vluxei16_v_u64m1(signs64, vidx_s2_2, 4); + vint8m1_t s2_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vreinterpret_v_u64m1_u8m1(vs2_64_1)); + vint8m1_t s2_2 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vreinterpret_v_u64m1_u8m1(vs2_64_2)); + + // Apply signs to q8 + vint8m1_t q8s_1 = __riscv_vmul_vv_i8m1(q8_1, s2_1, 32); + vint8m1_t q8s_2 = __riscv_vmul_vv_i8m1(q8_2, s2_2, 32); + + // multiplying q2 with q8 + vint16m2_t dot1 = __riscv_vwmul_vv_i16m2(q8s_1, q2_1, 32); + vint16m2_t dot2 = __riscv_vwmul_vv_i16m2(q8s_2, q2_2, 32); + + vint32m1_t zero_vec = __riscv_vmv_v_x_i32m1(0, 1); + vint32m1_t sumv1 = __riscv_vwredsum_vs_i16m2_i32m1(dot1, zero_vec, 32); + vint32m1_t sumv2 = __riscv_vwredsum_vs_i16m2_i32m1(dot2, zero_vec, 32); + int32_t scalar_sum1 = __riscv_vmv_x_s_i32m1_i32(sumv1); + int32_t scalar_sum2 = __riscv_vmv_x_s_i32m1_i32(sumv2); + int16_t scale1 = 2 * ((s_packed_1 >> 28) & 0xF) + 1; + int16_t scale2 = 2 * ((s_packed_2 >> 28) & 0xF) + 1; + + sum += scalar_sum1 * scale1 + scalar_sum2 * scale2; + q2_ptr += 16; + } + sumf += sum * combined_scale; + } + *s = 0.125f * sumf; +} + +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 128: + ggml_vec_dot_iq2_xxs_q8_K_vl128(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_iq2_xxs_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + ggml_vec_dot_iq2_xxs_q8_K(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +static void ggml_vec_dot_iq3_s_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq3_s * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + + const int nb = n / QK_K; + + const uint64_t * grid64 = (const uint64_t *)iq3s_grid; + + // --- Pre-load Constants --- + const uint16_t qh_bit_shifts_arr[16] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 + }; + vuint8m2_t v_sign_gather_indices = __riscv_vle8_v_u8m2(sign_gather_indices_arr, 64); + vuint8m2_t v_sign_masks = __riscv_vle8_v_u8m2(sign_bit_masks_arr, 64); + vuint16m1_t v_qh_shifts = __riscv_vle16_v_u16m1(qh_bit_shifts_arr, 16); + + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + const float d = GGML_CPU_FP16_TO_FP32(x[i].d); + const float combined_scale = d * y[i].d; + + const uint8_t * GGML_RESTRICT qs = x[i].qs; + const uint8_t * GGML_RESTRICT qh = x[i].qh; + const uint8_t * GGML_RESTRICT scales = x[i].scales; + const uint8_t * GGML_RESTRICT signs = x[i].signs; + const int8_t * GGML_RESTRICT q8 = y[i].qs; + + float sum_block = 0.0f; + + // Loop: Process 64 weights (16 mini-blocks of 4) per iteration + for (int ib = 0; ib < 4; ++ib) { + + vuint8mf2_t v_qs_u8 = __riscv_vle8_v_u8mf2(qs, 16); + qs += 16; + + uint16_t qh_val; + memcpy(&qh_val, qh, 2); + qh += 2; + + vuint16m1_t v_qh_val = __riscv_vmv_v_x_u16m1(qh_val, 16); + // Extract bits: (qh >> i) & 1 + v_qh_val = __riscv_vsrl_vv_u16m1(v_qh_val, v_qh_shifts, 16); + v_qh_val = __riscv_vand_vx_u16m1(v_qh_val, 1, 16); + + vuint16m1_t v_qs_u16 = __riscv_vwcvtu_x_x_v_u16m1(v_qs_u8, 16); + v_qs_u16 = __riscv_vsll_vx_u16m1(v_qs_u16, 2, 16); + v_qh_val = __riscv_vsll_vx_u16m1(v_qh_val, 10, 16); + vuint16m1_t v_grid_offsets = __riscv_vor_vv_u16m1(v_qs_u16, v_qh_val, 16); + + // Grid value is 4xuint8 + vuint32m2_t v_grid_packed = __riscv_vluxei16_v_u32m2((const uint32_t *)grid64, v_grid_offsets, 16); + vuint8m2_t v_grid_u8 = __riscv_vreinterpret_v_u32m2_u8m2(v_grid_packed); + vuint8mf4_t v_signs_raw = __riscv_vle8_v_u8mf4(signs, 8); + signs += 8; + + // Generate sign mask + vuint8m2_t v_signs_source = __riscv_vlmul_ext_v_u8mf4_u8m2(v_signs_raw); + vuint8m2_t v_signs_bcast = __riscv_vrgather_vv_u8m2(v_signs_source, v_sign_gather_indices, 64); + vuint8m2_t v_sign_bits = __riscv_vand_vv_u8m2(v_signs_bcast, v_sign_masks, 64); + vbool4_t m_negative = __riscv_vmsne_vx_u8m2_b4(v_sign_bits, 0, 64); + + vint8m2_t v_q8 = __riscv_vle8_v_i8m2(q8, 64); + q8 += 64; + + // Apply Signs + vint8m2_t v_q8_signed = __riscv_vrsub_vx_i8m2_mu(m_negative, v_q8, v_q8, 0, 64); + vint16m4_t v_dot = __riscv_vwmulsu_vv_i16m4(v_q8_signed, v_grid_u8, 64); + + // Reduction + vint16m2_t v_dot_lo = __riscv_vget_v_i16m4_i16m2(v_dot, 0); + vint16m2_t v_dot_hi = __riscv_vget_v_i16m4_i16m2(v_dot, 1); + vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, 1); + + int32_t s_lo = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m2_i32m1(v_dot_lo, v_zero, 32)); + int32_t s_hi = __riscv_vmv_x_s_i32m1_i32(__riscv_vwredsum_vs_i16m2_i32m1(v_dot_hi, v_zero, 32)); + + // Apply sub-scales + uint8_t sc_byte = *scales++; + int sc_lo = (sc_byte & 0xF) * 2 + 1; + int sc_hi = (sc_byte >> 4) * 2 + 1; + + sum_block += s_lo * sc_lo + s_hi * sc_hi; + } + sumf += sum_block * combined_scale; + } + *s = sumf; +} + +void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 256: + ggml_vec_dot_iq3_s_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +static void ggml_vec_dot_iq3_xxs_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq3_xxs * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + const int nb = n / QK_K; + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + const uint32_t * grid32 = (const uint32_t *)iq3xxs_grid; + + // constants for unpacking logic + const uint32_t shifts_val[8] = {0, 7, 14, 21, 0, 7, 14, 21}; + vuint32m1_t v_shifts = __riscv_vle32_v_u32m1(shifts_val, 8); + + const uint32_t gather_idx_val[8] = {0, 0, 0, 0, 1, 1, 1, 1}; + vuint32m1_t v_gather_idx = __riscv_vle32_v_u32m1(gather_idx_val, 8); + + uint32_t aux32[2]; + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * GGML_RESTRICT q3_indices = x[i].qs; + const uint8_t * GGML_RESTRICT metadata = x[i].qs + QK_K/4; + const int8_t * GGML_RESTRICT q8 = y[i].qs; + + float block_sum = 0.0f; + + for (int ib = 0; ib < QK_K / 64; ++ib) { + // Load q8 (64 bytes) + vint8m2_t v_q8 = __riscv_vle8_v_i8m2(q8, 64); + q8 += 64; + + // load of metadata via memcpy + memcpy(aux32, metadata, 2 * sizeof(uint32_t)); + metadata += 2 * sizeof(uint32_t); + + // Load q3 indices and gather magnitudes + vuint8mf2_t v_q3_idx_u8 = __riscv_vle8_v_u8mf2(q3_indices, 16); + q3_indices += 16; + + vuint16m1_t v_q3_idx_u16 = __riscv_vwmulu_vx_u16m1(v_q3_idx_u8, 4, 16); + vuint32m2_t v_q3_magnitudes_u32 = __riscv_vluxei16_v_u32m2(grid32, v_q3_idx_u16, 16); + vint8m2_t v_q3_magnitudes = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u32m2_u8m2(v_q3_magnitudes_u32)); + + // --- Unpacking of Sign Indices --- + + // 1. Load the 2 auxiliary 32-bit integers into a vector + vuint32m1_t v_aux = __riscv_vle32_v_u32m1(aux32, 2); + + // 2. Broadcast/Gather: replicate aux[0] to first 4 lanes, aux[1] to next 4 lanes + vuint32m1_t v_aux_expanded = __riscv_vrgather_vv_u32m1(v_aux, v_gather_idx, 8); + + // 3. Apply Shifts and Mask: ((val >> shift) & 127) + vuint32m1_t v_s_vals_raw = __riscv_vand_vx_u32m1(__riscv_vsrl_vv_u32m1(v_aux_expanded, v_shifts, 8), 127, 8); + + // 4. Narrow to u16 (required for vluxei index) and multiply by 8 (byte offset for u64 table) + vuint16mf2_t sign_indices_byte_offset = __riscv_vsll_vx_u16mf2(__riscv_vncvt_x_x_w_u16mf2(v_s_vals_raw, 8), 3, 8); + + // 5. Gather Signs + vuint64m2_t v_s_vals_u64 = __riscv_vluxei16_v_u64m2(signs64, sign_indices_byte_offset, 8); + vint8m2_t v_s_vals = __riscv_vreinterpret_v_u8m2_i8m2(__riscv_vreinterpret_v_u64m2_u8m2(v_s_vals_u64)); + + vint8m2_t v_q3_signed = __riscv_vmul_vv_i8m2(v_q3_magnitudes, v_s_vals, 64); + vint16m4_t v_dot = __riscv_vwmul_vv_i16m4(v_q8, v_q3_signed, 64); + + vint16m2_t v_dot_1 = __riscv_vget_v_i16m4_i16m2(v_dot, 0); + vint16m2_t v_dot_2 = __riscv_vget_v_i16m4_i16m2(v_dot, 1); + + vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, 1); + vint32m1_t v_sum_1 = __riscv_vwredsum_vs_i16m2_i32m1(v_dot_1, v_zero, 32); + vint32m1_t v_sum_2 = __riscv_vwredsum_vs_i16m2_i32m1(v_dot_2, v_zero, 32); + + int32_t sum1_i = __riscv_vmv_x_s_i32m1_i32(v_sum_1); + int32_t sum2_i = __riscv_vmv_x_s_i32m1_i32(v_sum_2); + + const float scale1_f = (float)(2 * (aux32[0] >> 28) + 1); + const float scale2_f = (float)(2 * (aux32[1] >> 28) + 1); + + block_sum += sum1_i * scale1_f + sum2_i * scale2_f; + } + + sumf += d * block_sum; + } + *s = 0.25f * sumf; +} + +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 256: + ggml_vec_dot_iq3_xxs_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_iq3_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + ggml_vec_dot_iq3_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +static void ggml_vec_dot_iq4_nl_q8_0_vl128(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK4_NL == 0); + static_assert(QK4_NL == QK8_0, "QK4_NL and QK8_0 must be the same"); + + const block_iq4_nl * GGML_RESTRICT x = vx; + const block_q8_0 * GGML_RESTRICT y = vy; + + const int nb = n / QK4_NL; + + int ib = 0; + float sumf = 0; + + // Load the lookup table once. + const vint8m2_t values = __riscv_vle8_v_i8m2(kvalues_iq4nl, 16); + int acc1, acc2; + + // We process 2 blocks at once. + for (; ib + 1 < nb; ib += 2) { + // Weights and activations. + vuint8m1_t iq4_packed1 = __riscv_vle8_v_u8m1(x[ib + 0].qs, 16); + vint8m2_t q8b1 = __riscv_vle8_v_i8m2(y[ib + 0].qs, 32); + vuint8m1_t iq4_packed2 = __riscv_vle8_v_u8m1(x[ib + 1].qs, 16); + vint8m2_t q8b2 = __riscv_vle8_v_i8m2(y[ib + 1].qs, 32); + + // Unpack the weight blocks. + vuint8m2_t iq4bits1; + iq4bits1 = __riscv_vset_v_u8m1_u8m2(iq4bits1, 0, __riscv_vand_vx_u8m1(iq4_packed1, 0xf, 16)); + iq4bits1 = __riscv_vset_v_u8m1_u8m2(iq4bits1, 1, __riscv_vsrl_vx_u8m1(iq4_packed1, 4, 16)); + vuint8m2_t iq4bits2; + iq4bits2 = __riscv_vset_v_u8m1_u8m2(iq4bits2, 0, __riscv_vand_vx_u8m1(iq4_packed2, 0xf, 16)); + iq4bits2 = __riscv_vset_v_u8m1_u8m2(iq4bits2, 1, __riscv_vsrl_vx_u8m1(iq4_packed2, 4, 16)); + + // Gather values from the lookup table. + vint8m2_t iq4b1 = __riscv_vrgather_vv_i8m2(values, iq4bits1, 32); + vint8m2_t iq4b2 = __riscv_vrgather_vv_i8m2(values, iq4bits2, 32); + + // Accumulation. + vint16m4_t sum1 = __riscv_vwmul_vv_i16m4(q8b1, iq4b1, 32); + vint16m4_t sum2 = __riscv_vwmul_vv_i16m4(q8b2, iq4b2, 32); + __riscv_vse32_v_i32m1(&acc1,__riscv_vwredsum_vs_i16m4_i32m1(sum1, __riscv_vmv_v_x_i32m1(0, 1), 32), 1); + __riscv_vse32_v_i32m1(&acc2,__riscv_vwredsum_vs_i16m4_i32m1(sum2, __riscv_vmv_v_x_i32m1(0, 1), 32), 1); + sumf += ((GGML_CPU_FP16_TO_FP32(x[ib + 0].d) * GGML_CPU_FP16_TO_FP32(y[ib + 0].d) * acc1)); + sumf += ((GGML_CPU_FP16_TO_FP32(x[ib + 1].d) * GGML_CPU_FP16_TO_FP32(y[ib + 1].d) * acc2)); + } + + *s = sumf; +} + +static void ggml_vec_dot_iq4_nl_q8_0_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK4_NL == 0); + static_assert(QK4_NL == QK8_0, "QK4_NL and QK8_0 must be the same"); + + const block_iq4_nl * GGML_RESTRICT x = vx; + const block_q8_0 * GGML_RESTRICT y = vy; + + const int nb = n / QK4_NL; + + int ib = 0; + float sumf = 0; + + // Load the lookup table once. + const vint8mf2_t values = __riscv_vle8_v_i8mf2(kvalues_iq4nl, 16); + int acc1, acc2; + + // We process 2 blocks at once. + for (; ib + 1 < nb; ib += 2) { + // Weights and activations. + vuint8mf2_t iq4_packed1 = __riscv_vle8_v_u8mf2(x[ib + 0].qs, 16); + vint8mf2_t q8b_lo1 = __riscv_vle8_v_i8mf2(y[ib + 0].qs, 16); + vint8mf2_t q8b_hi1 = __riscv_vle8_v_i8mf2(y[ib + 0].qs + 16, 16); + vuint8mf2_t iq4_packed2 = __riscv_vle8_v_u8mf2(x[ib + 1].qs, 16); + vint8mf2_t q8b_lo2 = __riscv_vle8_v_i8mf2(y[ib + 1].qs, 16); + vint8mf2_t q8b_hi2 = __riscv_vle8_v_i8mf2(y[ib + 1].qs + 16, 16); + + // Unpack the weight blocks. + vuint8mf2_t iq4bits_lo1 = __riscv_vand_vx_u8mf2(iq4_packed1, 0xf, 16); + vuint8mf2_t iq4bits_hi1 = __riscv_vsrl_vx_u8mf2(iq4_packed1, 4, 16); + vuint8mf2_t iq4bits_lo2 = __riscv_vand_vx_u8mf2(iq4_packed2, 0xf, 16); + vuint8mf2_t iq4bits_hi2 = __riscv_vsrl_vx_u8mf2(iq4_packed2, 4, 16); + + // Gather values from the lookup table. + vint8mf2_t iq4b_lo1 = __riscv_vrgather_vv_i8mf2(values, iq4bits_lo1, 16); + vint8mf2_t iq4b_hi1 = __riscv_vrgather_vv_i8mf2(values, iq4bits_hi1, 16); + vint8mf2_t iq4b_lo2 = __riscv_vrgather_vv_i8mf2(values, iq4bits_lo2, 16); + vint8mf2_t iq4b_hi2 = __riscv_vrgather_vv_i8mf2(values, iq4bits_hi2, 16); + + // Accumulation. + vint16m1_t sum1 = __riscv_vwmul_vv_i16m1(q8b_lo1, iq4b_lo1, 16); + sum1 = __riscv_vwmacc_vv_i16m1(sum1, q8b_hi1, iq4b_hi1, 16); + vint16m1_t sum2 = __riscv_vwmul_vv_i16m1(q8b_lo2, iq4b_lo2, 16); + sum2 = __riscv_vwmacc_vv_i16m1(sum2, q8b_hi2, iq4b_hi2, 16); + __riscv_vse32_v_i32m1(&acc1,__riscv_vwredsum_vs_i16m1_i32m1(sum1, __riscv_vmv_v_x_i32m1(0, 1), 16), 1); + __riscv_vse32_v_i32m1(&acc2,__riscv_vwredsum_vs_i16m1_i32m1(sum2, __riscv_vmv_v_x_i32m1(0, 1), 16), 1); + sumf += ((GGML_CPU_FP16_TO_FP32(x[ib + 0].d) * GGML_CPU_FP16_TO_FP32(y[ib + 0].d) * acc1)); + sumf += ((GGML_CPU_FP16_TO_FP32(x[ib + 1].d) * GGML_CPU_FP16_TO_FP32(y[ib + 1].d) * acc2)); + } + + *s = sumf; +} + +void ggml_vec_dot_iq4_nl_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 128: + ggml_vec_dot_iq4_nl_q8_0_vl128(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_iq4_nl_q8_0_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + ggml_vec_dot_iq4_nl_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +static void ggml_vec_dot_iq4_xs_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK_K == 0); + + const block_iq4_xs * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + + const int nb = n / QK_K; + +#if defined __riscv_v_intrinsic + const vint8m4_t values = __riscv_vle8_v_i8m4(kvalues_iq4nl, 16); + float sumf = 0; + int acc[4]; + + // Indices for re-ordering IQ4 data. + uint64_t index[16] = { + 0, 1, 8, 9, + 2, 3, 10, 11, + 4, 5,12, 13, + 6, 7, 14, 15, + }; + vuint64m4_t i_vec = __riscv_vle64_v_u64m4(index, 16); + + for (int ibl = 0; ibl < nb; ++ibl) { + const int8_t * q8 = y[ibl].qs; + const uint8_t * iq4 = x[ibl].qs; + uint16_t h = x[ibl].scales_h; + + int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0; + + for (int ib = 0; ib < QK_K / 128; ++ib) { + // Weights and activations. + vuint8m2_t iq4_packed = __riscv_vle8_v_u8m2(iq4, 64); + vint8m4_t q8b = __riscv_vle8_v_i8m4(q8, 128); + iq4 += 64; + q8 += 128; + + // Unpack the weight blocks. + vuint8m2_t iq4bits_lo = __riscv_vand_vx_u8m2(iq4_packed, 0xf, 64); + vuint8m2_t iq4bits_hi = __riscv_vsrl_vx_u8m2(iq4_packed, 4, 64); + vuint8m4_t iq4bits; + iq4bits = __riscv_vset_v_u8m2_u8m4(iq4bits, 0, iq4bits_lo); + iq4bits = __riscv_vset_v_u8m2_u8m4(iq4bits, 1, iq4bits_hi); + vuint8m4_t iq4bits_reorder = __riscv_vreinterpret_v_u64m4_u8m4(__riscv_vrgather_vv_u64m4(__riscv_vreinterpret_v_u8m4_u64m4(iq4bits), i_vec, 16)); + vint8m4_t iq4b = __riscv_vrgather_vv_i8m4(values, iq4bits_reorder, 128); + + // Multiply with activations. + vint16m8_t prod = __riscv_vwmul_vv_i16m8(iq4b, q8b, 128); + + // Reduce separately. + __riscv_vse32_v_i32m1(&acc[0],__riscv_vwredsum_vs_i16m2_i32m1(__riscv_vget_v_i16m8_i16m2(prod, 0), __riscv_vmv_v_x_i32m1(0, 1), 32), 1); + __riscv_vse32_v_i32m1(&acc[1],__riscv_vwredsum_vs_i16m2_i32m1(__riscv_vget_v_i16m8_i16m2(prod, 1), __riscv_vmv_v_x_i32m1(0, 1), 32), 1); + __riscv_vse32_v_i32m1(&acc[2],__riscv_vwredsum_vs_i16m2_i32m1(__riscv_vget_v_i16m8_i16m2(prod, 2), __riscv_vmv_v_x_i32m1(0, 1), 32), 1); + __riscv_vse32_v_i32m1(&acc[3],__riscv_vwredsum_vs_i16m2_i32m1(__riscv_vget_v_i16m8_i16m2(prod, 3), __riscv_vmv_v_x_i32m1(0, 1), 32), 1); + + int ls1 = ((x[ibl].scales_l[ib * 2 + 0] & 0xf) | ((h << 4) & 0x30)) - 32; + int ls2 = ((x[ibl].scales_l[ib * 2 + 0] >> 4) | ((h << 2) & 0x30)) - 32; + int ls3 = ((x[ibl].scales_l[ib * 2 + 1] & 0xf) | ((h << 0) & 0x30)) - 32; + int ls4 = ((x[ibl].scales_l[ib * 2 + 1] >> 4) | ((h >> 2) & 0x30)) - 32; + h >>= 8; + + sumi1 += acc[0] * ls1; + sumi2 += acc[1] * ls2; + sumi3 += acc[2] * ls3; + sumi4 += acc[3] * ls4; + } + + sumf += GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2 + sumi3 + sumi4); + } + + *s = sumf; + +#else + UNUSED(x); + UNUSED(y); + UNUSED(nb); + ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 256: + ggml_vec_dot_iq4_xs_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +static void ggml_vec_dot_tq1_0_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_tq1_0 * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + + const int nb = n / QK_K; + + float sumf = 0.0f; + uint8_t pow[16] = {1, 1, 1, 1, 3, 3, 3, 3, 9, 9, 9, 9, 27, 27, 27, 27}; + + for (int i = 0; i < nb; i++) { + // First loop. + vint32m4_t suml1; + { + const int vl = 32; + vuint8m1_t tq = __riscv_vle8_v_u8m1(x[i].qs, vl); + + vuint16m2_t tq0 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(tq, 3, vl), 8, vl); + vuint16m2_t tq1 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(__riscv_vmul_vx_u8m1(tq, 3, vl), 3, vl), 8, vl); + vuint16m2_t tq2 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(__riscv_vmul_vx_u8m1(tq, 9, vl), 3, vl), 8, vl); + vuint16m2_t tq3 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(__riscv_vmul_vx_u8m1(tq, 27, vl), 3, vl), 8, vl); + vuint16m2_t tq4 = __riscv_vsrl_vx_u16m2(__riscv_vwmulu_vx_u16m2(__riscv_vmul_vx_u8m1(tq, 81, vl), 3, vl), 8, vl); + + vint16m2_t q80 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 0, vl), vl); + vint16m2_t q81 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 32, vl), vl); + vint16m2_t q82 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 64, vl), vl); + vint16m2_t q83 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 96, vl), vl); + vint16m2_t q84 = __riscv_vwcvt_x_x_v_i16m2(__riscv_vle8_v_i8m1(y[i].qs + 128, vl), vl); + + vint16m2_t sum0 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq0, 1, vl)), q80, vl); + vint16m2_t sum1 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq1, 1, vl)), q81, vl); + vint16m2_t sum2 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq2, 1, vl)), q82, vl); + vint16m2_t sum3 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq3, 1, vl)), q83, vl); + vint16m2_t sum4 = __riscv_vmul_vv_i16m2(__riscv_vreinterpret_v_u16m2_i16m2(__riscv_vsub_vx_u16m2(tq4, 1, vl)), q84, vl); + + vint32m4_t sumi0 = __riscv_vwadd_vv_i32m4(sum0, sum1, vl); + vint32m4_t sumi1 = __riscv_vwadd_vv_i32m4(sum2, sum3, vl); + suml1 = __riscv_vadd_vv_i32m4(__riscv_vwcvt_x_x_v_i32m4(sum4, vl), __riscv_vadd_vv_i32m4(sumi0, sumi1, vl), vl); + } + + // Second loop. + vint32m2_t suml2; + { + const int vl = 16; + vuint8mf2_t tq = __riscv_vle8_v_u8mf2(x[i].qs + 32, vl); + + vuint16m1_t tq0 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(tq, 3 * 1, vl), 8, vl); + vuint16m1_t tq1 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vx_u8mf2(tq, 3, vl), 3, vl), 8, vl); + vuint16m1_t tq2 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vx_u8mf2(tq, 9, vl), 3, vl), 8, vl); + vuint16m1_t tq3 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vx_u8mf2(tq, 27, vl), 3, vl), 8, vl); + vuint16m1_t tq4 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vx_u8mf2(tq, 81, vl), 3, vl), 8, vl); + + vint16m1_t q80 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 160, vl), vl); + vint16m1_t q81 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 176, vl), vl); + vint16m1_t q82 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 192, vl), vl); + vint16m1_t q83 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 208, vl), vl); + vint16m1_t q84 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 224, vl), vl); + + vint16m1_t sum0 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq0, 1, vl)), q80, vl); + vint16m1_t sum1 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq1, 1, vl)), q81, vl); + vint16m1_t sum2 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq2, 1, vl)), q82, vl); + vint16m1_t sum3 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq3, 1, vl)), q83, vl); + vint16m1_t sum4 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq4, 1, vl)), q84, vl); + + vint32m2_t sumi0 = __riscv_vwadd_vv_i32m2(sum0, sum1, vl); + vint32m2_t sumi1 = __riscv_vwadd_vv_i32m2(sum2, sum3, vl); + suml2 = __riscv_vadd_vv_i32m2(__riscv_vwcvt_x_x_v_i32m2(sum4, vl), __riscv_vadd_vv_i32m2(sumi0, sumi1, vl), vl); + } + + // Third loop. + vint32m2_t suml3; + { + const int vl = 16; + + uint32_t qh; + memcpy(&qh, &x[i].qh[0], 4); + // Prevent fusion with vmv. + __asm__ __volatile__("" : "+r"(qh)); + vuint8mf2_t tq = __riscv_vreinterpret_v_u32mf2_u8mf2(__riscv_vmv_v_x_u32mf2(qh, vl / 4)); + + vuint8mf2_t p = __riscv_vle8_v_u8mf2(pow, vl); + + vuint16m1_t tq0 = __riscv_vsrl_vx_u16m1(__riscv_vwmulu_vx_u16m1(__riscv_vmul_vv_u8mf2(tq, p, vl), 3, vl), 8, vl); + + vint16m1_t q80 = __riscv_vwcvt_x_x_v_i16m1(__riscv_vle8_v_i8mf2(y[i].qs + 240, vl), vl); + + vint16m1_t sum0 = __riscv_vmul_vv_i16m1(__riscv_vreinterpret_v_u16m1_i16m1(__riscv_vsub_vx_u16m1(tq0, 1, vl)), q80, vl); + suml3 = __riscv_vwcvt_x_x_v_i32m2(sum0, vl); + } + + vint32m2_t sumb = __riscv_vadd_vv_i32m2(__riscv_vget_v_i32m4_i32m2(suml1, 0), __riscv_vget_v_i32m4_i32m2(suml1, 1), 16); + sumb = __riscv_vadd_vv_i32m2(sumb, suml2, 16); + sumb = __riscv_vadd_vv_i32m2(sumb, suml3, 16); + + vint32m1_t sum = __riscv_vredsum_vs_i32m2_i32m1(sumb, __riscv_vmv_v_x_i32m1(0, 1), 16); + sumf += __riscv_vmv_x_s_i32m1_i32(sum) * y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d); + } + + *s = sumf; +} + +void ggml_vec_dot_tq1_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 256: + ggml_vec_dot_tq1_0_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_tq1_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + ggml_vec_dot_tq1_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +static void ggml_vec_dot_tq2_0_q8_K_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_tq2_0 * GGML_RESTRICT x = vx; + const block_q8_K * GGML_RESTRICT y = vy; + + const int nb = n / QK_K; + + float sumf = 0.0f; + for (int i = 0; i < nb; ++i) { + int32_t sumi = 0; + + for (size_t j = 0; j < sizeof(x[0].qs); j += 32) { + const int8_t * py0 = &y[i].qs[j * 4 + 0 * 32]; + const int8_t * py1 = &y[i].qs[j * 4 + 1 * 32]; + const int8_t * py2 = &y[i].qs[j * 4 + 2 * 32]; + const int8_t * py3 = &y[i].qs[j * 4 + 3 * 32]; + const uint8_t* px = &x[i].qs[j]; + + size_t vlmax_16m2 = __riscv_vsetvl_e16m2(32); + vint16m2_t vacc16 = __riscv_vmv_v_x_i16m2(0, vlmax_16m2); + + size_t vl = __riscv_vsetvl_e8m1(32); + + vuint8m1_t vx_u8 = __riscv_vle8_v_u8m1(px, vl); + + vint8m1_t vy0 = __riscv_vle8_v_i8m1(py0 , vl); + vint8m1_t vy1 = __riscv_vle8_v_i8m1(py1, vl); + vint8m1_t vy2 = __riscv_vle8_v_i8m1(py2, vl); + vint8m1_t vy3 = __riscv_vle8_v_i8m1(py3, vl); + + // l=0 (bits 1:0) + vuint8m1_t t0 = __riscv_vand_vx_u8m1(vx_u8, 0x03, vl); + vint8m1_t vq0 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(t0), 1, vl); + + // l=1 (bits 3:2) + vuint8m1_t t1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vx_u8, 2, vl), 0x03, vl); + vint8m1_t vq1 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(t1), 1, vl); + + // l=2 (bits 5:4) + vuint8m1_t t2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(vx_u8, 4, vl), 0x03, vl); + vint8m1_t vq2 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(t2), 1, vl); + + // l=3 (bits 7:6) + vuint8m1_t t3 = __riscv_vsrl_vx_u8m1(vx_u8, 6, vl); // No final AND needed as vsrl shifts in zeros + vint8m1_t vq3 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(t3), 1, vl); + + // 4. Multiply and accumulate + vacc16 = __riscv_vwmacc_vv_i16m2(vacc16, vq0, vy0, vl); + vacc16 = __riscv_vwmacc_vv_i16m2(vacc16, vq1, vy1, vl); + vacc16 = __riscv_vwmacc_vv_i16m2(vacc16, vq2, vy2, vl); + vacc16 = __riscv_vwmacc_vv_i16m2(vacc16, vq3, vy3, vl); + + vlmax_16m2 = __riscv_vsetvl_e16m2(32); + vint32m1_t vzero32 = __riscv_vmv_v_x_i32m1(0, 1); + vint32m1_t vred32 = __riscv_vwredsum_vs_i16m2_i32m1(vacc16, vzero32, vlmax_16m2); + + sumi += __riscv_vmv_x_s_i32m1_i32(vred32); + } + const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d); + sumf += (float)sumi * d; + } + + *s = sumf; +} + +void ggml_vec_dot_tq2_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 256: + ggml_vec_dot_tq2_0_q8_K_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_tq2_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + ggml_vec_dot_tq2_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} + +static void ggml_vec_dot_mxfp4_q8_0_vl128(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK_MXFP4 == 0); + static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same"); + + const block_mxfp4 * GGML_RESTRICT x = vx; + const block_q8_0 * GGML_RESTRICT y = vy; + + const int nb = n / QK_MXFP4; + + int ib = 0; + float sumf = 0; + + // Load the lookup table once. + const vint8m2_t values = __riscv_vle8_v_i8m2(kvalues_mxfp4, 16); + int acc1, acc2; + + // We process 2 blocks at once. + for (; ib + 1 < nb; ib += 2) { + // Weights and activations. + vuint8m1_t mx_packed1 = __riscv_vle8_v_u8m1(x[ib + 0].qs, 16); + vint8m2_t q8b1 = __riscv_vle8_v_i8m2(y[ib + 0].qs, 32); + vuint8m1_t mx_packed2 = __riscv_vle8_v_u8m1(x[ib + 1].qs, 16); + vint8m2_t q8b2 = __riscv_vle8_v_i8m2(y[ib + 1].qs, 32); + + // Unpack the weight blocks. + vuint8m2_t mxbits1; + mxbits1 = __riscv_vset_v_u8m1_u8m2(mxbits1, 0, __riscv_vand_vx_u8m1(mx_packed1, 0xf, 16)); + mxbits1 = __riscv_vset_v_u8m1_u8m2(mxbits1, 1, __riscv_vsrl_vx_u8m1(mx_packed1, 4, 16)); + vuint8m2_t mxbits2; + mxbits2 = __riscv_vset_v_u8m1_u8m2(mxbits2, 0, __riscv_vand_vx_u8m1(mx_packed2, 0xf, 16)); + mxbits2 = __riscv_vset_v_u8m1_u8m2(mxbits2, 1, __riscv_vsrl_vx_u8m1(mx_packed2, 4, 16)); + + // Gather values from the lookup table. + vint8m2_t mxb1 = __riscv_vrgather_vv_i8m2(values, mxbits1, 32); + vint8m2_t mxb2 = __riscv_vrgather_vv_i8m2(values, mxbits2, 32); + + // Accumulation. + vint16m4_t sum1 = __riscv_vwmul_vv_i16m4(q8b1, mxb1, 32); + vint16m4_t sum2 = __riscv_vwmul_vv_i16m4(q8b2, mxb2, 32); + __riscv_vse32_v_i32m1(&acc1,__riscv_vwredsum_vs_i16m4_i32m1(sum1, __riscv_vmv_v_x_i32m1(0, 1), 32), 1); + __riscv_vse32_v_i32m1(&acc2,__riscv_vwredsum_vs_i16m4_i32m1(sum2, __riscv_vmv_v_x_i32m1(0, 1), 32), 1); + sumf += ((GGML_E8M0_TO_FP32_HALF(x[ib + 0].e) * GGML_CPU_FP16_TO_FP32(y[ib + 0].d) * acc1)); + sumf += ((GGML_E8M0_TO_FP32_HALF(x[ib + 1].e) * GGML_CPU_FP16_TO_FP32(y[ib + 1].d) * acc2)); + } + + *s = sumf; +} + +static void ggml_vec_dot_mxfp4_q8_0_vl256(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK_MXFP4 == 0); + static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same"); + + const block_mxfp4 * GGML_RESTRICT x = vx; + const block_q8_0 * GGML_RESTRICT y = vy; + + const int nb = n / QK_MXFP4; + + int ib = 0; + float sumf = 0; + + // Load the lookup table once. + const vint8mf2_t values = __riscv_vle8_v_i8mf2(kvalues_mxfp4, 16); + int acc1, acc2; + + // We process 2 blocks at once. + for (; ib + 1 < nb; ib+=2) { + // Weights and activations. + vuint8mf2_t mx_packed1 = __riscv_vle8_v_u8mf2(x[ib + 0].qs, 16); + vint8mf2_t q8b_lo1 = __riscv_vle8_v_i8mf2(y[ib + 0].qs, 16); + vint8mf2_t q8b_hi1 = __riscv_vle8_v_i8mf2(y[ib + 0].qs + 16, 16); + vuint8mf2_t mx_packed2 = __riscv_vle8_v_u8mf2(x[ib + 1].qs, 16); + vint8mf2_t q8b_lo2 = __riscv_vle8_v_i8mf2(y[ib + 1].qs, 16); + vint8mf2_t q8b_hi2 = __riscv_vle8_v_i8mf2(y[ib + 1].qs + 16, 16); + + // Unpack the weight blocks. + vuint8mf2_t mxbits_lo1 = __riscv_vand_vx_u8mf2(mx_packed1, 0xf, 16); + vuint8mf2_t mxbits_hi1 = __riscv_vsrl_vx_u8mf2(mx_packed1, 4, 16); + vuint8mf2_t mxbits_lo2 = __riscv_vand_vx_u8mf2(mx_packed2, 0xf, 16); + vuint8mf2_t mxbits_hi2 = __riscv_vsrl_vx_u8mf2(mx_packed2, 4, 16); + + // Gather values from the lookup table. + vint8mf2_t mxb_lo1 = __riscv_vrgather_vv_i8mf2(values, mxbits_lo1, 16); + vint8mf2_t mxb_hi1 = __riscv_vrgather_vv_i8mf2(values, mxbits_hi1, 16); + vint8mf2_t mxb_lo2 = __riscv_vrgather_vv_i8mf2(values, mxbits_lo2, 16); + vint8mf2_t mxb_hi2 = __riscv_vrgather_vv_i8mf2(values, mxbits_hi2, 16); + + // Accumulation. + vint16m1_t sum1 = __riscv_vwmul_vv_i16m1(q8b_lo1, mxb_lo1, 16); + sum1 = __riscv_vwmacc_vv_i16m1(sum1, q8b_hi1, mxb_hi1, 16); + vint16m1_t sum2 = __riscv_vwmul_vv_i16m1(q8b_lo2, mxb_lo2, 16); + sum2 = __riscv_vwmacc_vv_i16m1(sum2, q8b_hi2, mxb_hi2, 16); + __riscv_vse32_v_i32m1(&acc1,__riscv_vwredsum_vs_i16m1_i32m1(sum1, __riscv_vmv_v_x_i32m1(0, 1), 16), 1); + __riscv_vse32_v_i32m1(&acc2,__riscv_vwredsum_vs_i16m1_i32m1(sum2, __riscv_vmv_v_x_i32m1(0, 1), 16), 1); + sumf += ((GGML_E8M0_TO_FP32_HALF(x[ib + 0].e) * GGML_CPU_FP16_TO_FP32(y[ib + 0].d) * acc1)); + sumf += ((GGML_E8M0_TO_FP32_HALF(x[ib + 1].e) * GGML_CPU_FP16_TO_FP32(y[ib + 1].d) * acc2)); + } + + *s = sumf; +} + +void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { +#if defined __riscv_v_intrinsic + switch (__riscv_vlenb() * 8) { + case 128: + ggml_vec_dot_mxfp4_q8_0_vl128(n, s, bs, vx, bx, vy, by, nrc); + break; + default: + ggml_vec_dot_mxfp4_q8_0_vl256(n, s, bs, vx, bx, vy, by, nrc); + break; + } +#else + return ggml_vec_dot_mxfp4_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc); +#endif +} diff --git a/ggml/src/ggml-cpu/arch/riscv/repack.cpp b/ggml/src/ggml-cpu/arch/riscv/repack.cpp index 2a35ff9ad8..cd5807879e 100644 --- a/ggml/src/ggml-cpu/arch/riscv/repack.cpp +++ b/ggml/src/ggml-cpu/arch/riscv/repack.cpp @@ -24,6 +24,94 @@ #define UNUSED GGML_UNUSED +void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { + assert(QK8_0 == 32); + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + +#if defined(__riscv_v_intrinsic) + block_q8_0x4 * GGML_RESTRICT y = (block_q8_0x4 *) vy; + const size_t vl_calc = __riscv_vsetvl_e32m8(QK8_0); + const size_t vl_save = __riscv_vsetvl_e64m2(4); + vfloat32m1_t v_scalar_zero = __riscv_vfmv_s_f_f32m1(0.0f, __riscv_vsetvl_e32m1(1)); + + for (int i = 0; i < nb; i++) { + const float *x_block_base = x + i * QK8_0; + vint8m2_t q_r0, q_r1, q_r2, q_r3; + { + vfloat32m8_t v_src = __riscv_vle32_v_f32m8(x_block_base + 0 * k, vl_calc); + vfloat32m8_t v_abs = __riscv_vfabs_v_f32m8(v_src, vl_calc); + vfloat32m1_t v_max = __riscv_vfredmax_vs_f32m8_f32m1(v_abs, v_scalar_zero, vl_calc); + float amax = __riscv_vfmv_f_s_f32m1_f32(v_max); + + float d = amax / 127.0f; + y[i].d[0] = GGML_CPU_FP32_TO_FP16(d); + + float id = d ? 1.0f / d : 0.0f; + vfloat32m8_t v_scaled = __riscv_vfmul_vf_f32m8(v_src, id, vl_calc); + vint16m4_t v_i16 = __riscv_vfncvt_x_f_w_i16m4_rm(v_scaled, 4, vl_calc); + q_r0 = __riscv_vncvt_x_x_w_i8m2(v_i16, vl_calc); + } + asm volatile ("" ::: "memory"); + + { + vfloat32m8_t v_src = __riscv_vle32_v_f32m8(x_block_base + 1 * k, vl_calc); + vfloat32m8_t v_abs = __riscv_vfabs_v_f32m8(v_src, vl_calc); + vfloat32m1_t v_max = __riscv_vfredmax_vs_f32m8_f32m1(v_abs, v_scalar_zero, vl_calc); + float amax = __riscv_vfmv_f_s_f32m1_f32(v_max); + + float d = amax / 127.0f; + y[i].d[1] = GGML_CPU_FP32_TO_FP16(d); + float id = d ? 1.0f / d : 0.0f; + + vfloat32m8_t v_scaled = __riscv_vfmul_vf_f32m8(v_src, id, vl_calc); + vint16m4_t v_i16 = __riscv_vfncvt_x_f_w_i16m4_rm(v_scaled, 4, vl_calc); + q_r1 = __riscv_vncvt_x_x_w_i8m2(v_i16, vl_calc); + } + asm volatile ("" ::: "memory"); + { + vfloat32m8_t v_src = __riscv_vle32_v_f32m8(x_block_base + 2 * k, vl_calc); + vfloat32m8_t v_abs = __riscv_vfabs_v_f32m8(v_src, vl_calc); + vfloat32m1_t v_max = __riscv_vfredmax_vs_f32m8_f32m1(v_abs, v_scalar_zero, vl_calc); + float amax = __riscv_vfmv_f_s_f32m1_f32(v_max); + + float d = amax / 127.0f; + y[i].d[2] = GGML_CPU_FP32_TO_FP16(d); + float id = d ? 1.0f / d : 0.0f; + + vfloat32m8_t v_scaled = __riscv_vfmul_vf_f32m8(v_src, id, vl_calc); + vint16m4_t v_i16 = __riscv_vfncvt_x_f_w_i16m4_rm(v_scaled, 4, vl_calc); + q_r2 = __riscv_vncvt_x_x_w_i8m2(v_i16, vl_calc); + } + asm volatile ("" ::: "memory"); + { + vfloat32m8_t v_src = __riscv_vle32_v_f32m8(x_block_base + 3 * k, vl_calc); + vfloat32m8_t v_abs = __riscv_vfabs_v_f32m8(v_src, vl_calc); + vfloat32m1_t v_max = __riscv_vfredmax_vs_f32m8_f32m1(v_abs, v_scalar_zero, vl_calc); + float amax = __riscv_vfmv_f_s_f32m1_f32(v_max); + + float d = amax / 127.0f; + y[i].d[3] = GGML_CPU_FP32_TO_FP16(d); + float id = d ? 1.0f / d : 0.0f; + + vfloat32m8_t v_scaled = __riscv_vfmul_vf_f32m8(v_src, id, vl_calc); + vint16m4_t v_i16 = __riscv_vfncvt_x_f_w_i16m4_rm(v_scaled, 4, vl_calc); + q_r3 = __riscv_vncvt_x_x_w_i8m2(v_i16, vl_calc); + } + vint64m2_t v_q64_r0 = __riscv_vreinterpret_v_i8m2_i64m2(q_r0); + vint64m2_t v_q64_r1 = __riscv_vreinterpret_v_i8m2_i64m2(q_r1); + vint64m2_t v_q64_r2 = __riscv_vreinterpret_v_i8m2_i64m2(q_r2); + vint64m2_t v_q64_r3 = __riscv_vreinterpret_v_i8m2_i64m2(q_r3); + vint64m2x4_t v_quant_tuple = __riscv_vcreate_v_i64m2x4(v_q64_r0, v_q64_r1, v_q64_r2, v_q64_r3); + __riscv_vsseg4e64_v_i64m2x4((int64_t*)y[i].qs, v_quant_tuple, vl_save); + } +#else + UNUSED(nb); + UNUSED(y); + ggml_quantize_mat_q8_0_4x4_generic(x, vy, k); +#endif +} + void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; const int nb = n / qk; @@ -115,6 +203,486 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo ggml_gemv_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc); } +void ggml_gemv_q4_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined __riscv_v_intrinsic + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x16 * b_ptr = (const block_q4_0x16 *) vx + (x * nb); + + // 1x16 Accumulator + vfloat32m2_t sumf = __riscv_vfmv_v_f_f32m2(0.0f, 16); + + for (int l = 0; l < nb; l++) { + // 1x16 Integer Accumulator + vint16m1_t sumi_0_lo_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_0_hi_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + + // Accumulation loop. + for (int i = 0; i < QK4_0 / 2; i++) { + // Load `b_ptr`. + const vint8mf2_t b_0_packed = __riscv_vle8_v_i8mf2((const int8_t *)&b_ptr[l].qs[i * 16], 16); + const vint8mf2_t b_0_lo = __riscv_vsra_vx_i8mf2(__riscv_vsll_vx_i8mf2(b_0_packed, 4, 16), 4, 16); + const vint8mf2_t b_0_hi = __riscv_vsra_vx_i8mf2(b_0_packed, 4, 16); + + sumi_0_lo_16 = __riscv_vwmacc_vx_i16m1(sumi_0_lo_16, a_ptr[l].qs[i], b_0_lo, 16); + sumi_0_hi_16 = __riscv_vwmacc_vx_i16m1(sumi_0_hi_16, a_ptr[l].qs[16 + i], b_0_hi, 16); + } + + const vint32m2_t sumi = __riscv_vwadd_vv_i32m2(sumi_0_lo_16, sumi_0_hi_16, 16); + + const vfloat16m1_t b_d = __riscv_vle16_v_f16m1((const _Float16 *)b_ptr[l].d, 16); + const vfloat32m2_t d_0 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d, 16); + + sumf = __riscv_vfmacc_vv_f32m2(sumf, __riscv_vfcvt_f_x_v_f32m2(sumi, 16), d_0, 16); + } + + __riscv_vse32_v_f32m2(s + x * 16, sumf, 16); + } + return; +#endif + ggml_gemv_q4_0_16x1_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + +void ggml_gemv_q4_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK_K; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined __riscv_v_intrinsic + const block_q8_K * a_ptr = (const block_q8_K *) vy; + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_Kx16 * b_ptr = (const block_q4_Kx16 *) vx + (x * nb); + + // 1x16 Accumulator + vfloat32m2_t sumf = __riscv_vfmv_v_f_f32m2(0.0f, 16); + + for (int l = 0; l < nb; l++) { + vint32m2_t sumi = __riscv_vmv_v_x_i32m2(0, 16); + + // Load `dmin`. + const vfloat32m2_t dmins_d = __riscv_vfmul_vf_f32m2( + __riscv_vfwcvt_f_f_v_f32m2(__riscv_vle16_v_f16m1((const _Float16 *)b_ptr[l].dmin, 16), 16), a_ptr[l].d, 16); + + // We process 4 sub-blocks at once. + for (int j = 0; j < QK_K / 128; j++) { + // Extract the scales and the mins. + // + // Low bits. + vuint8m2_t scales_mins_lo = __riscv_vle8_v_u8m2(&b_ptr[l].scales[j * 64], 64); + vuint8m2_t scales_lo = __riscv_vand_vx_u8m2(scales_mins_lo, 0x0F, 64); + vuint8m2_t mins_lo = __riscv_vsrl_vx_u8m2(scales_mins_lo, 4, 64); + + // High bits. + vuint8m2_t scales_mins_hi = __riscv_vle8_v_u8m2(&b_ptr[l].scales[128], 64); + vuint8m2_t scales_hi; + vuint8m2_t mins_hi; + if (!j) { + scales_hi = __riscv_vsll_vx_u8m2(__riscv_vand_vx_u8m2(scales_mins_hi, 0x03, 64), 4, 64); + mins_hi = __riscv_vsll_vx_u8m2(__riscv_vand_vx_u8m2(scales_mins_hi, 0x0C, 64), 2, 64); + } else { + scales_hi = __riscv_vand_vx_u8m2(scales_mins_hi, 0x30, 64); + mins_hi = __riscv_vsrl_vx_u8m2(__riscv_vand_vx_u8m2(scales_mins_hi, 0xC0, 64), 2, 64); + } + vuint16m4_t scales = __riscv_vzext_vf2_u16m4(__riscv_vor_vv_u8m2(scales_hi, scales_lo, 64), 64); + vint16m4_t mins = __riscv_vreinterpret_v_u16m4_i16m4(__riscv_vzext_vf2_u16m4(__riscv_vor_vv_u8m2(mins_hi, mins_lo, 64), 64)); + + // Reduce the mins and multiply with `dmin`. + // + // Correct in `sumf`. + vint32m2_t bsums = __riscv_vmv_v_x_i32m2(0, 16); + bsums = __riscv_vwmacc_vx_i32m2(bsums, a_ptr[l].bsums[j * 8] + a_ptr[l].bsums[j * 8 + 1], __riscv_vget_v_i16m4_i16m1(mins, 0), 16); + bsums = __riscv_vwmacc_vx_i32m2(bsums, a_ptr[l].bsums[j * 8 + 2] + a_ptr[l].bsums[j * 8 + 3], __riscv_vget_v_i16m4_i16m1(mins, 1), 16); + bsums = __riscv_vwmacc_vx_i32m2(bsums, a_ptr[l].bsums[j * 8 + 4] + a_ptr[l].bsums[j * 8 + 5], __riscv_vget_v_i16m4_i16m1(mins, 2), 16); + bsums = __riscv_vwmacc_vx_i32m2(bsums, a_ptr[l].bsums[j * 8 + 6] + a_ptr[l].bsums[j * 8 + 7], __riscv_vget_v_i16m4_i16m1(mins, 3), 16); + + sumf = __riscv_vfsub_vv_f32m2(sumf, __riscv_vfmul_vv_f32m2(dmins_d, __riscv_vfcvt_f_x_v_f32m2(bsums, 16), 16), 16); + + // Accumulation for 2 sub-blocks. + // + // This might overflow, so we accumulate in two steps. + // + // Recheck. + for (int k = 0; k < 2; k++) { + vint16m1_t sumi_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + + for (int i = k * 16; i < k * 16 + QK4_0 / 2; i++) { + // Load `b_ptr`. + const vuint8mf2_t b_0_packed = __riscv_vle8_v_u8mf2(&b_ptr[l].qs[j * 1024 + i * 16], 16); + const vint8mf2_t b_s_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(b_0_packed, 0xF, 16)); + const vint8mf2_t b_s_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(b_0_packed, 4, 16)); + + sumi_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_s_0_16, a_ptr[l].qs[j * 128 + i], b_s_0, 16); + sumi_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_s_1_16, a_ptr[l].qs[j * 128 + 32 + i], b_s_1, 16); + } + + sumi = __riscv_vwmacc_vv_i32m2(sumi, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 0)), + sumi_s_0_16, 16); + sumi = __riscv_vwmacc_vv_i32m2(sumi, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 1)), + sumi_s_1_16, 16); + } + // Accumulation for 2 sub-blocks. + // + // This might overflow, so we accumulate in two steps. + // + // Recheck. + for (int k = 0; k < 2; k++) { + vint16m1_t sumi_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + + for (int i = k * 16; i < k * 16 + QK4_0 / 2; i++) { + // Load `b_ptr`. + const vuint8mf2_t b_0_packed = __riscv_vle8_v_u8mf2(&b_ptr[l].qs[j * 1024 + 512 + i * 16], 16); + const vint8mf2_t b_s_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(b_0_packed, 0xF, 16)); + const vint8mf2_t b_s_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(b_0_packed, 4, 16)); + + sumi_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_s_0_16, a_ptr[l].qs[j * 128 + 64 + i], b_s_0, 16); + sumi_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_s_1_16, a_ptr[l].qs[j * 128 + 96 + i], b_s_1, 16); + } + + sumi = __riscv_vwmacc_vv_i32m2(sumi, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 2)), + sumi_s_0_16, 16); + sumi = __riscv_vwmacc_vv_i32m2(sumi, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 3)), + sumi_s_1_16, 16); + } + } + + const vfloat32m2_t b_d = __riscv_vfwcvt_f_f_v_f32m2(__riscv_vle16_v_f16m1((const _Float16 *)&b_ptr[l].d[0], 16), 16); + const vfloat32m2_t d_0 = __riscv_vfmul_vf_f32m2(b_d, a_ptr[l].d, 16); + + sumf = __riscv_vfmacc_vv_f32m2(sumf, __riscv_vfcvt_f_x_v_f32m2(sumi, 16), d_0, 16); + } + + __riscv_vse32_v_f32m2(s + x * 16, sumf, 16); + } + return; +#endif + ggml_gemv_q4_K_16x1_q8_K_generic(n, s, bs, vx, vy, nr, nc); +} + +void ggml_gemv_iq4_nl_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined __riscv_v_intrinsic + const vint8mf2_t values = __riscv_vle8_v_i8mf2(kvalues_iq4nl, 16); + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx16 * b_ptr = (const block_iq4_nlx16 *) vx + (x * nb); + + // 1x16 Accumulator1 + vfloat32m2_t sumf = __riscv_vfmv_v_f_f32m2(0.0f, 16); + + for (int l = 0; l < nb; l++) { + // 1x16 integer accumulator + vint32m2_t sumi = __riscv_vmv_v_x_i32m2(0.0f, 16); + + // Accumulation loop. + for (int i = 0; i < QK4_NL / 2; i++) { + // Load `b_ptr`. + const vuint8mf2_t b_0_packed = __riscv_vle8_v_u8mf2((const uint8_t *)&b_ptr[l].qs[i * 16], 16); + const vint8mf2_t b_0_lo = __riscv_vrgather_vv_i8mf2(values, __riscv_vand_vx_u8mf2(b_0_packed, 0xf, 16), 16); + const vint8mf2_t b_0_hi = __riscv_vrgather_vv_i8mf2(values, __riscv_vsrl_vx_u8mf2(b_0_packed, 4, 16), 16); + // const vint16m1_t b_0_lo_16 = __riscv_vwcvt_x_x_v_i16m1(b_0_lo, 16); + // const vint16m1_t b_0_hi_16 = __riscv_vwcvt_x_x_v_i16m1(b_0_hi, 16); + + const vint16m1_t sumi_lo = __riscv_vwmul_vx_i16m1(b_0_lo, a_ptr[l].qs[i], 16); + const vint16m1_t sumi_hi = __riscv_vwmul_vx_i16m1(b_0_hi, a_ptr[l].qs[16 + i], 16); + sumi = __riscv_vadd_vv_i32m2(sumi, __riscv_vwadd_vv_i32m2(sumi_lo, sumi_hi, 16), 16); + } + + const vfloat16m1_t b_d = __riscv_vle16_v_f16m1((const _Float16 *)b_ptr[l].d, 16); + const vfloat32m2_t d_0 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d, 16); + + sumf = __riscv_vfmacc_vv_f32m2(sumf, __riscv_vfcvt_f_x_v_f32m2(sumi, 16), d_0, 16); + } + + __riscv_vse32_v_f32m2(s + x * 16, sumf, 16); + } + return; +#endif + ggml_gemv_iq4_nl_16x1_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + +void ggml_gemv_q8_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + UNUSED(bs); + +#if defined __riscv_v_intrinsic + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q8_0x16 * b_ptr = (const block_q8_0x16 *) vx + (x * nb); + + // 1x16 Accumulator + vfloat32m2_t sumf = __riscv_vfmv_v_f_f32m2(0.0f, 16); + + for (int l = 0; l < nb; l++) { + // 1x16 Integer Accumulator + vint32m2_t sumi = __riscv_vmv_v_x_i32m2(0.0f, 16); + + // Accumulation loop. + for (int i = 0; i < QK8_0; i++) { + // Load `b_ptr`. + const vint8mf2_t b_0 = __riscv_vle8_v_i8mf2((const int8_t *)&b_ptr[l].qs[i * 16], 16); + // const vint16m1_t b_0_16 = __riscv_vwcvt_x_x_v_i16m1(b_0, 16); + + sumi = __riscv_vwadd_wv_i32m2(sumi, __riscv_vwmul_vx_i16m1(b_0, a_ptr[l].qs[i], 16), 16); + } + + const vfloat16m1_t b_d = __riscv_vle16_v_f16m1((const _Float16 *)b_ptr[l].d, 16); + const vfloat32m2_t d_0 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d, 16); + + sumf = __riscv_vfmacc_vv_f32m2(sumf, __riscv_vfcvt_f_x_v_f32m2(sumi, 16), d_0, 16); + } + + __riscv_vse32_v_f32m2(s + x * 16, sumf, 16); + } + return; +#endif + ggml_gemv_q8_0_16x1_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + +void ggml_gemv_q2_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + assert(n % QK_K == 0); + assert(nr == 1); + assert(nc % 16 == 0); + + UNUSED(bs); + + const int N_COLS_TILE = 16; + const int num_k_blocks = n / QK_K; + + const size_t vl = __riscv_vsetvl_e32m2(N_COLS_TILE); + for (int col_tile = 0; col_tile < nc; col_tile += N_COLS_TILE) { + + const block_q8_K* lhs_base_ptr = (const block_q8_K*)vy; + const block_q2_Kx16* rhs_base_ptr = (const block_q2_Kx16*)vx + (col_tile / N_COLS_TILE) * num_k_blocks; + + vfloat32m2_t v_sumf = __riscv_vfmv_v_f_f32m2(0.0f, vl); + + for (int k_block = 0; k_block < num_k_blocks; ++k_block) { + const block_q8_K* lhs_current = &lhs_base_ptr[k_block]; + const block_q2_Kx16* rhs_current = &rhs_base_ptr[k_block]; + + // 1. Prepare Global Min Scales + vfloat16m1_t v_g_min_f16 = __riscv_vle16_v_f16m1((const _Float16*)rhs_current->dmin, vl); + vfloat32m2_t v_g_min_base = __riscv_vfwcvt_f_f_v_f32m2(v_g_min_f16, vl); + + vfloat32m2_t v_g_min_final = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d, vl); + + vint32m2_t v_isum = __riscv_vmv_v_x_i32m2(0, vl); + + const uint8_t* rhs_qs_ptr = rhs_current->qs; + const uint8_t* rhs_sc_ptr = rhs_current->scales; + const int8_t* lhs_qs_ptr = lhs_current->qs; + + // --- Phase Loop (4 phases x 64 elements) --- + for (int phase = 0; phase < 4; ++phase) { + + // A. Load Scales/Mins + vuint16m1_t v_d_sb_0, v_d_sb_1, v_d_sb_2, v_d_sb_3; + vuint16m1_t v_m_sb_0, v_m_sb_1, v_m_sb_2, v_m_sb_3; + + { + vuint8mf2_t v_raw; + // Sub-block 0 + v_raw = __riscv_vle8_v_u8mf2(rhs_sc_ptr + 0, vl); + v_d_sb_0 = __riscv_vzext_vf2_u16m1(__riscv_vand_vx_u8mf2(v_raw, 0xF, vl), vl); + v_m_sb_0 = __riscv_vzext_vf2_u16m1(__riscv_vsrl_vx_u8mf2(v_raw, 4, vl), vl); + + // Sub-block 1 + v_raw = __riscv_vle8_v_u8mf2(rhs_sc_ptr + 16, vl); + v_d_sb_1 = __riscv_vzext_vf2_u16m1(__riscv_vand_vx_u8mf2(v_raw, 0xF, vl), vl); + v_m_sb_1 = __riscv_vzext_vf2_u16m1(__riscv_vsrl_vx_u8mf2(v_raw, 4, vl), vl); + + // Sub-block 2 + v_raw = __riscv_vle8_v_u8mf2(rhs_sc_ptr + 32, vl); + v_d_sb_2 = __riscv_vzext_vf2_u16m1(__riscv_vand_vx_u8mf2(v_raw, 0xF, vl), vl); + v_m_sb_2 = __riscv_vzext_vf2_u16m1(__riscv_vsrl_vx_u8mf2(v_raw, 4, vl), vl); + + // Sub-block 3 + v_raw = __riscv_vle8_v_u8mf2(rhs_sc_ptr + 48, vl); + v_d_sb_3 = __riscv_vzext_vf2_u16m1(__riscv_vand_vx_u8mf2(v_raw, 0xF, vl), vl); + v_m_sb_3 = __riscv_vzext_vf2_u16m1(__riscv_vsrl_vx_u8mf2(v_raw, 4, vl), vl); + + rhs_sc_ptr += 64; + } + + int base_k_phase = (phase < 2) ? (phase * 16) : (128 + (phase-2)*16); + int k_offsets[4] = {0, 32, 64, 96}; + + // B. Inner Dot Product Loop + for (int l = 0; l < 16; ++l) { + vuint8mf2_t v_rhs_data = __riscv_vle8_v_u8mf2(rhs_qs_ptr, vl); + rhs_qs_ptr += 16; + + // Sub-block 0 + { + vuint8mf2_t v_q2 = __riscv_vand_vx_u8mf2(v_rhs_data, 3, vl); + vint16m1_t v_w = __riscv_vmul_vv_i16m1( + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(v_q2, vl)), + __riscv_vreinterpret_v_u16m1_i16m1(v_d_sb_0), vl); + + int8_t q8 = lhs_qs_ptr[base_k_phase + k_offsets[0] + l]; + v_isum = __riscv_vwmacc_vx_i32m2(v_isum, (int16_t)q8, v_w, vl); + } + // Sub-block 1 + { + vuint8mf2_t v_q2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(v_rhs_data, 2, vl), 3, vl); + vint16m1_t v_w = __riscv_vmul_vv_i16m1( + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(v_q2, vl)), + __riscv_vreinterpret_v_u16m1_i16m1(v_d_sb_1), vl); + + int8_t q8 = lhs_qs_ptr[base_k_phase + k_offsets[1] + l]; + v_isum = __riscv_vwmacc_vx_i32m2(v_isum, (int16_t)q8, v_w, vl); + } + // Sub-block 2 + { + vuint8mf2_t v_q2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(v_rhs_data, 4, vl), 3, vl); + vint16m1_t v_w = __riscv_vmul_vv_i16m1( + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(v_q2, vl)), + __riscv_vreinterpret_v_u16m1_i16m1(v_d_sb_2), vl); + + int8_t q8 = lhs_qs_ptr[base_k_phase + k_offsets[2] + l]; + v_isum = __riscv_vwmacc_vx_i32m2(v_isum, (int16_t)q8, v_w, vl); + } + // Sub-block 3 + { + vuint8mf2_t v_q2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(v_rhs_data, 6, vl), 3, vl); + vint16m1_t v_w = __riscv_vmul_vv_i16m1( + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(v_q2, vl)), + __riscv_vreinterpret_v_u16m1_i16m1(v_d_sb_3), vl); + + int8_t q8 = lhs_qs_ptr[base_k_phase + k_offsets[3] + l]; + v_isum = __riscv_vwmacc_vx_i32m2(v_isum, (int16_t)q8, v_w, vl); + } + } + + // correction + int sb_base_abs = base_k_phase / 16; + + // Sub-block 0 + { + int sb_idx = sb_base_abs + (k_offsets[0] / 16); + int16_t bsum = lhs_current->bsums[sb_idx]; + vint16m1_t v_min = __riscv_vreinterpret_v_u16m1_i16m1(v_m_sb_0); + vint32m2_t v_c = __riscv_vwmul_vx_i32m2(v_min, bsum, vl); + vfloat32m2_t vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min_final, vl); + v_sumf = __riscv_vfsub_vv_f32m2(v_sumf, vf_c, vl); + } + // Sub-block 1 + { + int sb_idx = sb_base_abs + (k_offsets[1] / 16); + int16_t bsum = lhs_current->bsums[sb_idx]; + vint16m1_t v_min = __riscv_vreinterpret_v_u16m1_i16m1(v_m_sb_1); + vint32m2_t v_c = __riscv_vwmul_vx_i32m2(v_min, bsum, vl); + vfloat32m2_t vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min_final, vl); + v_sumf = __riscv_vfsub_vv_f32m2(v_sumf, vf_c, vl); + } + // Sub-block 2 + { + int sb_idx = sb_base_abs + (k_offsets[2] / 16); + int16_t bsum = lhs_current->bsums[sb_idx]; + vint16m1_t v_min = __riscv_vreinterpret_v_u16m1_i16m1(v_m_sb_2); + vint32m2_t v_c = __riscv_vwmul_vx_i32m2(v_min, bsum, vl); + vfloat32m2_t vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min_final, vl); + v_sumf = __riscv_vfsub_vv_f32m2(v_sumf, vf_c, vl); + } + // Sub-block 3 + { + int sb_idx = sb_base_abs + (k_offsets[3] / 16); + int16_t bsum = lhs_current->bsums[sb_idx]; + vint16m1_t v_min = __riscv_vreinterpret_v_u16m1_i16m1(v_m_sb_3); + vint32m2_t v_c = __riscv_vwmul_vx_i32m2(v_min, bsum, vl); + vfloat32m2_t vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min_final, vl); + v_sumf = __riscv_vfsub_vv_f32m2(v_sumf, vf_c, vl); + } + + } // End Phase Loop + + // Apply global Scales + vfloat16m1_t v_g_all_f16 = __riscv_vle16_v_f16m1((const _Float16*)rhs_current->d, vl); + vfloat32m2_t v_g_all_base = __riscv_vfwcvt_f_f_v_f32m2(v_g_all_f16, vl); + + vfloat32m2_t v_g_all_final = __riscv_vfmul_vf_f32m2(v_g_all_base, lhs_current->d, vl); + vfloat32m2_t v_sum = __riscv_vfcvt_f_x_v_f32m2(v_isum, vl); + v_sum = __riscv_vfmul_vv_f32m2(v_sum, v_g_all_final, vl); + v_sumf = __riscv_vfadd_vv_f32m2(v_sumf, v_sum, vl); + + } // End K-Block + __riscv_vse32_v_f32m2(s + col_tile, v_sumf, vl); + + } +} + void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; const int nb = n / qk; @@ -340,3 +908,826 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo #endif ggml_gemm_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc); } + +void ggml_gemm_q4_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined __riscv_v_intrinsic + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x16 * b_ptr = (const block_q4_0x16 *) vx + (x * nb); + + // 4x16 Accumulators + vfloat32m2_t sumf_0 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_1 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_2 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_3 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + + for (int l = 0; l < nb; l++) { + // 4x16 integer accumulators + vint16m1_t sumi_0_lo_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_1_lo_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_2_lo_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_3_lo_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_0_hi_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_1_hi_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_2_hi_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_3_hi_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + + // Accumulation loop. + for (int i = 0; i < QK4_0 / 2; i++) { + // Load `b_ptr`. + const vint8mf2_t b_0_packed = __riscv_vle8_v_i8mf2((const int8_t *)&b_ptr[l].qs[i * 16], 16); + const vint8mf2_t b_0_lo = __riscv_vsra_vx_i8mf2(__riscv_vsll_vx_i8mf2(b_0_packed, 4, 16), 4, 16); + const vint8mf2_t b_0_hi = __riscv_vsra_vx_i8mf2(b_0_packed, 4, 16); + + sumi_0_lo_16 = __riscv_vwmacc_vx_i16m1(sumi_0_lo_16, a_ptr[l].qs[i * 4], b_0_lo, 16); + sumi_1_lo_16 = __riscv_vwmacc_vx_i16m1(sumi_1_lo_16, a_ptr[l].qs[i * 4 + 1], b_0_lo, 16); + sumi_2_lo_16 = __riscv_vwmacc_vx_i16m1(sumi_2_lo_16, a_ptr[l].qs[i * 4 + 2], b_0_lo, 16); + sumi_3_lo_16 = __riscv_vwmacc_vx_i16m1(sumi_3_lo_16, a_ptr[l].qs[i * 4 + 3], b_0_lo, 16); + + sumi_0_hi_16 = __riscv_vwmacc_vx_i16m1(sumi_0_hi_16, a_ptr[l].qs[64 + i * 4], b_0_hi, 16); + sumi_1_hi_16 = __riscv_vwmacc_vx_i16m1(sumi_1_hi_16, a_ptr[l].qs[64 + i * 4 + 1], b_0_hi, 16); + sumi_2_hi_16 = __riscv_vwmacc_vx_i16m1(sumi_2_hi_16, a_ptr[l].qs[64 + i * 4 + 2], b_0_hi, 16); + sumi_3_hi_16 = __riscv_vwmacc_vx_i16m1(sumi_3_hi_16, a_ptr[l].qs[64 + i * 4 + 3], b_0_hi, 16); + } + + // Do the final accumulation in i32 to prevent overflow. + const vint32m2_t sumi_0 = __riscv_vwadd_vv_i32m2(sumi_0_lo_16, sumi_0_hi_16, 16); + const vint32m2_t sumi_1 = __riscv_vwadd_vv_i32m2(sumi_1_lo_16, sumi_1_hi_16, 16); + const vint32m2_t sumi_2 = __riscv_vwadd_vv_i32m2(sumi_2_lo_16, sumi_2_hi_16, 16); + const vint32m2_t sumi_3 = __riscv_vwadd_vv_i32m2(sumi_3_lo_16, sumi_3_hi_16, 16); + + const vfloat16m1_t b_d = __riscv_vle16_v_f16m1((const _Float16 *)b_ptr[l].d, 16); + const vfloat32m2_t d_0 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[0], 16); + const vfloat32m2_t d_1 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[1], 16); + const vfloat32m2_t d_2 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[2], 16); + const vfloat32m2_t d_3 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[3], 16); + + sumf_0 = __riscv_vfmacc_vv_f32m2(sumf_0, __riscv_vfcvt_f_x_v_f32m2(sumi_0, 16), d_0, 16); + sumf_1 = __riscv_vfmacc_vv_f32m2(sumf_1, __riscv_vfcvt_f_x_v_f32m2(sumi_1, 16), d_1, 16); + sumf_2 = __riscv_vfmacc_vv_f32m2(sumf_2, __riscv_vfcvt_f_x_v_f32m2(sumi_2, 16), d_2, 16); + sumf_3 = __riscv_vfmacc_vv_f32m2(sumf_3, __riscv_vfcvt_f_x_v_f32m2(sumi_3, 16), d_3, 16); + } + + __riscv_vse32_v_f32m2(s + (y * 4 + 0) * bs + x * 16, sumf_0, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 1) * bs + x * 16, sumf_1, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 2) * bs + x * 16, sumf_2, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 3) * bs + x * 16, sumf_3, 16); + } + } + return; +#endif + ggml_gemm_q4_0_16x1_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + +void ggml_gemm_q4_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK_K; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined __riscv_v_intrinsic + for (int y = 0; y < nr / 4; y++) { + const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_Kx16 * b_ptr = (const block_q4_Kx16 *) vx + (x * nb); + + // 4x16 Accumulators + vfloat32m2_t sumf_0 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_1 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_2 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_3 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + + for (int l = 0; l < nb; l++) { + vint32m2_t sumi_0 = __riscv_vmv_v_x_i32m2(0, 16); + vint32m2_t sumi_1 = __riscv_vmv_v_x_i32m2(0, 16); + vint32m2_t sumi_2 = __riscv_vmv_v_x_i32m2(0, 16); + vint32m2_t sumi_3 = __riscv_vmv_v_x_i32m2(0, 16); + + // Load `dmin`. + const vfloat32m2_t dmins = __riscv_vfwcvt_f_f_v_f32m2(__riscv_vle16_v_f16m1((const _Float16 *)b_ptr[l].dmin, 16), 16); + + // We process 4 sub-blocks at once. + for (int j = 0; j < QK_K / 128; j++) { + // Extract the scales and the mins. + // + // Low bits. + vuint8m2_t scales_mins_lo = __riscv_vle8_v_u8m2(&b_ptr[l].scales[j * 64], 64); + vuint8m2_t scales_lo = __riscv_vand_vx_u8m2(scales_mins_lo, 0x0F, 64); + vuint8m2_t mins_lo = __riscv_vsrl_vx_u8m2(scales_mins_lo, 4, 64); + + // High bits. + vuint8m2_t scales_mins_hi = __riscv_vle8_v_u8m2(&b_ptr[l].scales[128], 64); + vuint8m2_t scales_hi; + vuint8m2_t mins_hi; + if (!j) { + scales_hi = __riscv_vsll_vx_u8m2(__riscv_vand_vx_u8m2(scales_mins_hi, 0x03, 64), 4, 64); + mins_hi = __riscv_vsll_vx_u8m2(__riscv_vand_vx_u8m2(scales_mins_hi, 0x0C, 64), 2, 64); + } else { + scales_hi = __riscv_vand_vx_u8m2(scales_mins_hi, 0x30, 64); + mins_hi = __riscv_vsrl_vx_u8m2(__riscv_vand_vx_u8m2(scales_mins_hi, 0xC0, 64), 2, 64); + } + vuint16m4_t scales = __riscv_vzext_vf2_u16m4(__riscv_vor_vv_u8m2(scales_hi, scales_lo, 64), 64); + vint16m4_t mins = __riscv_vreinterpret_v_u16m4_i16m4(__riscv_vzext_vf2_u16m4(__riscv_vor_vv_u8m2(mins_hi, mins_lo, 64), 64)); + + // Reduce the mins and multiply with `dmin`. + // + // Correct in `sumf`. + vint32m2_t bsums_0 = __riscv_vmv_v_x_i32m2(0, 16); + vint32m2_t bsums_1 = __riscv_vmv_v_x_i32m2(0, 16); + vint32m2_t bsums_2 = __riscv_vmv_v_x_i32m2(0, 16); + vint32m2_t bsums_3 = __riscv_vmv_v_x_i32m2(0, 16); + + bsums_0 = __riscv_vwmacc_vx_i32m2(bsums_0, + a_ptr[l].bsums[j * 32] + a_ptr[l].bsums[j * 32 + 4], + __riscv_vget_v_i16m4_i16m1(mins, 0), 16); + bsums_1 = __riscv_vwmacc_vx_i32m2(bsums_1, + a_ptr[l].bsums[j * 32 + 1] + a_ptr[l].bsums[j * 32 + 5], + __riscv_vget_v_i16m4_i16m1(mins, 0), 16); + bsums_2 = __riscv_vwmacc_vx_i32m2(bsums_2, + a_ptr[l].bsums[j * 32 + 2] + a_ptr[l].bsums[j * 32 + 6], + __riscv_vget_v_i16m4_i16m1(mins, 0), 16); + bsums_3 = __riscv_vwmacc_vx_i32m2(bsums_3, + a_ptr[l].bsums[j * 32 + 3] + a_ptr[l].bsums[j * 32 + 7], + __riscv_vget_v_i16m4_i16m1(mins, 0), 16); + bsums_0 = __riscv_vwmacc_vx_i32m2(bsums_0, + a_ptr[l].bsums[j * 32 + 8] + a_ptr[l].bsums[j * 32 + 8 + 4], + __riscv_vget_v_i16m4_i16m1(mins, 1), 16); + bsums_1 = __riscv_vwmacc_vx_i32m2(bsums_1, + a_ptr[l].bsums[j * 32 + 8 + 1] + a_ptr[l].bsums[j * 32 + 8 + 5], + __riscv_vget_v_i16m4_i16m1(mins, 1), 16); + bsums_2 = __riscv_vwmacc_vx_i32m2(bsums_2, + a_ptr[l].bsums[j * 32 + 8 + 2] + a_ptr[l].bsums[j * 32 + 8 + 6], + __riscv_vget_v_i16m4_i16m1(mins, 1), 16); + bsums_3 = __riscv_vwmacc_vx_i32m2(bsums_3, + a_ptr[l].bsums[j * 32 + 8 + 3] + a_ptr[l].bsums[j * 32 + 8 + 7], + __riscv_vget_v_i16m4_i16m1(mins, 1), 16); + bsums_0 = __riscv_vwmacc_vx_i32m2(bsums_0, + a_ptr[l].bsums[j * 32 + 16] + a_ptr[l].bsums[j * 32 + 16 + 4], + __riscv_vget_v_i16m4_i16m1(mins, 2), 16); + bsums_1 = __riscv_vwmacc_vx_i32m2(bsums_1, + a_ptr[l].bsums[j * 32 + 16 + 1] + a_ptr[l].bsums[j * 32 + 16 + 5], + __riscv_vget_v_i16m4_i16m1(mins, 2), 16); + bsums_2 = __riscv_vwmacc_vx_i32m2(bsums_2, + a_ptr[l].bsums[j * 32 + 16 + 2] + a_ptr[l].bsums[j * 32 + 16 + 6], + __riscv_vget_v_i16m4_i16m1(mins, 2), 16); + bsums_3 = __riscv_vwmacc_vx_i32m2(bsums_3, + a_ptr[l].bsums[j * 32 + 16 + 3] + a_ptr[l].bsums[j * 32 + 16 + 7], + __riscv_vget_v_i16m4_i16m1(mins, 2), 16); + bsums_0 = __riscv_vwmacc_vx_i32m2(bsums_0, + a_ptr[l].bsums[j * 32 + 24 + 0] + a_ptr[l].bsums[j * 32 + 24 + 4], + __riscv_vget_v_i16m4_i16m1(mins, 3), 16); + bsums_1 = __riscv_vwmacc_vx_i32m2(bsums_1, + a_ptr[l].bsums[j * 32 + 24 + 1] + a_ptr[l].bsums[j * 32 + 24 + 5], + __riscv_vget_v_i16m4_i16m1(mins, 3), 16); + bsums_2 = __riscv_vwmacc_vx_i32m2(bsums_2, + a_ptr[l].bsums[j * 32 + 24 + 2] + a_ptr[l].bsums[j * 32 + 24 + 6], + __riscv_vget_v_i16m4_i16m1(mins, 3), 16); + bsums_3 = __riscv_vwmacc_vx_i32m2(bsums_3, + a_ptr[l].bsums[j * 32 + 24 + 3] + a_ptr[l].bsums[j * 32 + 24 + 7], + __riscv_vget_v_i16m4_i16m1(mins, 3), 16); + + const vfloat32m2_t dmins_d_0 = __riscv_vfmul_vf_f32m2(dmins, a_ptr[l].d[0], 16); + const vfloat32m2_t dmins_d_1 = __riscv_vfmul_vf_f32m2(dmins, a_ptr[l].d[1], 16); + const vfloat32m2_t dmins_d_2 = __riscv_vfmul_vf_f32m2(dmins, a_ptr[l].d[2], 16); + const vfloat32m2_t dmins_d_3 = __riscv_vfmul_vf_f32m2(dmins, a_ptr[l].d[3], 16); + + sumf_0 = __riscv_vfsub_vv_f32m2(sumf_0, __riscv_vfmul_vv_f32m2(dmins_d_0, __riscv_vfcvt_f_x_v_f32m2(bsums_0, 16), 16), 16); + sumf_1 = __riscv_vfsub_vv_f32m2(sumf_1, __riscv_vfmul_vv_f32m2(dmins_d_1, __riscv_vfcvt_f_x_v_f32m2(bsums_1, 16), 16), 16); + sumf_2 = __riscv_vfsub_vv_f32m2(sumf_2, __riscv_vfmul_vv_f32m2(dmins_d_2, __riscv_vfcvt_f_x_v_f32m2(bsums_2, 16), 16), 16); + sumf_3 = __riscv_vfsub_vv_f32m2(sumf_3, __riscv_vfmul_vv_f32m2(dmins_d_3, __riscv_vfcvt_f_x_v_f32m2(bsums_3, 16), 16), 16); + + + // Accumulation for 2 sub-blocks. + // + // This might overflow, so we accumulate in two steps. + // + // Recheck. + for (int k = 0; k < 2; k++) { + // 4x16 integer accumulators + vint16m1_t sumi_0_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_1_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_2_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_3_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_0_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_1_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_2_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_3_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + + for (int i = k * 16; i < k * 16 + QK4_0 / 2; i++) { + // Load `b_ptr`. + const vuint8mf2_t b_0_packed = __riscv_vle8_v_u8mf2(&b_ptr[l].qs[j * 1024 + i * 16], 16); + const vint8mf2_t b_s_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(b_0_packed, 0xF, 16)); + const vint8mf2_t b_s_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(b_0_packed, 4, 16)); + + sumi_0_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_0_s_0_16, a_ptr[l].qs[j * 512 + i * 4], b_s_0, 16); + sumi_1_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_1_s_0_16, a_ptr[l].qs[j * 512 + i * 4 + 1], b_s_0, 16); + sumi_2_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_2_s_0_16, a_ptr[l].qs[j * 512 + i * 4 + 2], b_s_0, 16); + sumi_3_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_3_s_0_16, a_ptr[l].qs[j * 512 + i * 4 + 3], b_s_0, 16); + + sumi_0_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_0_s_1_16, a_ptr[l].qs[j * 512 + 128 + i * 4], b_s_1, 16); + sumi_1_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_1_s_1_16, a_ptr[l].qs[j * 512 + 128 + i * 4 + 1], b_s_1, 16); + sumi_2_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_2_s_1_16, a_ptr[l].qs[j * 512 + 128 + i * 4 + 2], b_s_1, 16); + sumi_3_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_3_s_1_16, a_ptr[l].qs[j * 512 + 128 + i * 4 + 3], b_s_1, 16); + } + + sumi_0 = __riscv_vwmacc_vv_i32m2(sumi_0, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 0)), + sumi_0_s_0_16, 16); + sumi_0 = __riscv_vwmacc_vv_i32m2(sumi_0, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 1)), + sumi_0_s_1_16, 16); + sumi_1 = __riscv_vwmacc_vv_i32m2(sumi_1, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 0)), + sumi_1_s_0_16, 16); + sumi_1 = __riscv_vwmacc_vv_i32m2(sumi_1, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 1)), + sumi_1_s_1_16, 16); + sumi_2 = __riscv_vwmacc_vv_i32m2(sumi_2, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 0)), + sumi_2_s_0_16, 16); + sumi_2 = __riscv_vwmacc_vv_i32m2(sumi_2, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 1)), + sumi_2_s_1_16, 16); + sumi_3 = __riscv_vwmacc_vv_i32m2(sumi_3, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 0)), + sumi_3_s_0_16, 16); + sumi_3 = __riscv_vwmacc_vv_i32m2(sumi_3, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 1)), + sumi_3_s_1_16, 16); + } + // Accumulation for 2 sub-blocks. + // + // This might overflow, so we accumulate in two steps. + // + // Recheck. + for (int k = 0; k < 2; k++) { + // 4x16 integer accumulators + vint16m1_t sumi_0_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_1_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_2_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_3_s_0_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_0_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_1_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_2_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + vint16m1_t sumi_3_s_1_16 = __riscv_vmv_v_x_i16m1(0.0f, 16); + + for (int i = k * 16; i < k * 16 + QK4_0 / 2; i++) { + // Load `b_ptr`. + const vuint8mf2_t b_0_packed = __riscv_vle8_v_u8mf2(&b_ptr[l].qs[j * 1024 + 512 + i * 16], 16); + const vint8mf2_t b_s_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(b_0_packed, 0xF, 16)); + const vint8mf2_t b_s_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(b_0_packed, 4, 16)); + + sumi_0_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_0_s_0_16, a_ptr[l].qs[j * 512 + 256 + i * 4], b_s_0, 16); + sumi_1_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_1_s_0_16, a_ptr[l].qs[j * 512 + 256 + i * 4 + 1], b_s_0, 16); + sumi_2_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_2_s_0_16, a_ptr[l].qs[j * 512 + 256 + i * 4 + 2], b_s_0, 16); + sumi_3_s_0_16 = __riscv_vwmacc_vx_i16m1(sumi_3_s_0_16, a_ptr[l].qs[j * 512 + 256 + i * 4 + 3], b_s_0, 16); + + sumi_0_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_0_s_1_16, a_ptr[l].qs[j * 512 + 384 + i * 4], b_s_1, 16); + sumi_1_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_1_s_1_16, a_ptr[l].qs[j * 512 + 384 + i * 4 + 1], b_s_1, 16); + sumi_2_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_2_s_1_16, a_ptr[l].qs[j * 512 + 384 + i * 4 + 2], b_s_1, 16); + sumi_3_s_1_16 = __riscv_vwmacc_vx_i16m1(sumi_3_s_1_16, a_ptr[l].qs[j * 512 + 384 + i * 4 + 3], b_s_1, 16); + } + + sumi_0 = __riscv_vwmacc_vv_i32m2(sumi_0, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 2)), + sumi_0_s_0_16, 16); + sumi_0 = __riscv_vwmacc_vv_i32m2(sumi_0, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 3)), + sumi_0_s_1_16, 16); + sumi_1 = __riscv_vwmacc_vv_i32m2(sumi_1, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 2)), + sumi_1_s_0_16, 16); + sumi_1 = __riscv_vwmacc_vv_i32m2(sumi_1, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 3)), + sumi_1_s_1_16, 16); + sumi_2 = __riscv_vwmacc_vv_i32m2(sumi_2, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 2)), + sumi_2_s_0_16, 16); + sumi_2 = __riscv_vwmacc_vv_i32m2(sumi_2, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 3)), + sumi_2_s_1_16, 16); + sumi_3 = __riscv_vwmacc_vv_i32m2(sumi_3, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 2)), + sumi_3_s_0_16, 16); + sumi_3 = __riscv_vwmacc_vv_i32m2(sumi_3, + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vget_v_u16m4_u16m1(scales, 3)), + sumi_3_s_1_16, 16); + } + } + + const vfloat32m2_t b_d = __riscv_vfwcvt_f_f_v_f32m2(__riscv_vle16_v_f16m1((const _Float16 *)b_ptr[l].d, 16), 16); + const vfloat32m2_t d_0 = __riscv_vfmul_vf_f32m2(b_d, a_ptr[l].d[0], 16); + const vfloat32m2_t d_1 = __riscv_vfmul_vf_f32m2(b_d, a_ptr[l].d[1], 16); + const vfloat32m2_t d_2 = __riscv_vfmul_vf_f32m2(b_d, a_ptr[l].d[2], 16); + const vfloat32m2_t d_3 = __riscv_vfmul_vf_f32m2(b_d, a_ptr[l].d[3], 16); + + sumf_0 = __riscv_vfmacc_vv_f32m2(sumf_0, __riscv_vfcvt_f_x_v_f32m2(sumi_0, 16), d_0, 16); + sumf_1 = __riscv_vfmacc_vv_f32m2(sumf_1, __riscv_vfcvt_f_x_v_f32m2(sumi_1, 16), d_1, 16); + sumf_2 = __riscv_vfmacc_vv_f32m2(sumf_2, __riscv_vfcvt_f_x_v_f32m2(sumi_2, 16), d_2, 16); + sumf_3 = __riscv_vfmacc_vv_f32m2(sumf_3, __riscv_vfcvt_f_x_v_f32m2(sumi_3, 16), d_3, 16); + } + + __riscv_vse32_v_f32m2(s + (y * 4 + 0) * bs + x * 16, sumf_0, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 1) * bs + x * 16, sumf_1, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 2) * bs + x * 16, sumf_2, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 3) * bs + x * 16, sumf_3, 16); + } + } + return; +#endif + ggml_gemm_q4_K_16x1_q8_K_generic(n, s, bs, vx, vy, nr, nc); +} + +void ggml_gemm_iq4_nl_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined __riscv_v_intrinsic + const vint8mf2_t values = __riscv_vle8_v_i8mf2(kvalues_iq4nl, 16); + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx16 * b_ptr = (const block_iq4_nlx16 *) vx + (x * nb); + + // 4x16 Accumulators + vfloat32m2_t sumf_0 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_1 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_2 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_3 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + + for (int l = 0; l < nb; l++) { + // 4x16 integer accumulators + vint32m2_t sumi_0 = __riscv_vmv_v_x_i32m2(0.0f, 16); + vint32m2_t sumi_1 = __riscv_vmv_v_x_i32m2(0.0f, 16); + vint32m2_t sumi_2 = __riscv_vmv_v_x_i32m2(0.0f, 16); + vint32m2_t sumi_3 = __riscv_vmv_v_x_i32m2(0.0f, 16); + + // Accumulation loop. + for (int i = 0; i < QK4_NL / 2; i++) { + // Load `b_ptr`. + const vuint8mf2_t b_0_packed = __riscv_vle8_v_u8mf2((const uint8_t *)&b_ptr[l].qs[i * 16], 16); + const vint8mf2_t b_0_lo = __riscv_vrgather_vv_i8mf2(values, __riscv_vand_vx_u8mf2(b_0_packed, 0xf, 16), 16); + const vint8mf2_t b_0_hi = __riscv_vrgather_vv_i8mf2(values, __riscv_vsrl_vx_u8mf2(b_0_packed, 4, 16), 16); + // const vint16m1_t b_0_lo_16 = __riscv_vwcvt_x_x_v_i16m1(b_0_lo, 16); + // const vint16m1_t b_0_hi_16 = __riscv_vwcvt_x_x_v_i16m1(b_0_hi, 16); + + const vint16m1_t sumi_0_lo = __riscv_vwmul_vx_i16m1(b_0_lo, a_ptr[l].qs[i * 4], 16); + const vint16m1_t sumi_1_lo = __riscv_vwmul_vx_i16m1(b_0_lo, a_ptr[l].qs[i * 4 + 1], 16); + const vint16m1_t sumi_2_lo = __riscv_vwmul_vx_i16m1(b_0_lo, a_ptr[l].qs[i * 4 + 2], 16); + const vint16m1_t sumi_3_lo = __riscv_vwmul_vx_i16m1(b_0_lo, a_ptr[l].qs[i * 4 + 3], 16); + + const vint16m1_t sumi_0_hi = __riscv_vwmul_vx_i16m1(b_0_hi, a_ptr[l].qs[64 + i * 4], 16); + const vint16m1_t sumi_1_hi = __riscv_vwmul_vx_i16m1(b_0_hi, a_ptr[l].qs[64 + i * 4 + 1], 16); + const vint16m1_t sumi_2_hi = __riscv_vwmul_vx_i16m1(b_0_hi, a_ptr[l].qs[64 + i * 4 + 2], 16); + const vint16m1_t sumi_3_hi = __riscv_vwmul_vx_i16m1(b_0_hi, a_ptr[l].qs[64 + i * 4 + 3], 16); + + sumi_0 = __riscv_vadd_vv_i32m2(sumi_0, __riscv_vwadd_vv_i32m2(sumi_0_lo, sumi_0_hi, 16), 16); + sumi_1 = __riscv_vadd_vv_i32m2(sumi_1, __riscv_vwadd_vv_i32m2(sumi_1_lo, sumi_1_hi, 16), 16); + sumi_2 = __riscv_vadd_vv_i32m2(sumi_2, __riscv_vwadd_vv_i32m2(sumi_2_lo, sumi_2_hi, 16), 16); + sumi_3 = __riscv_vadd_vv_i32m2(sumi_3, __riscv_vwadd_vv_i32m2(sumi_3_lo, sumi_3_hi, 16), 16); + } + + const vfloat16m1_t b_d = __riscv_vle16_v_f16m1((const _Float16 *)b_ptr[l].d, 16); + const vfloat32m2_t d_0 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[0], 16); + const vfloat32m2_t d_1 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[1], 16); + const vfloat32m2_t d_2 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[2], 16); + const vfloat32m2_t d_3 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[3], 16); + + sumf_0 = __riscv_vfmacc_vv_f32m2(sumf_0, __riscv_vfcvt_f_x_v_f32m2(sumi_0, 16), d_0, 16); + sumf_1 = __riscv_vfmacc_vv_f32m2(sumf_1, __riscv_vfcvt_f_x_v_f32m2(sumi_1, 16), d_1, 16); + sumf_2 = __riscv_vfmacc_vv_f32m2(sumf_2, __riscv_vfcvt_f_x_v_f32m2(sumi_2, 16), d_2, 16); + sumf_3 = __riscv_vfmacc_vv_f32m2(sumf_3, __riscv_vfcvt_f_x_v_f32m2(sumi_3, 16), d_3, 16); + } + + __riscv_vse32_v_f32m2(s + (y * 4 + 0) * bs + x * 16, sumf_0, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 1) * bs + x * 16, sumf_1, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 2) * bs + x * 16, sumf_2, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 3) * bs + x * 16, sumf_3, 16); + } + } + return; +#endif + ggml_gemm_iq4_nl_16x1_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + +void ggml_gemm_q8_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined __riscv_v_intrinsic + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q8_0x16 * b_ptr = (const block_q8_0x16 *) vx + (x * nb); + + // 4x16 Accumulators + vfloat32m2_t sumf_0 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_1 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_2 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + vfloat32m2_t sumf_3 = __riscv_vfmv_v_f_f32m2(0.0f, 16); + + for (int l = 0; l < nb; l++) { + // 4x16 Integer Accumulators + vint32m2_t sumi_0 = __riscv_vmv_v_x_i32m2(0.0f, 16); + vint32m2_t sumi_1 = __riscv_vmv_v_x_i32m2(0.0f, 16); + vint32m2_t sumi_2 = __riscv_vmv_v_x_i32m2(0.0f, 16); + vint32m2_t sumi_3 = __riscv_vmv_v_x_i32m2(0.0f, 16); + + // Accumulation loop. + for (int i = 0; i < QK8_0; i++) { + // Load `b_ptr`. + const vint8mf2_t b_0 = __riscv_vle8_v_i8mf2((const int8_t *)&b_ptr[l].qs[i * 16], 16); + // const vint16m1_t b_0_16 = __riscv_vwcvt_x_x_v_i16m1(b_0, 16); + + sumi_0 = __riscv_vwadd_wv_i32m2(sumi_0, __riscv_vwmul_vx_i16m1(b_0, a_ptr[l].qs[i * 4 + 0], 16), 16); + sumi_1 = __riscv_vwadd_wv_i32m2(sumi_1, __riscv_vwmul_vx_i16m1(b_0, a_ptr[l].qs[i * 4 + 1], 16), 16); + sumi_2 = __riscv_vwadd_wv_i32m2(sumi_2, __riscv_vwmul_vx_i16m1(b_0, a_ptr[l].qs[i * 4 + 2], 16), 16); + sumi_3 = __riscv_vwadd_wv_i32m2(sumi_3, __riscv_vwmul_vx_i16m1(b_0, a_ptr[l].qs[i * 4 + 3], 16), 16); + } + + const vfloat16m1_t b_d = __riscv_vle16_v_f16m1((const _Float16 *)b_ptr[l].d, 16); + const vfloat32m2_t d_0 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[0], 16); + const vfloat32m2_t d_1 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[1], 16); + const vfloat32m2_t d_2 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[2], 16); + const vfloat32m2_t d_3 = __riscv_vfwmul_vf_f32m2(b_d, *(const _Float16 *)&a_ptr[l].d[3], 16); + + sumf_0 = __riscv_vfmacc_vv_f32m2(sumf_0, __riscv_vfcvt_f_x_v_f32m2(sumi_0, 16), d_0, 16); + sumf_1 = __riscv_vfmacc_vv_f32m2(sumf_1, __riscv_vfcvt_f_x_v_f32m2(sumi_1, 16), d_1, 16); + sumf_2 = __riscv_vfmacc_vv_f32m2(sumf_2, __riscv_vfcvt_f_x_v_f32m2(sumi_2, 16), d_2, 16); + sumf_3 = __riscv_vfmacc_vv_f32m2(sumf_3, __riscv_vfcvt_f_x_v_f32m2(sumi_3, 16), d_3, 16); + } + + __riscv_vse32_v_f32m2(s + (y * 4 + 0) * bs + x * 16, sumf_0, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 1) * bs + x * 16, sumf_1, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 2) * bs + x * 16, sumf_2, 16); + __riscv_vse32_v_f32m2(s + (y * 4 + 3) * bs + x * 16, sumf_3, 16); + } + } + return; +#endif + ggml_gemm_q8_0_16x1_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + +void ggml_gemm_q2_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + assert(n % QK_K == 0); + const int num_k_blocks = n / QK_K; + const int N_ROWS_TILE = 4; + const int N_COLS_TILE = 16; + assert(nr % N_ROWS_TILE == 0); + assert(nc % N_COLS_TILE == 0); + + const size_t vl = __riscv_vsetvl_e32m2(N_COLS_TILE); + // --- Tiling Loops --- +#pragma GCC unroll 1 + for (int row_tile = 0; row_tile < nr; row_tile += N_ROWS_TILE) { +#pragma GCC unroll 1 + for (int col_tile = 0; col_tile < nc; col_tile += N_COLS_TILE) { + // Base Pointers + const block_q8_Kx4* lhs_base_ptr = (const block_q8_Kx4*)vy + (row_tile / N_ROWS_TILE) * num_k_blocks; + const block_q2_Kx16* rhs_base_ptr = (const block_q2_Kx16*)vx + (col_tile / N_COLS_TILE) * num_k_blocks; + + // Persistent Float Accumulators + vfloat32m2_t v_sumf_0 = __riscv_vfmv_v_f_f32m2(0.0f, vl); + vfloat32m2_t v_sumf_1 = __riscv_vfmv_v_f_f32m2(0.0f, vl); + vfloat32m2_t v_sumf_2 = __riscv_vfmv_v_f_f32m2(0.0f, vl); + vfloat32m2_t v_sumf_3 = __riscv_vfmv_v_f_f32m2(0.0f, vl); + + // --- Super-Block Loop (K=0..255) --- +#pragma GCC unroll 1 + for (int k_block = 0; k_block < num_k_blocks; ++k_block) { + const block_q8_Kx4* lhs_current = &lhs_base_ptr[k_block]; + const block_q2_Kx16* rhs_current = &rhs_base_ptr[k_block]; + + // 1. Load Global Min Scales (Keep as F16/LMUL=1 to save registers) + vfloat16m1_t v_g_min_f16 = __riscv_vle16_v_f16m1((const _Float16*)rhs_current->dmin, vl); + vfloat32m2_t v_g_min_base = __riscv_vfwcvt_f_f_v_f32m2(v_g_min_f16, vl); + + // 2. Initialize Integer Accumulators + vint32m2_t v_isum_0 = __riscv_vmv_v_x_i32m2(0, vl); + vint32m2_t v_isum_1 = __riscv_vmv_v_x_i32m2(0, vl); + vint32m2_t v_isum_2 = __riscv_vmv_v_x_i32m2(0, vl); + vint32m2_t v_isum_3 = __riscv_vmv_v_x_i32m2(0, vl); + + const uint8_t* rhs_qs_ptr = rhs_current->qs; + const uint8_t* rhs_sc_ptr = rhs_current->scales; + const int8_t* lhs_qs_ptr = lhs_current->qs; + + // --- Phase Loop (4 phases x 64 elements) --- +#pragma GCC unroll 1 + for (int phase = 0; phase < 4; ++phase) { + + // A. Load Scales/Mins for the 4 interleaved sub-blocks + vuint16m1_t v_d_sb_0, v_d_sb_1, v_d_sb_2, v_d_sb_3; + vuint16m1_t v_m_sb_0, v_m_sb_1, v_m_sb_2, v_m_sb_3; + + // Unrolled Load Logic + { + vuint8mf2_t v_raw; + // Sub-block 0 + v_raw = __riscv_vle8_v_u8mf2(rhs_sc_ptr + 0, vl); + v_d_sb_0 = __riscv_vzext_vf2_u16m1(__riscv_vand_vx_u8mf2(v_raw, 0xF, vl), vl); + v_m_sb_0 = __riscv_vzext_vf2_u16m1(__riscv_vsrl_vx_u8mf2(v_raw, 4, vl), vl); + + // Sub-block 1 + v_raw = __riscv_vle8_v_u8mf2(rhs_sc_ptr + 16, vl); + v_d_sb_1 = __riscv_vzext_vf2_u16m1(__riscv_vand_vx_u8mf2(v_raw, 0xF, vl), vl); + v_m_sb_1 = __riscv_vzext_vf2_u16m1(__riscv_vsrl_vx_u8mf2(v_raw, 4, vl), vl); + + // Sub-block 2 + v_raw = __riscv_vle8_v_u8mf2(rhs_sc_ptr + 32, vl); + v_d_sb_2 = __riscv_vzext_vf2_u16m1(__riscv_vand_vx_u8mf2(v_raw, 0xF, vl), vl); + v_m_sb_2 = __riscv_vzext_vf2_u16m1(__riscv_vsrl_vx_u8mf2(v_raw, 4, vl), vl); + + // Sub-block 3 + v_raw = __riscv_vle8_v_u8mf2(rhs_sc_ptr + 48, vl); + v_d_sb_3 = __riscv_vzext_vf2_u16m1(__riscv_vand_vx_u8mf2(v_raw, 0xF, vl), vl); + v_m_sb_3 = __riscv_vzext_vf2_u16m1(__riscv_vsrl_vx_u8mf2(v_raw, 4, vl), vl); + + rhs_sc_ptr += 64; + } + + int base_k_phase = (phase < 2) ? (phase * 16) : (128 + (phase-2)*16); + int k_offsets[4] = {0, 32, 64, 96}; + + // B. Inner Dot Product Loop +#pragma GCC unroll 1 + for (int l = 0; l < 16; ++l) { + vuint8mf2_t v_rhs_data = __riscv_vle8_v_u8mf2(rhs_qs_ptr, vl); + rhs_qs_ptr += 16; + + // Unroll over 4 sub-blocks (0, 1, 2, 3 relative to phase) + + // --- Sub-block 0 --- + { + vuint8mf2_t v_q2 = __riscv_vand_vx_u8mf2(v_rhs_data, 3, vl); + vint16m1_t v_w = __riscv_vmul_vv_i16m1( + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(v_q2, vl)), + __riscv_vreinterpret_v_u16m1_i16m1(v_d_sb_0), vl); + + const int8_t* q8 = &lhs_qs_ptr[(base_k_phase + k_offsets[0] + l) * 4]; + v_isum_0 = __riscv_vwmacc_vx_i32m2(v_isum_0, (int16_t)q8[0], v_w, vl); + v_isum_1 = __riscv_vwmacc_vx_i32m2(v_isum_1, (int16_t)q8[1], v_w, vl); + v_isum_2 = __riscv_vwmacc_vx_i32m2(v_isum_2, (int16_t)q8[2], v_w, vl); + v_isum_3 = __riscv_vwmacc_vx_i32m2(v_isum_3, (int16_t)q8[3], v_w, vl); + } + // --- Sub-block 1 --- + { + vuint8mf2_t v_q2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(v_rhs_data, 2, vl), 3, vl); + vint16m1_t v_w = __riscv_vmul_vv_i16m1( + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(v_q2, vl)), + __riscv_vreinterpret_v_u16m1_i16m1(v_d_sb_1), vl); + + const int8_t* q8 = &lhs_qs_ptr[(base_k_phase + k_offsets[1] + l) * 4]; + v_isum_0 = __riscv_vwmacc_vx_i32m2(v_isum_0, (int16_t)q8[0], v_w, vl); + v_isum_1 = __riscv_vwmacc_vx_i32m2(v_isum_1, (int16_t)q8[1], v_w, vl); + v_isum_2 = __riscv_vwmacc_vx_i32m2(v_isum_2, (int16_t)q8[2], v_w, vl); + v_isum_3 = __riscv_vwmacc_vx_i32m2(v_isum_3, (int16_t)q8[3], v_w, vl); + } + // --- Sub-block 2 --- + { + vuint8mf2_t v_q2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(v_rhs_data, 4, vl), 3, vl); + vint16m1_t v_w = __riscv_vmul_vv_i16m1( + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(v_q2, vl)), + __riscv_vreinterpret_v_u16m1_i16m1(v_d_sb_2), vl); + + const int8_t* q8 = &lhs_qs_ptr[(base_k_phase + k_offsets[2] + l) * 4]; + v_isum_0 = __riscv_vwmacc_vx_i32m2(v_isum_0, (int16_t)q8[0], v_w, vl); + v_isum_1 = __riscv_vwmacc_vx_i32m2(v_isum_1, (int16_t)q8[1], v_w, vl); + v_isum_2 = __riscv_vwmacc_vx_i32m2(v_isum_2, (int16_t)q8[2], v_w, vl); + v_isum_3 = __riscv_vwmacc_vx_i32m2(v_isum_3, (int16_t)q8[3], v_w, vl); + } + // --- Sub-block 3 --- + { + vuint8mf2_t v_q2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(v_rhs_data, 6, vl), 3, vl); + vint16m1_t v_w = __riscv_vmul_vv_i16m1( + __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(v_q2, vl)), + __riscv_vreinterpret_v_u16m1_i16m1(v_d_sb_3), vl); + + const int8_t* q8 = &lhs_qs_ptr[(base_k_phase + k_offsets[3] + l) * 4]; + v_isum_0 = __riscv_vwmacc_vx_i32m2(v_isum_0, (int16_t)q8[0], v_w, vl); + v_isum_1 = __riscv_vwmacc_vx_i32m2(v_isum_1, (int16_t)q8[1], v_w, vl); + v_isum_2 = __riscv_vwmacc_vx_i32m2(v_isum_2, (int16_t)q8[2], v_w, vl); + v_isum_3 = __riscv_vwmacc_vx_i32m2(v_isum_3, (int16_t)q8[3], v_w, vl); + } + } + + // C CORRECTION + int sb_base_abs = base_k_phase / 16; + + // --- Correction Sub-block 0 --- + { + int sb_abs = sb_base_abs + (k_offsets[0] / 16); + vint16m1_t v_min = __riscv_vreinterpret_v_u16m1_i16m1(v_m_sb_0); + + // Row 0 + vfloat32m2_t v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[0], vl); + vint32m2_t v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 0], vl); + vfloat32m2_t vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_0 = __riscv_vfsub_vv_f32m2(v_sumf_0, vf_c, vl); + + // Row 1 + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[1], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 1], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_1 = __riscv_vfsub_vv_f32m2(v_sumf_1, vf_c, vl); + + // Row 2 + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[2], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 2], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_2 = __riscv_vfsub_vv_f32m2(v_sumf_2, vf_c, vl); + + // Row 3 + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[3], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 3], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_3 = __riscv_vfsub_vv_f32m2(v_sumf_3, vf_c, vl); + } + + // --- Correction Sub-block 1 --- + { + int sb_abs = sb_base_abs + (k_offsets[1] / 16); + vint16m1_t v_min = __riscv_vreinterpret_v_u16m1_i16m1(v_m_sb_1); + + vfloat32m2_t v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[0], vl); + vint32m2_t v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 0], vl); + vfloat32m2_t vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_0 = __riscv_vfsub_vv_f32m2(v_sumf_0, vf_c, vl); + + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[1], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 1], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_1 = __riscv_vfsub_vv_f32m2(v_sumf_1, vf_c, vl); + + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[2], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 2], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_2 = __riscv_vfsub_vv_f32m2(v_sumf_2, vf_c, vl); + + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[3], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 3], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_3 = __riscv_vfsub_vv_f32m2(v_sumf_3, vf_c, vl); + } + + // --- Correction Sub-block 2 --- + { + int sb_abs = sb_base_abs + (k_offsets[2] / 16); + vint16m1_t v_min = __riscv_vreinterpret_v_u16m1_i16m1(v_m_sb_2); + + vfloat32m2_t v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[0], vl); + vint32m2_t v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 0], vl); + vfloat32m2_t vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_0 = __riscv_vfsub_vv_f32m2(v_sumf_0, vf_c, vl); + + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[1], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 1], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_1 = __riscv_vfsub_vv_f32m2(v_sumf_1, vf_c, vl); + + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[2], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 2], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_2 = __riscv_vfsub_vv_f32m2(v_sumf_2, vf_c, vl); + + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[3], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 3], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_3 = __riscv_vfsub_vv_f32m2(v_sumf_3, vf_c, vl); + } + + // --- Correction Sub-block 3 --- + { + int sb_abs = sb_base_abs + (k_offsets[3] / 16); + vint16m1_t v_min = __riscv_vreinterpret_v_u16m1_i16m1(v_m_sb_3); + + vfloat32m2_t v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[0], vl); + vint32m2_t v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 0], vl); + vfloat32m2_t vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_0 = __riscv_vfsub_vv_f32m2(v_sumf_0, vf_c, vl); + + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[1], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 1], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_1 = __riscv_vfsub_vv_f32m2(v_sumf_1, vf_c, vl); + + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[2], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 2], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_2 = __riscv_vfsub_vv_f32m2(v_sumf_2, vf_c, vl); + + v_g_min = __riscv_vfmul_vf_f32m2(v_g_min_base, lhs_current->d[3], vl); + v_c = __riscv_vwmul_vx_i32m2(v_min, lhs_current->bsums[sb_abs * 4 + 3], vl); + vf_c = __riscv_vfmul_vv_f32m2(__riscv_vfcvt_f_x_v_f32m2(v_c, vl), v_g_min, vl); + v_sumf_3 = __riscv_vfsub_vv_f32m2(v_sumf_3, vf_c, vl); + } + + } // End Phase Loop + + // --- Apply Main Scales --- + vfloat16m1_t v_g_all_f16 = __riscv_vle16_v_f16m1((const _Float16*)rhs_current->d, vl); + vfloat32m2_t v_g_all_base = __riscv_vfwcvt_f_f_v_f32m2(v_g_all_f16, vl); + + { + vfloat32m2_t v_g_all = __riscv_vfmul_vf_f32m2(v_g_all_base, lhs_current->d[0], vl); + vfloat32m2_t v_sum = __riscv_vfcvt_f_x_v_f32m2(v_isum_0, vl); + v_sum = __riscv_vfmul_vv_f32m2(v_sum, v_g_all, vl); + v_sumf_0 = __riscv_vfadd_vv_f32m2(v_sumf_0, v_sum, vl); + } + // Row 1 + { + vfloat32m2_t v_g_all = __riscv_vfmul_vf_f32m2(v_g_all_base, lhs_current->d[1], vl); + vfloat32m2_t v_sum = __riscv_vfcvt_f_x_v_f32m2(v_isum_1, vl); + v_sum = __riscv_vfmul_vv_f32m2(v_sum, v_g_all, vl); + v_sumf_1 = __riscv_vfadd_vv_f32m2(v_sumf_1, v_sum, vl); + } + // Row 2 + { + vfloat32m2_t v_g_all = __riscv_vfmul_vf_f32m2(v_g_all_base, lhs_current->d[2], vl); + vfloat32m2_t v_sum = __riscv_vfcvt_f_x_v_f32m2(v_isum_2, vl); + v_sum = __riscv_vfmul_vv_f32m2(v_sum, v_g_all, vl); + v_sumf_2 = __riscv_vfadd_vv_f32m2(v_sumf_2, v_sum, vl); + } + // Row 3 + { + vfloat32m2_t v_g_all = __riscv_vfmul_vf_f32m2(v_g_all_base, lhs_current->d[3], vl); + vfloat32m2_t v_sum = __riscv_vfcvt_f_x_v_f32m2(v_isum_3, vl); + v_sum = __riscv_vfmul_vv_f32m2(v_sum, v_g_all, vl); + v_sumf_3 = __riscv_vfadd_vv_f32m2(v_sumf_3, v_sum, vl); + } + + } // End K-Block + + __riscv_vse32_v_f32m2(s + (row_tile + 0) * bs + col_tile, v_sumf_0, vl); + __riscv_vse32_v_f32m2(s + (row_tile + 1) * bs + col_tile, v_sumf_1, vl); + __riscv_vse32_v_f32m2(s + (row_tile + 2) * bs + col_tile, v_sumf_2, vl); + __riscv_vse32_v_f32m2(s + (row_tile + 3) * bs + col_tile, v_sumf_3, vl); + } + } +} diff --git a/ggml/src/ggml-cpu/arch/s390/quants.c b/ggml/src/ggml-cpu/arch/s390/quants.c index 19d225a483..34184ed851 100644 --- a/ggml/src/ggml-cpu/arch/s390/quants.c +++ b/ggml/src/ggml-cpu/arch/s390/quants.c @@ -181,11 +181,11 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi const int8x16_t v_yh = vec_xl(QK8_0/2, y[ib].qs); const int16x8_t v_xylso = vec_mulo(v_xls, v_yl); - const int16x8_t v_xylse = vec_mule(v_xls, v_yl); + const int16x8_t v_xyl = vec_meadd(v_xls, v_yl, v_xylso); const int16x8_t v_xyhso = vec_mulo(v_xhs, v_yh); - const int16x8_t v_xyhse = vec_mule(v_xhs, v_yh); + const int16x8_t v_xyh = vec_meadd(v_xhs, v_yh, v_xyhso); - int16x8_t v_xy_ = v_xylso + v_xylse + v_xyhso + v_xyhse; v_xy_ += vec_reve(v_xy_); + int16x8_t v_xy_ = v_xyl + v_xyh; v_xy_ += vec_reve(v_xy_); const float32x4_t v_xy = vec_float(vec_unpackh(v_xy_)); const float32x4_t v_d = vec_splats(GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d)); @@ -890,8 +890,7 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi const int16x8_t v_minsh = (int16x8_t)vec_unpackh((uint8x16_t)v_mins8); const int32x4_t v_minso = vec_mulo(v_ysums, v_minsh); - const int32x4_t v_minse = vec_mule(v_ysums, v_minsh); - const int32x4_t v_mins = v_minso + v_minse; + const int32x4_t v_mins = vec_meadd(v_ysums, v_minsh, v_minso); sumf -= dmin * (v_mins[0] + v_mins[1] + v_mins[2] + v_mins[3]); const uint8_t * scales = (const uint8_t *)utmp; @@ -1004,8 +1003,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi const int16x8_t v_minsh = (int16x8_t)vec_unpackh(v_mins8); const int32x4_t v_minsho = vec_mulo(v_ysums, v_minsh); - const int32x4_t v_minshe = vec_mule(v_ysums, v_minsh); - const int32x4_t v_mins = vec_add(v_minsho, v_minshe); + const int32x4_t v_mins = vec_meadd(v_ysums, v_minsh, v_minsho); const int32_t mins = vec_hsum_i32x4(v_mins); const uint8_t * scales = (const uint8_t *)utmp; @@ -1110,10 +1108,10 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi const int16x8_t v_scaleh = vec_unpackl(v_scale); const int32x4_t v_minslo = vec_mulo(v_ysumsl, v_scalel); - const int32x4_t v_minsle = vec_mule(v_ysumsl, v_scalel); + const int32x4_t v_minsl = vec_meadd(v_ysumsl, v_scalel, v_minslo); const int32x4_t v_minsho = vec_mulo(v_ysumsh, v_scaleh); - const int32x4_t v_minshe = vec_mule(v_ysumsh, v_scaleh); - const int32x4_t v_mins = v_minslo + v_minsle + v_minsho + v_minshe; + const int32x4_t v_minsh = vec_meadd(v_ysumsh, v_scaleh, v_minsho); + const int32x4_t v_mins = vec_add(v_minsl, v_minsh); const int32_t mins = vec_hsum_i32x4(v_mins); diff --git a/ggml/src/ggml-cpu/arch/x86/repack.cpp b/ggml/src/ggml-cpu/arch/x86/repack.cpp index 7dda9eea0c..33c6cb6509 100644 --- a/ggml/src/ggml-cpu/arch/x86/repack.cpp +++ b/ggml/src/ggml-cpu/arch/x86/repack.cpp @@ -423,7 +423,7 @@ void ggml_quantize_mat_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTR quants_interleaved[j] = i0; } - // Masks to shuffle the quants of corresonding sub blocks for rearraning quants for vectorized bsums computation + // Masks to shuffle the quants of corresponding sub blocks for rearranging quants for vectorized bsums computation __m256i shuffle_mask_sb2 = _mm256_castsi128_si256(_mm_setr_epi8(0, 1, 0, 1, 4, 5, 6, 7, 8, 9, 8, 9, 12, 13, 14, 15)); shuffle_mask_sb2 = _mm256_permute2f128_si256(shuffle_mask_sb2, shuffle_mask_sb2, 0); __m256i shuffle_mask_sb3 = _mm256_castsi128_si256(_mm_setr_epi8(0, 1, 2, 3, 0, 1, 6, 7, 8, 9, 10, 11, 8, 9, 14, 15)); @@ -522,7 +522,8 @@ template static void gemv_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc, __m256i signextendlut) { static_assert( std::is_same_v || - std::is_same_v, + std::is_same_v || + std::is_same_v, "Unsupported block type"); const int qk = QK8_0; @@ -580,6 +581,18 @@ static void gemv_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t std::is_same_v || std::is_same_v) { col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask); + } else if constexpr (std::is_same_v) { + // Load 8 E8M0 exponents and convert to float via LUT + // Rearranged to match changemask order: 0,4,1,5,2,6,3,7 + col_scale_f32 = _mm256_set_ps( + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[7]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[3]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[6]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[2]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[5]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[1]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[4]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[0])); } // Load and convert to FP32 scale from block_q8_0 @@ -612,7 +625,7 @@ static void gemv_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t iacc = mul_sum_i8_pairs_acc_int32x8(iacc, _mm256_blend_epi32(rhs_vec_0123_3 ,_mm256_shuffle_epi32(rhs_vec_4567_3, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 170)); iacc = mul_sum_i8_pairs_acc_int32x8(iacc, _mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_3, 177) ,rhs_vec_4567_3, 170), _mm256_shuffle_epi32(lhs_vec_1, 255)); - // Accumulated values multipled with appropriate scales + // Accumulated values multiplied with appropriate scales acc_row = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc), _mm256_mul_ps(col_scale_f32, row_scale_f32), acc_row); } @@ -628,7 +641,8 @@ template static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc, __m256i signextendlut) { static_assert( std::is_same_v || - std::is_same_v, + std::is_same_v || + std::is_same_v, "Unsupported block type"); const int qk = QK8_0; @@ -749,6 +763,25 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t std::is_same_v || std::is_same_v) { col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); + } else if constexpr (std::is_same_v) { + //TODO: simd-ify + col_scale_f32 = _mm512_set_ps( + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[7]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[6]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[5]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[4]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[3]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[2]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[1]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[0]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[7]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[6]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[5]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[4]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[3]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[2]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[1]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[0])); } // Process LHS in pairs of rows @@ -835,7 +868,7 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68); const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); - // Multiply with appropiate scales and accumulate + // Multiply with appropriate scales and accumulate acc_rows[rp * 4] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); @@ -941,6 +974,25 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t std::is_same_v || std::is_same_v) { col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); + } else if constexpr (std::is_same_v) { + //TODO: simd-ify + col_scale_f32 = _mm512_set_ps( + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[7]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[6]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[5]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[4]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[3]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[2]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[1]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[0]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[7]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[6]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[5]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[4]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[3]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[2]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[1]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[0])); } // Load the four blocks of quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 @@ -1024,7 +1076,7 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68); const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); - // Multiply with appropiate scales and accumulate + // Multiply with appropriate scales and accumulate acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); @@ -1123,6 +1175,16 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t std::is_same_v || std::is_same_v) { col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); + } else if constexpr (std::is_same_v) { + col_scale_f32 = _mm256_set_ps( + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[7]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[6]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[5]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[4]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[3]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[2]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[1]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[0])); } // Process LHS in groups of four @@ -1195,7 +1257,7 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask); - // Multiply with appropiate scales and accumulate + // Multiply with appropriate scales and accumulate acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); @@ -1283,6 +1345,16 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t std::is_same_v || std::is_same_v) { col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); + } else if constexpr (std::is_same_v) { + col_scale_f32 = _mm256_set_ps( + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[7]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[6]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[5]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[4]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[3]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[2]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[1]), + GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[0])); } // Load the four blocks of quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 @@ -1356,7 +1428,7 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask); - // Multiply with appropiate scales and accumulate + // Multiply with appropriate scales and accumulate acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); @@ -1540,7 +1612,7 @@ void ggml_gemv_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo lhs_vec_11 = _mm256_permute2f128_si256(lhs_vec_11, lhs_vec_11, 0); // Dot product done within 32 bit lanes and accumulated in the same vector - // First done for first sub block and thenn for second sub block in each sb + // First done for first sub block and then for second sub block in each sb // B0(0-3) B4(0-3) B1(0-3) B5(0-3) B2(0-3) B6(0-3) B3(0-3) B7(0-3) with A0(0-3) // B0(4-7) B4(4-7) B1(4-7) B5(4-7) B2(4-7) B6(4-7) B3(4-7) B7(4-7) with A0(4-7) // ........................................................................... @@ -1625,6 +1697,19 @@ void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const ggml_gemv_iq4_nl_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc); } +void ggml_gemv_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { +#if defined(__AVX2__) + __m256i signextendlut = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i*)kvalues_mxfp4)); + signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); + + gemv_q4_b32_8x8_q8_0_lut_avx(n, s, bs, vx, vy, nr, nc, signextendlut); + + return; +#endif + + ggml_gemv_mxfp4_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK_K; const int nb = n / qk; @@ -2337,7 +2422,7 @@ void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo const __m256 row_scale_f32_ymm = _mm256_set_m128(row_scale_f32_sse, row_scale_f32_sse); const __m512 row_scale_f32 = _mm512_insertf32x8(_mm512_castps256_ps512(row_scale_f32_ymm), row_scale_f32_ymm, 1); - // Multiply with appropiate scales and accumulate (for both d and dmin) below + // Multiply with appropriate scales and accumulate (for both d and dmin) below acc_rows[rp * 4] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); @@ -2700,7 +2785,7 @@ void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo const __m256 row_scale_f32_ymm = _mm256_set_m128(row_scale_f32_sse, row_scale_f32_sse); const __m512 row_scale_f32 = _mm512_insertf32x8(_mm512_castps256_ps512(row_scale_f32_ymm), row_scale_f32_ymm, 1); - // Multiply with appropiate scales and accumulate (for both d and dmin) below + // Multiply with appropriate scales and accumulate (for both d and dmin) below acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); @@ -2717,7 +2802,7 @@ void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo acc_min_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_min_3), _mm512_mul_ps(col_dmin_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_min_rows[3]); } } - // Store accumlated values + // Store accumulated values for (int i = 0; i < 4; i++) { _mm512_storeu_ps((float * )(s + ((y * 4 + i) * bs + x * 8)), _mm512_sub_ps(acc_rows[i], acc_min_rows[i])); } @@ -3045,7 +3130,7 @@ void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo const __m128 row_scale_f32_sse = _mm_load_ps(a_ptrs[rp][b].d); const __m256 row_scale_f32 = _mm256_set_m128(row_scale_f32_sse, row_scale_f32_sse);//GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask); - // Multiply with appropiate scales and accumulate (for both d and dmin) below + // Multiply with appropriate scales and accumulate (for both d and dmin) below acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); @@ -3375,7 +3460,7 @@ void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo const __m128 row_scale_f32_sse = _mm_load_ps(a_ptr[b].d); const __m256 row_scale_f32 = _mm256_set_m128(row_scale_f32_sse, row_scale_f32_sse); //GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask); - // Multiply with appropiate scales and accumulate (for both d and dmin) below + // Multiply with appropriate scales and accumulate (for both d and dmin) below acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); @@ -3423,6 +3508,21 @@ void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc); } +void ggml_gemm_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { +#if defined(__AVX2__) || defined(__AVX512F__) + { + __m256i signextendlut = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i*)kvalues_mxfp4)); + signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); + + gemm_q4_b32_8x8_q8_0_lut_avx(n, s, bs, vx, vy, nr, nc, signextendlut); + + return; + } +#endif // defined(__AVX2__) || defined(__AVX512F__) + + ggml_gemm_mxfp4_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc); +} + void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK_K; const int nb = n / qk; @@ -4168,7 +4268,7 @@ void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo const __m256 row_scale_f32_ymm = _mm256_set_m128(row_scale_f32_sse, row_scale_f32_sse); const __m512 row_scale_f32 = _mm512_insertf32x8(_mm512_castps256_ps512(row_scale_f32_ymm), row_scale_f32_ymm, 1); - // Multiply with appropiate scales and accumulate (for both d and dmin) below + // Multiply with appropriate scales and accumulate (for both d and dmin) below acc_rows[rp * 4] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); @@ -4935,7 +5035,7 @@ void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo acc_min_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_min_3), _mm512_mul_ps(col_dmin_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_min_rows[3]); } } - // Store accumlated values + // Store accumulated values for (int i = 0; i < 4; i++) { _mm512_storeu_ps((float * )(s + ((y * 4 + i) * bs + x * 8)), _mm512_sub_ps(acc_rows[i], acc_min_rows[i])); } @@ -5577,7 +5677,7 @@ void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo const __m128 row_scale_f32_sse = _mm_load_ps(a_ptrs[rp][b].d); const __m256 row_scale_f32 = _mm256_set_m128(row_scale_f32_sse, row_scale_f32_sse); - // Multiply with appropiate scales and accumulate (for both d and dmin) below + // Multiply with appropriate scales and accumulate (for both d and dmin) below acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); @@ -6249,7 +6349,7 @@ void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo const __m128 row_scale_f32_sse = _mm_load_ps(a_ptr[b].d); const __m256 row_scale_f32 = _mm256_set_m128(row_scale_f32_sse, row_scale_f32_sse); - // Multiply with appropiate scales and accumulate (for both d and dmin) below + // Multiply with appropriate scales and accumulate (for both d and dmin) below acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 64eb01a4e1..8b323bd9b0 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -270,6 +270,12 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = { .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, }, + [GGML_TYPE_NVFP4] = { + .from_float = quantize_row_nvfp4, + .vec_dot = ggml_vec_dot_nvfp4_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + }, [GGML_TYPE_Q2_K] = { .from_float = quantize_row_q2_K, .vec_dot = ggml_vec_dot_q2_K_q8_K, @@ -2021,6 +2027,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_solve_tri(params, tensor); } break; + case GGML_OP_GATED_DELTA_NET: + { + ggml_compute_forward_gated_delta_net(params, tensor); + } break; case GGML_OP_MAP_CUSTOM1: { ggml_compute_forward_map_custom1(params, tensor); @@ -2200,6 +2210,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { } break; case GGML_OP_COUNT_EQUAL: case GGML_OP_SOLVE_TRI: + case GGML_OP_GATED_DELTA_NET: { n_tasks = n_threads; } break; @@ -2477,7 +2488,7 @@ static bool ggml_thread_apply_priority(int32_t prio) { if (prio != GGML_SCHED_PRIO_LOW) { // Tell Windows that this thread should not be throttled (needs its own CPU core). - // Newer Windows 11 versions aggresively park (offline) CPU cores and often place + // Newer Windows 11 versions aggressively park (offline) CPU cores and often place // all our threads onto the first 4 cores which results in terrible performance with // n_threads > 4 #if _WIN32_WINNT >= 0x0602 @@ -2905,6 +2916,11 @@ struct ggml_cplan ggml_graph_plan( { cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); } break; + case GGML_OP_GATED_DELTA_NET: + { + const int64_t S_v = node->src[2]->ne[0]; + cur = S_v * sizeof(float) * n_tasks; + } break; case GGML_OP_COUNT: { GGML_ABORT("fatal error"); diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.cpp b/ggml/src/ggml-cpu/kleidiai/kernels.cpp index d114f2d49b..8c4d7bc925 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kernels.cpp @@ -1,4 +1,4 @@ -// SPDX-FileCopyrightText: Copyright 2025 Arm Limited and/or its affiliates +// SPDX-FileCopyrightText: Copyright 2025-2026 Arm Limited and/or its affiliates // SPDX-License-Identifier: MIT // @@ -9,7 +9,6 @@ #include "kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod.h" #include "kai_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod.h" #include "kai_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm.h" -#include "kai_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa.h" #include "kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.h" #include "kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.h" #include "kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa.h" @@ -20,6 +19,7 @@ #include "kai_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm.h" #include "kai_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p8x8_16x8_sve_i8mm.h" #include "kai_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p8x8_1x8_sve_dotprod.h" +#include "kai_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa.h" #include "kai_lhs_pack_bf16p2vlx2_f32_sme.h" #include "kai_lhs_quant_pack_qsi8d32p_f32.h" @@ -31,6 +31,7 @@ #include "kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.h" #include "kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.h" #include "kai_rhs_pack_nxk_qsi8cxp_qsi8cx_neon.h" +#include "kai_lhs_pack_f16pmrx2_f32_neon.h" #include "kai_common.h" @@ -309,24 +310,24 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { { /* SME GEMM */ /* .kern_info = */ { - /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_mr = */ kai_get_mr_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa, - /* .get_lhs_offset_ex = */ &kernel_offs_fn3, - /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, - /* .run_kernel_ex = */ &kernel_run_fn11, + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa, + /* .get_lhs_offset_ex = */ &kernel_offs_fn3, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn3, + /* .run_kernel_ex = */ &kernel_run_fn11, }, /* .gemm_lhs_info = */ { - /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32_neon, - /* .get_packed_offset_ex = */ &lhs_offs_fn6, - /* .packed_size_ex = */ &lhs_ps_fn6, - /* .pack_func_ex = */ &lhs_pack_float_fn10, + /* .get_offset = */ kai_get_lhs_offset_lhs_pack_f16pmrx2_f32_neon, + /* .get_packed_offset_ex = */ &lhs_offs_fn6, + /* .packed_size_ex = */ &lhs_ps_fn6, + /* .pack_func_ex = */ &lhs_pack_void_fn10, }, /* SME GEMV */ /* .kern_info = */ { @@ -519,7 +520,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .packed_stride_ex = */ &rhs_stride_fn4, /* .pack_func_ex = */ &rhs_pack_fn12, }, - /* .required_cpu = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM, + /* .required_cpu = */ CPU_FEATURE_I8MM, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_Q4_0, /* .op_type = */ GGML_TYPE_F32, @@ -630,7 +631,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .packed_stride_ex = */ &rhs_stride_fn4, /* .pack_func_ex = */ &rhs_pack_fn12, }, - /* .required_cpu = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM, + /* .required_cpu = */ CPU_FEATURE_I8MM, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_Q4_0, /* .op_type = */ GGML_TYPE_F32, @@ -800,7 +801,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q8[] = { /* .packed_stride_ex = */ &rhs_stride_fn4, /* .pack_func_ex = */ &rhs_pack_scale_fn12, }, - /* .required_cpu = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM, + /* .required_cpu = */ CPU_FEATURE_I8MM, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_Q8_0, /* .op_type = */ GGML_TYPE_F32, diff --git a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp index ad23e73184..9bcc18d442 100644 --- a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp @@ -1,20 +1,31 @@ -// SPDX-FileCopyrightText: Copyright 2025 Arm Limited and/or its affiliates +// SPDX-FileCopyrightText: Copyright 2025-2026 Arm Limited and/or its affiliates // SPDX-License-Identifier: MIT // #include #include +#include #include #include -#include #include +#include #include #include #include #include #include +#include +#include +#include +#include +#include +#include +#include #if defined(__linux__) #include #include +#include +#include +#include #elif defined(__APPLE__) #include #include @@ -39,11 +50,18 @@ #define GGML_COMMON_DECL_CPP #include "ggml-common.h" +static constexpr int GGML_KLEIDIAI_MAX_KERNEL_SLOTS = 2; +static constexpr uint32_t GGML_KLEIDIAI_PACK_MAGIC = 0x4b4c4149; // "KLAI" +static constexpr uint16_t GGML_KLEIDIAI_PACK_VERSION = 1; +static constexpr size_t GGML_KLEIDIAI_PACK_ALIGN = 64; + struct ggml_kleidiai_context { cpu_feature features; ggml_kleidiai_kernels * kernels_q4; ggml_kleidiai_kernels * kernels_q8; -} static ctx = { CPU_FEATURE_NONE, NULL, NULL }; + int sme_thread_cap; // <= 0 means “SME disabled/unknown”; + int thread_hint; // <= 0 means “no hint” +} static ctx = { CPU_FEATURE_NONE, nullptr, nullptr, 0, -1 }; static const char* cpu_feature_to_string(cpu_feature f) { if (f == CPU_FEATURE_NONE) { @@ -63,41 +81,335 @@ static const char* cpu_feature_to_string(cpu_feature f) { } } -static void init_kleidiai_context(void) { +static size_t detect_num_smcus() { + if (!ggml_cpu_has_sme()) { + return 0; + } +#if defined(__linux__) && defined(__aarch64__) + // Linux/aarch64: Best-effort count of Streaming Mode Compute Units (SMCUs) via SMIDR_EL1 sysfs. + size_t num_private = 0; + std::set shared_ids; + + for (size_t cpu = 0;; ++cpu) { + const std::string path = + "/sys/devices/system/cpu/cpu" + std::to_string(cpu) + + "/regs/identification/smidr_el1"; + + std::ifstream file(path); + if (!file.is_open()) { + break; + } + + uint64_t smidr = 0; + if (!(file >> std::hex >> smidr)) { + continue; + } + + // Arm ARM: SMIDR_EL1 + const uint32_t sh = (uint32_t)((smidr >> 13) & 0x3); + // Build an "affinity-like" identifier for shared SMCUs. + // Keep the original packing logic, but isolate it here. + const uint32_t id = (uint32_t)((smidr & 0xFFFu) | ((smidr >> 20) & 0xFFFFF000u)); + + switch (sh) { + case 0b10: // private SMCU + ++num_private; + break; + case 0b11: // shared SMCU + shared_ids.emplace(id); + break; + case 0b00: + // Ambiguous / implementation-defined. Be conservative: + // treat id==0 as private, otherwise as shared. + if (id == 0) ++num_private; + else shared_ids.emplace(id); + break; + default: + break; + } + } + + return num_private + shared_ids.size(); + +#elif defined(__APPLE__) && defined(__aarch64__) + // table for known M4 variants. Users can override via GGML_KLEIDIAI_SME=. + char chip_name[256] = {}; + size_t size = sizeof(chip_name); + + if (sysctlbyname("machdep.cpu.brand_string", chip_name, &size, nullptr, 0) == 0) { + const std::string brand(chip_name); + + struct ModelSMCU { const char *match; size_t smcus; }; + static const ModelSMCU table[] = { + { "M4 Ultra", 2 }, + { "M4 Max", 2 }, + { "M4 Pro", 2 }, + { "M4", 1 }, + }; + + for (const auto &e : table) { + if (brand.find(e.match) != std::string::npos) { + return e.smcus; + } + } + } + return 1; + +#else + return 1; +#endif +} + +static int parse_uint_env(const char *s, const char *name, bool *ok) { + if (!s) { *ok = false; return 0; } + char *end = nullptr; + long v = strtol(s, &end, 10); + if (end == s || *end != '\0') { + GGML_LOG_WARN("kleidiai: invalid %s='%s' (expected integer)\n", name, s); + *ok = false; + return 0; + } + if (v < 0 || v > INT_MAX) { + GGML_LOG_WARN("kleidiai: out-of-range %s='%s'\n", name, s); + *ok = false; + return 0; + } + *ok = true; + return (int)v; +} + +static void init_kleidiai_context(void) { ggml_critical_section_start(); static bool initialized = false; if (!initialized) { initialized = true; - const char *env_var = getenv("GGML_KLEIDIAI_SME"); - int sme_enabled = 0; + + const char *env_sme = getenv("GGML_KLEIDIAI_SME"); + const char *env_threads = getenv("GGML_TOTAL_THREADS"); + + const bool cpu_has_sme = ggml_cpu_has_sme(); + size_t detected_smcus = 0; ctx.features = (ggml_cpu_has_dotprod() ? CPU_FEATURE_DOTPROD : CPU_FEATURE_NONE) | (ggml_cpu_has_matmul_int8() ? CPU_FEATURE_I8MM : CPU_FEATURE_NONE) | ((ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) ? CPU_FEATURE_SVE : CPU_FEATURE_NONE); - if (env_var) { - sme_enabled = atoi(env_var); + if (env_threads) { + bool ok = false; + int hint = parse_uint_env(env_threads, "GGML_TOTAL_THREADS", &ok); + if (ok && hint > 0) { + ctx.thread_hint = hint; + } } - if (sme_enabled != 0) { - ctx.features |= ggml_cpu_has_sme() ? CPU_FEATURE_SME : CPU_FEATURE_NONE; + // SME policy: + // - If CPU doesn't support SME: SME always off. + // - Else: + // - env unset => auto-detect cores; enable if detected > 0. + // - env=0 => force off. + // - env>0 => force N cores (skip detection). + int sme_cores = 0; + bool sme_env_ok = false; + bool sme_env_set = (env_sme != nullptr); + + if (!cpu_has_sme) { + if (sme_env_set) { + bool ok = false; + int req = parse_uint_env(env_sme, "GGML_KLEIDIAI_SME", &ok); + if (ok && req > 0) { + GGML_LOG_WARN("kleidiai: GGML_KLEIDIAI_SME=%d but SME is not supported on this CPU; disabling SME\n", req); + } + } + sme_cores = 0; + } else { + if (sme_env_set) { + bool ok = false; + int v = parse_uint_env(env_sme, "GGML_KLEIDIAI_SME", &ok); + sme_env_ok = ok; + + if (!ok) { + GGML_LOG_WARN("kleidiai: GGML_KLEIDIAI_SME set but parsing failed; falling back to runtime SME-core detection\n"); + detected_smcus = detect_num_smcus(); + sme_cores = detected_smcus > 0 ? (int)detected_smcus : 0; + } else if (v == 0) { + sme_cores = 0; + } else { + sme_cores = v; + } + } else { + detected_smcus = detect_num_smcus(); + sme_cores = detected_smcus > 0 ? (int)detected_smcus : 0; + } + + if (!sme_env_set && sme_cores == 0) { + GGML_LOG_WARN("kleidiai: SME supported but runtime SME-core detection returned 0; falling back to NEON\n"); + } + + if (sme_cores > 0) { + ctx.features |= CPU_FEATURE_SME; + } } + + // Kernel selection ctx.kernels_q4 = ggml_kleidiai_select_kernels_q4_0(ctx.features); ctx.kernels_q8 = ggml_kleidiai_select_kernels_q8_0(ctx.features); -#ifndef NDEBUG - if (ctx.kernels_q4) { - GGML_LOG_DEBUG("kleidiai: using q4 kernel with CPU feature %s\n", cpu_feature_to_string(ctx.kernels_q4->required_cpu)); + + if (!ctx.kernels_q4) { + GGML_LOG_INFO("kleidiai: no compatible q4 kernels found for CPU features mask %d\n", (int)ctx.features); + } else { + GGML_LOG_INFO("kleidiai: primary q4 kernel feature %s\n", cpu_feature_to_string(ctx.kernels_q4->required_cpu)); } - if (ctx.kernels_q8) { - GGML_LOG_DEBUG("kleidiai: using q8 kernel with CPU feature %s\n", cpu_feature_to_string(ctx.kernels_q8->required_cpu)); + + if (!ctx.kernels_q8) { + GGML_LOG_INFO("kleidiai: no compatible q8 kernels found for CPU features mask %d\n", (int)ctx.features); + } else { + GGML_LOG_INFO("kleidiai: primary q8 kernel feature %s\n", cpu_feature_to_string(ctx.kernels_q8->required_cpu)); + } + + ctx.sme_thread_cap = (ctx.features & CPU_FEATURE_SME) ? sme_cores : 0; + + if (ctx.features & CPU_FEATURE_SME) { + if (sme_env_set && sme_env_ok && sme_cores > 0) { + GGML_LOG_INFO("kleidiai: SME enabled (GGML_KLEIDIAI_SME=%d override)\n", sme_cores); + } else { + GGML_LOG_INFO("kleidiai: SME enabled (runtime-detected SME cores=%d)\n", sme_cores); + } + } else { + GGML_LOG_INFO("kleidiai: SME disabled\n"); } -#endif } + ggml_critical_section_end(); } +static inline int kleidiai_sme_thread_cap() { + return ctx.sme_thread_cap; +} + +static inline size_t align_up(size_t value, size_t alignment) { + if (alignment == 0) { + return value; + } + const size_t remainder = value % alignment; + return remainder == 0 ? value : value + (alignment - remainder); +} + +static inline bool kleidiai_pack_fallback_allowed() { + if (ctx.sme_thread_cap <= 0) { + return false; + } + if (ctx.thread_hint <= 0) { + return true; + } + return ctx.thread_hint > ctx.sme_thread_cap; +} + +struct kleidiai_weight_header { + uint32_t magic; + uint16_t version; + uint16_t slot_count; + uint64_t offsets[GGML_KLEIDIAI_MAX_KERNEL_SLOTS]; + uint64_t sizes[GGML_KLEIDIAI_MAX_KERNEL_SLOTS]; +}; + +static inline kleidiai_weight_header * kleidiai_weight_header_from_ptr(void * data) { + return reinterpret_cast(data); +} + +static inline const kleidiai_weight_header * kleidiai_weight_header_from_ptr(const void * data) { + return reinterpret_cast(data); +} + +static inline bool kleidiai_is_weight_header_valid(const kleidiai_weight_header * header) { + if (!header) { + return false; + } + if (header->magic != GGML_KLEIDIAI_PACK_MAGIC || header->version != GGML_KLEIDIAI_PACK_VERSION) { + return false; + } + if (header->slot_count == 0 || header->slot_count > GGML_KLEIDIAI_MAX_KERNEL_SLOTS) { + return false; + } + return true; +} + +static inline uint8_t * kleidiai_weight_slot_ptr(kleidiai_weight_header * header, int slot) { + if (!kleidiai_is_weight_header_valid(header)) { + return nullptr; + } + if (slot < 0 || slot >= header->slot_count) { + return nullptr; + } + return reinterpret_cast(header) + header->offsets[slot]; +} + +static inline const uint8_t * kleidiai_weight_slot_ptr(const kleidiai_weight_header * header, int slot) { + if (!kleidiai_is_weight_header_valid(header)) { + return nullptr; + } + if (slot < 0 || slot >= header->slot_count) { + return nullptr; + } + return reinterpret_cast(header) + header->offsets[slot]; +} + +static inline ggml_kleidiai_kernels * kleidiai_primary_kernel_q4() { + return ctx.kernels_q4; +} + +static inline ggml_kleidiai_kernels * kleidiai_primary_kernel_q8() { + return ctx.kernels_q8; +} + +template +static int kleidiai_collect_kernel_chain_common( + ggml_kleidiai_kernels * primary, + cpu_feature features, + std::array & out, + SelectFallback select_fallback) { + int count = 0; + if (!primary) { + return 0; + } + out[count++] = primary; + + if ((primary->required_cpu & CPU_FEATURE_SME) == CPU_FEATURE_SME) { + const cpu_feature fallback_mask = static_cast(features & ~CPU_FEATURE_SME); + if (fallback_mask != CPU_FEATURE_NONE) { + ggml_kleidiai_kernels * fallback = select_fallback(fallback_mask); + if (fallback && fallback != primary && + fallback->lhs_type == primary->lhs_type && + fallback->rhs_type == primary->rhs_type && + fallback->op_type == primary->op_type) { + out[count++] = fallback; + } + } + } + + return count; +} + +static int kleidiai_collect_kernel_chain(const struct ggml_tensor * op, + std::array & out) { + ggml_kleidiai_kernels * primary = ggml_kleidiai_select_kernels(ctx.features, op); + return kleidiai_collect_kernel_chain_common(primary, ctx.features, out, + [&](cpu_feature mask) { return ggml_kleidiai_select_kernels(mask, op); }); +} + +static int kleidiai_collect_q4_chain(std::array & out) { + ggml_kleidiai_kernels * primary = kleidiai_primary_kernel_q4(); + return kleidiai_collect_kernel_chain_common(primary, ctx.features, out, + [&](cpu_feature mask) { return ggml_kleidiai_select_kernels_q4_0(mask); }); +} + +static int kleidiai_collect_q8_chain(std::array & out) { + ggml_kleidiai_kernels * primary = kleidiai_primary_kernel_q8(); + return kleidiai_collect_kernel_chain_common(primary, ctx.features, out, + [&](cpu_feature mask) { return ggml_kleidiai_select_kernels_q8_0(mask); }); +} + static inline int64_t ggml_ne(const ggml_tensor * tensor, int dim) { GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS); return tensor->ne[dim]; @@ -126,49 +438,108 @@ class tensor_traits : public ggml::cpu::tensor_traits { if (op->op != GGML_OP_MUL_MAT) { return false; } - ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, op); - if (!kernels) { + + std::array kernel_chain; + const int slot_count = kleidiai_collect_kernel_chain(op, kernel_chain); + if (slot_count == 0) { return false; } - bool is_gemv = op->src[1]->ne[1] == 1; - kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm; - lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info; - size_t k = op->src[0]->ne[0]; - size_t n = op->src[0]->ne[1]; - size_t m = op->src[1]->ne[1]; + const bool is_gemv = op->src[1]->ne[1] == 1; + const size_t k = op->src[0]->ne[0]; + const size_t n = op->src[0]->ne[1]; + const size_t m = op->src[1]->ne[1]; - size_t mr = kernel->get_mr(); - size_t kr = kernel->get_kr(); - size_t sr = kernel->get_sr(); + if (op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_Q8_0) { + const size_t qk = (op->src[0]->type == GGML_TYPE_Q4_0) ? QK4_0 : QK8_0; - if (kernels->rhs_type == GGML_TYPE_Q4_0) { - if (!lhs_info->packed_size_ex) return false; - size = lhs_info->packed_size_ex(m, k, QK4_0, mr, kr, sr); - } else if (kernels->rhs_type == GGML_TYPE_Q8_0) { - if (!lhs_info->packed_size_ex) return false; - size = lhs_info->packed_size_ex(m, k, QK8_0, mr, kr, sr); - } else if (kernels->rhs_type == GGML_TYPE_F16) { - if (!lhs_info->packed_size_ex || !kernels->rhs_info.packed_size_ex) return false; + size_t cursor = 0; + bool any_slot = false; + + for (int slot = 0; slot < slot_count; ++slot) { + ggml_kleidiai_kernels * kernels = kernel_chain[slot]; + lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info; + kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm; + + if (!lhs_info || !lhs_info->packed_size_ex || !kernel) { + return false; + } + + const size_t mr = kernel->get_mr(); + const size_t kr = kernel->get_kr(); + const size_t sr = kernel->get_sr(); + + const size_t packed = lhs_info->packed_size_ex(m, k, qk, mr, kr, sr); + + cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); + cursor += packed; + any_slot = true; + } + + if (!any_slot) { + return false; + } + + size = cursor; + return true; + } + + if (op->src[0]->type == GGML_TYPE_F16) { const int64_t lhs_batch_size0 = op->src[1]->ne[2]; const int64_t rhs_batch_size0 = op->src[0]->ne[2]; + GGML_ASSERT(rhs_batch_size0 > 0); const int64_t r = lhs_batch_size0 / rhs_batch_size0; - size = lhs_info->packed_size_ex(m * r, k, 0, mr, kr, sr) + - kernels->rhs_info.packed_size_ex(n, k, kernel->get_nr(), kernel->get_kr(), 0) + - k * n * sizeof(float) + n * sizeof(float); - } else { - return false; + + size_t cursor = 0; + bool any_slot = false; + + for (int slot = 0; slot < slot_count; ++slot) { + ggml_kleidiai_kernels * kernels = kernel_chain[slot]; + lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info; + kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm; + if (!lhs_info || !lhs_info->packed_size_ex || !kernels->rhs_info.packed_size_ex || !kernel) { + return false; + } + + const size_t mr = kernel->get_mr(); + const size_t kr = kernel->get_kr(); + const size_t sr = kernel->get_sr(); + + cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); + cursor += lhs_info->packed_size_ex(m * r, k, 0, mr, kr, sr); + any_slot = true; + } + + for (int slot = 0; slot < slot_count; ++slot) { + ggml_kleidiai_kernels * kernels = kernel_chain[slot]; + kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm; + if (!kernel || !kernels->rhs_info.packed_size_ex) { + return false; + } + cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); + cursor += kernels->rhs_info.packed_size_ex(n, k, kernel->get_nr(), kernel->get_kr(), 0); + } + + cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); + cursor += k * n * sizeof(float); + cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); + cursor += n * sizeof(float); + + if (!any_slot) { + return false; + } + + size = cursor; + return true; } - return true; + return false; } bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * dst) override { if (dst->op == GGML_OP_MUL_MAT) { - if (dst->src[0]->type == GGML_TYPE_Q4_0) { - return compute_forward_q4_0(params, dst); - } else if (dst->src[0]->type == GGML_TYPE_Q8_0) { - return compute_forward_q8_0(params, dst); + if (dst->src[0]->type == GGML_TYPE_Q4_0 || dst->src[0]->type == GGML_TYPE_Q8_0) { + return compute_forward_qx(params, dst); } else if (dst->src[0]->type == GGML_TYPE_F16) { return compute_forward_fp16(params, dst); } @@ -331,204 +702,457 @@ class tensor_traits : public ggml::cpu::tensor_traits { return true; } - bool compute_forward_q4_0(struct ggml_compute_params * params, struct ggml_tensor * dst) { - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_Q4_0); + bool compute_forward_qx(struct ggml_compute_params * params, struct ggml_tensor * dst) { + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_Q4_0 || dst->src[0]->type == GGML_TYPE_Q8_0); const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS - ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst); - if (!kernels) { - return false; + const kleidiai_weight_header * header = kleidiai_weight_header_from_ptr(src0->data); + const bool has_header = kleidiai_is_weight_header_valid(header); + const bool is_gemv = src1->ne[1] == 1; + std::array kernel_chain; + const int slot_total = kleidiai_collect_kernel_chain(dst, kernel_chain); + + auto weight_for_slot = [&](int slot_index, size_t & size_out) -> const uint8_t * { + if (slot_index < 0 || slot_index >= slot_total) { + return nullptr; + } + if (has_header) { + if (slot_index < header->slot_count) { + size_out = static_cast(header->sizes[slot_index]); + return kleidiai_weight_slot_ptr(header, slot_index); + } + return nullptr; + } + if (slot_index == 0) { + size_out = ggml_nbytes(src0); + return static_cast(src0->data); + } + return nullptr; + }; + + struct runtime_slot { + int slot_index; + ggml_kleidiai_kernels * kernels; + kernel_info * kernel; + lhs_packing_info * lhs_info; + size_t mr; + size_t nr; + size_t kr; + size_t sr; + size_t n_step; + size_t lhs_packed_size; + size_t lhs_offset; + size_t n_offset; + size_t n_cols; + int assigned_threads; + int thread_begin; + int thread_end; + const uint8_t * rhs_base; + }; + + std::array runtime{}; + int runtime_count = 0; + + for (int slot = 0; slot < slot_total && runtime_count < GGML_KLEIDIAI_MAX_KERNEL_SLOTS; ++slot) { + ggml_kleidiai_kernels * kernels = kernel_chain[slot]; + kernel_info * kinfo = is_gemv ? &kernels->gemv : &kernels->gemm; + lhs_packing_info * linfo = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info; + if (!kinfo || !linfo || !linfo->packed_size_ex || !linfo->pack_func_ex || !linfo->get_offset || + !kinfo->get_rhs_packed_offset_ex || !kinfo->run_kernel_ex || !kinfo->get_dst_offset) { + continue; + } + + size_t rhs_size = 0; + const uint8_t * rhs_ptr = weight_for_slot(slot, rhs_size); + if (!rhs_ptr || rhs_size == 0) { + continue; + } + + runtime[runtime_count] = { + slot, + kernels, + kinfo, + linfo, + kinfo->get_mr(), + kinfo->get_nr(), + kinfo->get_kr(), + kinfo->get_sr(), + kinfo->get_n_step(), + 0, + 0, + 0, + 0, + 0, + 0, + 0, + rhs_ptr + }; + ++runtime_count; } - bool is_gemv = src1->ne[1] == 1; - kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm; - lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info; - - GGML_ASSERT(kernel); - if (!lhs_info->get_packed_offset_ex || !lhs_info->pack_func_ex || - !kernel->get_rhs_packed_offset_ex || !kernel->run_kernel_ex || !kernel->get_dst_offset) { - return false; + if (runtime_count == 0) { + ggml_kleidiai_kernels * fallback = ggml_kleidiai_select_kernels(ctx.features, dst); + if (!fallback) { + return false; + } + kernel_info * kinfo = is_gemv ? &fallback->gemv : &fallback->gemm; + lhs_packing_info * linfo = is_gemv ? &fallback->gemv_lhs_info : &fallback->gemm_lhs_info; + rhs_packing_info * rinfo = &fallback->rhs_info; + if (!kinfo || !linfo || !linfo->packed_size_ex || !linfo->pack_func_ex || + !kinfo->get_rhs_packed_offset_ex || !kinfo->run_kernel_ex || !kinfo->get_dst_offset || + !rinfo || !rinfo->pack_func_ex || !rinfo->packed_size_ex) { + return false; + } + kernel_chain[0] = fallback; + runtime[0] = { + 0, + fallback, + kinfo, + linfo, + kinfo->get_mr(), + kinfo->get_nr(), + kinfo->get_kr(), + kinfo->get_sr(), + kinfo->get_n_step(), + 0, + 0, + 0, + 0, + 0, + 0, + 0, + nullptr + }; + size_t rhs_size_fallback = 0; + const uint8_t * rhs_base = weight_for_slot(0, rhs_size_fallback); + if (!rhs_base) { + rhs_base = static_cast(src0->data); + } + runtime[0].rhs_base = rhs_base; + runtime_count = 1; } - const int ith = params->ith; - const int nth_raw = params->nth; - const int nth = nth_raw > 0 ? nth_raw : 1; + const int nth_total = params->nth > 0 ? params->nth : 1; + const int ith_total = params->ith; + + int sme_slot = -1; + for (int i = 0; i < runtime_count; ++i) { + if ((runtime[i].kernels->required_cpu & CPU_FEATURE_SME) == CPU_FEATURE_SME) { + sme_slot = i; + break; + } + } + + const int sme_cap_limit = ctx.sme_thread_cap; + const bool use_hybrid = sme_cap_limit > 0 && + runtime_count > 1 && + nth_total > sme_cap_limit; + // Heuristic: disable hybrid for very small workloads where per-slot overhead dominates. + // If rows are small or average columns per thread are small, keep single-slot. + size_t min_cols_per_thread = 0; + if (runtime_count > 0 && nth_total > 0) { + min_cols_per_thread = (size_t) std::max(1, (int64_t)ne01 / (int64_t)nth_total); + } + const bool too_small_for_hybrid = (min_cols_per_thread < 2) || (ne11 < 128); + + const bool hybrid_enabled = use_hybrid && !too_small_for_hybrid; + + if (!hybrid_enabled) { + int chosen_slot = 0; + if (too_small_for_hybrid && sme_slot != -1) { + chosen_slot = sme_slot; + } else if (runtime_count > 1 && ctx.sme_thread_cap > 0 && nth_total > ctx.sme_thread_cap) { + chosen_slot = 1; + } + if (chosen_slot != 0 && chosen_slot < runtime_count) { + runtime[0] = runtime[chosen_slot]; + } + runtime_count = runtime_count > 0 ? 1 : 0; + + // Recompute SME slot based on the collapsed runtime[0] + sme_slot = -1; + if (runtime_count > 0 && + (runtime[0].kernels->required_cpu & CPU_FEATURE_SME) == CPU_FEATURE_SME) { + sme_slot = 0; + } + } + + int sme_cap = kleidiai_sme_thread_cap(); + if (sme_cap < 0) { + sme_cap = nth_total; + } + sme_cap = std::min(sme_cap, nth_total); + + int threads_remaining = nth_total; + if (sme_slot != -1) { + int sme_threads = std::min(std::max(sme_cap, 0), threads_remaining); + runtime[sme_slot].assigned_threads = sme_threads; + threads_remaining -= sme_threads; + } + + int fallback_indices[GGML_KLEIDIAI_MAX_KERNEL_SLOTS]; + int fallback_count = 0; + for (int i = 0; i < runtime_count; ++i) { + if (i == sme_slot) { + continue; + } + fallback_indices[fallback_count++] = i; + } + + for (int fi = 0; fi < fallback_count; ++fi) { + if (threads_remaining <= 0) { + break; + } + const int slot_index = fallback_indices[fi]; + const int slots_left = fallback_count - fi; + int share = (threads_remaining + slots_left - 1) / slots_left; + share = std::min(share, threads_remaining); + runtime[slot_index].assigned_threads = share; + threads_remaining -= share; + } + + if (threads_remaining > 0) { + const int fallback_slot = (sme_slot != -1) ? sme_slot : 0; + runtime[fallback_slot].assigned_threads += threads_remaining; + threads_remaining = 0; + } + + int thread_cursor = 0; + for (int i = 0; i < runtime_count; ++i) { + runtime[i].thread_begin = thread_cursor; + thread_cursor += runtime[i].assigned_threads; + runtime[i].thread_end = thread_cursor; + } + + if (thread_cursor < nth_total && runtime_count > 0) { + runtime[runtime_count - 1].assigned_threads += nth_total - thread_cursor; + runtime[runtime_count - 1].thread_end = nth_total; + } + + int local_slot = -1; + int local_ith = 0; + for (int i = 0; i < runtime_count; ++i) { + if (ith_total >= runtime[i].thread_begin && ith_total < runtime[i].thread_end) { + local_slot = i; + local_ith = ith_total - runtime[i].thread_begin; + break; + } + } + if (local_slot == -1) { + return false; + } const size_t k = ne00; const size_t m = ne11; const size_t n = ne01; - size_t mr = kernel->get_mr(); - size_t kr = kernel->get_kr(); - size_t sr = kernel->get_sr(); + size_t cursor = 0; + for (int i = 0; i < runtime_count; ++i) { + const ggml_type slot_rhs_type = runtime[i].kernels->rhs_type; + const size_t slot_pack_size_arg = slot_rhs_type == GGML_TYPE_Q4_0 ? QK4_0 : + slot_rhs_type == GGML_TYPE_Q8_0 ? QK8_0 : 0; + runtime[i].lhs_packed_size = runtime[i].lhs_info->packed_size_ex(m, k, slot_pack_size_arg, runtime[i].mr, runtime[i].kr, runtime[i].sr); + cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); + runtime[i].lhs_offset = cursor; + cursor += runtime[i].lhs_packed_size; + } - const uint8_t * lhs = static_cast(src1->data); - uint8_t * lhs_packed = (uint8_t*)params->wdata; - const uint8_t * rhs_packed = static_cast(src0->data); + GGML_ASSERT(cursor <= params->wsize); + uint8_t * scratch = static_cast(params->wdata); - const size_t n_step = kernel->get_n_step(); - const size_t num_n_per_thread = kai_roundup(kai_roundup(n, nth) / nth, n_step); - const size_t n_start = ith * num_n_per_thread; - - size_t n_to_process = 0; - if (n_start < n) { - n_to_process = num_n_per_thread; - if ((n_start + n_to_process) > n) { - n_to_process = n - n_start; + size_t assigned_cols = 0; + uint64_t weighted_total = 0; + if (runtime_count > 1 && sme_slot != -1) { + for (int i = 0; i < runtime_count; ++i) { + const uint64_t weight = (i == sme_slot) ? (sme_cap << 1) : 1; + weighted_total += (uint64_t)runtime[i].assigned_threads * weight; } } - - // Calculate number of columns to be processed per thread - const size_t num_m_per_thread = kai_roundup(m, mr * nth) / nth; - const size_t m_start = ith * num_m_per_thread; - size_t m_to_process = num_m_per_thread; - if ((m_start + m_to_process) > m) { - m_to_process = m - m_start; + for (int i = 0; i < runtime_count; ++i) { + runtime[i].n_offset = assigned_cols; + if (runtime[i].assigned_threads == 0) { + runtime[i].n_cols = 0; + continue; + } + const size_t remaining_cols = n - assigned_cols; + if (remaining_cols == 0) { + runtime[i].n_cols = 0; + continue; + } + const size_t step = runtime[i].n_step ? runtime[i].n_step : 1; + size_t target = 0; + if (weighted_total > 0) { + const uint64_t weight = (i == sme_slot) ? (sme_cap << 1) : 1; + target = (size_t)(((uint64_t)n * runtime[i].assigned_threads * weight) / weighted_total); + } else { + target = (size_t)(((uint64_t)n * runtime[i].assigned_threads) / nth_total); + } + target = std::min(target, remaining_cols); + size_t aligned = round_down(target, step); + if (aligned == 0 && remaining_cols >= step) { + aligned = step; + } + runtime[i].n_cols = aligned; + assigned_cols += aligned; } - if (m_start < m) { - // Transform LHS - const size_t src_stride = src1->nb[1]; - const float * src_ptr = reinterpret_cast(lhs + lhs_info->get_offset(m_start, dst->src[1]->nb[1])); - const size_t lhs_packed_offset = lhs_info->get_packed_offset_ex(m_start, k, QK4_0, mr, kr, sr); - void * lhs_packed_ptr = static_cast(lhs_packed + lhs_packed_offset); - - // Pack this thread's chunk with m_idx_start = 0 and per-thread output pointer - lhs_info->pack_func_ex(m_to_process, k, QK4_0, mr, kr, sr, 0, src_ptr, src_stride, lhs_packed_ptr); - } - - ggml_barrier(params->threadpool); - - // Perform the operation - const size_t dst_stride = dst->nb[1]; - const size_t lhs_packed_offset = lhs_info->get_packed_offset_ex(0, k, QK4_0, mr, kr, sr); - const size_t rhs_packed_offset = kernel->get_rhs_packed_offset_ex(n_start, k, QK4_0); - const size_t dst_offset = kernel->get_dst_offset(0, n_start, dst_stride); - const void * rhs_ptr = static_cast(rhs_packed + rhs_packed_offset); - const void* lhs_ptr = (const void*)((const char *)lhs_packed + lhs_packed_offset); - float *dst_ptr = reinterpret_cast(static_cast(dst->data) + dst_offset); - - if (n_to_process > 0) { - kernel->run_kernel_ex(m, n_to_process, k, QK4_0, lhs_ptr, rhs_ptr, dst_ptr, dst_stride, - sizeof(float), -FLT_MAX, FLT_MAX); - } - - return true; - } - - bool compute_forward_q8_0(struct ggml_compute_params * params, struct ggml_tensor * dst) { - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_Q8_0); - - const ggml_tensor * src0 = dst->src[0]; - const ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst); - if (!kernels) { - return false; - } - - bool is_gemv = src1->ne[1] == 1; - kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm; - lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info; - - if (!kernel || !lhs_info->get_packed_offset_ex || !lhs_info->pack_func_ex || - !kernel->get_rhs_packed_offset_ex || !kernel->run_kernel_ex || !kernel->get_dst_offset) { - return false; - } - - const int ith = params->ith; - const int nth_raw = params->nth; - const int nth = nth_raw > 0 ? nth_raw : 1; - - const size_t k = ne00; - const size_t m = ne11; - const size_t n = ne01; - - size_t mr = kernel->get_mr(); - size_t kr = kernel->get_kr(); - size_t sr = kernel->get_sr(); - - const uint8_t * lhs = static_cast(src1->data); - uint8_t * lhs_packed = static_cast(params->wdata); - const uint8_t * rhs_packed = static_cast(src0->data); - - const size_t n_step = kernel->get_n_step(); - const size_t num_n_per_thread = kai_roundup(kai_roundup(n, nth) / nth, n_step); - const size_t n_start = ith * num_n_per_thread; - - size_t n_to_process = 0; - if (n_start < n) { - n_to_process = num_n_per_thread; - if ((n_start + n_to_process) > n) { - n_to_process = n - n_start; + if (assigned_cols < n) { + for (int i = runtime_count - 1; i >= 0; --i) { + if (runtime[i].assigned_threads > 0) { + runtime[i].n_cols += n - assigned_cols; + break; + } } } + const size_t dst_stride = dst->nb[1]; - const size_t num_m_per_thread = kai_roundup(m, mr * nth) / nth; - const size_t m_start = ith * num_m_per_thread; - size_t m_to_process = num_m_per_thread; - if ((m_start + m_to_process) > m) { - m_to_process = m - m_start; - } + for (int64_t batch_idx = 0; batch_idx < ne12; ++batch_idx) { + const uint8_t * lhs_batch_base = static_cast(src1->data) + batch_idx * src1->nb[2]; + uint8_t * dst_batch_base = static_cast(dst->data) + batch_idx * dst->nb[2]; - if (m_start < m) { - const size_t src_stride = src1->nb[1]; - const float * src_ptr = reinterpret_cast(lhs + lhs_info->get_offset(m_start, dst->src[1]->nb[1])); - const size_t lhs_packed_offset = lhs_info->get_packed_offset_ex(m_start, k, 0, mr, kr, sr); - void * lhs_packed_ptr = static_cast(lhs_packed + lhs_packed_offset); + if (runtime[local_slot].assigned_threads > 0) { + runtime_slot & slot = runtime[local_slot]; + const ggml_type slot_rhs_type = slot.kernels->rhs_type; + const size_t slot_lhs_exec_arg = slot_rhs_type == GGML_TYPE_Q4_0 ? QK4_0 : + slot_rhs_type == GGML_TYPE_Q8_0 ? 0 : 0; + const int64_t m_roundup_mr = kai_roundup((int64_t)m, (int64_t)slot.mr); + int64_t max_threads = slot.mr ? (m_roundup_mr / (int64_t)slot.mr) : slot.assigned_threads; + max_threads = std::max(1, max_threads); + const int64_t use_threads = std::min(slot.assigned_threads, max_threads); - lhs_info->pack_func_ex(m_to_process, k, 0, mr, kr, sr, 0, src_ptr, src_stride, lhs_packed_ptr); - } + if (local_ith < use_threads) { + const int64_t num_m_per_thread0 = round_down((size_t)(m_roundup_mr / use_threads), slot.mr); + const int64_t num_m_per_threadN_1 = (int64_t)m - (use_threads - 1) * num_m_per_thread0; - ggml_barrier(params->threadpool); + const int64_t m_start = (int64_t)local_ith * num_m_per_thread0; + const int64_t m_count = (local_ith == use_threads - 1) ? num_m_per_threadN_1 : num_m_per_thread0; - const size_t dst_stride = dst->nb[1]; - const size_t lhs_packed_offset = lhs_info->get_packed_offset_ex(0, k, 0, mr, kr, sr); - const size_t rhs_packed_offset = kernel->get_rhs_packed_offset_ex(n_start, k, 0); - const size_t dst_offset = kernel->get_dst_offset(0, n_start, dst_stride); - const void * rhs_ptr = static_cast(rhs_packed + rhs_packed_offset); - const void * lhs_ptr = static_cast(lhs_packed + lhs_packed_offset); - float * dst_ptr = reinterpret_cast(static_cast(dst->data) + dst_offset); + const size_t base_packed_off = slot.lhs_info->get_packed_offset_ex(m_start, k, slot_lhs_exec_arg, slot.mr, slot.kr, slot.sr); + const size_t next_block_off = slot.lhs_info->get_packed_offset_ex(m_start + slot.mr, k, slot_lhs_exec_arg, slot.mr, slot.kr, slot.sr); + const size_t row_stride_bytes = slot.mr ? (next_block_off - base_packed_off) / slot.mr : 0; - if (n_to_process > 0) { - kernel->run_kernel_ex(m, n_to_process, k, 0, lhs_ptr, rhs_ptr, dst_ptr, dst_stride, - sizeof(float), -FLT_MAX, FLT_MAX); + int64_t remaining = m_count; + int64_t cur = m_start; + + uint8_t * lhs_packed = scratch + slot.lhs_offset; + while (remaining > 0) { + const int64_t row_in_group = cur; + const int64_t avail = (int64_t)m - row_in_group; + const int64_t take = std::min(avail, remaining); + + const size_t src_off = slot.lhs_info->get_offset(row_in_group, src1->nb[1]); + const void * src_ptr = lhs_batch_base + src_off; + const size_t dst_off = base_packed_off + (size_t)(cur - m_start) * row_stride_bytes; + void * dst_ptr = lhs_packed + dst_off; + + slot.lhs_info->pack_func_ex(take, k, slot_lhs_exec_arg, slot.mr, slot.kr, slot.sr, 0, src_ptr, src1->nb[1], dst_ptr); + + cur += take; + remaining -= take; + } + } + } + + ggml_barrier(params->threadpool); + + runtime_slot & slot = runtime[local_slot]; + if (slot.n_cols > 0 && slot.assigned_threads > 0) { + int64_t active_threads = slot.assigned_threads; + const int64_t max_threads = slot.n_step ? (slot.n_cols / slot.n_step) : slot.assigned_threads; + if (max_threads > 0) { + active_threads = std::min(active_threads, std::max(1, max_threads)); + } + active_threads = std::max(1, active_threads); + + if (local_ith < active_threads) { + const size_t step = slot.n_step ? slot.n_step : 1; + const size_t chunk0 = round_down((size_t)(slot.n_cols / active_threads), step); + const size_t chunkN = slot.n_cols - (active_threads - 1) * chunk0; + const size_t local_start = (size_t)local_ith * chunk0; + const size_t cols = (local_ith == active_threads - 1) ? chunkN : chunk0; + + if (cols > 0) { + const ggml_type slot_rhs_type = slot.kernels->rhs_type; + const size_t slot_lhs_exec_arg = slot_rhs_type == GGML_TYPE_Q4_0 ? QK4_0 : + slot_rhs_type == GGML_TYPE_Q8_0 ? 0 : 0; + const size_t slot_rhs_block_arg = slot_rhs_type == GGML_TYPE_Q4_0 ? QK4_0 : + slot_rhs_type == GGML_TYPE_Q8_0 ? 0 : 0; + const size_t global_start = slot.n_offset + local_start; + const size_t lhs_packed_offset = slot.lhs_info->get_packed_offset_ex(0, k, slot_lhs_exec_arg, slot.mr, slot.kr, slot.sr); + const size_t rhs_packed_offset = slot.kernel->get_rhs_packed_offset_ex(global_start, k, slot_rhs_block_arg); + const size_t dst_offset = slot.kernel->get_dst_offset(0, global_start, dst_stride); + + const uint8_t * lhs_ptr = scratch + slot.lhs_offset + lhs_packed_offset; + const uint8_t * rhs_ptr = slot.rhs_base + rhs_packed_offset; + float * dst_ptr = reinterpret_cast(dst_batch_base + dst_offset); + + slot.kernel->run_kernel_ex(m, cols, k, slot_rhs_block_arg, + lhs_ptr, + rhs_ptr, + dst_ptr, + dst_stride, + sizeof(float), + -FLT_MAX, + FLT_MAX); + } + } + } + + if (batch_idx != ne12 - 1) { + ggml_barrier(params->threadpool); + } } return true; } bool compute_forward_get_rows(struct ggml_compute_params * params, struct ggml_tensor * dst) { + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_Q4_0 || dst->src[0]->type == GGML_TYPE_Q8_0); const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS - ggml_kleidiai_kernels * kernels = nullptr; - size_t block_len = 0; - size_t num_bytes_multiplier = 0; + const kleidiai_weight_header * header = kleidiai_weight_header_from_ptr(src0->data); + const bool has_header = kleidiai_is_weight_header_valid(header); - if (dst->src[0]->type == GGML_TYPE_Q4_0) { - if (!ctx.kernels_q4) { - return false; + std::array kernel_chain; + const bool want_q8 = src0->type == GGML_TYPE_Q8_0; + const int chain_count = want_q8 ? kleidiai_collect_q8_chain(kernel_chain) + : kleidiai_collect_q4_chain(kernel_chain); + + ggml_kleidiai_kernels * kernels = nullptr; + const uint8_t * packed_base = static_cast(src0->data); + + if (has_header && chain_count > 0) { + int select_slot = 0; + if (select_slot >= header->slot_count) { + select_slot = header->slot_count - 1; } - kernels = ctx.kernels_q4; - block_len = QK4_0; - num_bytes_multiplier = sizeof(uint16_t); - } else if (dst->src[0]->type == GGML_TYPE_Q8_0) { - if (!ctx.kernels_q8) { - return false; + if (select_slot >= 0 && select_slot < chain_count) { + kernels = kernel_chain[select_slot]; + const uint8_t * slot_ptr = kleidiai_weight_slot_ptr(header, select_slot); + if (slot_ptr) { + packed_base = slot_ptr; + } } - kernels = ctx.kernels_q8; - block_len = QK8_0; - num_bytes_multiplier = sizeof(float); - } else { + } + + if (!kernels && chain_count > 0) { + kernels = kernel_chain[0]; + if (has_header) { + const uint8_t * slot_ptr = kleidiai_weight_slot_ptr(header, 0); + if (slot_ptr) { + packed_base = slot_ptr; + } + } + } + + if (!kernels) { return false; } @@ -541,6 +1165,19 @@ class tensor_traits : public ggml::cpu::tensor_traits { const int64_t nc = ne00; const int64_t nr = ggml_nelements(src1); + const ggml_type rhs_type = kernels->rhs_type; + size_t block_len = 0; + size_t num_bytes_multiplier = 0; + if (rhs_type == GGML_TYPE_Q4_0) { + block_len = QK4_0; + num_bytes_multiplier = sizeof(uint16_t); + } else if (rhs_type == GGML_TYPE_Q8_0) { + block_len = QK8_0; + num_bytes_multiplier = sizeof(float); + } else { + return false; + } + const size_t block_rows = kernel->get_nr(); const size_t kr = kernel->get_kr(); @@ -559,7 +1196,7 @@ class tensor_traits : public ggml::cpu::tensor_traits { GGML_ASSERT(row_idx >= 0 && row_idx < src0->ne[1]); float *out = (float *)((char *)dst->data + i * nb1); - rhs_info->to_float(src0->data, row_idx, nc, out, block_rows, packed_stride, kr, block_len, num_bytes_multiplier); + rhs_info->to_float(packed_base, row_idx, nc, out, block_rows, packed_stride, kr, block_len, num_bytes_multiplier); } return true; @@ -567,36 +1204,39 @@ class tensor_traits : public ggml::cpu::tensor_traits { public: int repack(struct ggml_tensor * tensor, const void * data, size_t data_size) { + GGML_ASSERT(tensor->type == GGML_TYPE_Q4_0 || tensor->type == GGML_TYPE_Q8_0); const size_t n = tensor->ne[1]; const size_t k = tensor->ne[0]; - if (tensor->type == GGML_TYPE_Q4_0) { - if (!ctx.kernels_q4) { - return -1; - } - size_t nr = ctx.kernels_q4->gemm.get_nr(); - size_t kr = ctx.kernels_q4->gemm.get_kr(); - size_t sr = ctx.kernels_q4->gemm.get_sr(); + kleidiai_weight_header * header = kleidiai_weight_header_from_ptr(tensor->data); + if (!header) { + return -1; + } - struct kai_rhs_pack_qs4cxs1s0_param params; - params.lhs_zero_point = 1; - params.rhs_zero_point = 8; - ctx.kernels_q4->rhs_info.pack_func_ex(1, n, k, nr, kr, sr, QK4_0, 0, - static_cast(data), - nullptr, nullptr, tensor->data, 0, ¶ms); - GGML_UNUSED(data_size); - return 0; - } else if (tensor->type == GGML_TYPE_Q8_0) { - if (!ctx.kernels_q8) { - return -1; - } + header->magic = GGML_KLEIDIAI_PACK_MAGIC; + header->version = GGML_KLEIDIAI_PACK_VERSION; + header->slot_count = 0; + + uint8_t * base_ptr = static_cast(tensor->data); + size_t cursor = sizeof(kleidiai_weight_header); + cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); + + std::array kernel_chain; + const bool want_q8 = tensor->type == GGML_TYPE_Q8_0; + const int slot_total = want_q8 ? kleidiai_collect_q8_chain(kernel_chain) + : kleidiai_collect_q4_chain(kernel_chain); + const bool allow_fallback = kleidiai_pack_fallback_allowed(); + + std::vector qdata; + std::vector scales; + + if (want_q8 && slot_total > 0) { + qdata.resize(n * k, 0); + scales.resize(n, 0.0f); const size_t row_stride = tensor->nb[1]; const size_t k_blocks = (k + QK8_0 - 1) / QK8_0; - std::vector qdata(n * k, 0); - std::vector scales(n, 0.0f); - for (size_t row = 0; row < n; ++row) { const auto * row_blocks = reinterpret_cast( static_cast(data) + row * row_stride); @@ -610,7 +1250,7 @@ public: if (linear_idx >= k) { break; } - const float value = d * blk.qs[l]; + const float value = d * static_cast(blk.qs[l]); max_abs = std::max(max_abs, std::fabs(value)); } } @@ -627,31 +1267,73 @@ public: if (linear_idx >= k) { break; } - const float value = d * blk.qs[l]; + const float value = d * static_cast(blk.qs[l]); int32_t q = scale > 0.0f ? static_cast(std::lround(value * inv_scale)) : 0; q = std::clamp(q, -127, 127); qdata[row * k + linear_idx] = static_cast(q); } } } - - size_t nr = ctx.kernels_q8->gemm.get_nr(); - size_t kr = ctx.kernels_q8->gemm.get_kr(); - size_t sr = ctx.kernels_q8->gemm.get_sr(); - - struct kai_rhs_pack_qsi8cx_params params; - params.lhs_zero_point = 1; - params.scale_multiplier = 1.0f; - - ctx.kernels_q8->rhs_info.pack_func_ex(1, n, k, nr, kr, sr, 0, 0, - qdata.data(), nullptr, scales.data(), - tensor->data, 0, ¶ms); - GGML_UNUSED(data_size); - return 0; } - GGML_UNUSED(data_size); - return -1; + for (int slot = 0; slot < slot_total && slot < GGML_KLEIDIAI_MAX_KERNEL_SLOTS; ++slot) { + if (!allow_fallback && slot > 0) { + break; + } + ggml_kleidiai_kernels * kernels = kernel_chain[slot]; + kernel_info * kernel = &kernels->gemm; + rhs_packing_info * rhs_info = &kernels->rhs_info; + if (!rhs_info || !rhs_info->pack_func_ex || !rhs_info->packed_size_ex || !kernel) { + continue; + } + + const size_t nr = kernel->get_nr(); + const size_t kr = kernel->get_kr(); + const size_t sr = kernel->get_sr(); + const ggml_type rhs_type = kernels->rhs_type; + const size_t block_len = rhs_type == GGML_TYPE_Q8_0 ? QK8_0 : + rhs_type == GGML_TYPE_Q4_0 ? QK4_0 : 0; + if (block_len == 0) { + continue; + } + + const size_t packed_size = rhs_info->packed_size_ex(n, k, nr, kr, block_len); + const size_t aligned_cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); + + uint8_t * dst_ptr = base_ptr + aligned_cursor; + + if (rhs_type == GGML_TYPE_Q4_0) { + struct kai_rhs_pack_qs4cxs1s0_param params; + params.lhs_zero_point = 1; + params.rhs_zero_point = 8; + rhs_info->pack_func_ex(1, n, k, nr, kr, sr, QK4_0, 0, + static_cast(data), nullptr, nullptr, + dst_ptr, 0, ¶ms); + } else if (rhs_type == GGML_TYPE_Q8_0) { + struct kai_rhs_pack_qsi8cx_params params; + params.lhs_zero_point = 1; + params.scale_multiplier = 1.0f; + rhs_info->pack_func_ex(1, n, k, nr, kr, sr, 0, 0, + qdata.data(), nullptr, scales.data(), + dst_ptr, 0, ¶ms); + } else { + continue; + } + + header->offsets[header->slot_count] = aligned_cursor; + header->sizes[header->slot_count] = packed_size; + ++header->slot_count; + + cursor = aligned_cursor + packed_size; + } + + if (header->slot_count == 0) { + header->magic = 0; + header->version = 0; + memcpy(tensor->data, data, data_size); + } + + return 0; } }; @@ -681,9 +1363,8 @@ static void ggml_backend_cpu_kleidiai_buffer_set_tensor(ggml_backend_buffer_t bu } static const char * ggml_backend_cpu_kleidiai_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "CPU_KLEIDIAI"; - GGML_UNUSED(buft); + return "CPU_KLEIDIAI"; } static ggml_backend_buffer_t ggml_backend_cpu_kleidiai_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { @@ -702,49 +1383,78 @@ static ggml_backend_buffer_t ggml_backend_cpu_kleidiai_buffer_type_alloc_buffer( } static size_t ggml_backend_cpu_kleidiai_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return TENSOR_ALIGNMENT; - GGML_UNUSED(buft); + return TENSOR_ALIGNMENT; } static size_t ggml_backend_cpu_kleidiai_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor) { GGML_UNUSED(buft); + if (tensor->type != GGML_TYPE_Q4_0 && tensor->type != GGML_TYPE_Q8_0) { + return ggml_nbytes(tensor); + } + const size_t n = tensor->ne[1]; const size_t k = tensor->ne[0]; - ggml_kleidiai_kernels * kernels = nullptr; - size_t block_len = 0; + size_t cursor = sizeof(kleidiai_weight_header); + cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); - if (tensor->type == GGML_TYPE_Q4_0) { - GGML_ASSERT(ctx.kernels_q4); - kernels = ctx.kernels_q4; - block_len = QK4_0; - } else if (tensor->type == GGML_TYPE_Q8_0) { - GGML_ASSERT(ctx.kernels_q8); - kernels = ctx.kernels_q8; - block_len = QK8_0; - } else { - return 0; + std::array kernel_chain; + const bool want_q8 = tensor->type == GGML_TYPE_Q8_0; + const int slot_total = want_q8 ? kleidiai_collect_q8_chain(kernel_chain) + : kleidiai_collect_q4_chain(kernel_chain); + const bool allow_fallback = kleidiai_pack_fallback_allowed(); + + size_t slot_count = 0; + for (int slot = 0; slot < slot_total; ++slot) { + if (!allow_fallback && slot > 0) { + break; + } + ggml_kleidiai_kernels * kernels = kernel_chain[slot]; + if (!kernels) { + continue; + } + kernel_info * kernel = &kernels->gemm; + rhs_packing_info * rhs_info = &kernels->rhs_info; + if (!kernel || !rhs_info || !rhs_info->packed_size_ex) { + continue; + } + + const ggml_type rhs_type = kernels->rhs_type; + const size_t block_len = rhs_type == GGML_TYPE_Q4_0 ? QK4_0 : + rhs_type == GGML_TYPE_Q8_0 ? QK8_0 : 0; + if (block_len == 0) { + continue; + } + + cursor = align_up(cursor, GGML_KLEIDIAI_PACK_ALIGN); + cursor += rhs_info->packed_size_ex(n, k, kernel->get_nr(), kernel->get_kr(), block_len); + ++slot_count; } - const size_t nr = kernels->gemm.get_nr(); - const size_t kr = kernels->gemm.get_kr(); - const size_t packed = kernels->rhs_info.packed_size_ex(n, k, nr, kr, block_len); - const size_t raw = ggml_nbytes(tensor); + if (slot_count == 0) { + return ggml_nbytes(tensor); + } - return packed > raw ? packed : raw; + return std::max(cursor, ggml_nbytes(tensor)); } namespace ggml::cpu::kleidiai { class extra_buffer_type : ggml::cpu::extra_buffer_type { bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override { + std::array kernel_chain; + const int slot_total = kleidiai_collect_kernel_chain(op, kernel_chain); if ((op->op == GGML_OP_MUL_MAT || op->op == GGML_OP_GET_ROWS) && (op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_Q8_0) && op->src[0]->buffer && (ggml_n_dims(op->src[0]) == 2) && - op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type()) { - if (((op->src[0]->type == GGML_TYPE_Q4_0) ? ctx.kernels_q4 : ctx.kernels_q8) == nullptr) { + op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type() && + slot_total > 0) { + if (op->src[0]->type == GGML_TYPE_Q4_0 && ctx.kernels_q4 == nullptr) { + return false; + } + if (op->src[0]->type == GGML_TYPE_Q8_0 && ctx.kernels_q8 == nullptr) { return false; } if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) { @@ -762,14 +1472,17 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type { if (op->op == GGML_OP_MUL_MAT || op->op == GGML_OP_GET_ROWS) { if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type()) { return (ggml::cpu::tensor_traits *) op->src[0]->extra; - } - else if (ggml_kleidiai_select_kernels(ctx.features, op) && op->src[1]->ne[1] > 1) { - if ((op->src[0]->nb[1] * op->src[0]->ne[1] != op->src[0]->nb[2]) || - (op->src[1]->nb[1] * op->src[1]->ne[1] != op->src[1]->nb[2])) { - return nullptr; + } else { + std::array kernel_chain; + const int slot_total = kleidiai_collect_kernel_chain(op, kernel_chain); + const bool has_kernel = slot_total > 0; + if (has_kernel && op->src[1]->ne[1] > 1) { + if ((op->src[0]->nb[1] * op->src[0]->ne[1] != op->src[0]->nb[2]) || + (op->src[1]->nb[1] * op->src[1]->ne[1] != op->src[1]->nb[2])) { + return nullptr; + } + return ggml::cpu::kleidiai::get_tensor_traits(NULL, NULL); } - - return ggml::cpu::kleidiai::get_tensor_traits(NULL, NULL); } } return nullptr; diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index da412fd009..c89e5076f2 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -533,7 +533,7 @@ class tinyBLAS { if constexpr (RN > 1) { return mnpack(m, n, SIZE_N, BN); } else { - GGML_LOG_ERROR("mnpack<%d, %d> bloc size not supported\n", RM, (int)SIZE_N); + GGML_LOG_ERROR("mnpack<%d, %d> block size not supported\n", RM, (int)SIZE_N); GGML_ASSERT(false); // we have miss something. } } @@ -711,7 +711,7 @@ class tinyBLAS_RVV { if constexpr (RN > 1) { return mnpack(m, n, SIZE_N, BN); } else { - GGML_LOG_ERROR("mnpack<%d, %d> bloc size not supported\n", RM, (int)SIZE_N); + GGML_LOG_ERROR("mnpack<%d, %d> block size not supported\n", RM, (int)SIZE_N); GGML_ASSERT(false); // we have miss something. } } @@ -2497,7 +2497,7 @@ class tinyBLAS_Q0_PPC { for (int r = 0; r < 8; r++) { const block_q4_0 * current_blk = rows_base[r] + blk; vector float v_scale = vec_extract_fp32_from_shorth(vec_splats(current_blk->d)); - vector signed char v_qs = reinterpret_cast(vec_xl(0, current_blk->qs)); + vector signed char v_qs = vec_xl(0, (const vector signed char *)current_blk->qs); vector signed char c1, c2; unpack_q4_to_q8(v_qs, c1, c2); convert_and_scale_q8(c1, v_scale, hp_res[r][0], hp_res[r][1]); @@ -2611,14 +2611,14 @@ class tinyBLAS_Q0_PPC { i = (cols >> 2); if (i > 0) { do { - c1[1] = reinterpret_cast(vec_xl(0, aoffset1->qs)); - c2[1] = reinterpret_cast(vec_xl(0, aoffset2->qs)); - c3[1] = reinterpret_cast(vec_xl(0, aoffset3->qs)); - c4[1] = reinterpret_cast(vec_xl(0, aoffset4->qs)); - c5[1] = reinterpret_cast(vec_xl(0, aoffset5->qs)); - c6[1] = reinterpret_cast(vec_xl(0, aoffset6->qs)); - c7[1] = reinterpret_cast(vec_xl(0, aoffset7->qs)); - c8[1] = reinterpret_cast(vec_xl(0, aoffset8->qs)); + c1[1] = vec_xl(0, (const vector signed char *)aoffset1->qs); + c2[1] = vec_xl(0, (const vector signed char *)aoffset2->qs); + c3[1] = vec_xl(0, (const vector signed char *)aoffset3->qs); + c4[1] = vec_xl(0, (const vector signed char *)aoffset4->qs); + c5[1] = vec_xl(0, (const vector signed char *)aoffset5->qs); + c6[1] = vec_xl(0, (const vector signed char *)aoffset6->qs); + c7[1] = vec_xl(0, (const vector signed char *)aoffset7->qs); + c8[1] = vec_xl(0, (const vector signed char *)aoffset8->qs); process_q4_elements(c1, & comparray[0]); process_q4_elements(c2, & comparray[1]); @@ -2657,10 +2657,10 @@ class tinyBLAS_Q0_PPC { i = (cols >> 2); if (i > 0) { do { - c1[1] = reinterpret_cast(vec_xl(0, aoffset1->qs)); - c2[1] = reinterpret_cast(vec_xl(0, aoffset2->qs)); - c3[1] = reinterpret_cast(vec_xl(0, aoffset3->qs)); - c4[1] = reinterpret_cast(vec_xl(0, aoffset4->qs)); + c1[1] = vec_xl(0, (const vector signed char *)aoffset1->qs); + c2[1] = vec_xl(0, (const vector signed char *)aoffset2->qs); + c3[1] = vec_xl(0, (const vector signed char *)aoffset3->qs); + c4[1] = vec_xl(0, (const vector signed char *)aoffset4->qs); process_q4_elements(c1, & comparray[0]); process_q4_elements(c2, & comparray[1]); @@ -2686,9 +2686,9 @@ class tinyBLAS_Q0_PPC { if (i > 0) { do { switch(rows) { - case 3: c3[1] = reinterpret_cast(vec_xl(0, aoffset3->qs)); - case 2: c2[1] = reinterpret_cast(vec_xl(0, aoffset2->qs)); - case 1: c1[1] = reinterpret_cast(vec_xl(0, aoffset1->qs)); + case 3: c3[1] = vec_xl(0, (const vector signed char *)aoffset3->qs); + case 2: c2[1] = vec_xl(0, (const vector signed char *)aoffset2->qs); + case 1: c1[1] = vec_xl(0, (const vector signed char *)aoffset1->qs); break; } process_q4_elements(c1, & comparray[0]); diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index b7a70e06f1..314cc1088a 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -375,7 +375,7 @@ static void ggml_compute_forward_dup_bytes( const size_t rs = ne00 * type_size; if (nb00 == type_size) { - // src0 is contigous on first dimension, copy by rows + // src0 is contiguous on first dimension, copy by rows for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { id += rs * ir0; @@ -670,6 +670,7 @@ void ggml_compute_forward_add( case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: @@ -1119,6 +1120,7 @@ void ggml_compute_forward_add1( case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: @@ -1247,6 +1249,7 @@ void ggml_compute_forward_acc( case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: @@ -1795,7 +1798,7 @@ void ggml_compute_forward_repeat( { ggml_compute_forward_repeat_f32(params, dst); } break; - // TODO: templateify the implemenation and support for I64 + // TODO: templateify the implementation and support for I64 // ref https://github.com/ggml-org/llama.cpp/pull/14274#discussion_r2169492225 //case GGML_TYPE_I64: // { @@ -2129,12 +2132,12 @@ static void ggml_compute_forward_gelu_f32( #ifndef NDEBUG for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + const float x = ((float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*(dst->nb[1])))[k]; GGML_UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -2176,13 +2179,13 @@ static void ggml_compute_forward_gelu_f16( #ifndef NDEBUG for (int k = 0; k < nc; k++) { - const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*( dst->nb[1])))[k]; const float v = GGML_CPU_FP16_TO_FP32(x); GGML_UNUSED(v); assert(!isnan(v)); assert(!isinf(v)); } -#endif +#endif // NDEBUG } } @@ -2325,12 +2328,12 @@ static void ggml_compute_forward_gelu_erf_f32( #ifndef NDEBUG for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + const float x = ((float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*(dst->nb[1])))[k]; GGML_UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -2372,13 +2375,13 @@ static void ggml_compute_forward_gelu_erf_f16( #ifndef NDEBUG for (int k = 0; k < nc; k++) { - const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*( dst->nb[1])))[k]; const float v = GGML_CPU_FP16_TO_FP32(x); GGML_UNUSED(v); assert(!isnan(v)); assert(!isinf(v)); } -#endif +#endif // NDEBUG } } @@ -2444,12 +2447,12 @@ static void ggml_compute_forward_gelu_quick_f32( #ifndef NDEBUG for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + const float x = ((float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*(dst->nb[1])))[k]; GGML_UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -2491,13 +2494,13 @@ static void ggml_compute_forward_gelu_quick_f16( #ifndef NDEBUG for (int k = 0; k < nc; k++) { - const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*( dst->nb[1])))[k]; const float v = GGML_CPU_FP16_TO_FP32(x); GGML_UNUSED(v); assert(!isnan(v)); assert(!isinf(v)); } -#endif +#endif // NDEBUG } } @@ -2563,12 +2566,12 @@ static void ggml_compute_forward_silu_f32( #ifndef NDEBUG for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; + const float x = ((float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*(dst->nb[1])))[k]; GGML_UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -2610,13 +2613,13 @@ static void ggml_compute_forward_silu_f16( #ifndef NDEBUG for (int k = 0; k < nc; k++) { - const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*(dst->nb[1])))[k]; + const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*( dst->nb[1])))[k]; const float v = GGML_CPU_FP16_TO_FP32(x); GGML_UNUSED(v); assert(!isnan(v)); assert(!isinf(v)); } -#endif +#endif // NDEBUG } } @@ -2766,7 +2769,7 @@ static void ggml_compute_forward_silu_back_f32( assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -2802,7 +2805,7 @@ static void ggml_compute_forward_silu_back_f16( (ggml_fp16_t *) ((char *) src1->data + i1*(src1->nb[1])), (ggml_fp16_t *) ((char *) grad->data + i1*(grad->nb[1]))); - #ifndef NDEBUG +#ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k]; const float v = GGML_CPU_FP16_TO_FP32(x); @@ -2810,7 +2813,7 @@ static void ggml_compute_forward_silu_back_f16( assert(!isnan(v)); assert(!isinf(v)); } - #endif +#endif // NDEBUG } } @@ -2893,7 +2896,7 @@ static void ggml_compute_forward_reglu_f32( assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -2953,7 +2956,7 @@ static void ggml_compute_forward_reglu_f16( assert(!isnan(v)); assert(!isinf(v)); } -#endif +#endif // NDEBUG } } @@ -3036,7 +3039,7 @@ static void ggml_compute_forward_geglu_f32( assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -3096,7 +3099,7 @@ static void ggml_compute_forward_geglu_f16( assert(!isnan(v)); assert(!isinf(v)); } -#endif +#endif // NDEBUG } } @@ -3179,7 +3182,7 @@ static void ggml_compute_forward_swiglu_f32( assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -3239,7 +3242,7 @@ static void ggml_compute_forward_swiglu_f16( assert(!isnan(v)); assert(!isinf(v)); } -#endif +#endif // NDEBUG } } @@ -3330,7 +3333,7 @@ static void ggml_compute_forward_swiglu_oai_f32( assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -3409,7 +3412,7 @@ static void ggml_compute_forward_geglu_erf_f32( assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -3469,7 +3472,7 @@ static void ggml_compute_forward_geglu_erf_f16( assert(!isnan(v)); assert(!isinf(v)); } -#endif +#endif // NDEBUG } } @@ -3552,7 +3555,7 @@ static void ggml_compute_forward_geglu_quick_f32( assert(!isnan(x)); assert(!isinf(x)); } -#endif +#endif // NDEBUG } } @@ -3612,7 +3615,7 @@ static void ggml_compute_forward_geglu_quick_f16( assert(!isnan(v)); assert(!isinf(v)); } -#endif +#endif // NDEBUG } } @@ -4334,6 +4337,7 @@ void ggml_compute_forward_out_prod( case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: @@ -4609,6 +4613,7 @@ void ggml_compute_forward_set( case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: @@ -4831,6 +4836,7 @@ void ggml_compute_forward_get_rows( case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: @@ -5303,7 +5309,7 @@ static void ggml_compute_forward_soft_max_f32( //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(wp[i])); } -#endif +#endif // NDEBUG float max = -INFINITY; ggml_vec_max_f32(ne00, &max, wp); @@ -5328,7 +5334,7 @@ static void ggml_compute_forward_soft_max_f32( assert(!isnan(dp[i])); assert(!isinf(dp[i])); } -#endif +#endif // NDEBUG } } } @@ -5402,7 +5408,7 @@ static void ggml_compute_forward_soft_max_ext_back_f32( assert(!isnan(dy[i])); assert(!isnan(y[i])); } -#endif +#endif // NDEBUG // Jii = yi - yi*yi // Jij = -yi*yj // J = diag(y)-y.T*y @@ -5435,7 +5441,7 @@ static void ggml_compute_forward_soft_max_ext_back_f32( assert(!isnan(dx[i])); assert(!isinf(dx[i])); } -#endif +#endif // NDEBUG } } @@ -5555,6 +5561,7 @@ void ggml_compute_forward_clamp( case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: @@ -5803,28 +5810,33 @@ static void ggml_compute_forward_rope_flt( const int32_t * pos = (const int32_t *) src1->data; + int64_t last_i2 = -1; + for (int64_t i3 = 0; i3 < ne3; i3++) { // batch for (int64_t i2 = 0; i2 < ne2; i2++) { // seq-len - - float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - if (!mrope_used) { - const int64_t p = pos[i2]; - ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - } - else { - const int64_t p_t = pos[i2]; - const int64_t p_h = pos[i2 + ne2]; - const int64_t p_w = pos[i2 + ne2 * 2]; - const int64_t p_e = pos[i2 + ne2 * 3]; - ggml_mrope_cache_init( - p_t, p_h, p_w, p_e, sections, is_imrope, is_vision, - freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - } - for (int64_t i1 = 0; i1 < ne1; i1++) { // attn-heads - if (ir++ < ir0) continue; + if (ir++ < ir0) continue; // skip rows mapped to other threads if (ir > ir1) break; + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + if (last_i2 != i2) { + if (!mrope_used) { + const int64_t p = pos[i2]; + ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + else { + const int64_t p_t = pos[i2]; + const int64_t p_h = pos[i2 + ne2]; + const int64_t p_w = pos[i2 + ne2 * 2]; + const int64_t p_e = pos[i2 + ne2 * 3]; + ggml_mrope_cache_init( + p_t, p_h, p_w, p_e, sections, is_imrope, is_vision, + freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + } + + last_i2 = i2; + } + T * src = (T *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); T * dst_data = (T *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); @@ -9612,7 +9624,7 @@ void ggml_compute_forward_win_unpart( } } -//gmml_compute_forward_unary +//ggml_compute_forward_unary void ggml_compute_forward_unary( const ggml_compute_params * params, @@ -10375,6 +10387,195 @@ void ggml_compute_forward_solve_tri(const struct ggml_compute_params * params, s } } +// ggml_compute_forward_gated_delta_net +static void ggml_compute_forward_gated_delta_net_one_chunk( + const ggml_compute_params * params, + ggml_tensor * dst, + int64_t ir0, + int64_t ir1) { + + ggml_tensor * src_q = dst->src[0]; + ggml_tensor * src_k = dst->src[1]; + ggml_tensor * src_v = dst->src[2]; + ggml_tensor * src_g = dst->src[3]; + ggml_tensor * src_beta = dst->src[4]; + ggml_tensor * src_state = dst->src[5]; + + const int64_t S_v = src_v->ne[0]; + const int64_t H = src_v->ne[1]; + const int64_t n_tokens = src_v->ne[2]; + const int64_t n_seqs = src_v->ne[3]; + + GGML_ASSERT(ggml_is_contiguous_rows(src_q)); + GGML_ASSERT(ggml_is_contiguous_rows(src_k)); + GGML_ASSERT(ggml_is_contiguous_rows(src_v)); + GGML_ASSERT(ggml_is_contiguous(src_g)); + GGML_ASSERT(ggml_is_contiguous(src_beta)); + GGML_ASSERT(ggml_is_contiguous(src_state)); + + GGML_ASSERT(src_g->ne[0] == 1 || src_g->ne[0] == S_v); + GGML_ASSERT(src_beta->ne[0] == 1); + + GGML_TENSOR_LOCALS(int64_t, neq, src_q, ne); + GGML_TENSOR_LOCALS(size_t, nbq, src_q, nb); + GGML_TENSOR_LOCALS(int64_t, nek, src_k, ne); + GGML_TENSOR_LOCALS(size_t, nbk, src_k, nb); + GGML_TENSOR_LOCALS(int64_t, nev, src_v, ne); + GGML_TENSOR_LOCALS(size_t, nbv, src_v, nb); + GGML_TENSOR_LOCALS(int64_t, neg, src_g, ne); + GGML_TENSOR_LOCALS(size_t, nbg, src_g, nb); + GGML_TENSOR_LOCALS(size_t, nbb, src_beta, nb); + + const bool kda = (neg0 == S_v); + + // scratch layout per thread: [delta(S_v)] + const int64_t scratch_per_thread = S_v; + const int ith = params->ith; + + float * delta = (float *)params->wdata + ith * scratch_per_thread + CACHE_LINE_SIZE_F32; + + // output layout: [attn_scores | new_states] + // attn_scores: S_v * H * n_tokens * n_seqs floats + // new_states: S_v * S_v * H * n_seqs floats + const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs; + float * attn_out_base = (float *)dst->data; + float * state_out_base = (float *)dst->data + attn_score_elems; + + const float * state_in_base = (const float *)src_state->data; + + //const int64_t rq1 = nev1 / neq1; + //const int64_t rk1 = nev1 / nek1; + const int64_t rq3 = nev3 / neq3; + const int64_t rk3 = nev3 / nek3; + + const float scale = 1.0f / sqrtf((float) S_v); + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t iv1 = ir % H; // head_index + const int64_t iv3 = ir / H; // sequence + + const int64_t iq1 = iv1 % neq1; + const int64_t ik1 = iv1 % nek1; + + const int64_t iq3 = iv3 / rq3; + const int64_t ik3 = iv3 / rk3; + + float * s_out = state_out_base + (iv3 * H + iv1) * S_v * S_v; + + // copy input state into output buffer and operate in-place + const float * s_in = state_in_base + (iv3 * H + iv1) * S_v * S_v; + memcpy(s_out, s_in, S_v * S_v * sizeof(float)); + + // attn output pointer for first token of this (head, seq) + float * attn_data = attn_out_base + (iv3 * n_tokens * H + iv1) * S_v; + + for (int64_t t = 0; t < n_tokens; t++) { + const float * q_d = (const float *)((const char *)src_q->data + iq3 * nbq3 + t * nbq2 + iq1 * nbq1); + const float * k_d = (const float *)((const char *)src_k->data + ik3 * nbk3 + t * nbk2 + ik1 * nbk1); + const float * v_d = (const float *)((const char *)src_v->data + iv3 * nbv3 + t * nbv2 + iv1 * nbv1); + + const float beta_val = *(const float *)((const char *)src_beta->data + iv3 * nbb3 + t * nbb2 + iv1 * nbb1); + const float * g_d = (const float *)((const char *)src_g->data + iv3 * nbg3 + t * nbg2 + iv1 * nbg1); + + // state is stored transposed: s_out[j*S_v + i] = S[i][j] + // so row j of s_out = column j of S (contiguous access) + + if (kda) { + // precompute exp(g) into delta scratch (reused below) + for (int64_t i = 0; i < S_v; ++i) { + delta[i] = expf(g_d[i]); + } + // S[i][:] *= exp(g[i]) => for each row j of M: M[j][i] *= exp(g[i]) + for (int64_t j = 0; j < S_v; ++j) { + ggml_vec_mul_f32(S_v, &s_out[j * S_v], &s_out[j * S_v], delta); + } + } else { + ggml_vec_scale_f32(S_v * S_v, s_out, expf(g_d[0])); + } + + // delta[j] = sum_i S[i][j] * k[i] = dot(row j of M, k) + for (int64_t j = 0; j < S_v; ++j) { + float sum = 0.0f; + ggml_vec_dot_f32(S_v, &sum, 0, &s_out[j * S_v], 0, k_d, 0, 1); + delta[j] = (v_d[j] - sum) * beta_val; + } + + // outer product: S[i][j] += k[i] * delta[j] => M[j][i] += delta[j] * k[i] + for (int64_t j = 0; j < S_v; ++j) { + ggml_vec_mad_f32(S_v, &s_out[j * S_v], k_d, delta[j]); + } + + // attn_out[j] = sum_i S[i][j] * q[i] = dot(row j of M, q) + for (int64_t j = 0; j < S_v; ++j) { + float sum = 0.0f; + ggml_vec_dot_f32(S_v, &sum, 0, &s_out[j * S_v], 0, q_d, 0, 1); + attn_data[j] = sum * scale; + } + + attn_data += S_v * H; // advance to next token + } + } +} + + +static void ggml_compute_forward_gated_delta_net_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + ggml_tensor * V = dst->src[2]; + int64_t nr = V->ne[1] * V->ne[3]; + + // disable for NUMA + const bool disable_chunking = ggml_is_numa(); + + int nth = params->nth; + int ith = params->ith; + + // 4x chunks per thread + int nth_scaled = nth * 4; + int64_t chunk_size = (nr + nth_scaled - 1) / nth_scaled; + int64_t nchunk = (nr + chunk_size - 1) / chunk_size; + + if (nth == 1 || nchunk < nth || disable_chunking) { + nchunk = nth; + } + + if (ith == 0) { + ggml_threadpool_chunk_set(params->threadpool, nth); + } + + ggml_barrier(params->threadpool); + + const int64_t dr = (nr + nchunk - 1) / nchunk; + + int current_chunk = ith; + + while (current_chunk < nchunk) { + const int64_t ir0 = dr * current_chunk; + const int64_t ir1 = MIN(ir0 + dr, nr); + + ggml_compute_forward_gated_delta_net_one_chunk(params, dst, ir0, ir1); + current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1); + } +} + +void ggml_compute_forward_gated_delta_net( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_gated_delta_net_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + // ggml_compute_forward_rwkv_wkv7 static void ggml_compute_forward_rwkv_wkv7_f32( @@ -10700,7 +10901,7 @@ static void ggml_compute_forward_cross_entropy_loss_f32( assert(!isnan(s0[i])); assert(!isnan(s1[i])); } -#endif +#endif // NDEBUG float max = -INFINITY; ggml_vec_max_f32(nc, &max, s0); @@ -10719,7 +10920,7 @@ static void ggml_compute_forward_cross_entropy_loss_f32( assert(!isnan(st[i])); assert(!isinf(st[i])); } -#endif +#endif // NDEBUG } sums[ith] = sum_thread; ggml_barrier(params->threadpool); @@ -10792,7 +10993,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( assert(!isnan(s0[i])); assert(!isnan(s1[i])); } -#endif +#endif // NDEBUG // soft_max float max = -INFINITY; @@ -10810,7 +11011,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( assert(!isnan(ds0[i])); assert(!isinf(ds0[i])); } -#endif +#endif // NDEBUG } } diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h index 0fdfee7976..3fa1443abc 100644 --- a/ggml/src/ggml-cpu/ops.h +++ b/ggml/src/ggml-cpu/ops.h @@ -102,6 +102,7 @@ void ggml_compute_forward_rwkv_wkv6(const struct ggml_compute_params * params, s void ggml_compute_forward_rwkv_wkv7(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_solve_tri(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_gla(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_gated_delta_net(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom3(const struct ggml_compute_params * params, struct ggml_tensor * dst); diff --git a/ggml/src/ggml-cpu/quants.c b/ggml/src/ggml-cpu/quants.c index 365cb36d2d..7ebbb9c6f1 100644 --- a/ggml/src/ggml-cpu/quants.c +++ b/ggml/src/ggml-cpu/quants.c @@ -50,6 +50,10 @@ void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, i quantize_row_mxfp4_ref(x, y, k); } +void quantize_row_nvfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) { + quantize_row_nvfp4_ref(x, y, k); +} + // // 2-6 bit quantization in super-blocks // @@ -216,6 +220,42 @@ void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, *s = sumf; } +// NVFP4: super-block of 64 elements = 4 sub-blocks of 16 = 2 q8_0 blocks +void ggml_vec_dot_nvfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK_NVFP4 == 0); + + const block_nvfp4 * GGML_RESTRICT x = vx; + const block_q8_0 * GGML_RESTRICT y = vy; + + const int nb = n / QK_NVFP4; + + float sumf = 0; + + for (int ib = 0; ib < nb; ++ib) { + for (int s_idx = 0; s_idx < 4; ++s_idx) { + const float d = ggml_ue4m3_to_fp32(x[ib].d[s_idx]); + const int q8_block = s_idx / 2; + const int q8_off = (s_idx % 2) * QK_NVFP4_SUB; + const float dy = GGML_CPU_FP16_TO_FP32(y[2*ib + q8_block].d); + + int sumi_lo = 0, sumi_hi = 0; + for (int j = 0; j < QK_NVFP4_SUB/2; ++j) { + const uint8_t qv = x[ib].qs[s_idx*(QK_NVFP4_SUB/2) + j]; + sumi_lo += y[2*ib + q8_block].qs[q8_off + j + 0] * kvalues_mxfp4[qv & 0xf]; + sumi_hi += y[2*ib + q8_block].qs[q8_off + j + QK_NVFP4_SUB/2] * kvalues_mxfp4[qv >> 4]; + } + + sumf += dy * d * (sumi_lo + sumi_hi); + } + } + *s = sumf; +} + void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { const int qk = QK8_0; const int nb = n / qk; diff --git a/ggml/src/ggml-cpu/quants.h b/ggml/src/ggml-cpu/quants.h index d83eb1b144..3584aaa43e 100644 --- a/ggml/src/ggml-cpu/quants.h +++ b/ggml/src/ggml-cpu/quants.h @@ -20,6 +20,7 @@ void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_nvfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); @@ -42,6 +43,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_nvfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); @@ -73,6 +75,7 @@ void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_nvfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp index f94426ddd7..6b76ab3bfb 100644 --- a/ggml/src/ggml-cpu/repack.cpp +++ b/ggml/src/ggml-cpu/repack.cpp @@ -48,6 +48,90 @@ static inline int nearest_int(float fval) { extern "C" { +#if defined __riscv_zvfh +void ggml_quantize_mat_q8_0_4x1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { + assert(QK8_0 == 32); + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + block_q8_0x4 * GGML_RESTRICT y = (block_q8_0x4 *) vy; + + // scalar + const int blck_size_interleave = 1; + float srcv[4][QK8_0]; + float id[4]; + + for (int i = 0; i < nb; i++) { + for (int row_iter = 0; row_iter < 4; row_iter++) { + float amax = 0.0f; // absolute max + + for (int j = 0; j < QK8_0; j++) { + srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j]; + amax = MAX(amax, fabsf(srcv[row_iter][j])); + } + + const float d = amax / ((1 << 7) - 1); + id[row_iter] = d ? 1.0f / d : 0.0f; + + y[i].d[row_iter] = GGML_CPU_FP32_TO_FP16(d); + } + + for (int j = 0; j < QK8_0 * 4; j++) { + int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave; + int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave; + src_offset += (j % blck_size_interleave); + + float x0 = srcv[src_id][src_offset] * id[src_id]; + y[i].qs[j] = roundf(x0); + } + } +} + +void ggml_quantize_mat_q8_K_4x1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { + assert(QK_K == 256); + assert(k % QK_K == 0); + const int nb = k / QK_K; + + block_q8_Kx4 * GGML_RESTRICT y = (block_q8_Kx4 *) vy; + + const int blck_size_interleave = 1; + float srcv[4][QK_K]; + float iscale[4]; + + for (int i = 0; i < nb; i++) { + for (int row_iter = 0; row_iter < 4; row_iter++) { + float amax = 0.0f; // absolute max + float max = 0; + + for (int j = 0; j < QK_K; j++) { + srcv[row_iter][j] = x[row_iter * k + i * QK_K + j]; + // Update the maximum value of the corresponding super block + if(amax < fabsf(srcv[row_iter][j])) { + amax = fabsf(srcv[row_iter][j]); + max = srcv[row_iter][j]; + } + } + + iscale[row_iter] = amax ? -127.f/max : 0; + y[i].d[row_iter] = amax ? 1/iscale[row_iter] : 0; + } + + for (int j = 0; j < QK_K / 4; j++) { + y[i].bsums[j] = 0; + } + for (int j = 0; j < QK_K * 4; j++) { + int src_id = j % 4; + int src_offset = j / 4; + int index = ((j >> 6) << 2) + (j & 3); + + float x0 = srcv[src_id][src_offset] * iscale[src_id]; + y[i].qs[j] = nearest_int(x0); + y[i].bsums[index] += y[i].qs[j]; + } + } +} +#endif + void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { assert(QK8_0 == 32); assert(k % QK8_0 == 0); @@ -124,7 +208,6 @@ void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GG } } - void ggml_quantize_mat_q8_K_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { assert(QK_K == 256); assert(k % QK_K == 0); @@ -256,6 +339,20 @@ template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_K>(const float * GGML_RESTR ggml_quantize_mat_q8_K_4x8(x, vy, n_per_row); } +#if defined __riscv_zvfh +template <> void ggml_quantize_mat_t<1, GGML_TYPE_Q8_0>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) { + assert(nrow == 4); + UNUSED(nrow); + ggml_quantize_mat_q8_0_4x1(x, vy, n_per_row); +} + +template <> void ggml_quantize_mat_t<1, GGML_TYPE_Q8_K>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) { + assert(nrow == 4); + UNUSED(nrow); + ggml_quantize_mat_q8_K_4x1(x, vy, n_per_row); +} +#endif + template static void ggml_gemv_q6_K_NxM_q8_K_generic_impl(int n, float * GGML_RESTRICT s, @@ -450,6 +547,208 @@ static void ggml_gemm_q6_K_NxM_q8_K_generic_impl(int n, } } +template +static void ggml_gemv_q5_K_NxM_q8_K_generic_impl(int n, + float * GGML_RESTRICT s, + size_t bs, + const void * GGML_RESTRICT vx, + const void * GGML_RESTRICT vy, + int nr, + int nc) { + constexpr int blocklen = M; + constexpr int ncols_interleaved = N; + const int qk = QK_K; + const int nb = n / qk; + static const uint32_t kmask1 = 0x3f3f3f3f; + static const uint32_t kmask2 = 0x0f0f0f0f; + static const uint32_t kmask3 = 0x03030303; + + assert(n % qk == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(bs); + UNUSED(nr); + + float sumf[ncols_interleaved]; + float sum_minf[ncols_interleaved]; + uint32_t utmp[32]; + int sumi1; + int sumi2; + int sumi; + + const block_q8_K * a_ptr = (const block_q8_K *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q5_Kx8 * b_ptr = (const block_q5_Kx8 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) { + sumf[j] = 0.0; + sum_minf[j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int sb = 0; sb < 8; sb++) { + memcpy(utmp + sb * 4, b_ptr[l].scales + sb * K_SCALE_SIZE, K_SCALE_SIZE); + utmp[sb * 4 + 3] = ((utmp[sb * 4 + 2] >> 4) & kmask2) | (((utmp[sb * 4 + 1] >> 6) & kmask3) << 4); + const uint32_t uaux_0 = utmp[sb * 4 + 1] & kmask1; + utmp[sb * 4 + 1] = (utmp[sb * 4 + 2] & kmask2) | (((utmp[sb * 4 + 0] >> 6) & kmask3) << 4); + utmp[sb * 4 + 2] = uaux_0; + utmp[sb * 4 + 0] &= kmask1; + } + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + constexpr int scale_stride = 32; + uint8_t * scales_0 = (uint8_t *) utmp + (k / (32 / blocklen)) * scale_stride; + uint8_t * scales_1 = (uint8_t *) utmp + (k / (32 / blocklen)) * scale_stride + 16; + + const int qh_shift = (k / (32 / blocklen)) * 2; + for (int j = 0; j < ncols_interleaved; j++) { + sumi1 = 0; + sumi2 = 0; + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int b_qs_offset = k * ncols_interleaved * blocklen + j * blocklen + i; + + const int qh_idx = (k * blocklen + i) % 32; + const int qh_chunk = qh_idx / blocklen; + const int qh_pos = qh_idx % blocklen; + const int b_qh_offset = qh_chunk * (blocklen * ncols_interleaved) + j * blocklen + qh_pos; + + const uint8_t qh_val = b_ptr[l].qh[b_qh_offset]; + const uint8_t h0 = (qh_val >> qh_shift) & 1; + const uint8_t h1 = (qh_val >> (qh_shift + 1)) & 1; + + const int v0 = (int8_t) ((b_ptr[l].qs[b_qs_offset] & 0xF) | (h0 << 4)); + const int v1 = (int8_t) ((b_ptr[l].qs[b_qs_offset] >> 4) | (h1 << 4)); + + const int q8_offset = (k / (32 / blocklen)) * 64 + (k % (32 / blocklen)) * blocklen + i; + + sumi1 = (v0 * a_ptr[l].qs[q8_offset]); + sumi2 = (v1 * a_ptr[l].qs[q8_offset + 32]); + sumi1 = sumi1 * scales_0[j]; + sumi2 = sumi2 * scales_1[j]; + sumi += sumi1 + sumi2; + } + sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d; + } + } + for (int sb = 0; sb < 8; sb++) { + uint8_t * mins = (uint8_t *) utmp + 8 + sb * 16; + for (int j = 0; j < ncols_interleaved; j++) { + sum_minf[j] += mins[j] * (a_ptr[l].bsums[sb * 2] + a_ptr[l].bsums[sb * 2 + 1]) * + GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d; + } + } + } + for (int j = 0; j < ncols_interleaved; j++) { + s[x * ncols_interleaved + j] = sumf[j] - sum_minf[j]; + } + } +} + +template +static void ggml_gemm_q5_K_NxM_q8_K_generic_impl(int n, + float * GGML_RESTRICT s, + size_t bs, + const void * GGML_RESTRICT vx, + const void * GGML_RESTRICT vy, + int nr, + int nc) { + constexpr int blocklen = M; + constexpr int ncols_interleaved = N; + const int qk = QK_K; + const int nb = n / qk; + static const uint32_t kmask1 = 0x3f3f3f3f; + static const uint32_t kmask2 = 0x0f0f0f0f; + static const uint32_t kmask3 = 0x03030303; + + assert(n % qk == 0); + assert(nr % 4 == 0); + assert(nc % ncols_interleaved == 0); + + float sumf[4][ncols_interleaved]; + float sum_minf[4][ncols_interleaved]; + uint32_t utmp[32]; + int sumi1; + int sumi2; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q5_Kx8 * b_ptr = (const block_q5_Kx8 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumf[m][j] = 0.0; + sum_minf[m][j] = 0.0; + } + } + for (int l = 0; l < nb; l++) { + for (int sb = 0; sb < 8; sb++) { + memcpy(utmp + sb * 4, b_ptr[l].scales + sb * K_SCALE_SIZE, K_SCALE_SIZE); + utmp[sb * 4 + 3] = ((utmp[sb * 4 + 2] >> 4) & kmask2) | (((utmp[sb * 4 + 1] >> 6) & kmask3) << 4); + const uint32_t uaux_0 = utmp[sb * 4 + 1] & kmask1; + utmp[sb * 4 + 1] = (utmp[sb * 4 + 2] & kmask2) | (((utmp[sb * 4 + 0] >> 6) & kmask3) << 4); + utmp[sb * 4 + 2] = uaux_0; + utmp[sb * 4 + 0] &= kmask1; + } + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + constexpr int scale_stride = 32; + uint8_t * scales_0 = (uint8_t *) utmp + (k / (32 / blocklen)) * scale_stride; + uint8_t * scales_1 = (uint8_t *) utmp + (k / (32 / blocklen)) * scale_stride + 16; + + const int qh_shift = (k / (32 / blocklen)) * 2; + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi1 = 0; + sumi2 = 0; + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int b_qs_offset = k * ncols_interleaved * blocklen + j * blocklen + i; + + const int qh_idx = (k * blocklen + i) % 32; + const int qh_chunk = qh_idx / blocklen; + const int qh_pos = qh_idx % blocklen; + const int b_qh_offset = + qh_chunk * (blocklen * ncols_interleaved) + j * blocklen + qh_pos; + + const uint8_t qh_val = b_ptr[l].qh[b_qh_offset]; + const uint8_t h0 = (qh_val >> qh_shift) & 1; + const uint8_t h1 = (qh_val >> (qh_shift + 1)) & 1; + + const int v0 = (int8_t) ((b_ptr[l].qs[b_qs_offset] & 0xF) | (h0 << 4)); + const int v1 = (int8_t) ((b_ptr[l].qs[b_qs_offset] >> 4) | (h1 << 4)); + + const int q8_offset = (k / (32 / blocklen)) * 256 + + (k % (32 / blocklen)) * 4 * blocklen + m * blocklen + i; + + sumi1 = (v0 * a_ptr[l].qs[q8_offset]); + sumi2 = (v1 * a_ptr[l].qs[q8_offset + 128]); + sumi1 = sumi1 * scales_0[j]; + sumi2 = sumi2 * scales_1[j]; + sumi += sumi1 + sumi2; + } + sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d[m]; + } + } + } + for (int sb = 0; sb < 8; sb++) { + uint8_t * mins = (uint8_t *) utmp + 8 + sb * 16; + for (int m = 0; m < 4; m++) { + const int16_t * bsums = a_ptr[l].bsums + (sb * 8) + (m * 4) - ((sb % 2) * 6); + for (int j = 0; j < ncols_interleaved; j++) { + sum_minf[m][j] += mins[j] * (bsums[0] + bsums[1]) * + GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d[m]; + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j] - sum_minf[m][j]; + } + } + } + } +} + extern "C" { void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { @@ -803,98 +1102,12 @@ void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, } } -void ggml_gemv_q5_K_8x8_q8_K_generic(int n, - float * GGML_RESTRICT s, - size_t bs, - const void * GGML_RESTRICT vx, - const void * GGML_RESTRICT vy, - int nr, - int nc) { - const int qk = QK_K; - const int nb = n / qk; - const int ncols_interleaved = 8; - const int blocklen = 8; - static const uint32_t kmask1 = 0x3f3f3f3f; - static const uint32_t kmask2 = 0x0f0f0f0f; - static const uint32_t kmask3 = 0x03030303; +void ggml_gemv_q5_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + ggml_gemv_q5_K_NxM_q8_K_generic_impl<4, 8>(n, s, bs, vx, vy, nr, nc); +} - assert(n % qk == 0); - assert(nc % ncols_interleaved == 0); - - UNUSED(bs); - UNUSED(nr); - - float sumf[8]; - float sum_minf[8]; - uint32_t utmp[32]; - int sumi1; - int sumi2; - int sumi; - - const block_q8_K * a_ptr = (const block_q8_K *) vy; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q5_Kx8 * b_ptr = (const block_q5_Kx8 *) vx + (x * nb); - - for (int j = 0; j < ncols_interleaved; j++) { - sumf[j] = 0.0; - sum_minf[j] = 0.0; - } - for (int l = 0; l < nb; l++) { - for (int sb = 0; sb < 8; sb++) { - memcpy(utmp + sb * 4, b_ptr[l].scales + sb * 12, 12); - utmp[sb * 4 + 3] = ((utmp[sb * 4 + 2] >> 4) & kmask2) | (((utmp[sb * 4 + 1] >> 6) & kmask3) << 4); - const uint32_t uaux_0 = utmp[sb * 4 + 1] & kmask1; - utmp[sb * 4 + 1] = (utmp[sb * 4 + 2] & kmask2) | (((utmp[sb * 4 + 0] >> 6) & kmask3) << 4); - utmp[sb * 4 + 2] = uaux_0; - utmp[sb * 4 + 0] &= kmask1; - } - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - uint8_t * scales_0 = (uint8_t *) utmp + (k / 4) * 32; - uint8_t * scales_1 = (uint8_t *) utmp + (k / 4) * 32 + 16; - - const int qh_shift = (k / 4) * 2; - for (int j = 0; j < ncols_interleaved; j++) { - sumi1 = 0; - sumi2 = 0; - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int b_qs_offset = k * ncols_interleaved * blocklen + j * blocklen + i; - - const int qh_idx = (k * 8 + i) % 32; - const int qh_chunk = qh_idx / 8; - const int qh_pos = qh_idx % 8; - const int b_qh_offset = qh_chunk * 64 + j * 8 + qh_pos; - - const uint8_t qh_val = b_ptr[l].qh[b_qh_offset]; - const uint8_t h0 = (qh_val >> qh_shift) & 1; - const uint8_t h1 = (qh_val >> (qh_shift + 1)) & 1; - - const int v0 = (int8_t) ((b_ptr[l].qs[b_qs_offset] & 0xF) | (h0 << 4)); - const int v1 = (int8_t) ((b_ptr[l].qs[b_qs_offset] >> 4) | (h1 << 4)); - - const int q8_offset = (k >> 2) * 64 + (k % 4) * blocklen + i; - - sumi1 = (v0 * a_ptr[l].qs[q8_offset]); - sumi2 = (v1 * a_ptr[l].qs[q8_offset + 32]); - sumi1 = sumi1 * scales_0[j]; - sumi2 = sumi2 * scales_1[j]; - sumi += sumi1 + sumi2; - } - sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d; - } - } - for (int sb = 0; sb < 8; sb++) { - uint8_t * mins = (uint8_t *) utmp + 8 + sb * 16; - for (int j = 0; j < ncols_interleaved; j++) { - sum_minf[j] += mins[j] * (a_ptr[l].bsums[sb * 2] + a_ptr[l].bsums[sb * 2 + 1]) * - GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d; - } - } - } - for (int j = 0; j < ncols_interleaved; j++) { - s[x * ncols_interleaved + j] = sumf[j] - sum_minf[j]; - } - } +void ggml_gemv_q5_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + ggml_gemv_q5_K_NxM_q8_K_generic_impl<8, 8>(n, s, bs, vx, vy, nr, nc); } @@ -982,6 +1195,82 @@ void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs } } +void ggml_gemv_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert(nr == 1); + assert(n % qk == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(bs); + UNUSED(nr); + + float sumf[4]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_mxfp4x4 * b_ptr = (const block_mxfp4x4 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])); + } + sumf[j] += sumi * GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } +} + +void ggml_gemv_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 8; + const int blocklen = 8; + + assert(nr == 1); + assert(n % qk == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(bs); + UNUSED(nr); + + float sumf[8]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_mxfp4x8 * b_ptr = (const block_mxfp4x8 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])); + } + sumf[j] += sumi * GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } +} + void ggml_gemv_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, @@ -1076,6 +1365,294 @@ void ggml_gemv_q8_0_4x8_q8_0_generic(int n, } } +#if defined __riscv_zvfh +void ggml_gemv_q4_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + + float sumf[16]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x16 * b_ptr = (const block_q4_0x16 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; + } + sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } +} + +void ggml_gemv_q4_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK_K; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + float sumf[16]; + float sum_minf[16]; + uint8_t scales[128]; + uint8_t mins[128]; + int sumi1; + int sumi2; + int sumi; + const block_q8_K * a_ptr = (const block_q8_K *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_Kx16 * b_ptr = (const block_q4_Kx16 *) vx + (x * nb); + for (int j = 0; j < ncols_interleaved; j++) { + sumf[j] = 0.0f; + sum_minf[j] = 0.0f; + } + for (int l = 0; l < nb; l++) { + for (int i = 0; i < 128; i++) { + scales[i] = b_ptr[l].scales[i] & 0x0F; + mins[i] = b_ptr[l].scales[i] >> 4; + } + for (int i = 0; i < 64; i++) { + scales[i] |= (b_ptr[l].scales[128 + i] & 0x03) << 4; + mins[i] |= (b_ptr[l].scales[128 + i] & 0x0C) << 2; + scales[i + 64] |= (b_ptr[l].scales[128 + i] & 0x30); + mins[i + 64] |= (b_ptr[l].scales[128 + i] & 0xC0) >> 2; + } + for (int sb = 0; sb < 8; sb++) { + uint8_t *min = &mins[sb * 16]; + for (int j = 0; j < ncols_interleaved; j++) { + sum_minf[j] += min[j] * (a_ptr[l].bsums[sb * 2] + a_ptr[l].bsums[sb * 2 + 1]) * GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d; + } + } + for (int sb = 0; sb < 8; sb += 2) { + uint8_t *scales_0 = &scales[sb * 16]; + uint8_t *scales_1 = &scales[(sb + 1) * 16]; + for (int i = 0; i < QK4_0; i++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi1 = 0; + sumi2 = 0; + sumi = 0; + const int v0 = (int8_t) (b_ptr[l].qs[sb * 256 + i * 16 + j] & 0xF); + const int v1 = (int8_t) (b_ptr[l].qs[sb * 256 + i * 16 + j] >> 4); + sumi1 = (v0 * a_ptr[l].qs[sb * 32 + i]); + sumi2 = (v1 * a_ptr[l].qs[sb * 32 + 32 + i]); + sumi1 = sumi1 * scales_0[j]; + sumi2 = sumi2 * scales_1[j]; + sumi += sumi1 + sumi2; + sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d; + } + } + } + } + for (int j = 0; j < ncols_interleaved; j++) { + s[x * ncols_interleaved + j] = sumf[j] - sum_minf[j]; + } + } +} + +void ggml_gemv_iq4_nl_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert(nr == 1); + assert(n % qk == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(bs); + UNUSED(nr); + + float sumf[16]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx16 * b_ptr = (const block_iq4_nlx16 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])); + } + sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } +} + +void ggml_gemv_q8_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert(nr == 1); + assert(n % qk == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(bs); + UNUSED(nr); + + float sumf[16]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q8_0x16 * b_ptr = (const block_q8_0x16 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) { + sumf[j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / blocklen); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i]; + sumi += v0 * a_ptr[l].qs[k * blocklen + i]; + } + sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) { + s[x * ncols_interleaved + j] = sumf[j]; + } + } +} + +void ggml_gemv_q2_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + assert(n % QK_K == 0); + assert(nr == 1); + assert(nc % 16 == 0); + + UNUSED(bs); + + const int nb = n / QK_K; + const block_q2_Kx16 * x = (const block_q2_Kx16 *)vx; + const block_q8_K * y = (const block_q8_K *)vy; + + // Layout: Even-Low(0,2,4,6), Odd-Low(1,3,5,7), Even-High(8...), Odd-High(9...) + const int sb_perm[16] = { + 0, 4, 1, 5, 2, 6, 3, 7, // 0-7 + 8, 12, 9, 13, 10, 14, 11, 15 // 8-15 + }; + + for (int col_tile = 0; col_tile < nc; col_tile += 16) { + const block_q2_Kx16 * x_ptr = x + (col_tile / 16) * nb; + const block_q8_K * y_ptr = y; + + float sumf[16] = {0}; + + // Loop over K-blocks + for (int k_block = 0; k_block < nb; ++k_block) { + int32_t isum[16] = {0}; + int32_t summs[16] = {0}; + + const uint8_t * qs_rhs = x_ptr[k_block].qs; + const uint8_t * sc_rhs = x_ptr[k_block].scales; + const int8_t * qs_lhs = y_ptr[k_block].qs; + const int16_t * bs_lhs = y_ptr[k_block].bsums; + + // Iterate over sub-blocks 0..15 + for (int sb = 0; sb < 16; ++sb) { + // Correction Term + int16_t bsum = bs_lhs[sb]; + int scale_offset = sb_perm[sb] * 16; + + for (int col = 0; col < 16; ++col) { + uint8_t sc_val = sc_rhs[scale_offset + col]; + summs[col] += bsum * (sc_val >> 4); // Min is high 4 bits + } + + // Main Dot Product + // Calculate base offsets for Q2 unpacking based on SB + int byte_base; + if (sb < 8) byte_base = (sb % 2 == 0) ? 0 : 16; + else byte_base = (sb % 2 == 0) ? 32 : 48; + + int shift = ((sb / 2) % 4) * 2; + + for (int col = 0; col < 16; ++col) { + uint8_t sc_val = sc_rhs[scale_offset + col]; + int32_t d_sb = sc_val & 0xF; // Scale is low 4 bits + + // Process 16 elements (l=0..15) + for (int l = 0; l < 16; ++l) { + // Q2: Interleaved by column. Byte `l` contains 4 k-values. + int qs_idx = (byte_base + l) * 16 + col; + uint8_t q2_val = (qs_rhs[qs_idx] >> shift) & 3; + + // Q8: Linear access + int k = sb * 16 + l; + int8_t q8_val = qs_lhs[k]; + + isum[col] += q8_val * q2_val * d_sb; + } + } + } + + // Finalize K-Block + for (int col = 0; col < 16; ++col) { + float d_lhs = y_ptr[k_block].d; + float d_rhs = GGML_FP16_TO_FP32(x_ptr[k_block].d[col]); + float dm_rhs = GGML_FP16_TO_FP32(x_ptr[k_block].dmin[col]); + + float d_all = d_lhs * d_rhs; + float d_min = d_lhs * dm_rhs; + + sumf[col] += (isum[col] * d_all) - (summs[col] * d_min); + } + } + + for (int col = 0; col < 16; ++col) { + s[col_tile + col] = sumf[col]; + } + } +} +#endif + void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; const int nb = n / qk; @@ -1494,107 +2071,12 @@ void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, } } -void ggml_gemm_q5_K_8x8_q8_K_generic(int n, - float * GGML_RESTRICT s, - size_t bs, - const void * GGML_RESTRICT vx, - const void * GGML_RESTRICT vy, - int nr, - int nc) { - const int qk = QK_K; - const int nb = n / qk; - const int ncols_interleaved = 8; - const int blocklen = 8; +void ggml_gemm_q5_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + ggml_gemm_q5_K_NxM_q8_K_generic_impl<4, 8>(n, s, bs, vx, vy, nr, nc); +} - constexpr uint32_t kmask1 = 0x3f3f3f3f; - constexpr uint32_t kmask2 = 0x0f0f0f0f; - constexpr uint32_t kmask3 = 0x03030303; - - assert(n % qk == 0); - assert(nr % 4 == 0); - assert(nc % ncols_interleaved == 0); - - float sumf[4][8]; - float sum_minf[4][8]; - uint32_t utmp[32]; - int sumi1; - int sumi2; - int sumi; - - for (int y = 0; y < nr / 4; y++) { - const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q5_Kx8 * b_ptr = (const block_q5_Kx8 *) vx + (x * nb); - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumf[m][j] = 0.0; - sum_minf[m][j] = 0.0; - } - } - for (int l = 0; l < nb; l++) { - for (int sb = 0; sb < 8; sb++) { - memcpy(utmp + sb * 4, b_ptr[l].scales + sb * 12, 12); - utmp[sb * 4 + 3] = ((utmp[sb * 4 + 2] >> 4) & kmask2) | (((utmp[sb * 4 + 1] >> 6) & kmask3) << 4); - const uint32_t uaux_0 = utmp[sb * 4 + 1] & kmask1; - utmp[sb * 4 + 1] = (utmp[sb * 4 + 2] & kmask2) | (((utmp[sb * 4 + 0] >> 6) & kmask3) << 4); - utmp[sb * 4 + 2] = uaux_0; - utmp[sb * 4 + 0] &= kmask1; - } - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - uint8_t * scales_0 = (uint8_t *) utmp + (k / 4) * 32; - uint8_t * scales_1 = (uint8_t *) utmp + (k / 4) * 32 + 16; - - const int qh_shift = (k / 4) * 2; - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi1 = 0; - sumi2 = 0; - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int b_qs_offset = k * ncols_interleaved * blocklen + j * blocklen + i; - - const int qh_idx = (k * 8 + i) % 32; - const int qh_chunk = qh_idx / 8; - const int qh_pos = qh_idx % 8; - const int b_qh_offset = qh_chunk * 64 + j * 8 + qh_pos; - - const uint8_t qh_val = b_ptr[l].qh[b_qh_offset]; - const uint8_t h0 = (qh_val >> qh_shift) & 1; - const uint8_t h1 = (qh_val >> (qh_shift + 1)) & 1; - - const int v0 = (int8_t) ((b_ptr[l].qs[b_qs_offset] & 0xF) | (h0 << 4)); - const int v1 = (int8_t) ((b_ptr[l].qs[b_qs_offset] >> 4) | (h1 << 4)); - - const int q8_offset = (k >> 2) * 256 + (k % 4) * 4 * blocklen + m * blocklen + i; - - sumi1 = (v0 * a_ptr[l].qs[q8_offset]); - sumi2 = (v1 * a_ptr[l].qs[q8_offset + 128]); - sumi1 = sumi1 * scales_0[j]; - sumi2 = sumi2 * scales_1[j]; - sumi += sumi1 + sumi2; - } - sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d[m]; - } - } - } - for (int sb = 0; sb < 8; sb++) { - uint8_t * mins = (uint8_t *) utmp + 8 + sb * 16; - for (int m = 0; m < 4; m++) { - const int16_t * bsums = a_ptr[l].bsums + (sb * 8) + (m * 4) - ((sb % 2) * 6); - for (int j = 0; j < ncols_interleaved; j++) { - sum_minf[m][j] += mins[j] * (bsums[0] + bsums[1]) * - GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d[m]; - } - } - } - } - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j] - sum_minf[m][j]; - } - } - } - } +void ggml_gemm_q5_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + ggml_gemm_q5_K_NxM_q8_K_generic_impl<8, 8>(n, s, bs, vx, vy, nr, nc); } void ggml_gemm_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { @@ -1705,6 +2187,94 @@ void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs } } +void ggml_gemm_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert(n % qk == 0); + assert(nr % 4 == 0); + assert(nc % ncols_interleaved == 0); + + float sumf[4][4]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_mxfp4x4 * b_ptr = (const block_mxfp4x4 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])); + } + sumf[m][j] += sumi * GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } +} + +void ggml_gemm_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 8; + const int blocklen = 8; + + assert(n % qk == 0); + assert(nr % 4 == 0); + assert(nc % ncols_interleaved == 0); + + float sumf[4][8]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_mxfp4x8 * b_ptr = (const block_mxfp4x8 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])); + } + sumf[m][j] += sumi * GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } +} + void ggml_gemm_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, @@ -1757,6 +2327,8 @@ void ggml_gemm_q8_0_4x4_q8_0_generic(int n, } } + + void ggml_gemm_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, @@ -1809,6 +2381,342 @@ void ggml_gemm_q8_0_4x8_q8_0_generic(int n, } } +#if defined __riscv_zvfh +void ggml_gemm_q4_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + + float sumf[4][16]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x16 * b_ptr = (const block_q4_0x16 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + } + sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } +} + +void ggml_gemm_q4_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK_K; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + + float sumf[4][16]; + float sum_minf[4][16]; + uint8_t scales[128]; + uint8_t mins[128]; + int sumi1; + int sumi2; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_Kx16 * b_ptr = (const block_q4_Kx16 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumf[m][j] = 0.0; + sum_minf[m][j] = 0.0; + } + } + for (int l = 0; l < nb; l++) { + for (int i = 0; i < 128; i++) { + scales[i] = b_ptr[l].scales[i] & 0x0F; + mins[i] = b_ptr[l].scales[i] >> 4; + } + for (int i = 0; i < 64; i++) { + scales[i] |= (b_ptr[l].scales[128 + i] & 0x03) << 4; + mins[i] |= (b_ptr[l].scales[128 + i] & 0x0C) << 2; + scales[i + 64] |= (b_ptr[l].scales[128 + i] & 0x30); + mins[i + 64] |= (b_ptr[l].scales[128 + i] & 0xC0) >> 2; + } + + for (int sb = 0; sb < 8; sb++) { + uint8_t *min = &mins[sb * 16]; + for(int m = 0; m < 4; m++) { + const int16_t bsums = a_ptr[l].bsums[sb * 8 + m] + a_ptr[l].bsums[sb * 8 + m + 4]; + for(int j = 0; j < ncols_interleaved; j++) { + sum_minf[m][j] += min[j] * bsums * GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d[m]; + } + } + } + + for (int sb = 0; sb < 8; sb += 2) { + uint8_t *scales_0 = &scales[sb * 16]; + uint8_t *scales_1 = &scales[(sb + 1) * 16]; + + for (int i = 0; i < QK4_0; i++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi1 = 0; + sumi2 = 0; + sumi = 0; + + const int v0 = (int8_t) (b_ptr[l].qs[sb * 256 + i * 16 + j] & 0xF); + const int v1 = (int8_t) (b_ptr[l].qs[sb * 256 + i * 16 + j] >> 4); + sumi1 = (v0 * a_ptr[l].qs[sb * 4 * 32 + i * 4 + m]); + sumi2 = (v1 * a_ptr[l].qs[sb * 4 * 32 + 32 * 4 + i * 4 + m]); + sumi1 = sumi1 * scales_0[j]; + sumi2 = sumi2 * scales_1[j]; + sumi += sumi1 + sumi2; + + sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d[m]; + } + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j] - sum_minf[m][j]; + } + } + } + } +} + +void ggml_gemm_iq4_nl_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert(n % qk == 0); + assert(nr % 4 == 0); + assert(nc % ncols_interleaved == 0); + + float sumf[4][16]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx16 * b_ptr = (const block_iq4_nlx16 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + (qk / 2) * 4])); + } + sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } +} + +void ggml_gemm_q8_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 16; + const int blocklen = 1; + + assert(n % qk == 0); + assert(nr % 4 == 0); + assert(nc % ncols_interleaved == 0); + + float sumf[4][16]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q8_0x16 * b_ptr = (const block_q8_0x16 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumf[m][j] = 0.0; + } + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / blocklen); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i]; + sumi += v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]; + } + sumf[m][j] += + sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } + } +} + + +void ggml_gemm_q2_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { + assert(n % QK_K == 0); + assert(nr % 4 == 0); + assert(nc % 16 == 0); + const int nb = n / QK_K; + const block_q2_Kx16 * x = (const block_q2_Kx16 *)vx; + const block_q8_Kx4 * y = (const block_q8_Kx4 *)vy; + + const int sb_perm[16] = { + 0, 4, 1, 5, 2, 6, 3, 7, + 8, 12, 9, 13, 10, 14, 11, 15 + }; + + // Iterate Rows in tiles of 4 + for (int row_tile = 0; row_tile < nr; row_tile += 4) { + // Iterate Columns in tiles of 16 + for (int col_tile = 0; col_tile < nc; col_tile += 16) { + + const block_q2_Kx16 * x_ptr = x + (col_tile / 16) * nb; + const block_q8_Kx4 * y_ptr = y + (row_tile / 4) * nb; + + float sumf[4][16]; + memset(sumf, 0, sizeof(sumf)); + + for (int k_block = 0; k_block < nb; ++k_block) { + int32_t isum[4][16]; + int32_t summs[4][16]; + memset(isum, 0, sizeof(isum)); + memset(summs, 0, sizeof(summs)); + + const uint8_t * qs_rhs = x_ptr[k_block].qs; + const uint8_t * sc_rhs = x_ptr[k_block].scales; + const int8_t * qs_lhs = y_ptr[k_block].qs; + const int16_t * bs_lhs = y_ptr[k_block].bsums; + + for (int sb = 0; sb < 16; ++sb) { + int scale_offset = sb_perm[sb] * 16; + + int byte_base; + if (sb < 8) byte_base = (sb % 2 == 0) ? 0 : 16; + else byte_base = (sb % 2 == 0) ? 32 : 48; + int shift = ((sb / 2) % 4) * 2; + + for (int col = 0; col < 16; ++col) { + uint8_t sc_val = sc_rhs[scale_offset + col]; + int32_t d_sb = sc_val & 0xF; + int32_t m_sb = sc_val >> 4; + + // Correction Term + for (int r = 0; r < 4; ++r) { + int bsum_idx = (sb / 4) * 16 + r * 4 + (sb % 4); + summs[r][col] += bs_lhs[bsum_idx] * m_sb; + } + + // Main Dot Product + for (int l = 0; l < 16; ++l) { + int qs_idx = (byte_base + l) * 16 + col; + uint8_t q2_val = (qs_rhs[qs_idx] >> shift) & 3; + + // Calculate Q8 index for this specific k and row + int k = sb * 16 + l; + int q8_idx = (k / 4) * 16 + (k % 4); + + for (int r = 0; r < 4; ++r) { + // Add r*4 to jump to the correct row within the 4x4 chunk + int8_t q8_val = qs_lhs[q8_idx + r * 4]; + isum[r][col] += q8_val * q2_val * d_sb; + } + } + } + } + + // Finalize K-Block + for (int col = 0; col < 16; ++col) { + float d_rhs = GGML_FP16_TO_FP32(x_ptr[k_block].d[col]); + float dm_rhs = GGML_FP16_TO_FP32(x_ptr[k_block].dmin[col]); + + for (int r = 0; r < 4; ++r) { + float d_lhs = y_ptr[k_block].d[r]; + float d_all = d_lhs * d_rhs; + float d_min = d_lhs * dm_rhs; + sumf[r][col] += (isum[r][col] * d_all) - (summs[r][col] * d_min); + } + } + } + + for (int r = 0; r < 4; ++r) { + for (int col = 0; col < 16; ++col) { + s[(row_tile + r) * bs + (col_tile + col)] = sumf[r][col]; + } + } + } + } +} +#endif + } // extern "C" static block_q8_0x4 make_block_q8_0x4(block_q8_0 * in, unsigned int blck_size_interleave) { @@ -1897,6 +2805,31 @@ static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_in return out; } +static block_q4_0x16 make_block_q4_0x16(block_q4_0 * in, unsigned int blck_size_interleave) { + block_q4_0x16 out; + + for (int i = 0; i < 16; i++) { + out.d[i] = in[i].d; + } + + const int end = QK4_0 * 8 / blck_size_interleave; + + if (blck_size_interleave == 1) { + const uint8_t xor_mask = 0x88; + for (int i = 0; i < end; ++i) { + int src_id = i % 16; + int src_offset = i / 16; + int dst_offset = i; + + out.qs[dst_offset] = in[src_id].qs[src_offset] ^ xor_mask; + } + } else { + GGML_ASSERT(false); + } + + return out; +} + static block_q4_Kx8 make_block_q4_Kx8(block_q4_K * in, unsigned int blck_size_interleave) { block_q4_Kx8 out; //Delta(scale) and dmin values of the eight Q4_K structures are copied onto the output interleaved structure @@ -1974,6 +2907,58 @@ static block_q4_Kx8 make_block_q4_Kx8(block_q4_K * in, unsigned int blck_size_in return out; } +static block_q4_Kx16 make_block_q4_Kx16(block_q4_K * in, unsigned int blck_size_interleave) { + block_q4_Kx16 out; + //Delta(scale) and dmin values of the 16 Q4_K structures are copied onto the output interleaved structure + for (int i = 0; i < 16; i++) { + out.d[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d; + } + + for (int i = 0; i < 16; i++) { + out.dmin[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin; + } + + const int end = QK_K * 8 / blck_size_interleave; + + if (blck_size_interleave == 1) { + for (int i = 0; i < end; ++i) { + int src_id = i % 16; + int src_offset = i / 16; + int dst_offset = i; + + out.qs[dst_offset] = in[src_id].qs[src_offset]; + } + + // RVV repacking. + // + // Extract sums and mins for all 8 sub-blocks for each block of Q4_K. + uint8_t s[128], m[128]; + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 16; j++) { + s[i * 16 + j] = in[j].scales[i] & 63; + m[i * 16 + j] = in[j].scales[i + 4] & 63; + } + } + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 16; j++) { + s[64 + i * 16 + j] = ((in[j].scales[i] & 192) >> 2) | (in[j].scales[i+8] & 15); + m[64 + i * 16 + j] = ((in[j].scales[i + 4] & 192) >> 2) | ((in[j].scales[i+8] & 240) >> 4); + } + } + + for (int i = 0; i < 128; i++) { + out.scales[i] = (s[i] & 15) | ((m[i] & 15) << 4); + } + for (int i = 0; i < 64; i++) { + out.scales[128 + i] = ((s[i] & 48) >> 4) | ((m[i] & 48) >> 2) | (s[64 + i] & 48) | ((m[64 + i] & 48) << 2); + } + } else { + GGML_ASSERT(false); + } + + return out; +} + static block_q2_Kx8 make_block_q2_Kx8(block_q2_K * in, unsigned int blck_size_interleave) { block_q2_Kx8 out; @@ -2029,18 +3014,16 @@ static block_q5_Kx8 make_block_q5_Kx8(block_q5_K * in, unsigned int blck_size_in const int end = QK_K * 4 / blck_size_interleave; - // Interleave Q5_K quants by taking 8 bytes at a time + // Interleave Q5_K quants by taking blck_size_interleave bytes at a time for (int i = 0; i < end; ++i) { int src_id = i % 8; int src_offset = (i / 8) * blck_size_interleave; int dst_offset = i * blck_size_interleave; - uint64_t elems; - memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t)); - memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t)); + memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], blck_size_interleave); } - // Repeat for low bits 8 bytes at a time as well, since + // Repeat for high bits with the same chunk size, since // the high bits are interleaved in Q5_K and the index is // qh_idx = (qs_idx % 32); // qh_val = qh[qh_idx] >> (qs_idx / 32); @@ -2049,9 +3032,7 @@ static block_q5_Kx8 make_block_q5_Kx8(block_q5_K * in, unsigned int blck_size_in int src_offset = (i / 8) * blck_size_interleave; int dst_offset = i * blck_size_interleave; - uint64_t elems; - memcpy(&elems, &in[src_id].qh[src_offset], sizeof(uint64_t)); - memcpy(&out.qh[dst_offset], &elems, sizeof(uint64_t)); + memcpy(&out.qh[dst_offset], &in[src_id].qh[src_offset], blck_size_interleave); } // The below logic is copied over from Q4_K @@ -2151,6 +3132,68 @@ static block_q6_Kx8 make_block_q6_Kx8(block_q6_K * in, unsigned int blck_size_in return out; } +static block_q2_Kx16 make_block_q2_Kx16(const block_q2_K * in, unsigned int blck_size_interleave) { + block_q2_Kx16 out; + constexpr int N_COLS = 16; + + // 1. Copy Super-Scales (d) and Super-Mins (dmin) + for (int i = 0; i < N_COLS; i++) { + out.d[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d; + out.dmin[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin; + } + + // 2. Interleave Q2_K Data + const int bytes_per_col = 64; + const int total_bytes = N_COLS * bytes_per_col; + const int end = total_bytes / blck_size_interleave; + + for (int i = 0; i < end; ++i) { + int src_col_id = i % N_COLS; + int src_offset = (i / N_COLS) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + memcpy(&out.qs[dst_offset], &in[src_col_id].qs[src_offset], blck_size_interleave); + } + + // 3. Repack Scales into the Optimized "Sequential-Parallel" Layout + int out_idx = 0; + + // Arrays define the sub-block order for each group + const int even_low_sbs[] = {0, 2, 4, 6}; + const int odd_low_sbs[] = {1, 3, 5, 7}; + const int even_high_sbs[] = {8, 10, 12, 14}; + const int odd_high_sbs[] = {9, 11, 13, 15}; + + // Pack Group 1: Even-Low + for (int sb : even_low_sbs) { + for (int col = 0; col < N_COLS; col++) { + out.scales[out_idx++] = in[col].scales[sb]; + } + } + + // Pack Group 2: Odd-Low + for (int sb : odd_low_sbs) { + for (int col = 0; col < N_COLS; col++) { + out.scales[out_idx++] = in[col].scales[sb]; + } + } + + // Pack Group 3: Even-High + for (int sb : even_high_sbs) { + for (int col = 0; col < N_COLS; col++) { + out.scales[out_idx++] = in[col].scales[sb]; + } + } + + // Pack Group 4: Odd-High + for (int sb : odd_high_sbs) { + for (int col = 0; col < N_COLS; col++) { + out.scales[out_idx++] = in[col].scales[sb]; + } + } + + return out; +} + static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { GGML_ASSERT(t->type == GGML_TYPE_Q4_0); GGML_ASSERT(interleave_block == 4 || interleave_block == 8); @@ -2213,6 +3256,36 @@ static int repack_q4_K_to_q4_K_8_bl(struct ggml_tensor * t, int interleave_block GGML_UNUSED(data_size); } +static int repack_q4_K_to_q4_K_16_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_Q4_K); + constexpr int nrows_interleaved = 16; + + block_q4_Kx16 * dst = (block_q4_Kx16*)t->data; + const block_q4_K * src = (const block_q4_K*) data; + block_q4_K dst_tmp[16]; + int nrow = ggml_nrows(t); + int nblocks = t->ne[0] / QK_K; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_K)); + + if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++ ) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_q4_Kx16(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + static int repack_q2_K_to_q2_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { GGML_ASSERT(t->type == GGML_TYPE_Q2_K); GGML_ASSERT(interleave_block == 8); @@ -2244,12 +3317,77 @@ static int repack_q2_K_to_q2_K_8_bl(struct ggml_tensor * t, int interleave_block GGML_UNUSED(data_size); } +static int repack_q2_K_to_q2_K_16_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_Q2_K); + constexpr int nrows_interleaved = 16; + + block_q2_Kx16 * dst = (block_q2_Kx16*)t->data; + const block_q2_K * src = (const block_q2_K*) data; + + block_q2_K dst_tmp[nrows_interleaved]; + + int nrow = ggml_nrows(t); + int nblocks = t->ne[0] / QK_K; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q2_K)); + + if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + // This loop gathers 16 separate blocks (one from each column) + // that correspond to the same K-dimension chunk. + for (int i = 0; i < nrows_interleaved; i++ ) { + dst_tmp[i] = src[x + i * nblocks]; + } + + *dst++ = make_block_q2_Kx16(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + +static int repack_q4_0_to_q4_0_16_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_Q4_0); + constexpr int nrows_interleaved = 16; + + block_q4_0x16 * dst = (block_q4_0x16*)t->data; + const block_q4_0 * src = (const block_q4_0*) data; + block_q4_0 dst_tmp[16]; + int nrow = ggml_nrows(t); + int nblocks = t->ne[0] / QK4_0; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0)); + + if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++ ) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_q4_0x16(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + static int repack_q5_K_to_q5_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { GGML_ASSERT(t->type == GGML_TYPE_Q5_K); - GGML_ASSERT(interleave_block == 8); + GGML_ASSERT(interleave_block == 4 || interleave_block == 8); constexpr int nrows_interleaved = 8; block_q5_Kx8 * dst = (block_q5_Kx8 *) t->data; @@ -2368,6 +3506,60 @@ static int repack_q8_0_to_q8_0_4_bl(struct ggml_tensor * t, return 0; } +static block_q8_0x16 make_block_q8_0x16(block_q8_0 * in, unsigned int blck_size_interleave) { + block_q8_0x16 out; + + for (int i = 0; i < 16; i++) { + out.d[i] = in[i].d; + } + + const int end = QK8_0 * 16 / blck_size_interleave; + + if (blck_size_interleave == 1) { + for (int i = 0; i < end; ++i) { + int src_id = i % 16; + int src_offset = i / 16; + int dst_offset = i; + out.qs[dst_offset] = in[src_id].qs[src_offset]; + } + } else { + GGML_ASSERT(false); + } + + return out; +} + +static int repack_q8_0_to_q8_0_16_bl(struct ggml_tensor * t, + int interleave_block, + const void * GGML_RESTRICT data, + size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_Q8_0); + constexpr int nrows_interleaved = 16; + + block_q8_0x16 * dst = (block_q8_0x16 *) t->data; + const block_q8_0 * src = (const block_q8_0 *) data; + block_q8_0 dst_tmp[16]; + int nrow = ggml_nrows(t); + int nblocks = t->ne[0] / QK8_0; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q8_0)); + + if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_q8_0x16(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; +} + static block_iq4_nlx4 make_block_iq4_nlx4(block_iq4_nl * in, unsigned int blck_size_interleave) { block_iq4_nlx4 out; @@ -2493,6 +3685,177 @@ static int repack_iq4_nl_to_iq4_nl_8_bl(struct ggml_tensor * t, int interleave_b GGML_UNUSED(data_size); } +static block_iq4_nlx16 make_block_iq4_nlx16(block_iq4_nl * in, unsigned int blck_size_interleave) { + block_iq4_nlx16 out; + + for (int i = 0; i < 16; i++) { + out.d[i] = in[i].d; + } + + const int end = QK4_NL * 8 / blck_size_interleave; + + if (blck_size_interleave == 1) { + for (int i = 0; i < end; ++i) { + int src_id = i % 16; + int src_offset = i / 16; + int dst_offset = i; + + out.qs[dst_offset] = in[src_id].qs[src_offset]; + } + } else { + GGML_ASSERT(false); + } + + return out; +} + +static int repack_iq4_nl_to_iq4_nl_16_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_IQ4_NL); + GGML_ASSERT(interleave_block == 1); + + const block_iq4_nl * src = (const block_iq4_nl *)data; + block_iq4_nlx16 * dst = ( block_iq4_nlx16 *)t->data; + + block_iq4_nl dst_tmp[16]; + + int nrow = ggml_nrows(t); + int nrows_interleaved = 16; + int nblocks = t->ne[0] / QK4_NL; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_iq4_nl)); + + if (t->ne[1] % nrows_interleaved != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_iq4_nlx16(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + +static block_mxfp4x4 make_block_mxfp4x4(block_mxfp4 * in, unsigned int blck_size_interleave) { + block_mxfp4x4 out; + + for (int i = 0; i < 4; i++) { + out.e[i] = in[i].e; + } + + const int end = QK_MXFP4 * 2 / blck_size_interleave; + + if (blck_size_interleave == 4) { + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint32_t)); + } + } else { + GGML_ASSERT(false); + } + + return out; +} + +static int repack_mxfp4_to_mxfp4_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_MXFP4); + GGML_ASSERT(interleave_block == 4); + + const block_mxfp4 * src = (const block_mxfp4 *)data; + block_mxfp4x4 * dst = ( block_mxfp4x4 *)t->data; + + block_mxfp4 dst_tmp[4]; + + int nrow = ggml_nrows(t); + int nrows_interleaved = 4; + int nblocks = t->ne[0] / QK_MXFP4; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_mxfp4)); + + if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_mxfp4x4(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + +static block_mxfp4x8 make_block_mxfp4x8(block_mxfp4 * in, unsigned int blck_size_interleave) { + block_mxfp4x8 out; + + for (int i = 0; i < 8; i++) { + out.e[i] = in[i].e; + } + + const int end = QK_MXFP4 * 4 / blck_size_interleave; + + if (blck_size_interleave == 8) { + for (int i = 0; i < end; ++i) { + int src_id = i % 8; + int src_offset = (i / 8) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint64_t)); + } + } else { + GGML_ASSERT(false); + } + + return out; +} + +static int repack_mxfp4_to_mxfp4_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_MXFP4); + GGML_ASSERT(interleave_block == 8); + + const block_mxfp4 * src = (const block_mxfp4 *)data; + block_mxfp4x8 * dst = ( block_mxfp4x8 *)t->data; + + block_mxfp4 dst_tmp[8]; + + int nrow = ggml_nrows(t); + int nrows_interleaved = 8; + int nblocks = t->ne[0] / QK_MXFP4; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_mxfp4)); + + if (t->ne[1] % nrows_interleaved != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_mxfp4x8(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + namespace ggml::cpu::repack { // repack template @@ -2523,6 +3886,10 @@ template <> int repack(struct ggml_tensor * t, const void * da return repack_q2_K_to_q2_K_8_bl(t, 8, data, data_size); } +template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { + return repack_q5_K_to_q5_K_8_bl(t, 4, data, data_size); +} + template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { return repack_q5_K_to_q5_K_8_bl(t, 8, data, data_size); } @@ -2548,6 +3915,14 @@ template <> int repack(struct ggml_tensor * t, const void * return repack_iq4_nl_to_iq4_nl_8_bl(t, 8, data, data_size); } +template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { + return repack_mxfp4_to_mxfp4_4_bl(t, 4, data, data_size); +} + +template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { + return repack_mxfp4_to_mxfp4_8_bl(t, 8, data, data_size); +} + template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { return repack_q8_0_to_q8_0_4_bl(t, 4, data, data_size); } @@ -2556,6 +3931,28 @@ template <> int repack(struct ggml_tensor * t, const void * da return repack_q8_0_to_q8_0_4_bl(t, 8, data, data_size); } +#if defined __riscv_zvfh +template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { + return repack_q4_0_to_q4_0_16_bl(t, 1, data, data_size); +} + +template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { + return repack_q4_K_to_q4_K_16_bl(t, 1, data, data_size); +} + +template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { + return repack_iq4_nl_to_iq4_nl_16_bl(t, 1, data, data_size); +} + +template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { + return repack_q8_0_to_q8_0_16_bl(t, 1, data, data_size); +} + +template <> int repack(struct ggml_tensor * t, const void * data, size_t data_size) { + return repack_q2_K_to_q2_K_16_bl(t, 1, data, data_size); +} +#endif + // gemv template void gemv(int, float *, size_t, const void *, const void *, int, int); @@ -2591,6 +3988,10 @@ template <> void gemv(int n, float * s, size_t ggml_gemv_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc); } +template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemv_q5_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc); +} + template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { ggml_gemv_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc); } @@ -2611,6 +4012,14 @@ template <> void gemv(int n, float * s, size ggml_gemv_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc); } +template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemv_mxfp4_4x4_q8_0(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemv_mxfp4_8x8_q8_0(n, s, bs, vx, vy, nr, nc); +} + template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { ggml_gemv_q8_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc); } @@ -2619,6 +4028,28 @@ template <> void gemv(int n, float * s, size_t ggml_gemv_q8_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc); } +#if defined __riscv_zvfh +template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemv_q4_0_16x1_q8_0(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemv_q4_K_16x1_q8_K(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemv_iq4_nl_16x1_q8_0(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemv_q8_0_16x1_q8_0(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemv(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemv_q2_K_16x1_q8_K(n, s, bs, vx, vy, nr, nc); +} +#endif + // gemm template void gemm(int, float *, size_t, const void *, const void *, int, int); @@ -2654,6 +4085,10 @@ template <> void gemm(int n, float * s, size_t ggml_gemm_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc); } +template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemm_q5_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc); +} + template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { ggml_gemm_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc); } @@ -2674,6 +4109,14 @@ template <> void gemm(int n, float * s, size ggml_gemm_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc); } +template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemm_mxfp4_4x4_q8_0(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemm_mxfp4_8x8_q8_0(n, s, bs, vx, vy, nr, nc); +} + template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { ggml_gemm_q8_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc); } @@ -2682,6 +4125,28 @@ template <> void gemm(int n, float * s, size_t ggml_gemm_q8_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc); } +#if defined __riscv_zvfh +template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemm_q4_0_16x1_q8_0(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemm_q4_K_16x1_q8_K(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemm_iq4_nl_16x1_q8_0(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemm_q8_0_16x1_q8_0(n, s, bs, vx, vy, nr, nc); +} + +template <> void gemm(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) { + ggml_gemm_q2_K_16x1_q8_K(n, s, bs, vx, vy, nr, nc); +} +#endif + class tensor_traits_base : public ggml::cpu::tensor_traits { public: virtual int repack(struct ggml_tensor * t, const void * data, size_t data_size) = 0; @@ -2700,7 +4165,7 @@ template src[1])); - size = GGML_PAD(size, sizeof(int64_t)); // + padding for next bloc. + size = GGML_PAD(size, sizeof(int64_t)); // + padding for next block. const int64_t ne02 = op->src[0]->ne[2]; // n_as, n_expert const int64_t ne12 = op->src[1]->ne[2]; // n_tokens @@ -2965,7 +4430,7 @@ template wdata; auto * wdata_src1_end = (char *)wdata + GGML_PAD(nbw3, sizeof(int64_t)); - // total of [n_as][ne12 + 1] elemets of type mmid_row_mapping (2*int32_t = int64_t) + // total of [n_as][ne12 + 1] elements of type mmid_row_mapping (2*int32_t = int64_t) auto * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *) (matrix_row_counts + n_as); // [n_as][ne12] @@ -3068,6 +4533,7 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons static const ggml::cpu::repack::tensor_traits q4_K_8x8_q8_K; // instance for Q5_K + static const ggml::cpu::repack::tensor_traits q5_K_8x4_q8_K; static const ggml::cpu::repack::tensor_traits q5_K_8x8_q8_K; // instance for Q6_K @@ -3081,13 +4547,28 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons static const ggml::cpu::repack::tensor_traits iq4_nl_4x4_q8_0; static const ggml::cpu::repack::tensor_traits iq4_nl_8x8_q8_0; + // instance for MXFP4 + static const ggml::cpu::repack::tensor_traits mxfp4_4x4_q8_0; + static const ggml::cpu::repack::tensor_traits mxfp4_8x8_q8_0; + // instance for Q8_0 static const ggml::cpu::repack::tensor_traits q8_0_4x4_q8_0; static const ggml::cpu::repack::tensor_traits q8_0_4x8_q8_0; + // instances for RISC-V + // + // These implement outer-product style matrix multiplication kernels with + // an interleave of 1. +#if defined __riscv_zvfh + static const ggml::cpu::repack::tensor_traits q4_0_16x1_q8_0; + static const ggml::cpu::repack::tensor_traits q4_K_16x1_q8_K; + static const ggml::cpu::repack::tensor_traits iq4_nl_16x1_q8_0; + static const ggml::cpu::repack::tensor_traits q8_0_16x1_q8_0; + static const ggml::cpu::repack::tensor_traits q2_K_16x1_q8_K; +#endif + if (cur->type == GGML_TYPE_Q4_0) { - if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) - || (ggml_cpu_has_riscv_v() && (ggml_cpu_get_rvv_vlen() >= QK4_0))) { + if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) { if (cur->ne[1] % 8 == 0) { return &q4_0_8x8_q8_0; } @@ -3102,6 +4583,17 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons return &q4_0_4x4_q8_0; } } + if (ggml_cpu_has_riscv_v()) { + #if defined __riscv_zvfh + switch (__riscv_vlenb() * 8) { + case 128: { break; } // TODO + case 256: { if (cur->ne[1] % 16 == 0) { return &q4_0_16x1_q8_0; } break; } + case 512: { break; } // TODO + case 1024: { break; } // TODO + default: { return nullptr; } + } + #endif + } } else if (cur->type == GGML_TYPE_Q4_K) { if (ggml_cpu_has_avx2()) { if (cur->ne[1] % 8 == 0) { @@ -3118,18 +4610,45 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons return &q4_K_8x4_q8_K; } } + if (ggml_cpu_has_riscv_v()) { + #if defined __riscv_zvfh + switch (__riscv_vlenb() * 8) { + case 128: { break; } // TODO + case 256: { if (cur->ne[1] % 16 == 0) { return &q4_K_16x1_q8_K; } break; } + case 512: { break; } // TODO + case 1024: { break; } // TODO + default: { return nullptr; } + } + #endif + } } else if (cur->type == GGML_TYPE_Q2_K) { if (ggml_cpu_has_avx512()) { if (cur->ne[1] % 8 == 0) { return &q2_K_8x8_q8_K; } } + if (ggml_cpu_has_riscv_v()) { + #if defined __riscv_zvfh + switch (__riscv_vlenb() * 8) { + case 128: { break; } // TODO + case 256: { if (cur->ne[1] % 16 == 0) { return &q2_K_16x1_q8_K; } break; } + case 512: { break; } // TODO + case 1024: { break; } // TODO + default: { return nullptr; } + } + #endif + } } else if (cur->type == GGML_TYPE_Q5_K) { if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { if (cur->ne[1] % 8 == 0) { return &q5_K_8x8_q8_K; } } + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + if (cur->ne[1] % 8 == 0) { + return &q5_K_8x4_q8_K; + } + } } else if (cur->type == GGML_TYPE_Q6_K) { if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { if (cur->ne[1] % 8 == 0) { @@ -3152,6 +4671,28 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons return &iq4_nl_4x4_q8_0; } } + if (ggml_cpu_has_riscv_v()) { + #if defined __riscv_zvfh + switch (__riscv_vlenb() * 8) { + case 128: { break; } // TODO + case 256: { if (cur->ne[1] % 16 == 0) { return &iq4_nl_16x1_q8_0; } break; } + case 512: { break; } // TODO + case 1024: { break; } // TODO + default: { return nullptr; } + } + #endif + } + } else if (cur->type == GGML_TYPE_MXFP4) { + if (ggml_cpu_has_avx2()) { + if (cur->ne[1] % 8 == 0) { + return &mxfp4_8x8_q8_0; + } + } + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + if (cur->ne[1] % 4 == 0) { + return &mxfp4_4x4_q8_0; + } + } } else if (cur->type == GGML_TYPE_Q8_0) { if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { if (cur->ne[1] % 4 == 0) { @@ -3163,6 +4704,17 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons return &q8_0_4x4_q8_0; } } + if (ggml_cpu_has_riscv_v()) { + #if defined __riscv_zvfh + switch (__riscv_vlenb() * 8) { + case 128: { break; } // TODO + case 256: { if (cur->ne[1] % 16 == 0) { return &q8_0_16x1_q8_0; } break; } + case 512: { break; } // TODO + case 1024: { break; } // TODO + default: { return nullptr; } + } + #endif + } } return nullptr; diff --git a/ggml/src/ggml-cpu/repack.h b/ggml/src/ggml-cpu/repack.h index 39b6b48238..cb21edf623 100644 --- a/ggml/src/ggml-cpu/repack.h +++ b/ggml/src/ggml-cpu/repack.h @@ -28,13 +28,17 @@ template struct block { // control size static_assert(sizeof(block<4, 4>) == 4 * sizeof(ggml_half) + QK8_0 * 2, "wrong block<4,4> size/padding"); static_assert(sizeof(block<4, 8>) == 8 * sizeof(ggml_half) + QK8_0 * 4, "wrong block<4,8> size/padding"); +static_assert(sizeof(block<4, 16>) == 16 * sizeof(ggml_half) + QK8_0 * 8, "wrong block<4,16> size/padding"); static_assert(sizeof(block<8, 4>) == 4 * sizeof(ggml_half) + QK8_0 * 4, "wrong block<8,4> size/padding"); static_assert(sizeof(block<8, 8>) == 8 * sizeof(ggml_half) + QK8_0 * 8, "wrong block<8,8> size/padding"); +static_assert(sizeof(block<8, 16>) == 16 * sizeof(ggml_half) + QK8_0 * 16, "wrong block<8,16> size/padding"); using block_q4_0x4 = block<4, 4>; using block_q4_0x8 = block<4, 8>; +using block_q4_0x16 = block<4, 16>; using block_q8_0x4 = block<8, 4>; using block_q8_0x8 = block<8, 8>; +using block_q8_0x16 = block<8, 16>; struct block_q4_Kx8 { ggml_half d[8]; // super-block scale for quantized scales @@ -44,7 +48,14 @@ struct block_q4_Kx8 { }; static_assert(sizeof(block_q4_Kx8) == sizeof(ggml_half) * 16 + K_SCALE_SIZE * 8 + QK_K * 4, "wrong q4_K block size/padding"); +struct block_q4_Kx16 { + ggml_half d[16]; // super-block scale for quantized scales + ggml_half dmin[16]; // super-block scale for quantized mins + uint8_t scales[192]; // scales and mins, quantized with 6 bits + uint8_t qs[2048]; // 4--bit quants +}; +static_assert(sizeof(block_q4_Kx16) == sizeof(ggml_half) * 32 + K_SCALE_SIZE * 16 + QK_K * 8, "wrong q4_K block size/padding"); struct block_q2_Kx8 { ggml_half d[8]; // super-block scale for quantized scales ggml_half dmin[8]; // super-block scale for quantized mins @@ -53,6 +64,13 @@ struct block_q2_Kx8 { }; static_assert(sizeof(block_q2_Kx8) == sizeof(ggml_half) * 16 + QK_K/2 + QK_K * 2, "wrong q2_K block size/padding"); +struct block_q2_Kx16 { + ggml_half d[16]; // Super-block scale for quantized scales + ggml_half dmin[16]; // Super-block scale for quantized mins + uint8_t scales[256]; // Sub-block scales (16 cols * 16 sub-blocks) + uint8_t qs[1024]; // Data (16 cols * 64 bytes per block) +}; +static_assert(sizeof(block_q2_Kx16) == sizeof(ggml_half) * 32 + QK_K + QK_K * 4, "wrong q2_K block size/padding"); struct block_q5_Kx8 { ggml_half d[8]; // super-block scale for quantized scales @@ -97,6 +115,24 @@ struct block_iq4_nlx8 { static_assert(sizeof(block_iq4_nlx8) == 8 * sizeof(ggml_half) + QK4_NL * 4, "wrong iq4_nlx8 block size/padding"); +struct block_iq4_nlx16 { + ggml_half d[16]; // deltas for 16 iq4_nl blocks + uint8_t qs[QK4_NL * 8]; // nibbles / quants for 16 iq4_nl blocks +}; + +static_assert(sizeof(block_iq4_nlx16) == 16 * sizeof(ggml_half) + QK4_NL * 8, "wrong iq4_nlx16 block size/padding"); +struct block_mxfp4x4 { + uint8_t e[4]; + uint8_t qs[QK_MXFP4 * 2]; +}; +static_assert(sizeof(block_mxfp4x4) == 4 + QK_MXFP4 * 2, "wrong mxfp4x4 block size/padding"); + +struct block_mxfp4x8 { + uint8_t e[8]; + uint8_t qs[QK_MXFP4 * 4]; +}; +static_assert(sizeof(block_mxfp4x8) == 8 + QK_MXFP4 * 4, "wrong mxfp4x8 block size/padding"); + #if defined(__cplusplus) extern "C" { #endif @@ -111,26 +147,46 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q5_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q5_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q6_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q6_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q5_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q5_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q6_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q6_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemv_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemv_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +#if defined __riscv_zvfh +void ggml_quantize_mat_q8_0_4x1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); +void ggml_quantize_mat_q8_K_4x1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); +void ggml_gemv_q4_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q4_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_iq4_nl_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q8_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q2_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q4_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q4_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_iq4_nl_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q8_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q2_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +#endif // Native implementations void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); @@ -143,26 +199,46 @@ void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q5_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q5_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q5_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q5_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemv_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemv_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +#if defined __riscv_zvfh +void ggml_quantize_mat_q8_0_4x1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); +void ggml_quantize_mat_q8_K_4x1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); +void ggml_gemv_q4_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q4_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q8_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q2_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_iq4_nl_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q4_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q4_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q8_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q2_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_iq4_nl_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +#endif #if defined(__cplusplus) } // extern "C" diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h index 22de55700d..0deda93098 100644 --- a/ggml/src/ggml-cpu/simd-mappings.h +++ b/ggml/src/ggml-cpu/simd-mappings.h @@ -479,13 +479,51 @@ do { \ // F16 AVX512 -// F16 AVX +#if defined(__AVX512FP16__) + +#define GGML_F16_STEP 128 +#define GGML_F16_EPR 32 + +#define GGML_F16x32 __m512h +#define GGML_F16x32_ZERO _mm512_setzero_ph() +#define GGML_F16x32_SET1(x) _mm512_set1_ph(__extension__(_Float16)(x)) +#define GGML_F16x32_LOAD(x) _mm512_loadu_ph(x) +#define GGML_F16x32_STORE(x, y) _mm512_storeu_ph(x, y) +#define GGML_F16x32_FMA(a, b, c) _mm512_fmadd_ph(b, c, a) +#define GGML_F16x32_ADD _mm512_add_ph +#define GGML_F16x32_MUL _mm512_mul_ph +#define GGML_F16x32_REDUCE(res, x) \ +do { \ + int offset = GGML_F16_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ph(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ph(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ph(x[i], x[offset+i]); \ + } \ + res = (ggml_float) _mm512_reduce_add_ph(x[0]); \ +} while (0) + +#define GGML_F16_VEC GGML_F16x32 +#define GGML_F16_VEC_ZERO GGML_F16x32_ZERO +#define GGML_F16_VEC_SET1 GGML_F16x32_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F16x32_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x32_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F16x32_FMA +#define GGML_F16_VEC_ADD GGML_F16x32_ADD +#define GGML_F16_VEC_MUL GGML_F16x32_MUL +#define GGML_F16_VEC_REDUCE GGML_F16x32_REDUCE + +#else // Fallback FP16 <-> FP32 #define GGML_F16_STEP 64 #define GGML_F16_EPR 16 -// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead - #define GGML_F32Cx16 __m512 #define GGML_F32Cx16_ZERO _mm512_setzero_ps() #define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) @@ -525,6 +563,8 @@ do { \ #define GGML_F16_VEC_MUL GGML_F32Cx16_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE + +#endif // __AVX512FP16__ #elif defined(__AVX__) #define GGML_SIMD diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu index 09b6d5db6a..b70492c7d6 100644 --- a/ggml/src/ggml-cuda/convert.cu +++ b/ggml/src/ggml-cuda/convert.cu @@ -16,27 +16,27 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __ return; } - const int64_t i01 = blockIdx.y; + for (int64_t i01 = blockIdx.y; i01 < ne01; i01 += gridDim.y) { + for (int64_t i0203 = blockIdx.z; i0203 < ne0203; i0203 += gridDim.z) { + const uint2 dm = fast_div_modulo((uint32_t)i0203, ne02); + const int64_t i02 = dm.y; + const int64_t i03 = dm.x; - for (int64_t i0203 = blockIdx.z; i0203 < ne0203; i0203 += gridDim.z) { - const uint2 dm = fast_div_modulo((uint32_t)i0203, ne02); - const int64_t i02 = dm.y; - const int64_t i03 = dm.x; + const int64_t ibx0 = i03*s03 + i02*s02 + i01*s01; - const int64_t ibx0 = i03*s03 + i02*s02 + i01*s01; + const int64_t ib = ibx0 + i00/qk; // block index + const int64_t iqs = (i00%qk)/qr; // quant index + const int64_t iybs = i00 - i00%qk; // y block start index + const int64_t y_offset = qr == 1 ? 1 : qk/2; - const int64_t ib = ibx0 + i00/qk; // block index - const int64_t iqs = (i00%qk)/qr; // quant index - const int64_t iybs = i00 - i00%qk; // y block start index - const int64_t y_offset = qr == 1 ? 1 : qk/2; + // dequantize + float2 v; + dequantize_kernel(vx, ib, iqs, v); - // dequantize - float2 v; - dequantize_kernel(vx, ib, iqs, v); - - const int64_t iy0 = (i0203*ne01 + i01)*ne00 + iybs + iqs; - y[iy0 + 0] = ggml_cuda_cast(v.x); - y[iy0 + y_offset] = ggml_cuda_cast(v.y); + const int64_t iy0 = (i0203*ne01 + i01)*ne00 + iybs + iqs; + y[iy0 + 0] = ggml_cuda_cast(v.x); + y[iy0 + y_offset] = ggml_cuda_cast(v.y); + } } } @@ -492,7 +492,7 @@ static void dequantize_block_cuda(const void * vx, dst_t * y, const int64_t s01, const int64_t s02, const int64_t s03, cudaStream_t stream) { const int64_t ne0203 = ne02*ne03; const uint3 ne02_fdv = init_fastdiv_values(ne02); - const dim3 num_blocks((ne00 + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE), ne01, (int)std::min(ne0203, (int64_t)65535)); + const dim3 num_blocks((ne00 + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE), (int)std::min(ne01, (int64_t)65535), (int)std::min(ne0203, (int64_t)65535)); dequantize_block<<>> (vx, y, ne00, ne01, ne0203, ne02_fdv, s01, s02, s03); } @@ -628,18 +628,18 @@ static __global__ void convert_unary( return; } - const int64_t i01 = blockIdx.y; - const src_t * x = (const src_t *) vx; - for (int64_t i0203 = blockIdx.z; i0203 < ne0203; i0203 += gridDim.z) { - const uint2 dm = fast_div_modulo((uint32_t)i0203, ne02); - const int64_t i02 = dm.y; - const int64_t i03 = dm.x; + for (int64_t i01 = blockIdx.y; i01 < ne01; i01 += gridDim.y) { + for (int64_t i0203 = blockIdx.z; i0203 < ne0203; i0203 += gridDim.z) { + const uint2 dm = fast_div_modulo((uint32_t)i0203, ne02); + const int64_t i02 = dm.y; + const int64_t i03 = dm.x; - const int64_t ix = i03*s03 + i02*s02 + i01*s01 + i00; - const int64_t iy = (i0203*ne01 + i01)*ne00 + i00; - y[iy] = ggml_cuda_cast(x[ix]); + const int64_t ix = i03*s03 + i02*s02 + i01*s01 + i00; + const int64_t iy = (i0203*ne01 + i01)*ne00 + i00; + y[iy] = ggml_cuda_cast(x[ix]); + } } } @@ -649,7 +649,7 @@ static void convert_unary_cuda(const void * vx, dst_t * y, const int64_t s01, const int64_t s02, const int64_t s03, cudaStream_t stream) { const int64_t ne0203 = ne02*ne03; const uint3 ne02_fdv = init_fastdiv_values(ne02); - const dim3 num_blocks((ne00 + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE, ne01, (int)std::min(ne0203, (int64_t)65535)); + const dim3 num_blocks((ne00 + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE, (int)std::min(ne01, (int64_t)65535), (int)std::min(ne0203, (int64_t)65535)); convert_unary<<>> (vx, y, ne00, ne01, ne0203, ne02_fdv, s01, s02, s03); } diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu index ee84303ef0..d208acf2d5 100644 --- a/ggml/src/ggml-cuda/cpy.cu +++ b/ggml/src/ggml-cuda/cpy.cu @@ -56,7 +56,8 @@ static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const const int tx = blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.x; // transpose block offset const int ty = blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.y; - __shared__ float tile[CUDA_CPY_TILE_DIM_2D][CUDA_CPY_TILE_DIM_2D+1]; + __shared__ float tile[2][CUDA_CPY_TILE_DIM_2D][CUDA_CPY_TILE_DIM_2D+1]; + int cur_tile_buf = 0; #pragma unroll for (int i = 0; i < CUDA_CPY_BLOCK_NM; ++i) { @@ -70,7 +71,7 @@ static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const if(x < ne01 && y + j < ne00){ const int row = threadIdx.y+j; const int col = threadIdx.x * sizeof(float)/sizeof(T); - T *tile2 = reinterpret_cast(tile[row]); + T *tile2 = reinterpret_cast(tile[cur_tile_buf][row]); tile2[col] = src[imat*n + (y+j)*ne01 + x]; } } @@ -81,10 +82,12 @@ static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const for (int j = 0; j < CUDA_CPY_TILE_DIM_2D; j += CUDA_CPY_BLOCK_ROWS) { if (ty + j < ne01 && tx < ne00) { const int col = (threadIdx.y+j)*sizeof(float)/sizeof(T); - const T *tile2 = reinterpret_cast(tile[threadIdx.x]); + const T *tile2 = reinterpret_cast(tile[cur_tile_buf][threadIdx.x]); dst[imat*n + (ty+j)*ne00 + tx] = tile2[col]; } } + + cur_tile_buf = (cur_tile_buf + 1) % 2; } GGML_UNUSED_VARS(ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index b6a7460da8..e9abdf288c 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -892,7 +892,7 @@ void launch_fattn( const int ntiles_x = ((Q->ne[1] + ncols1 - 1) / ncols1); const int gqa_ratio = Q->ne[2] / K->ne[2]; const int ntiles_z_gqa = ((gqa_ratio + ncols2 - 1) / ncols2); - const int ntiles_total = ntiles_x * ntiles_z_gqa * K->ne[2] * Q->ne[3]; + const int ntiles_dst = ntiles_x * ntiles_z_gqa * K->ne[2] * Q->ne[3]; // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped. // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or @@ -919,37 +919,37 @@ void launch_fattn( GGML_ASSERT(max_blocks_per_sm > 0); int parallel_blocks = max_blocks_per_sm; + const int ntiles_KV = (K->ne[1] + nbatch_fa - 1) / nbatch_fa; // Max. number of parallel blocks limited by KV cache length. + dim3 blocks_num; if (stream_k) { // For short contexts it can be faster to have the SMs work on whole tiles because this lets us skip the fixup. const int max_blocks = max_blocks_per_sm*nsm; - const int tiles_nwaves = (ntiles_total + max_blocks - 1) / max_blocks; - const int tiles_efficiency_percent = 100 * ntiles_total / (max_blocks*tiles_nwaves); + const int tiles_nwaves = (ntiles_dst + max_blocks - 1) / max_blocks; + const int tiles_efficiency_percent = 100 * ntiles_dst / (max_blocks*tiles_nwaves); - const int nblocks_stream_k = max_blocks; + const int nblocks_stream_k = std::min(max_blocks, ntiles_KV*ntiles_dst); const bool use_stream_k = cc >= GGML_CUDA_CC_ADA_LOVELACE || amd_wmma_available(cc) || tiles_efficiency_percent < 75; - blocks_num.x = use_stream_k ? nblocks_stream_k : ntiles_total; + blocks_num.x = use_stream_k ? nblocks_stream_k : ntiles_dst; blocks_num.y = 1; blocks_num.z = 1; - if (ntiles_total % blocks_num.x != 0) { // Fixup is only needed if the SMs work on fractional tiles. + if (ntiles_dst % blocks_num.x != 0) { // Fixup is only needed if the SMs work on fractional tiles. dst_tmp_meta.alloc((size_t(blocks_num.x) * ncols * (2 + DV/2))); } } else { - const int ntiles_KQ = (K->ne[1] + nbatch_fa - 1) / nbatch_fa; // Max. number of parallel blocks limited by tensor size. - // parallel_blocks must not be larger than what the tensor size allows: - parallel_blocks = std::min(parallel_blocks, ntiles_KQ); + parallel_blocks = std::min(parallel_blocks, ntiles_KV); // If ntiles_total % blocks_per_wave != 0 then some efficiency is lost due to tail effects. // Test whether parallel_blocks can be set to a higher value for better efficiency. const int blocks_per_wave = nsm * max_blocks_per_sm; int nwaves_best = 0; int efficiency_percent_best = 0; - for (int parallel_blocks_test = parallel_blocks; parallel_blocks_test <= ntiles_KQ; ++parallel_blocks_test) { - const int nblocks_total = ntiles_total * parallel_blocks_test; + for (int parallel_blocks_test = parallel_blocks; parallel_blocks_test <= ntiles_KV; ++parallel_blocks_test) { + const int nblocks_total = ntiles_dst * parallel_blocks_test; const int nwaves = (nblocks_total + blocks_per_wave - 1) / blocks_per_wave; const int efficiency_percent = 100 * nblocks_total / (nwaves*blocks_per_wave); @@ -1015,7 +1015,7 @@ void launch_fattn( CUDA_CHECK(cudaGetLastError()); if (stream_k) { - if (ntiles_total % blocks_num.x != 0) { // Fixup is only needed if the SMs work on fractional tiles. + if (ntiles_dst % blocks_num.x != 0) { // Fixup is only needed if the SMs work on fractional tiles. const dim3 block_dim_combine(DV, 1, 1); const dim3 blocks_num_combine = {blocks_num.x, ncols1, ncols2}; diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh index 0b8ef90794..fff70c8eb8 100644 --- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh @@ -111,6 +111,44 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols); } +static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_cdna(const int DKQ, const int DV, const int ncols) { + // Conservative configs for CDNA (MI100+): 64KB LDS, wavefront64, nstages=1 (no cp.async). + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 8, 128, 2, 128, 32, 32, 32, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 16, 128, 2, 64, 32, 32, 32, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 32, 128, 2, 64, 32, 32, 32, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 64, 256, 2, 64, 32, 32, 32, 1, true); + + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 8, 128, 2, 128, 40, 40, 40, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 16, 128, 2, 64, 40, 40, 40, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 32, 128, 2, 64, 40, 40, 40, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 64, 256, 2, 64, 40, 40, 40, 1, true); + + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 8, 128, 2, 128, 48, 48, 48, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 16, 128, 2, 64, 48, 48, 48, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 32, 128, 2, 64, 48, 48, 48, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 64, 256, 2, 64, 48, 48, 48, 1, true); + + GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 8, 128, 2, 128, 56, 56, 56, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 16, 128, 2, 64, 56, 56, 56, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 32, 128, 2, 64, 56, 56, 56, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 64, 256, 2, 64, 56, 56, 56, 1, true); + + GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 8, 128, 2, 128, 64, 64, 64, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 16, 128, 2, 64, 64, 64, 64, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 32, 128, 2, 64, 64, 64, 64, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 64, 256, 2, 64, 64, 64, 64, 1, true); + + GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 8, 64, 4, 64, 128, 128, 128, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16, 64, 4, 32, 128, 128, 128, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2, 32, 128, 128, 128, 1, true); + GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 256, 2, 32, 128, 128, 128, 1, true); + + // Fallback for unsupported DKQ values (e.g. 576). Must return non-zero values to satisfy + // compile-time static_asserts even though the kernel guard prevents runtime execution. + // nthreads=256 gives nwarps=4 (warp_size=64) or 8 (warp_size=32), nbatch_fa=128 satisfies np*16 divisibility. + return fattn_mma_config(256, 1, 128, 4, 4, 4, 1, false); +} + static __host__ fattn_mma_config ggml_cuda_fattn_mma_get_config(const int DKQ, const int DV, const int ncols, const int cc) { if (ampere_mma_available(cc)) { return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols); @@ -118,6 +156,9 @@ static __host__ fattn_mma_config ggml_cuda_fattn_mma_get_config(const int DKQ, c if (turing_mma_available(cc)) { return ggml_cuda_fattn_mma_get_config_turing(DKQ, DV, ncols); } + if (amd_mfma_available(cc)) { + return ggml_cuda_fattn_mma_get_config_cdna(DKQ, DV, ncols); + } if (amd_wmma_available(cc)) { return ggml_cuda_fattn_mma_get_config_rdna(DKQ, DV, ncols); } @@ -130,6 +171,8 @@ static constexpr __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config(cons return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols); #elif defined(TURING_MMA_AVAILABLE) return ggml_cuda_fattn_mma_get_config_turing(DKQ, DV, ncols); +#elif defined(AMD_MFMA_AVAILABLE) + return ggml_cuda_fattn_mma_get_config_cdna(DKQ, DV, ncols); #elif defined(VOLTA_MMA_AVAILABLE) return ggml_cuda_fattn_mma_get_config_volta(DKQ, DV, ncols); #elif defined(AMD_WMMA_AVAILABLE) @@ -205,15 +248,15 @@ static constexpr __device__ bool ggml_cuda_fattn_mma_get_Q_in_reg(const int DKQ, } static constexpr __device__ int get_cols_per_thread() { -#if defined(AMD_WMMA_AVAILABLE) - return 1; // RDNA has a single column. +#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) + return 1; // AMD has a single column per thread. #else return 2; // This is specifically KQ columns, Volta only has a single VKQ column. -#endif // defined(AMD_WMMA_AVAILABLE) +#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) } static __host__ int get_cols_per_warp(const int cc) { - if (turing_mma_available(cc) || amd_wmma_available(cc)) { + if (turing_mma_available(cc) || amd_wmma_available(cc) || amd_mfma_available(cc)) { return 16; } else { // Volta @@ -241,6 +284,7 @@ static constexpr __device__ int ggml_cuda_fattn_mma_get_nstages(const int DKQ, c template static __device__ __forceinline__ void flash_attn_ext_f16_load_tile( const half2 * const __restrict__ KV, half2 * const __restrict__ tile_KV, const int D2, const int stride_KV, const int i_sup) { + constexpr int warp_size = ggml_cuda_get_physical_warp_size(); // K/V data is loaded with decreasing granularity for D for better memory bandwidth. // The minimum granularity with cp.async is 16 bytes, with synchronous data loading it's 4 bytes. if constexpr (use_cp_async) { @@ -252,10 +296,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile( const unsigned int tile_KV_32 = ggml_cuda_cvta_generic_to_shared(tile_KV); auto load = [&] __device__ (auto n) { - const int stride_k = WARP_SIZE >> n; - const int k0_start = stride_k == WARP_SIZE ? 0 : chunks_per_row - chunks_per_row % (2*stride_k); + const int stride_k = warp_size >> n; + const int k0_start = stride_k == warp_size ? 0 : chunks_per_row - chunks_per_row % (2*stride_k); const int k0_stop = chunks_per_row - chunks_per_row % (1*stride_k); - const int stride_i = WARP_SIZE / stride_k; + const int stride_i = warp_size / stride_k; if (k0_start == k0_stop) { return; @@ -263,7 +307,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile( #pragma unroll for (int i0 = 0; i0 < nbatch_fa; i0 += nwarps*stride_i) { - const int i = i0 + threadIdx.y*stride_i + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k); + const int i = i0 + threadIdx.y*stride_i + (stride_k == warp_size ? 0 : threadIdx.x / stride_k); if (i0 + nwarps*stride_i > nbatch_fa && i >= nbatch_fa) { break; @@ -271,7 +315,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile( #pragma unroll for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) { - const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k); + const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k); cp_async_cg_16(tile_KV_32 + i*(stride_tile*sizeof(half2)) + k*16, KV + i*stride_KV + k*h2_per_chunk); } @@ -287,10 +331,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile( } else { // TODO use ggml_cuda_memcpy_1 auto load = [&] __device__ (const int n) { - const int stride_k = WARP_SIZE >> n; - const int k0_start = stride_k == WARP_SIZE ? 0 : D2 - D2 % (2*stride_k); + const int stride_k = warp_size >> n; + const int k0_start = stride_k == warp_size ? 0 : D2 - D2 % (2*stride_k); const int k0_stop = D2 - D2 % (1*stride_k); - const int stride_i = WARP_SIZE / stride_k; + const int stride_i = warp_size / stride_k; if (k0_start == k0_stop) { return; @@ -298,7 +342,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile( #pragma unroll for (int i0 = 0; i0 < nbatch_fa; i0 += nwarps*stride_i) { - const int i = i0 + threadIdx.y*stride_i + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k); + const int i = i0 + threadIdx.y*stride_i + (stride_k == warp_size ? 0 : threadIdx.x / stride_k); if (i0 + nwarps*stride_i > nbatch_fa && i >= nbatch_fa) { break; @@ -306,7 +350,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile( #pragma unroll for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) { - const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k); + const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k); tile_KV[i*stride_tile + k] = !oob_check || i < i_sup ? KV[i*stride_KV + k] : make_half2(0.0f, 0.0f); } @@ -324,18 +368,19 @@ template= 32 ? nbatch_fa * sizeof(half) : 64; - constexpr int cols_per_warp = 8*WARP_SIZE/nbatch_fa; + constexpr int cols_per_warp = 8*warp_size/nbatch_fa; constexpr int stride_j = nwarps * cols_per_warp; const unsigned int tile_mask_32 = ggml_cuda_cvta_generic_to_shared(tile_mask); #pragma unroll for (int j1 = 0; j1 < ncols1; j1 += stride_j) { - const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (WARP_SIZE/cols_per_warp); + const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (warp_size/cols_per_warp); const int j_vram = fastmodulo(j0 + j_sram, ne01); if (j1 + stride_j > ncols1 && j_sram >= ncols1) { @@ -357,25 +402,25 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask( } #pragma unroll - for (int i0 = 0; i0 < nbatch_fa; i0 += WARP_SIZE) { + for (int i0 = 0; i0 < nbatch_fa; i0 += warp_size) { const int i = i0 + threadIdx.x; tile_mask[j_sram*(nbatch_fa + 8) + i] = i < i_sup ? mask_h[j_vram*stride_mask + i] : half(0.0f); } } - } else if constexpr (nbatch_fa < 2*WARP_SIZE) { - constexpr int cols_per_warp = 2*WARP_SIZE/nbatch_fa; + } else if constexpr (nbatch_fa < 2*warp_size) { + constexpr int cols_per_warp = 2*warp_size/nbatch_fa; constexpr int stride_j = nwarps * cols_per_warp; #pragma unroll for (int j1 = 0; j1 < ncols1; j1 += stride_j) { - const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (WARP_SIZE/cols_per_warp); + const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (warp_size/cols_per_warp); const int j_vram = fastmodulo(j0 + j_sram, ne01); if (j1 + stride_j > ncols1 && j_sram >= ncols1) { break; } - const int i = threadIdx.x % (WARP_SIZE/cols_per_warp); + const int i = threadIdx.x % (warp_size/cols_per_warp); ggml_cuda_memcpy_1(tile_mask + j_sram*(nbatch_fa + 8) + 2*i, mask_h + j_vram*stride_mask + 2*i); } @@ -390,7 +435,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask( } #pragma unroll - for (int i0 = 0; i0 < nbatch_fa; i0 += 2*WARP_SIZE) { + for (int i0 = 0; i0 < nbatch_fa; i0 += 2*warp_size) { const int i = i0 + 2*threadIdx.x; ggml_cuda_memcpy_1(tile_mask + j_sram*(nbatch_fa + 8) + i, mask_h + j_vram*stride_mask + i); @@ -428,7 +473,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( const int jt, const int kb0, const int k_VKQ_sup) { -#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) +#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE) + constexpr int warp_size = ggml_cuda_get_physical_warp_size(); constexpr int ncols = ncols1 * ncols2; constexpr int cols_per_warp = T_B_KQ::I; constexpr int cols_per_thread = get_cols_per_thread(); @@ -447,7 +493,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( const int k_VKQ_0 = kb0 * nbatch_fa; #if defined(TURING_MMA_AVAILABLE) T_C_KQ KQ_C[nbatch_fa/(np*(cols_per_warp == 8 ? T_C_KQ::I : T_C_KQ::J))]; -#elif defined(AMD_WMMA_AVAILABLE) +#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) T_C_KQ KQ_C[nbatch_fa/(np*T_C_KQ::J)]; #else // Volta T_C_KQ KQ_C[nbatch_fa/(np*T_C_KQ::J)]; @@ -500,13 +546,13 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[k_KQ_0/T_A_KQ::J]); } else { // Wide version of KQ_C is column-major -#if defined(AMD_WMMA_AVAILABLE) - // RDNA matrix C is column-major. +#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) + // AMD matrix C is column-major. mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[k_KQ_0/T_A_KQ::J]); #else // swap A and B for CUDA. mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], Q_B[k_KQ_0/T_A_KQ::J], K_A); -#endif // defined(AMD_WMMA_AVAILABLE) +#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) } } } @@ -526,13 +572,13 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[0]); } else { // Wide version of KQ_C is column-major -#if defined(AMD_WMMA_AVAILABLE) - // RDNA matrix C is column-major. +#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) + // AMD matrix C is column-major. mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[0]); #else // swap A and B for CUDA. mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], Q_B[0], K_A); -#endif // defined(AMD_WMMA_AVAILABLE) +#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) } } } @@ -585,12 +631,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( #pragma unroll for (int l = 0; l < T_C_KQ::ne; ++l) { if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::I + T_C_KQ::get_i(l) < k_VKQ_sup) { -#if defined(AMD_WMMA_AVAILABLE) +#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) constexpr int KQ_idx = 0; #else // Turing + Volta: const int KQ_idx = l % 2; -#endif // defined(AMD_WMMA_AVAILABLE) +#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) KQ_max_new[KQ_idx] = fmaxf(KQ_max_new[KQ_idx], KQ_C[k0/(np*T_C_KQ::I)].x[l] + FATTN_KQ_MAX_OFFSET); } } @@ -601,7 +647,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( for (int col = 0; col < cols_per_thread; ++col) { #pragma unroll for (int offset = 16; offset >= 4; offset >>= 1) { - KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, WARP_SIZE)); + KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, warp_size)); } } @@ -611,12 +657,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( #pragma unroll for (int l = 0; l < T_C_KQ::ne; ++l) { if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::I + T_C_KQ::get_i(l) < k_VKQ_sup) { -#if defined(AMD_WMMA_AVAILABLE) +#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) constexpr int KQ_idx = 0; #else // Turing + Volta: const int KQ_idx = l % 2; -#endif // defined(AMD_WMMA_AVAILABLE) +#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) KQ_C[k0/(np*T_C_KQ::I)].x[l] = expf(KQ_C[k0/(np*T_C_KQ::I)].x[l] - KQ_max_new[KQ_idx]); KQ_rowsum_add[KQ_idx] += KQ_C[k0/(np*T_C_KQ::I)].x[l]; } else { @@ -649,12 +695,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( #pragma unroll for (int l = 0; l < T_C_KQ::ne; ++l) { if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::J + T_C_KQ::get_j(l) < k_VKQ_sup) { -#if defined(AMD_WMMA_AVAILABLE) +#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) constexpr int KQ_idx = 0; #else // Turing + Volta: const int KQ_idx = (l/2) % 2; -#endif // defined(AMD_WMMA_AVAILABLE) +#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) KQ_max_new[KQ_idx] = fmaxf(KQ_max_new[KQ_idx], KQ_C[(k0/(np*T_C_KQ::J))].x[l] + FATTN_KQ_MAX_OFFSET); } } @@ -666,6 +712,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( // Values per KQ column are spread across 4 threads: constexpr int offset_first = 2; constexpr int offset_last = 1; +#elif defined(AMD_MFMA_AVAILABLE) + // MFMA: 4 threads per Q column (threadIdx.x % 16 == col, spaced by 16). + constexpr int offset_first = 32; + constexpr int offset_last = 16; #elif defined(AMD_WMMA_AVAILABLE) // Values per KQ column are spread across 2 threads: constexpr int offset_first = 16; @@ -677,7 +727,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( #endif // defined(TURING_MMA_AVAILABLE) #pragma unroll for (int offset = offset_first; offset >= offset_last; offset >>= 1) { - KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, WARP_SIZE)); + KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, warp_size)); } } @@ -687,12 +737,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( #pragma unroll for (int l = 0; l < T_C_KQ::ne; ++l) { if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::J + T_C_KQ::get_j(l) < k_VKQ_sup) { -#if defined(AMD_WMMA_AVAILABLE) +#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) constexpr int KQ_idx = 0; #else // Turing + Volta: const int KQ_idx = (l/2) % 2; -#endif // defined(AMD_WMMA_AVAILABLE) +#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) KQ_C[(k0/(np*T_C_KQ::J))].x[l] = expf(KQ_C[(k0/(np*T_C_KQ::J))].x[l] - KQ_max_new[KQ_idx]); KQ_rowsum_add[KQ_idx] += KQ_C[(k0/(np*T_C_KQ::J))].x[l]; } else { @@ -739,7 +789,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( } } } -#elif defined(AMD_WMMA_AVAILABLE) +#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) const half2 KQ_max_scale_h2 = make_half2( KQ_max_scale[0], KQ_max_scale[0]); #pragma unroll @@ -818,7 +868,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( } const half2 * tile_V_i = !V_is_K_view || i0_stop > 2*nbatch_K2 ? tile_V : tile_V + i0_start/2; -#if defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) +#if defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) constexpr int i0_stride = cols_per_warp == 8 ? T_C_VKQ::I : 2*T_C_VKQ::J; #pragma unroll for (int i_VKQ_0 = i0_start; i_VKQ_0 < i0_stop; i_VKQ_0 += i0_stride) { @@ -830,24 +880,38 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( T_A_VKQ A; // Transposed in SRAM but not in registers, gets transposed on load. #if defined(LDMATRIX_TRANS_AVAILABLE) load_ldmatrix_trans(A, tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2, stride_tile_V); +#elif defined(AMD_MFMA_AVAILABLE) + // MFMA A register layout: A_mat[i=lane%16][k=4*(lane/16)+reg]. + // Normal load gives A_mat[seq][dv] but we need A_mat[dv][seq] = V^T. + // Load with transposed addressing: 4 strided half loads. + { + const half2 * xs0 = tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2; + const half * xs0_h = (const half *) xs0; + const int stride_h = stride_tile_V * 2; // stride in half units + half * A_h = (half *) A.x; +#pragma unroll + for (int l = 0; l < 4; ++l) { + A_h[l] = xs0_h[(4*(threadIdx.x / 16) + l) * stride_h + threadIdx.x % 16]; + } + } #else // TODO: Try to transpose tile_V when loading gmem to smem. // Use mma to transpose T_A_VKQ for RDNA. T_A_VKQ A_trans; load_ldmatrix(A_trans, tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2, stride_tile_V); mma(A, A_trans, A_identity); -#endif // defined(TURING_MMA_AVAILABLE) +#endif // defined(LDMATRIX_TRANS_AVAILABLE) if constexpr (T_B_KQ::I == 8) { mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]); } else { // Wide version of VKQ_C is column-major. -#if defined(AMD_WMMA_AVAILABLE) - // RDNA matrix C is column-major. +#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) + // AMD matrix C is column-major. mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]); #else // swap A and B for CUDA. mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::J)], A); -#endif // defined(AMD_WMMA_AVAILABLE) +#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) } } } @@ -866,7 +930,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::I)], A); } } -#endif // defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) +#endif // defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) if constexpr (nstages <= 1) { __syncthreads(); // Only needed if tile_K == tile_V. @@ -879,7 +943,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter( tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0); NO_DEVICE_CODE; -#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) +#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE) } #if defined(TURING_MMA_AVAILABLE) @@ -899,7 +963,7 @@ template<> struct mma_tile_sizes<8> { using T_B_VKQ = tile< 8, 8, half2>; // column-major using T_C_VKQ = tile<16, 4, half2>; // row-major }; -#elif defined(AMD_WMMA_AVAILABLE) +#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) template struct mma_tile_sizes { using T_A_KQ = tile<16, 8, half2>; // row-major using T_B_KQ = tile<16, 8, half2>; // column-major @@ -944,9 +1008,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const int zt_gqa, const int kb0_start, const int kb0_stop) { -#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) +#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE) //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + constexpr int warp_size = ggml_cuda_get_physical_warp_size(); constexpr int ncols = ncols1 * ncols2; using T_A_KQ = typename mma_tile_sizes::T_A_KQ; using T_B_KQ = typename mma_tile_sizes::T_B_KQ; @@ -986,7 +1051,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( T_B_KQ Q_B[(Q_in_reg ? DKQ/(2*T_B_KQ::J) : 1)]; #if defined(TURING_MMA_AVAILABLE) T_C_VKQ VKQ_C[cols_per_warp == 8 ? DV/T_C_VKQ::I : DV/(2*T_C_VKQ::J)]; -#elif defined(AMD_WMMA_AVAILABLE) +#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) T_C_VKQ VKQ_C[ DV/(2*T_C_VKQ::J)]; #else // Volta T_C_VKQ VKQ_C[ DV/(2*T_C_VKQ::J)]; @@ -1004,10 +1069,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( // The loading is done with decreasing granularity for D for better memory bandwidth. const half2 scale_h2 = make_half2(scale, scale); #pragma unroll - for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) { - const int k0_start = stride_k == WARP_SIZE ? 0 : DKQ/2 - (DKQ/2) % (2*stride_k); + for (int stride_k : {warp_size, warp_size/2, warp_size/4, warp_size/8}) { + const int k0_start = stride_k == warp_size ? 0 : DKQ/2 - (DKQ/2) % (2*stride_k); const int k0_stop = DKQ/2 - (DKQ/2) % (1*stride_k); - const int stride_jc = WARP_SIZE / stride_k; + const int stride_jc = warp_size / stride_k; if (k0_start == k0_stop) { continue; @@ -1015,7 +1080,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( #pragma unroll for (int jc0 = 0; jc0 < ncols; jc0 += nwarps*stride_jc) { - const int jc = jc0 + threadIdx.y*stride_jc + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k); + const int jc = jc0 + threadIdx.y*stride_jc + (stride_k == warp_size ? 0 : threadIdx.x / stride_k); if (jc0 + nwarps*stride_jc > ncols && jc >= ncols) { break; @@ -1027,7 +1092,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( if ((ncols1 == 1 || jt*ncols1 + j < int(ne01.z)) && (ncols2 == 1 || zt_gqa*ncols2 + c < gqa_ratio)) { #pragma unroll for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) { - const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k); + const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k); const float2 tmp = Q_f2[(jt*ncols1 + j)*stride_Q1 + c*stride_Q2 + k]; tile_Q[jc*stride_tile_Q + k] = scale_h2 * make_half2(tmp.x, tmp.y); @@ -1035,7 +1100,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( } else { #pragma unroll for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) { - const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k); + const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k); tile_Q[jc*stride_tile_Q + k] = make_half2(0.0f, 0.0f); } @@ -1127,6 +1192,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( // The partial sums are spread across 8/4 threads. constexpr int offset_first = cols_per_warp == 8 ? 16 : 2; constexpr int offset_last = cols_per_warp == 8 ? 4 : 1; +#elif defined(AMD_MFMA_AVAILABLE) + // The partial sums are spread across 4 threads (wavefront64, 16 cols). + constexpr int offset_first = 32; + constexpr int offset_last = 16; #elif defined(AMD_WMMA_AVAILABLE) // The partial sums are spread across 2 threads. constexpr int offset_first = 16; @@ -1140,13 +1209,13 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( for (int col = 0; col < cols_per_thread; ++col) { #pragma unroll for (int offset = offset_first; offset >= offset_last; offset >>= 1) { - KQ_rowsum[col] += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum[col], offset, WARP_SIZE); + KQ_rowsum[col] += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum[col], offset, warp_size); } } } // If attention sinks are used, potentially re-scale if KQ_max is small. - // Also add the sink as a value to KQ_rowsum, this is done after synchonization of KQ_rowsum + // Also add the sink as a value to KQ_rowsum, this is done after synchronization of KQ_rowsum // so it's being done unconditionally for every thread. if (!is_fixup && (np == 1 || threadIdx.y % np == 0) && sinks_f) { float KQ_max_scale[cols_per_thread]; @@ -1189,7 +1258,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( } } } -#elif defined(AMD_WMMA_AVAILABLE) +#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[0]); #pragma unroll for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) { @@ -1249,7 +1318,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const int jc_cwm = threadIdx.y*cols_per_warp + T_C_VKQ::get_i(threadIdx.x % 4); const float2 KQ_cmr = make_float2(KQ_max[threadIdx.x % cols_per_thread], KQ_rowsum[threadIdx.x % cols_per_thread]); const bool thread_should_write = threadIdx.x % 4 < cols_per_thread; -#elif defined(AMD_WMMA_AVAILABLE) +#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) const int jc_cwm = threadIdx.y*cols_per_warp + T_C_VKQ::get_i(0); const float2 KQ_cmr = make_float2(KQ_max[0], KQ_rowsum[0]); const bool thread_should_write = threadIdx.x / 16 < cols_per_thread; @@ -1283,14 +1352,14 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( // Warps with threadIdx.y % np != 0 must NOT return early. // All threads must return simultaneously to avoid race conditions with work on the next tile. - constexpr int nmeta = np*cols_per_warp >= WARP_SIZE ? np*cols_per_warp/WARP_SIZE : 1; + constexpr int nmeta = np*cols_per_warp >= warp_size ? np*cols_per_warp/warp_size : 1; - const int jc_meta = threadIdx.y*cols_per_warp + (np*cols_per_warp < WARP_SIZE ? threadIdx.x % (np*cols_per_warp) : threadIdx.x); + const int jc_meta = threadIdx.y*cols_per_warp + (np*cols_per_warp < warp_size ? threadIdx.x % (np*cols_per_warp) : threadIdx.x); float2 * const meta_ptr = ((float2 *) tile_Q) + jc_meta*(tile_stride/2) + nbatch_combine/2; float2 meta[nmeta]; #pragma unroll for (int imeta = 0; imeta < nmeta; ++imeta) { - meta[imeta] = meta_ptr[imeta * WARP_SIZE * tile_stride/2]; + meta[imeta] = meta_ptr[imeta * warp_size * tile_stride/2]; } float KQ_cmn = meta[0].x; // KQ combine max new, max between all parallel warps. @@ -1300,8 +1369,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( } #pragma unroll for (int offset = np*cols_per_warp/2; offset >= cols_per_warp; offset >>= 1) { - if (offset < WARP_SIZE) { - KQ_cmn = fmaxf(KQ_cmn, __shfl_xor_sync(0xFFFFFFFF, KQ_cmn, offset, WARP_SIZE)); + if (offset < warp_size) { + KQ_cmn = fmaxf(KQ_cmn, __shfl_xor_sync(0xFFFFFFFF, KQ_cmn, offset, warp_size)); } } @@ -1318,8 +1387,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( } #pragma unroll for (int offset = np*cols_per_warp/2; offset >= cols_per_warp; offset >>= 1) { - if (offset < WARP_SIZE) { - KQ_crs += __shfl_xor_sync(0xFFFFFFFF, KQ_crs, offset, WARP_SIZE); + if (offset < warp_size) { + KQ_crs += __shfl_xor_sync(0xFFFFFFFF, KQ_crs, offset, warp_size); } } @@ -1328,19 +1397,19 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( // Write back combined meta data: #pragma unroll for (int imeta = 0; imeta < nmeta; ++imeta) { - if (np*cols_per_warp >= WARP_SIZE || threadIdx.x < np*cols_per_warp) { + if (np*cols_per_warp >= warp_size || threadIdx.x < np*cols_per_warp) { // Combined KQ max scale + rowsum. - meta_ptr[imeta * WARP_SIZE * tile_stride/2] = make_float2(KQ_cms[imeta], KQ_crs); + meta_ptr[imeta * warp_size * tile_stride/2] = make_float2(KQ_cms[imeta], KQ_crs); } } // Combined KQ max + rowsum. - static_assert(cols_per_warp <= WARP_SIZE); - if (needs_fixup && (cols_per_warp == WARP_SIZE || threadIdx.x < cols_per_warp)) { + static_assert(cols_per_warp <= warp_size); + if (needs_fixup && (cols_per_warp == warp_size || threadIdx.x < cols_per_warp)) { float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols; dstk_fixup_meta[(threadIdx.y/np)*cols_per_warp + threadIdx.x] = make_float2(KQ_cmn, KQ_crs); } - if (is_fixup && (cols_per_warp == WARP_SIZE || threadIdx.x < cols_per_warp)) { + if (is_fixup && (cols_per_warp == warp_size || threadIdx.x < cols_per_warp)) { float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols; dstk_fixup_meta[(threadIdx.y/np)*cols_per_warp + threadIdx.x] = make_float2(KQ_cmn, KQ_crs); } @@ -1388,10 +1457,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( float2 * dstk_fixup_data = dstk_fixup + gridDim.x*(2*ncols) + blockIdx.x*(ncols*(DV/2)); #pragma unroll - for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) { - const int k0_start = stride_k == WARP_SIZE ? 0 : nbatch_combine - nbatch_combine % (2*stride_k); + for (int stride_k : {warp_size, warp_size/2, warp_size/4, warp_size/8}) { + const int k0_start = stride_k == warp_size ? 0 : nbatch_combine - nbatch_combine % (2*stride_k); const int k0_stop = nbatch_combine - nbatch_combine % (1*stride_k); - const int stride_jc = WARP_SIZE / stride_k; + const int stride_jc = warp_size / stride_k; if (k0_start == k0_stop) { continue; @@ -1399,7 +1468,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( #pragma unroll for (int jc0_dst = 0; jc0_dst < ncols; jc0_dst += (nwarps/np)*stride_jc) { - const int jc_dst = jc0_dst + (threadIdx.y/np)*stride_jc + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k); + const int jc_dst = jc0_dst + (threadIdx.y/np)*stride_jc + (stride_k == warp_size ? 0 : threadIdx.x / stride_k); if (jc0_dst + (nwarps/np)*stride_jc > ncols && jc_dst >= ncols) { break; @@ -1417,7 +1486,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( const float * meta_j = (const float *) tile_Q + jc_tile_K*tile_stride + nbatch_combine; #pragma unroll for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) { - const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k); + const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k); float2 dstk_val = make_float2(0.0f, 0.0f); #pragma unroll @@ -1453,7 +1522,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile( stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start, kb0_stop); NO_DEVICE_CODE; -#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) +#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE) } template @@ -1480,7 +1549,7 @@ static __global__ void flash_attn_ext_f16( const int32_t nb21, const int32_t nb22, const int64_t nb23, const int32_t ne31, const int32_t ne32, const int32_t ne33, const int32_t nb31, const int32_t nb32, const int64_t nb33) { -#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))) +#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)) // Skip unused kernel variants for faster compilation: if (use_logit_softcap && !(DKQ == 128 || DKQ == 256)) { @@ -1508,10 +1577,18 @@ static __global__ void flash_attn_ext_f16( } #endif // defined(AMD_WMMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) + if (DKQ != 64 && DKQ != 80 && DKQ != 96 && DKQ != 112 && DKQ != 128) { + NO_DEVICE_CODE; + return; + } +#endif // defined(AMD_MFMA_AVAILABLE) + + constexpr int warp_size = ggml_cuda_get_physical_warp_size(); constexpr int ncols = ncols1 * ncols2; constexpr int nbatch_fa = ggml_cuda_fattn_mma_get_nbatch_fa(DKQ, DV, ncols); constexpr int nthreads = ggml_cuda_fattn_mma_get_nthreads(DKQ, DV, ncols); - constexpr int nwarps = nthreads / WARP_SIZE; + constexpr int nwarps = nthreads / warp_size; const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. @@ -1624,7 +1701,7 @@ static __global__ void flash_attn_ext_f16( ne31, ne32, ne33, nb31, nb32, nb33); NO_DEVICE_CODE; -#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))) +#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)) } template @@ -1644,7 +1721,8 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml const int nstages = ggml_cuda_fattn_mma_get_nstages (DKQ, DV, ncols1, ncols2, cc); const int cols_per_warp = std::min(ncols, get_cols_per_warp(cc)); - const int nwarps = nthreads / WARP_SIZE; + const int warp_size_host = ggml_cuda_info().devices[ctx.device].warp_size; + const int nwarps = nthreads / warp_size_host; constexpr bool V_is_K_view = DKQ == 576; // Guaranteed by the kernel selection logic in fattn.cu @@ -1694,7 +1772,7 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml } launch_fattn - (ctx, dst, fattn_kernel, nwarps, nbytes_shared_total, nbatch_fa, true, true, true); + (ctx, dst, fattn_kernel, nwarps, nbytes_shared_total, nbatch_fa, true, true, true, warp_size_host); } diff --git a/ggml/src/ggml-cuda/fattn-vec.cuh b/ggml/src/ggml-cuda/fattn-vec.cuh index 3f4a78cc6e..7cbe32633e 100644 --- a/ggml/src/ggml-cuda/fattn-vec.cuh +++ b/ggml/src/ggml-cuda/fattn-vec.cuh @@ -10,7 +10,7 @@ static constexpr __device__ int ggml_cuda_fattn_vec_get_nthreads_device() { return 128; } -// Currenlty llvm with the amdgcn target does not support unrolling loops +// Currently llvm with the amdgcn target does not support unrolling loops // that contain a break that can not be resolved at compile time. #ifdef __clang__ #pragma clang diagnostic push diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh index cd3bfd4051..aaf711a618 100644 --- a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh @@ -18,7 +18,7 @@ #if defined(RDNA4) && ROCWMMA_VERSION_MAJOR > 1 #define GGML_USE_WMMA_FATTN #elif defined(RDNA4) -#warning "rocwmma fattn is not suported on RDNA4 on rocwmma < v2.0.0, expect degraded performance" +#warning "rocwmma fattn is not supported on RDNA4 on rocwmma < v2.0.0, expect degraded performance" #endif // defined(RDNA4) && ROCWMMA_VERSION_MAJOR > 1 #endif // defined(GGML_HIP_ROCWMMA_FATTN) diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 721edd9994..85c177f496 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -440,6 +440,18 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const return BEST_FATTN_KERNEL_MMA_F16; } + // Use MFMA flash attention for CDNA (MI100+): + if (amd_mfma_available(cc) && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 256 && Q->ne[0] != 576) { + const int64_t eff_nq = Q->ne[1] * (gqa_opt_applies ? gqa_ratio : 1); + // MMA vs tile crossover benchmarked on MI300X @ d32768: + // hsk=64 (gqa=4): MMA wins at eff >= 128 (+11%) + // hsk=128 (gqa=4): MMA wins at eff >= 128 (+4%) + if (eff_nq >= (GGML_CUDA_CC_IS_CDNA1(cc) && Q->ne[0] == 64 ? 64 : 128)) { + return BEST_FATTN_KERNEL_MMA_F16; + } + // Fall through to tile kernel for small effective batch sizes. + } + // If there are no tensor cores available, use the generic tile kernel: if (can_use_vector_kernel) { if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) { diff --git a/ggml/src/ggml-cuda/gated_delta_net.cu b/ggml/src/ggml-cuda/gated_delta_net.cu new file mode 100644 index 0000000000..6b44bec731 --- /dev/null +++ b/ggml/src/ggml-cuda/gated_delta_net.cu @@ -0,0 +1,273 @@ +#include "gated_delta_net.cuh" + +template +__global__ void __launch_bounds__((ggml_cuda_get_physical_warp_size() < S_v ? ggml_cuda_get_physical_warp_size() : S_v) * 4, 2) +gated_delta_net_cuda(const float * q, + const float * k, + const float * v, + const float * g, + const float * beta, + const float * curr_state, + float * dst, + int64_t H, + int64_t n_tokens, + int64_t n_seqs, + int64_t sq1, + int64_t sq2, + int64_t sq3, + int64_t sv1, + int64_t sv2, + int64_t sv3, + int64_t sb1, + int64_t sb2, + int64_t sb3, + const uint3 neqk1_magic, + const uint3 rq3_magic, + float scale) { + const uint32_t h_idx = blockIdx.x; + const uint32_t sequence = blockIdx.y; + // each warp owns one column, using warp-level primitives to reduce across rows + const int lane = threadIdx.x; + const int col = blockIdx.z * blockDim.y + threadIdx.y; + + const uint32_t iq1 = fastmodulo(h_idx, neqk1_magic); + const uint32_t iq3 = fastdiv(sequence, rq3_magic); + + const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs; + float * attn_data = dst; + float * state = dst + attn_score_elems; + + const int64_t state_offset = (sequence * H + h_idx) * S_v * S_v; + state += state_offset; + curr_state += state_offset + col * S_v; + attn_data += (sequence * n_tokens * H + h_idx) * S_v; + + constexpr int warp_size = ggml_cuda_get_physical_warp_size() < S_v ? ggml_cuda_get_physical_warp_size() : S_v; + static_assert(S_v % warp_size == 0, "S_v must be a multiple of warp_size"); + constexpr int rows_per_lane = (S_v + warp_size - 1) / warp_size; + float s_shard[rows_per_lane]; + // state is stored transposed: M[col][i] = S[i][col], row col is contiguous + +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + s_shard[r] = curr_state[i]; + } + + for (int t = 0; t < n_tokens; t++) { + const float * q_t = q + iq3 * sq3 + t * sq2 + iq1 * sq1; + const float * k_t = k + iq3 * sq3 + t * sq2 + iq1 * sq1; + const float * v_t = v + sequence * sv3 + t * sv2 + h_idx * sv1; + + const int64_t gb_offset = sequence * sb3 + t * sb2 + h_idx * sb1; + const float * beta_t = beta + gb_offset; + const float * g_t = g + gb_offset * (KDA ? S_v : 1); + + const float beta_val = *beta_t; + + // Cache k and q in registers + float k_reg[rows_per_lane]; + float q_reg[rows_per_lane]; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + k_reg[r] = k_t[i]; + q_reg[r] = q_t[i]; + } + + if constexpr (!KDA) { + const float g_val = expf(*g_t); + + // kv[col] = (S^T @ k)[col] = sum_i S[i][col] * k[i] + float kv_shard = 0.0f; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + kv_shard += s_shard[r] * k_reg[r]; + } + float kv_col = warp_reduce_sum(kv_shard); + + // delta[col] = (v[col] - g * kv[col]) * beta + float delta_col = (v_t[col] - g_val * kv_col) * beta_val; + + // fused: S[i][col] = g * S[i][col] + k[i] * delta[col] + // attn[col] = (S^T @ q)[col] = sum_i S[i][col] * q[i] + float attn_partial = 0.0f; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + s_shard[r] = g_val * s_shard[r] + k_reg[r] * delta_col; + attn_partial += s_shard[r] * q_reg[r]; + } + + float attn_col = warp_reduce_sum(attn_partial); + + if (lane == 0) { + attn_data[col] = attn_col * scale; + } + } else { + // kv[col] = sum_i g[i] * S[i][col] * k[i] + float kv_shard = 0.0f; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + kv_shard += expf(g_t[i]) * s_shard[r] * k_reg[r]; + } + + float kv_col = warp_reduce_sum(kv_shard); + + // delta[col] = (v[col] - kv[col]) * beta + float delta_col = (v_t[col] - kv_col) * beta_val; + + // fused: S[i][col] = g[i] * S[i][col] + k[i] * delta[col] + // attn[col] = (S^T @ q)[col] = sum_i S[i][col] * q[i] + float attn_partial = 0.0f; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + s_shard[r] = expf(g_t[i]) * s_shard[r] + k_reg[r] * delta_col; + attn_partial += s_shard[r] * q_reg[r]; + } + + float attn_col = warp_reduce_sum(attn_partial); + + if (lane == 0) { + attn_data[col] = attn_col * scale; + } + } + + attn_data += S_v * H; + } + + // Write state back to global memory (transposed layout) +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + state[col * S_v + i] = s_shard[r]; + } +} + +template +static void launch_gated_delta_net( + const float * q_d, const float * k_d, const float * v_d, + const float * g_d, const float * b_d, const float * s_d, + float * dst_d, + int64_t S_v, int64_t H, int64_t n_tokens, int64_t n_seqs, + int64_t sq1, int64_t sq2, int64_t sq3, + int64_t sv1, int64_t sv2, int64_t sv3, + int64_t sb1, int64_t sb2, int64_t sb3, + int64_t neqk1, int64_t rq3, + float scale, cudaStream_t stream) { + //TODO: Add chunked kernel for even faster pre-fill + const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size; + const int num_warps = 4; + dim3 grid_dims(H, n_seqs, (S_v + num_warps - 1) / num_warps); + dim3 block_dims(warp_size <= S_v ? warp_size : S_v, num_warps, 1); + + const uint3 neqk1_magic = init_fastdiv_values(neqk1); + const uint3 rq3_magic = init_fastdiv_values(rq3); + + int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + + switch (S_v) { + case 16: + gated_delta_net_cuda<16, KDA><<>>( + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, + n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, + sb1, sb2, sb3, neqk1_magic, rq3_magic, scale); + break; + case 32: + gated_delta_net_cuda<32, KDA><<>>( + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, + n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, + sb1, sb2, sb3, neqk1_magic, rq3_magic, scale); + break; + case 64: { + gated_delta_net_cuda<64, KDA><<>>( + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, + n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, + sb1, sb2, sb3, neqk1_magic, rq3_magic, scale); + break; + } + case 128: { + gated_delta_net_cuda<128, KDA><<>>( + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, + n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, + sb1, sb2, sb3, neqk1_magic, rq3_magic, scale); + break; + } + default: + GGML_ABORT("fatal error"); + break; + } +} + +void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * src_q = dst->src[0]; + ggml_tensor * src_k = dst->src[1]; + ggml_tensor * src_v = dst->src[2]; + ggml_tensor * src_g = dst->src[3]; + ggml_tensor * src_beta = dst->src[4]; + ggml_tensor * src_state = dst->src[5]; + + GGML_TENSOR_LOCALS(int64_t, neq, src_q, ne); + GGML_TENSOR_LOCALS(size_t , nbq, src_q, nb); + GGML_TENSOR_LOCALS(int64_t, nek, src_k, ne); + GGML_TENSOR_LOCALS(size_t , nbk, src_k, nb); + GGML_TENSOR_LOCALS(int64_t, nev, src_v, ne); + GGML_TENSOR_LOCALS(size_t, nbv, src_v, nb); + GGML_TENSOR_LOCALS(size_t, nbb, src_beta, nb); + + const int64_t S_v = nev0; + const int64_t H = nev1; + const int64_t n_tokens = nev2; + const int64_t n_seqs = nev3; + + const bool kda = (src_g->ne[0] == S_v); + + GGML_ASSERT(neq1 == nek1); + const int64_t neqk1 = neq1; + + const int64_t rq3 = nev3 / neq3; + + const float * q_d = (const float *) src_q->data; + const float * k_d = (const float *) src_k->data; + const float * v_d = (const float *) src_v->data; + const float * g_d = (const float *) src_g->data; + const float * b_d = (const float *) src_beta->data; + + const float * s_d = (const float *) src_state->data; + float * dst_d = (float *) dst->data; + + GGML_ASSERT(ggml_is_contiguous_rows(src_q)); + GGML_ASSERT(ggml_is_contiguous_rows(src_k)); + GGML_ASSERT(ggml_is_contiguous_rows(src_v)); + GGML_ASSERT(ggml_are_same_stride(src_q, src_k)); + GGML_ASSERT(src_g->ne[0] == 1 || kda); + GGML_ASSERT(ggml_is_contiguous(src_g)); + GGML_ASSERT(ggml_is_contiguous(src_beta)); + GGML_ASSERT(ggml_is_contiguous(src_state)); + + // strides in floats (beta strides used for both g and beta offset computation) + const int64_t sq1 = nbq1 / sizeof(float); + const int64_t sq2 = nbq2 / sizeof(float); + const int64_t sq3 = nbq3 / sizeof(float); + const int64_t sv1 = nbv1 / sizeof(float); + const int64_t sv2 = nbv2 / sizeof(float); + const int64_t sv3 = nbv3 / sizeof(float); + const int64_t sb1 = nbb1 / sizeof(float); + const int64_t sb2 = nbb2 / sizeof(float); + const int64_t sb3 = nbb3 / sizeof(float); + + const float scale = 1.0f / sqrtf((float) S_v); + + cudaStream_t stream = ctx.stream(); + + if (kda) { + launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, + S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, + sb1, sb2, sb3, neqk1, rq3, scale, stream); + } else { + launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, + S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, + sb1, sb2, sb3, neqk1, rq3, scale, stream); + } +} diff --git a/ggml/src/ggml-cuda/gated_delta_net.cuh b/ggml/src/ggml-cuda/gated_delta_net.cuh new file mode 100644 index 0000000000..7375e81c0c --- /dev/null +++ b/ggml/src/ggml-cuda/gated_delta_net.cuh @@ -0,0 +1,4 @@ +#include "common.cuh" +#include "ggml.h" + +void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 7e6d330354..5a0be4a472 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -53,6 +53,7 @@ #include "ggml-cuda/upscale.cuh" #include "ggml-cuda/wkv.cuh" #include "ggml-cuda/gla.cuh" +#include "ggml-cuda/gated_delta_net.cuh" #include "ggml-cuda/set.cuh" #include "ggml-cuda/set-rows.cuh" #include "ggml-cuda/pad_reflect_1d.cuh" @@ -123,7 +124,10 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) err = cudaMallocManaged(ptr, size); #if defined(GGML_USE_HIP) if (err == hipSuccess) { - CUDA_CHECK(cudaMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device)); + // hipMemAdviseSetCoarseGrain is an optional performance hint; + // ignore errors (e.g. hipErrorInvalidValue on some APU/iGPU configs). + cudaMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device); + (void)hipGetLastError(); // clear any error } // fall back to cudaMalloc if not supported (e.g. on Windows) @@ -204,7 +208,14 @@ static ggml_cuda_device_info ggml_cuda_init() { GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES); int64_t total_vram = 0; - GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); + for (int id = 0; id < info.device_count; ++id) { + cudaDeviceProp prop; + CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); + total_vram += prop.totalGlobalMem; + } + GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices (Total VRAM: %zu MiB):\n", + __func__, info.device_count, (size_t)(total_vram / (1024 * 1024))); + total_vram = 0; std::vector> turing_devices_without_mma; for (int id = 0; id < info.device_count; ++id) { @@ -242,6 +253,7 @@ static ggml_cuda_device_info ggml_cuda_init() { #else info.devices[id].supports_cooperative_launch = false; #endif // !(GGML_USE_MUSA) + #if defined(GGML_USE_HIP) info.devices[id].smpbo = prop.sharedMemPerBlock; @@ -256,22 +268,25 @@ static ggml_cuda_device_info ggml_cuda_init() { info.devices[id].cc += prop.minor * 0x10; } } - GGML_LOG_INFO(" Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d\n", + GGML_LOG_INFO(" Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d, VRAM: %zu MiB\n", id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff, - device_vmm ? "yes" : "no", prop.warpSize); + device_vmm ? "yes" : "no", prop.warpSize, + (size_t)(prop.totalGlobalMem / (1024 * 1024))); #elif defined(GGML_USE_MUSA) // FIXME: Ensure compatibility with varying warp sizes across different MUSA archs. info.devices[id].warp_size = 32; info.devices[id].smpbo = prop.sharedMemPerBlockOptin; info.devices[id].cc = GGML_CUDA_CC_OFFSET_MTHREADS + prop.major * 0x100; info.devices[id].cc += prop.minor * 0x10; - GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", - id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); + GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB\n", + id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no", + (size_t)(prop.totalGlobalMem / (1024 * 1024))); #else info.devices[id].smpbo = prop.sharedMemPerBlockOptin; info.devices[id].cc = 100*prop.major + 10*prop.minor; - GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", - id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); + GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB\n", + id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no", + (size_t)(prop.totalGlobalMem / (1024 * 1024))); std::string device_name(prop.name); if (device_name == "NVIDIA GeForce MX450") { turing_devices_without_mma.push_back({ id, device_name }); @@ -286,6 +301,7 @@ static ggml_cuda_device_info ggml_cuda_init() { // TODO: Check for future drivers the default scheduling strategy and // remove this call again when cudaDeviceScheduleSpin is default. if (prop.major == 12 && prop.minor == 1) { + CUDA_CHECK(cudaSetDevice(id)); CUDA_CHECK(cudaSetDeviceFlags(cudaDeviceScheduleSpin)); } @@ -1225,6 +1241,34 @@ static cudaError_t ggml_cuda_cpy_tensor_2d( } } +struct cublas_force_compute_type { + bool fp32 = false; + bool fp16 = false; +}; + +static const cublas_force_compute_type & ggml_cuda_cublas_get_force_compute_type() { + static const cublas_force_compute_type compute_type = [] { + cublas_force_compute_type result; + + const bool ggml_cuda_force_cublas_compute_32f_env = getenv("GGML_CUDA_FORCE_CUBLAS_COMPUTE_32F") != nullptr; + const bool ggml_cuda_force_cublas_compute_16f_env = getenv("GGML_CUDA_FORCE_CUBLAS_COMPUTE_16F") != nullptr; + + GGML_ASSERT(ggml_cuda_force_cublas_compute_16f_env == false || ggml_cuda_force_cublas_compute_32f_env == false); + + if (ggml_cuda_force_cublas_compute_32f_env) { + GGML_LOG_INFO("Detected GGML_CUDA_FORCE_CUBLAS_COMPUTE_32F\n"); + result.fp32 = true; + } else if (ggml_cuda_force_cublas_compute_16f_env) { + GGML_LOG_INFO("Detected GGML_CUDA_FORCE_CUBLAS_COMPUTE_16F\n"); + result.fp16 = true; + } + + return result; + }(); + + return compute_type; +} + static void ggml_cuda_op_mul_mat_cublas( ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, @@ -1307,7 +1351,13 @@ static void ggml_cuda_op_mul_mat_cublas( CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream)); - if (GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) { + const auto & force_compute_type = ggml_cuda_cublas_get_force_compute_type(); + + if (!force_compute_type.fp16 && (GGML_CUDA_CC_IS_CDNA(cc) + || GGML_CUDA_CC_IS_RDNA4(cc) + || cc == GGML_CUDA_CC_VOLTA + || force_compute_type.fp32)) + { const float alpha = 1.0f; const float beta = 0.0f; CUBLAS_CHECK( @@ -1906,10 +1956,23 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct cudaDataType_t cu_data_type_b = traits::data_type; const void * alpha = traits::get_alpha(); const void * beta = traits::get_beta(); - const float alpha_f32 = 1.0f; - const float beta_f32 = 0.0f; - if (dst->op_params[0] == GGML_PREC_DEFAULT) { + const auto & force_compute_type = ggml_cuda_cublas_get_force_compute_type(); + + int id = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[id].cc; + static constexpr bool is_src0_type_f16 = src0_type == GGML_TYPE_F16; + + // bf16 and fp32 are already being computed in fp32 (ensure it using static_assert), + // so checking necessity of forced fp32 only for fp16 src0_type + static_assert(is_src0_type_f16 || traits::compute_type == CUBLAS_COMPUTE_32F); + + const bool need_compute_32f = is_src0_type_f16 && !force_compute_type.fp16 && (GGML_CUDA_CC_IS_CDNA(cc) + || GGML_CUDA_CC_IS_RDNA4(cc) + || cc == GGML_CUDA_CC_VOLTA + || force_compute_type.fp32); + + if (dst->op_params[0] == GGML_PREC_DEFAULT && !need_compute_32f) { if constexpr (src0_type == GGML_TYPE_F32) { dst_t = (char *) dst_ddf; // Direct F32 output } else { @@ -1919,18 +1982,10 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct } } else { dst_t = (char *) dst_ddf; - cu_compute_type = CUBLAS_COMPUTE_32F; - cu_data_type = CUDA_R_32F; - alpha = &alpha_f32; - beta = &beta_f32; - } - - int id = ggml_cuda_get_device(); - const int cc = ggml_cuda_info().devices[id].cc; - if (GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) { - cu_compute_type = CUBLAS_COMPUTE_32F; - alpha = &alpha_f32; - beta = &beta_f32; + cu_compute_type = batched_mul_mat_traits::compute_type; + cu_data_type = batched_mul_mat_traits::data_type; + alpha = batched_mul_mat_traits::get_alpha(); + beta = batched_mul_mat_traits::get_beta(); } GGML_ASSERT(ne12 % ne02 == 0); @@ -2733,6 +2788,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_GATED_LINEAR_ATTN: ggml_cuda_op_gated_linear_attn(ctx, dst); break; + case GGML_OP_GATED_DELTA_NET: + ggml_cuda_op_gated_delta_net(ctx, dst); + break; case GGML_OP_RWKV_WKV7: ggml_cuda_op_rwkv_wkv7(ctx, dst); break; @@ -3330,7 +3388,7 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, return false; } - //rms_norm kernel assumes contigous rows + //rms_norm kernel assumes contiguous rows if (!ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) { return false; } @@ -3342,6 +3400,46 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, return true; } + if (ops.size() == 2 && ops.begin()[0] == GGML_OP_SSM_CONV && ops.begin()[1] == GGML_OP_UNARY + && unary_ops.size() == 1 && unary_ops.begin()[0] == GGML_UNARY_OP_SILU) { + const ggml_tensor * ssm_conv = cgraph->nodes[node_idx]; + const ggml_tensor * silu = cgraph->nodes[node_idx+1]; + + if (ssm_conv->type != GGML_TYPE_F32 || silu->type != GGML_TYPE_F32) { + return false; + } + + return true; + } + + if (ops.size() == 2 && ops.begin()[0] == GGML_OP_UNARY && ops.begin()[1] == GGML_OP_MUL + && unary_ops.size() == 1 && (unary_ops.begin()[0] == GGML_UNARY_OP_SILU || unary_ops.begin()[0] == GGML_UNARY_OP_SIGMOID || unary_ops.begin()[0] == GGML_UNARY_OP_SOFTPLUS)) { + const ggml_tensor * unary = cgraph->nodes[node_idx]; + const ggml_tensor * mul = cgraph->nodes[node_idx+1]; + + if (ggml_get_unary_op(unary) != unary_ops.begin()[0]) { + return false; + } + + if (unary->type != GGML_TYPE_F32 && unary->type != GGML_TYPE_F16) { + return false; + } + + if (unary->type != mul->type) { + return false; + } + + const ggml_tensor * other = (mul->src[0] == unary) ? mul->src[1] : mul->src[0]; + if (other->type != unary->type) { + return false; + } + if (!ggml_is_contiguous_1(other) || !ggml_is_contiguous_1(unary->src[0]) || !ggml_are_same_shape(other, unary)) { + return false; + } + + return true; + } + if (ops.size() == 3 && ops.begin()[0] == GGML_OP_SCALE && ops.begin()[1] == GGML_OP_UNARY && ops.begin()[2] == GGML_OP_SCALE && unary_ops.size() == 1 && unary_ops.begin()[0] == GGML_UNARY_OP_TANH) { const ggml_tensor *scale = cgraph->nodes[node_idx]; @@ -3366,6 +3464,69 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, return false; } +// returns whether the write (out) nodes overwrite the read nodes in operation +static bool ggml_cuda_check_fusion_memory_ranges(ggml_cgraph * cgraph, + int node_idx, + int node_count, + int * out_nodes, + int out_count) { + auto nodes_overlap = [&](const ggml_tensor * a, const ggml_tensor * b) { + const int64_t a_start = (int64_t) a->data; + const int64_t a_end = a_start + ggml_nbytes(a); + + const int64_t b_start = (int64_t) b->data; + const int64_t b_end = b_start + ggml_nbytes(b); + + if ((b_start <= a_start && a_start < b_end) || (a_start <= b_start && b_start < a_end)) { + return true; + } + + return false; + }; + + bool is_ok = true; + // for nrows=1, all fusion operations correctly read the src before writing dst or do it elementwise, so we should be ok + if (ggml_nrows(cgraph->nodes[node_idx]) == 1) { + return true; + } + + for (int i = 0; i < out_count; ++i) { + const ggml_tensor * dst = cgraph->nodes[out_nodes[i]]; + + for (int j = node_idx; j < node_idx + node_count; ++j) { + // Loop over all srcs of all nodes in the fusion. If the src overlaps + // the destination and the src is not an intermediate node that's being + // elided, then disable fusion. + + for (int src_idx = 0; src_idx < GGML_MAX_SRC; ++src_idx) { + const ggml_tensor * src = cgraph->nodes[j]->src[src_idx]; + + if (!src || src->op == GGML_OP_NONE) { + continue; + } + + if (nodes_overlap(dst, src)) { + bool found = false; + + for (int k = node_idx; k < j; ++k) { + if (cgraph->nodes[k] == src) { + found = true; + break; + } + } + + if (!found) { + is_ok = false; + break; + } + } + } + } + } + + return is_ok; +} + static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, const bool use_cuda_graph, const bool cuda_graph_update_required, const void * graph_key) { bool graph_evaluated_or_captured = false; @@ -3562,7 +3723,8 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud out_nodes[1] = i + ops.size() - 1; if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) && - ggml_cuda_should_use_topk_moe(node, logits, weights, ids)) { + ggml_cuda_should_use_topk_moe(node, logits, weights, ids) && + ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2)) { ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args); i += ops.size() - 1; continue; @@ -3577,7 +3739,8 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud int out_nodes[2] = { i + 1, i + 5 }; if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) && - ggml_cuda_should_use_topk_moe(softmax, logits, weights, ids)) { + ggml_cuda_should_use_topk_moe(softmax, logits, weights, ids) && + ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2)) { ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args); i += ops.size() - 1; continue; @@ -3830,6 +3993,20 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud continue; } + if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SSM_CONV, GGML_OP_UNARY }, { GGML_UNARY_OP_SILU })) { + ggml_cuda_op_ssm_conv(*cuda_ctx, node, cgraph->nodes[i+1]); + i++; + continue; + } + + if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SILU }) || + ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SIGMOID }) || + ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SOFTPLUS })) { + ggml_cuda_op_unary_mul(*cuda_ctx, node, cgraph->nodes[i+1]); + i++; + continue; + } + if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SCALE, GGML_OP_UNARY, GGML_OP_SCALE }, { GGML_UNARY_OP_TANH })) { i += 2; ggml_cuda_op_softcap(*cuda_ctx, cgraph->nodes[i], node); @@ -4849,6 +5026,13 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_GATED_LINEAR_ATTN: case GGML_OP_RWKV_WKV7: return true; + case GGML_OP_GATED_DELTA_NET: + //TODO: enable once MUSA compiler is solved https://github.com/ggml-org/llama.cpp/pull/19504#issuecomment-4018634327 +#ifdef GGML_USE_MUSA + return false; +#else + return true; +#endif // GGML_USE_MUSA case GGML_OP_FLASH_ATTN_EXT: return ggml_cuda_flash_attn_ext_supported(dev_ctx->device, op); case GGML_OP_CROSS_ENTROPY_LOSS: diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh index dd45d6c78f..5d1dadd3e4 100644 --- a/ggml/src/ggml-cuda/mma.cuh +++ b/ggml/src/ggml-cuda/mma.cuh @@ -668,7 +668,7 @@ namespace ggml_cuda_mma { return ret; } -#elif defined(AMD_WMMA_AVAILABLE) +#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) template static __device__ __forceinline__ tile get_half2(const tile & tile_float) { tile ret; @@ -964,6 +964,34 @@ namespace ggml_cuda_mma { GGML_UNUSED_VARS(D, A, B); NO_DEVICE_CODE; #endif // defined(RDNA4) +#elif defined(AMD_MFMA_AVAILABLE) + // MFMA: FP16 input, FP32 accumulate, convert back to half2. + using halfx4_t = __attribute__((ext_vector_type(4))) _Float16; + using floatx4_t = __attribute__((ext_vector_type(4))) float; + + // Convert existing half2 accumulator to float for MFMA: + floatx4_t acc_f32; + { + const halfx4_t acc_h = reinterpret_cast(D.x[0]); +#pragma unroll + for (int i = 0; i < 4; ++i) { + acc_f32[i] = (float)acc_h[i]; + } + } + + const halfx4_t& a_frag = reinterpret_cast(A.x[0]); + const halfx4_t& b_frag = reinterpret_cast(B.x[0]); + acc_f32 = __builtin_amdgcn_mfma_f32_16x16x16f16(a_frag, b_frag, acc_f32, 0, 0, 0); + + // Convert back to half2: + { + halfx4_t result_h; +#pragma unroll + for (int i = 0; i < 4; ++i) { + result_h[i] = (_Float16)acc_f32[i]; + } + reinterpret_cast(D.x[0]) = result_h; + } #else GGML_UNUSED_VARS(D, A, B); NO_DEVICE_CODE; diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index ce25ccf427..632246e43f 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -60,11 +60,17 @@ static constexpr __device__ int get_vdr_mmvq(ggml_type type) { enum mmvq_parameter_table_id { MMVQ_PARAMETERS_GENERIC = 0, MMVQ_PARAMETERS_GCN, - MMVQ_PARAMETERS_RDNA2 + MMVQ_PARAMETERS_RDNA2, + MMVQ_PARAMETERS_RDNA3_0, + MMVQ_PARAMETERS_RDNA4 }; static constexpr __device__ mmvq_parameter_table_id get_device_table_id() { -#if defined(RDNA2) || defined(RDNA3) || defined(RDNA4) +#if defined(RDNA4) + return MMVQ_PARAMETERS_RDNA4; +#elif defined(RDNA3_0) + return MMVQ_PARAMETERS_RDNA3_0; +#elif defined(RDNA2) || defined(RDNA3_5) return MMVQ_PARAMETERS_RDNA2; #elif defined(GCN) || defined(CDNA) return MMVQ_PARAMETERS_GCN; @@ -74,7 +80,13 @@ static constexpr __device__ mmvq_parameter_table_id get_device_table_id() { } static __host__ mmvq_parameter_table_id get_device_table_id(int cc) { - if (GGML_CUDA_CC_IS_RDNA2(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) { + if (GGML_CUDA_CC_IS_RDNA4(cc)) { + return MMVQ_PARAMETERS_RDNA4; + } + if (GGML_CUDA_CC_IS_RDNA3_0(cc)) { + return MMVQ_PARAMETERS_RDNA3_0; + } + if (GGML_CUDA_CC_IS_RDNA2(cc) || GGML_CUDA_CC_IS_RDNA3_5(cc)) { return MMVQ_PARAMETERS_RDNA2; } if (GGML_CUDA_CC_IS_GCN(cc) || GGML_CUDA_CC_IS_CDNA(cc)) { @@ -83,7 +95,7 @@ static __host__ mmvq_parameter_table_id get_device_table_id(int cc) { return MMVQ_PARAMETERS_GENERIC; } -static constexpr __host__ __device__ int calc_nwarps(int ncols_dst, mmvq_parameter_table_id table_id) { +static constexpr __host__ __device__ int calc_nwarps(ggml_type type, int ncols_dst, mmvq_parameter_table_id table_id) { if (table_id == MMVQ_PARAMETERS_GENERIC) { switch (ncols_dst) { case 1: @@ -114,6 +126,50 @@ static constexpr __host__ __device__ int calc_nwarps(int ncols_dst, mmvq_paramet return 1; } } + if (table_id == MMVQ_PARAMETERS_RDNA4) { + // nwarps=8 benefits types with simple vec_dot on RDNA4 (ncols_dst=1). + // Types with complex vec_dot (Q3_K, IQ2_*, IQ3_*) regress due to register + // pressure and lookup table contention at higher thread counts. + if (ncols_dst == 1) { + switch (type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + return 8; + default: + return 1; + } + } + return 1; + } + if (table_id == MMVQ_PARAMETERS_RDNA3_0) { + // RDNA3 (W7900): stricter whitelist than RDNA4. + // Q2_K / Q5_K / IQ4_XS regress in full quant sweeps. + if (ncols_dst == 1) { + switch (type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ4_NL: + return 8; + default: + return 1; + } + } + return 1; + } return 1; } @@ -138,7 +194,7 @@ static constexpr __host__ __device__ int calc_rows_per_block(int ncols_dst, int } template -__launch_bounds__(calc_nwarps(ncols_dst, get_device_table_id())*ggml_cuda_get_physical_warp_size(), 1) +__launch_bounds__(calc_nwarps(type, ncols_dst, get_device_table_id())*ggml_cuda_get_physical_warp_size(), 1) static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, const int32_t * __restrict__ ids, const ggml_cuda_mm_fusion_args_device fusion, float * __restrict__ dst, const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t stride_row_x, const uint32_t stride_col_y, @@ -151,7 +207,7 @@ static __global__ void mul_mat_vec_q( constexpr int qi = ggml_cuda_type_traits::qi; constexpr int vdr = get_vdr_mmvq(type); constexpr mmvq_parameter_table_id table_id = get_device_table_id(); - constexpr int nwarps = calc_nwarps(ncols_dst, table_id); + constexpr int nwarps = calc_nwarps(type, ncols_dst, table_id); constexpr int rows_per_cuda_block = calc_rows_per_block(ncols_dst, table_id); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); @@ -355,12 +411,13 @@ static __global__ void mul_mat_vec_q( } } +template static std::pair calc_launch_params( const int ncols_dst, const int nrows_x, const int nchannels_dst, const int nsamples_or_ntokens, const int warp_size, const mmvq_parameter_table_id table_id) { const int64_t nblocks = (nrows_x + calc_rows_per_block(ncols_dst, table_id) - 1) / calc_rows_per_block(ncols_dst, table_id); const dim3 block_nums(nblocks, nchannels_dst, nsamples_or_ntokens); - const dim3 block_dims(warp_size, calc_nwarps(ncols_dst, table_id), 1); + const dim3 block_dims(warp_size, calc_nwarps(type, ncols_dst, table_id), 1); return {block_nums, block_dims}; } @@ -420,7 +477,7 @@ static void mul_mat_vec_q_switch_ncols_dst( if (has_ids && ncols_dst > 1) { // Multi-token MUL_MAT_ID path only - single-token goes through regular path below constexpr int c_ncols_dst = 1; - std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, ncols_dst, warp_size, table_id); + std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, ncols_dst, warp_size, table_id); mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst, channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, @@ -431,7 +488,7 @@ static void mul_mat_vec_q_switch_ncols_dst( switch (ncols_dst) { case 1: { constexpr int c_ncols_dst = 1; - std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); + std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst, channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, @@ -439,7 +496,7 @@ static void mul_mat_vec_q_switch_ncols_dst( } break; case 2: { constexpr int c_ncols_dst = 2; - std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); + std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst, channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, @@ -447,7 +504,7 @@ static void mul_mat_vec_q_switch_ncols_dst( } break; case 3: { constexpr int c_ncols_dst = 3; - std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); + std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst, channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, @@ -455,7 +512,7 @@ static void mul_mat_vec_q_switch_ncols_dst( } break; case 4: { constexpr int c_ncols_dst = 4; - std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); + std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst, channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, @@ -463,7 +520,7 @@ static void mul_mat_vec_q_switch_ncols_dst( } break; case 5: { constexpr int c_ncols_dst = 5; - std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); + std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst, channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, @@ -471,7 +528,7 @@ static void mul_mat_vec_q_switch_ncols_dst( } break; case 6: { constexpr int c_ncols_dst = 6; - std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); + std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst, channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, @@ -479,7 +536,7 @@ static void mul_mat_vec_q_switch_ncols_dst( } break; case 7: { constexpr int c_ncols_dst = 7; - std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); + std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst, channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, @@ -487,7 +544,7 @@ static void mul_mat_vec_q_switch_ncols_dst( } break; case 8: { constexpr int c_ncols_dst = 8; - std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); + std::pair dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id); mul_mat_vec_q_switch_fusion(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst, channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu index a8c68e44b1..4300ffc148 100644 --- a/ggml/src/ggml-cuda/quantize.cu +++ b/ggml/src/ggml-cuda/quantize.cu @@ -235,7 +235,7 @@ static __global__ void quantize_mmq_q8_1( q.z = roundf(xi.z*d_inv); q.w = roundf(xi.w*d_inv); - // Write back 4 int8 values as a single 32 bit value for better memroy bandwidth: + // Write back 4 int8 values as a single 32 bit value for better memory bandwidth: char4 * yqs4 = (char4 *) y[ib].qs; yqs4[iqs/4] = q; diff --git a/ggml/src/ggml-cuda/softmax.cu b/ggml/src/ggml-cuda/softmax.cu index dc06d06930..285c0e9543 100644 --- a/ggml/src/ggml-cuda/softmax.cu +++ b/ggml/src/ggml-cuda/softmax.cu @@ -46,7 +46,7 @@ struct soft_max_params { }; // When ncols_template == 0 the bounds for the loops in this function are not known and can't be unrolled. -// As we want to keep pragma unroll for all other cases we supress the clang transformation warning here. +// As we want to keep pragma unroll for all other cases we suppress the clang transformation warning here. #ifdef __clang__ #pragma clang diagnostic push #pragma clang diagnostic ignored "-Wpass-failed" diff --git a/ggml/src/ggml-cuda/solve_tri.cu b/ggml/src/ggml-cuda/solve_tri.cu index 177ffc268f..07ca33f513 100644 --- a/ggml/src/ggml-cuda/solve_tri.cu +++ b/ggml/src/ggml-cuda/solve_tri.cu @@ -83,7 +83,7 @@ static void solve_tri_f32_cublas(ggml_backend_cuda_context & ctx, // ====================== // When ncols_template == 0 the bounds for the loops in this function are not // known and can't be unrolled. As we want to keep pragma unroll for all other -// cases we supress the clang transformation warning here. +// cases we suppress the clang transformation warning here. #ifdef __clang__ # pragma clang diagnostic push # pragma clang diagnostic ignored "-Wpass-failed" diff --git a/ggml/src/ggml-cuda/ssm-conv.cu b/ggml/src/ggml-cuda/ssm-conv.cu index 6d5ea704c6..69985cd335 100644 --- a/ggml/src/ggml-cuda/ssm-conv.cu +++ b/ggml/src/ggml-cuda/ssm-conv.cu @@ -1,6 +1,7 @@ #include "ssm-conv.cuh" +#include "unary.cuh" -template +template static __global__ void ssm_conv_f32(const float * __restrict__ src0, const float * __restrict__ src1, const int src0_nb0, const int src0_nb1, const int src0_nb2, const int src1_nb1, float * __restrict__ dst, const int dst_nb0, const int dst_nb1, const int dst_nb2, @@ -41,11 +42,11 @@ static __global__ void ssm_conv_f32(const float * __restrict__ src0, const float for (size_t j = 0; j < d_conv; j++) { sumf += x[(i + j) % d_conv] * w[j]; } - y_block[i * stride_y + tid] = sumf; + y_block[i * stride_y + tid] = apply_silu ? ggml_cuda_op_silu_single(sumf) : sumf; } } -template +template static __global__ void ssm_conv_long_token_f32(const float * __restrict__ src0, const float * __restrict__ src1, const int src0_nb0, const int src0_nb1, const int src0_nb2, const int src1_nb1, float * __restrict__ dst, const int dst_nb0, @@ -65,36 +66,49 @@ static __global__ void ssm_conv_long_token_f32(const float * __restrict__ src0, const int stride_w = src1_nb1 / sizeof(float); const int stride_y = dst_nb1 / sizeof(float); - float x[d_conv] = { 0.0f }; - float w[d_conv] = { 0.0f }; + const int64_t local_n_t = min(split_n_t, n_t - bidz * split_n_t); + const int n_cols = d_conv - 1 + split_n_t; + extern __shared__ float smem[]; + + constexpr int load_cols = d_conv - 1 + split_n_t; + constexpr int total_elems = split_d_inner * load_cols; + int row = tid / load_cols; + int col = tid % load_cols; +#pragma unroll + for (int idx = 0; idx < total_elems; idx += split_d_inner) { + if (row < (int)split_d_inner) { + smem[row * n_cols + col] = x_block[row * stride_x + col]; + } + + col += split_d_inner; + row += col / load_cols; + col = col % load_cols; + if (idx >= total_elems - tid - split_d_inner) { + break; + } + } + __syncthreads(); + + // Load weights into registers (done once, small) + float w[d_conv] = { 0.0f }; #pragma unroll for (size_t j = 0; j < d_conv; j++) { w[j] = w_block[tid * stride_w + j]; } + // Compute from shared memory + for (int64_t i = 0; i < local_n_t; i++) { + float sumf = 0.0f; #pragma unroll - for (int64_t i = 0; i < split_n_t; i++) { - if (bidz * split_n_t + i < n_t) { - float sumf = 0.0f; - - if (i == 0) { - for (size_t j = 0; j < d_conv; j++) { - x[j] = x_block[tid * stride_x + j]; - } - } else { - x[(i - 1) % d_conv] = x_block[tid * stride_x + i + d_conv - 1]; - } - -#pragma unroll - for (size_t j = 0; j < d_conv; j++) { - sumf += x[(i + j) % d_conv] * w[j]; - } - y_block[i * stride_y + tid] = sumf; + for (size_t j = 0; j < d_conv; j++) { + sumf += smem[tid * n_cols + i + j] * w[j]; } + y_block[i * stride_y + tid] = apply_silu ? ggml_cuda_op_silu_single(sumf) : sumf; } } +template static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int src0_nb0, const int src0_nb1, const int src0_nb2, const int src1_nb1, float * dst, const int dst_nb0, const int dst_nb1, const int dst_nb2, const int64_t nc, const int64_t nr, const int64_t n_t, @@ -106,12 +120,13 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int constexpr int kNC = decltype(NC)::value; if (n_t <= 32) { const dim3 blocks(n_s, (nr + threads - 1) / threads, 1); - ssm_conv_f32<<>>(src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, + ssm_conv_f32<<>>(src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t); } else { const int64_t split_n_t = 32; dim3 blocks(n_s, (nr + threads - 1) / threads, (n_t + split_n_t - 1) / split_n_t); - ssm_conv_long_token_f32<<>>( + const size_t smem_size = threads * (kNC - 1 + split_n_t) * sizeof(float); + ssm_conv_long_token_f32<<>>( src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t); } }; @@ -124,27 +139,36 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int } } -void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { +void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * silu_dst) { const struct ggml_tensor * src0 = dst->src[0]; // conv_x const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight + const bool fuse_silu = silu_dst != nullptr; + + // When fusing, write to silu_dst (the node downstream references). + const struct ggml_tensor * out = fuse_silu ? silu_dst : dst; const int64_t nc = src1->ne[0]; // d_conv const int64_t nr = src0->ne[1]; // d_inner - const int64_t n_t = dst->ne[1]; // tokens per sequence - const int64_t n_s = dst->ne[2]; // number of sequences in the batch + const int64_t n_t = out->ne[1]; // tokens per sequence + const int64_t n_s = out->ne[2]; // number of sequences in the batch - GGML_ASSERT(dst->ne[0] == nr); + GGML_ASSERT(out->ne[0] == nr); GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(src1->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float)); const float * src0_d = (const float *) src0->data; const float * src1_d = (const float *) src1->data; - float * dst_d = (float *) dst->data; + float * dst_d = (float *) out->data; cudaStream_t stream = ctx.stream(); GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - ssm_conv_f32_cuda(src0_d, src1_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, dst->nb[0], dst->nb[1], - dst->nb[2], nc, nr, n_t, n_s, stream); + GGML_ASSERT(out->type == GGML_TYPE_F32); + if (fuse_silu) { + ssm_conv_f32_cuda(src0_d, src1_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1], + out->nb[2], nc, nr, n_t, n_s, stream); + } else { + ssm_conv_f32_cuda(src0_d, src1_d, src0->nb[0], src0->nb[1], src0->nb[2], src1->nb[1], dst_d, out->nb[0], out->nb[1], + out->nb[2], nc, nr, n_t, n_s, stream); + } } diff --git a/ggml/src/ggml-cuda/ssm-conv.cuh b/ggml/src/ggml-cuda/ssm-conv.cuh index 8e6c1f00bf..f96a1cd248 100644 --- a/ggml/src/ggml-cuda/ssm-conv.cuh +++ b/ggml/src/ggml-cuda/ssm-conv.cuh @@ -1,3 +1,3 @@ #include "common.cuh" -void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * silu_dst = nullptr); diff --git a/ggml/src/ggml-cuda/topk-moe.cu b/ggml/src/ggml-cuda/topk-moe.cu index 08a88990dd..3020e5c743 100644 --- a/ggml/src/ggml-cuda/topk-moe.cu +++ b/ggml/src/ggml-cuda/topk-moe.cu @@ -119,6 +119,18 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * } } + // Sanitize NaN to -FLT_MAX so the iterative argmax produces unique expert IDs. + // NaN comparisons always return false, which would cause the same expert to be + // selected repeatedly. -FLT_MAX compares normally and is still excluded by the + // -INFINITY sentinel used after each selection round. + // More relevant for the cuBLAS path. See https://github.com/ggml-org/llama.cpp/issues/19659 +#pragma unroll + for (int i = 0; i < experts_per_thread; i++) { + if (__isnanf(wt[i])) { + wt[i] = -FLT_MAX; + } + } + // selection_wt is only needed when bias is present (selection uses wt + bias) // when no bias, we use wt directly for both selection and weight values float selection_wt[has_bias ? experts_per_thread : 1]; diff --git a/ggml/src/ggml-cuda/unary.cu b/ggml/src/ggml-cuda/unary.cu index d4866067a4..4ad30fa1f3 100644 --- a/ggml/src/ggml-cuda/unary.cu +++ b/ggml/src/ggml-cuda/unary.cu @@ -560,3 +560,58 @@ void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) leaky_relu_cuda((const float *)src0_d, (float *)dst_d, ggml_nelements(src0), negative_slope, stream); } } + +/* fused unary + mul */ + +template +static void ggml_cuda_op_unary_mul_impl(ggml_backend_cuda_context & ctx, ggml_tensor * unary_node, ggml_tensor * mul_node) { + // unary_node: UNARY op applied to unary_node->src[0] + // mul_node: MUL(a, b) where one of a/b is unary_node + // Output goes to mul_node->data + + const ggml_tensor * unary_src = unary_node->src[0]; // input to the unary op + const ggml_tensor * other_src = (mul_node->src[0] == unary_node) ? mul_node->src[1] : mul_node->src[0]; + + GGML_ASSERT(ggml_is_contiguous_1(unary_src)); + GGML_ASSERT(unary_src->nb[0] == ggml_element_size(unary_src)); + GGML_ASSERT(ggml_is_contiguous_1(other_src)); + GGML_ASSERT(other_src->nb[0] == ggml_element_size(other_src)); + GGML_ASSERT(ggml_are_same_shape(unary_src, other_src)); + + GGML_ASSERT(unary_src->type == GGML_TYPE_F32 || unary_src->type == GGML_TYPE_F16); + GGML_ASSERT(unary_src->type == other_src->type); + GGML_ASSERT(unary_src->type == mul_node->type); + + cudaStream_t stream = ctx.stream(); + + const int64_t k = ggml_nelements(mul_node); + const int64_t nc = unary_src->ne[0]; + const int64_t unary_stride = unary_src->nb[1]; + const int64_t other_stride = other_src->nb[1]; + + if (unary_src->type == GGML_TYPE_F16) { + unary_gated_cuda((const half *) unary_src->data, (const half *) other_src->data, + (half *) mul_node->data, k, nc, + unary_stride / sizeof(half), other_stride / sizeof(half), stream); + } else { + unary_gated_cuda((const float *) unary_src->data, (const float *) other_src->data, + (float *) mul_node->data, k, nc, + unary_stride / sizeof(float), other_stride / sizeof(float), stream); + } +} + +void ggml_cuda_op_unary_mul(ggml_backend_cuda_context & ctx, ggml_tensor * unary_node, ggml_tensor * mul_node) { + switch (ggml_get_unary_op(unary_node)) { + case GGML_UNARY_OP_SILU: + ggml_cuda_op_unary_mul_impl(ctx, unary_node, mul_node); + break; + case GGML_UNARY_OP_SIGMOID: + ggml_cuda_op_unary_mul_impl(ctx, unary_node, mul_node); + break; + case GGML_UNARY_OP_SOFTPLUS: + ggml_cuda_op_unary_mul_impl(ctx, unary_node, mul_node); + break; + default: + GGML_ABORT("Unsupported unary op for fused unary+mul"); + } +} diff --git a/ggml/src/ggml-cuda/unary.cuh b/ggml/src/ggml-cuda/unary.cuh index 609046e569..f1dd2183a6 100644 --- a/ggml/src/ggml-cuda/unary.cuh +++ b/ggml/src/ggml-cuda/unary.cuh @@ -89,6 +89,8 @@ void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst void ggml_cuda_op_xielu(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +void ggml_cuda_op_unary_mul(ggml_backend_cuda_context & ctx, ggml_tensor * unary_node, ggml_tensor * mul_node); + __device__ __forceinline__ float ggml_cuda_op_silu_single(float x) { return x / (1.0f + expf(-x)); } diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h index 5cc1b54319..35d1e1a063 100644 --- a/ggml/src/ggml-cuda/vendors/hip.h +++ b/ggml/src/ggml-cuda/vendors/hip.h @@ -207,6 +207,14 @@ #define RDNA3 #endif // defined(__GFX11__) +#if defined(__gfx1150__) || defined(__gfx1151__) +#define RDNA3_5 +#endif // defined(__gfx1150__) || defined(__gfx1151__) + +#if defined(RDNA3) && !defined(RDNA3_5) +#define RDNA3_0 +#endif // defined(RDNA3) && !defined(RDNA3_5) + #if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \ defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__) #define RDNA2 diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 54f9986498..19917cb114 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -139,7 +139,7 @@ struct ggml_hexagon_session { }; void ggml_hexagon_session::enqueue(struct htp_general_req &req, struct dspqueue_buffer *bufs, uint32_t n_bufs, bool sync) { - // Bump pending flag (cleared in the session::flush once we get the responce) + // Bump pending flag (cleared in the session::flush once we get the response) this->op_pending++; // atomic inc int err = dspqueue_write(this->queue, @@ -402,6 +402,7 @@ static void pack_q4_0_quants(block_q4_0 * x, const uint8_t * qs, unsigned int bi static void repack_row_q4x4x2(uint8_t * y, const block_q4_0 * x, int64_t k) { static const int qk = QK_Q4_0x4x2; const int nb = (k + qk - 1) / qk; // number of blocks (padded) + const int nloe = k % qk; // leftovers const int dblk_size = 8 * 2; // 8x __fp16 const int qblk_size = qk / 2; // int4 @@ -435,15 +436,17 @@ static void repack_row_q4x4x2(uint8_t * y, const block_q4_0 * x, int64_t k) { unpack_q4_0_quants(qs, &x[i * 8 + 6], 6); unpack_q4_0_quants(qs, &x[i * 8 + 7], 7); + bool partial = (nloe && i == nb-1); + uint8_t * q = y_q + (i * qblk_size); for (int j = 0; j < qk / 2; j++) { - q[j] = (qs[j + 128] << 4) | qs[j]; + q[j] = partial ? (qs[j*2+1] << 4) | qs[j*2+0] : (qs[j+128] << 4) | qs[j+000]; } } // Repack the scales // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2) - // the last block is truncated and overriden by the scales. + // the last block is truncated and overridden by the scales. for (int i = 0; i < nb; i++) { // Repack the scales ggml_half * d = (ggml_half *) (y_d + i * dblk_size); @@ -467,6 +470,7 @@ static void repack_row_q4x4x2(uint8_t * y, const block_q4_0 * x, int64_t k) { static void unpack_row_q4x4x2(block_q4_0 * x, const uint8_t * y, int64_t k) { static const int qk = QK_Q4_0x4x2; const int nb = (k + qk - 1) / qk; // number of blocks (padded) + const int nloe = k % qk; // leftovers const int dblk_size = 8 * 2; // 8x __fp16 const int qblk_size = qk / 2; // int4 @@ -485,10 +489,17 @@ static void unpack_row_q4x4x2(block_q4_0 * x, const uint8_t * y, int64_t k) { for (int i = 0; i < nb; i++) { uint8_t qs[QK_Q4_0x4x2]; // unpacked quants + bool partial = (nloe && i == nb-1); + const uint8_t * q = y_q + (i * qblk_size); for (int j = 0; j < qk / 2; j++) { - qs[j] = q[j] & 0xf; - qs[j + 128] = q[j] >> 4; + if (partial) { + qs[j*2+0] = q[j] & 0xf; + qs[j*2+1] = q[j] >> 4; + } else { + qs[j+000] = q[j] & 0xf; + qs[j+128] = q[j] >> 4; + } } pack_q4_0_quants(&x[i * 8 + 0], qs, 0); @@ -503,7 +514,7 @@ static void unpack_row_q4x4x2(block_q4_0 * x, const uint8_t * y, int64_t k) { // Repack the scales // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2) - // the last block is truncated and overriden by the scales. + // the last block is truncated and overridden by the scales. for (int i = 0; i < nb; i++) { // Unpack the scales const ggml_half * d = (const ggml_half *) (y_d + i * dblk_size); @@ -552,7 +563,7 @@ static void init_row_q4x4x2(block_q4_0 * x, int64_t k) { // Init the scales // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2) - // the last block is truncated and overriden by the scales. + // the last block is truncated and overridden by the scales. for (int i = 0; i < nb; i++) { // Unpack the scales x[i * 8 + 0].d = 0; @@ -770,7 +781,7 @@ static void repack_row_q8x4x2(uint8_t * y, const block_q8_0 * x, int64_t k) { // Repack the scales // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2) - // the last block is truncated and overriden by the scales. + // the last block is truncated and overridden by the scales. for (int i = 0; i < nb; i++) { // Repack the scales ggml_half * d = (ggml_half *) (y_d + i * dblk_size); @@ -829,7 +840,7 @@ static void unpack_row_q8x4x2(block_q8_0 * x, const uint8_t * y, int64_t k) { // Repack the scales // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2) - // the last block is truncated and overriden by the scales. + // the last block is truncated and overridden by the scales. for (int i = 0; i < nb; i++) { // Unpack the scales const ggml_half * d = (const ggml_half *) (y_d + i * dblk_size); @@ -878,7 +889,7 @@ static void init_row_q8x4x2(block_q8_0 * x, int64_t k) { // Init the scales // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q8_0x4x2) - // the last block is truncated and overriden by the scales. + // the last block is truncated and overridden by the scales. for (int i = 0; i < nb; i++) { // Unpack the scales x[i * 8 + 0].d = 0; @@ -1078,6 +1089,7 @@ static void pack_mxfp4_quants(block_mxfp4 * x, const uint8_t * qs, unsigned int static void repack_row_mxfp4x4x2(uint8_t * y, const block_mxfp4 * x, int64_t k) { static const int qk = QK_MXFP4x4x2; const int nb = (k + qk - 1) / qk; // number of blocks (padded) + const int nloe = k % qk; // leftovers const int eblk_size = 8 * 1; // 8x E8M0 const int qblk_size = qk / 2; // int4 @@ -1112,15 +1124,17 @@ static void repack_row_mxfp4x4x2(uint8_t * y, const block_mxfp4 * x, int64_t k) unpack_mxfp4_quants(qs, &x[i * 8 + 6], 6); unpack_mxfp4_quants(qs, &x[i * 8 + 7], 7); + bool partial = (nloe && i == nb-1); + uint8_t * q = y_q + (i * qblk_size); for (int j = 0; j < qk / 2; j++) { - q[j] = (qs[j + 128] << 4) | qs[j]; + q[j] = partial ? (qs[j*2+1] << 4) | qs[j*2+0] : (qs[j+128] << 4) | qs[j+000]; } } // Repack the scales // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_MXFP4x4x2) - // the last block is truncated and overriden by the scales. + // the last block is truncated and overridden by the scales. for (int i = 0; i < nb; i++) { // Repack the scales uint8_t * e = (uint8_t *) (y_e + i * eblk_size); @@ -1144,6 +1158,7 @@ static void repack_row_mxfp4x4x2(uint8_t * y, const block_mxfp4 * x, int64_t k) static void unpack_row_mxfp4x4x2(block_mxfp4 * x, const uint8_t * y, int64_t k) { static const int qk = QK_MXFP4x4x2; const int nb = (k + qk - 1) / qk; // number of blocks (padded) + const int nloe = k % qk; // leftovers const int eblk_size = 8 * 1; // 8x E8M0 const int qblk_size = qk / 2; // int4 @@ -1162,10 +1177,17 @@ static void unpack_row_mxfp4x4x2(block_mxfp4 * x, const uint8_t * y, int64_t k) for (int i = 0; i < nb; i++) { uint8_t qs[QK_MXFP4x4x2]; // unpacked quants + bool partial = (nloe && i == nb-1); + const uint8_t * q = y_q + (i * qblk_size); for (int j = 0; j < qk / 2; j++) { - qs[j] = q[j] & 0xf; - qs[j + 128] = q[j] >> 4; + if (partial) { + qs[j*2+0] = q[j] & 0xf; + qs[j*2+1] = q[j] >> 4; + } else { + qs[j+000] = q[j] & 0xf; + qs[j+128] = q[j] >> 4; + } } pack_mxfp4_quants(&x[i * 8 + 0], qs, 0); @@ -1180,7 +1202,7 @@ static void unpack_row_mxfp4x4x2(block_mxfp4 * x, const uint8_t * y, int64_t k) // Repack the scales // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_MXFP4_0x4x2) - // the last block is truncated and overriden by the scales. + // the last block is truncated and overridden by the scales. for (int i = 0; i < nb; i++) { // Unpack the scales const uint8_t * e = (const uint8_t *) (y_e + i * eblk_size); @@ -1229,7 +1251,7 @@ static void init_row_mxfp4x4x2(block_mxfp4 * x, int64_t k) { // Init the scales // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_MXFP4x4x2) - // the last block is truncated and overriden by the scales. + // the last block is truncated and overridden by the scales. for (int i = 0; i < nb; i++) { // Unpack the scales x[i * 8 + 0].e = 0; @@ -1749,23 +1771,6 @@ static inline bool ggml_backend_buffer_is_hexagon_repack(const struct ggml_backe return b->buft->iface.alloc_buffer == ggml_backend_hexagon_repack_buffer_type_alloc_buffer; } -static bool hex_supported_dims2(const struct ggml_tensor * x, const struct ggml_tensor * y) { - if (x->ne[0] != y->ne[0]) { - return false; - } - if (x->ne[1] != y->ne[1]) { - return false; - } - if (x->ne[2] != y->ne[2]) { - return false; - } - if (x->ne[3] != y->ne[3]) { - return false; - } - - return true; -} - static bool ggml_hexagon_supported_flash_attn_ext(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { const struct ggml_tensor * src0 = op->src[0]; const struct ggml_tensor * src1 = op->src[1]; @@ -1797,43 +1802,6 @@ static bool ggml_hexagon_supported_flash_attn_ext(const struct ggml_hexagon_sess return opt_experimental; } -static bool hex_supported_src0_type(ggml_type t) { - return t == GGML_TYPE_F32; -} - -static bool hex_supported_src1_type(ggml_type t) { - return t == GGML_TYPE_F32; -} - -static bool hex_supported_src2_type(ggml_type t) { - return t == GGML_TYPE_F32; -} - -static bool hex_supported_src1_type2(ggml_type t) { - return t == GGML_TYPE_F16; -} - -static bool hex_supported_src1_type3(ggml_type t) { - return t == GGML_TYPE_I32; -} - -static bool hex_supported_dst_type(ggml_type t) { - return t == GGML_TYPE_F32; -} - -static bool hex_supported_dims(const struct ggml_tensor * x, const struct ggml_tensor * y) { - // TODO: support broadcast for ne[2 and 3] - if (x->ne[0] != y->ne[0]) { - return false; - } - if (x->ne[2] != y->ne[2]) { - return false; - } - if (x->ne[3] != y->ne[3]) { - return false; - } - return true; -} static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * sess, const struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; @@ -1855,12 +1823,12 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s return false; } - if (src0->ne[1] > 16 * 1024) { + if (ggml_nrows(src0) > 16 * 1024) { return false; // typically the lm-head which would be too large for VTCM } - if ((src1->ne[2] != 1 || src1->ne[3] != 1)) { - return false; + if (ggml_nrows(src1) > 1024 || src1->ne[2] != 1 || src1->ne[3] != 1) { + return false; // no huge batches or broadcasting (for now) } // src0 (weights) must be repacked @@ -1874,6 +1842,9 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: permuted F16 src0 not supported\n"); return false; } + if (ggml_nrows(src1) > 1024) { + return false; // no huge batches (for now) + } break; default: @@ -1919,19 +1890,30 @@ static bool ggml_hexagon_supported_binary(const struct ggml_hexagon_session * se const struct ggml_tensor * src1 = op->src[1]; const struct ggml_tensor * dst = op; - if (!hex_supported_src0_type(src0->type)) { + if (src0->type == GGML_TYPE_F32) { + if (src1->type != GGML_TYPE_F32) { + return false; + } + if (dst->type != GGML_TYPE_F32) { + return false; + } + } + else if (src0->type == GGML_TYPE_F16) { + if (src1->type != GGML_TYPE_F16) { + return false; + } + if (dst->type != GGML_TYPE_F16) { + return false; + } + } + else { return false; } - if (!hex_supported_src1_type(src1->type)) { + + if (!ggml_are_same_shape(src0, dst)) { return false; } - if (!hex_supported_dst_type(dst->type)) { - return false; - } - if (!hex_supported_dims2(src0, dst)) { - return false; - } - if (!ggml_can_repeat(src1, src0)) { + if (!ggml_can_repeat(src1, src0) || ggml_is_permuted(src1)) { return false; } @@ -1943,16 +1925,16 @@ static bool ggml_hexagon_supported_add_id(const struct ggml_hexagon_session * se const struct ggml_tensor * src1 = op->src[1]; const struct ggml_tensor * dst = op; - if (!hex_supported_src0_type(src0->type)) { + if (src0->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_src1_type(src1->type)) { + if (src1->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_dst_type(dst->type)) { + if (dst->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_dims2(src0, dst)) { + if (!ggml_are_same_shape(src0, dst)) { return false; } @@ -1968,13 +1950,13 @@ static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * ses const struct ggml_tensor * src0 = op->src[0]; const struct ggml_tensor * dst = op; - if (!hex_supported_src0_type(src0->type)) { + if (src0->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_dst_type(dst->type)) { + if (dst->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_dims2(src0, dst)) { + if (!ggml_are_same_shape(src0, dst)) { return false; } @@ -1990,10 +1972,10 @@ static bool ggml_hexagon_supported_sum_rows(const struct ggml_hexagon_session * const struct ggml_tensor * src0 = op->src[0]; const struct ggml_tensor * dst = op; - if (!hex_supported_src0_type(src0->type)) { + if (src0->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_dst_type(dst->type)) { + if (dst->type != GGML_TYPE_F32) { return false; } @@ -2011,10 +1993,10 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session const struct ggml_tensor * src1 = op->src[1]; const struct ggml_tensor * dst = op; - if (!hex_supported_src0_type(src0->type)) { + if (src0->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_dst_type(dst->type)) { + if (dst->type != GGML_TYPE_F32) { return false; } @@ -2023,10 +2005,10 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session } if (src1) { - if (!hex_supported_src1_type(src1->type)) { + if (src1->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_dims2(src0, src1)) { + if (!ggml_are_same_shape(src0, src1)) { return false; } if (!ggml_is_contiguous(src1)) { @@ -2047,15 +2029,15 @@ static bool ggml_hexagon_supported_softmax(const struct ggml_hexagon_session * s return false; // FIXME: add support for sinks } - if (!hex_supported_src0_type(src0->type)) { + if (src0->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_dst_type(dst->type)) { + if (dst->type != GGML_TYPE_F32) { return false; } if (src1) { - if (!hex_supported_src1_type(src1->type) && !hex_supported_src1_type2(src1->type)) { + if (src1->type != GGML_TYPE_F32 && src1->type != GGML_TYPE_F16) { return false; } if (src0->ne[0] != src1->ne[0]) { @@ -2162,17 +2144,17 @@ static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess const struct ggml_tensor * src2 = op->src[2]; const struct ggml_tensor * dst = op; - if (!hex_supported_src0_type(src0->type)) { + if (src0->type != GGML_TYPE_F32) { return false; // FIXME: add support for GGML_TYPE_F16 for src0 } - if (!hex_supported_dst_type(dst->type)) { + if (dst->type != GGML_TYPE_F32) { return false; } - if (!hex_supported_src1_type3(src1->type)) { + if (src1->type != GGML_TYPE_I32) { return false; } if (src2) { - if (!hex_supported_src2_type(src2->type)) { + if (src2->type != GGML_TYPE_F32) { return false; } int n_dims = op_params[1]; @@ -2195,6 +2177,44 @@ static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess return true; } +static bool ggml_hexagon_supported_ssm_conv(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { + const struct ggml_tensor * src0 = op->src[0]; + const struct ggml_tensor * src1 = op->src[1]; + const struct ggml_tensor * dst = op; + + // Only support FP32 for now + if (src0->type != GGML_TYPE_F32 || src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) { + return false; + } + + // Check IO tensor shapes and dims + if (src0->ne[3] != 1 || src1->ne[2] != 1 || src1->ne[3] != 1 || dst->ne[3] != 1) { + return false; // src0 should be effectively 3D + } + + const int d_conv = src1->ne[0]; + const int d_inner = src0->ne[1]; + const int n_t = dst->ne[1]; + const int n_s = dst->ne[2]; + + if (src0->ne[0] != d_conv - 1 + n_t || src0->ne[1] != d_inner || src0->ne[2] != n_s) { + return false; + } + if (src1->ne[0] != d_conv || src1->ne[1] != d_inner) { + return false; + } + if (dst->ne[0] != d_inner || dst->ne[1] != n_t || dst->ne[2] != n_s) { + return false; + } + + // TODO: add support for non-contiguous tensors + if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) { + return false; + } + + return true; +} + enum dspqbuf_type { DSPQBUF_TYPE_DSP_WRITE_CPU_READ = 0, DSPQBUF_TYPE_CPU_WRITE_DSP_READ, @@ -2511,6 +2531,17 @@ static inline size_t init_flash_attn_ext_req(htp_general_req * req, dspqueue_buf return n_bufs; } +static inline size_t init_ssm_conv_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) { + req->op = HTP_OP_SSM_CONV; + + size_t n_bufs = 0; + n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->src1, &bufs[n_bufs], t->src[1], DSPQBUF_TYPE_CONSTANT); + n_bufs += htp_req_buff_init(&req->dst, &bufs[n_bufs], t, DSPQBUF_TYPE_DSP_WRITE_CPU_READ); + + return n_bufs; +} + static const char * ggml_backend_hexagon_name(ggml_backend_t backend) { auto sess = static_cast(backend->context); return sess->name.c_str(); @@ -2649,6 +2680,10 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg ggml_hexagon_dispatch_op(sess, node, flags); break; + case GGML_OP_SSM_CONV: + ggml_hexagon_dispatch_op(sess, node, flags); + break; + default: GGML_ABORT("\nggml-hex: graph-compute %s is not supported\n", ggml_op_desc(node)); } @@ -2724,7 +2759,7 @@ static std::vector ggml_hexagon_graph_optimize_reorder(const std::vectornb[2]; \ const uint32_t nb3 = dst->nb[3]; -static void glu_swiglu_f32_per_thread(const struct htp_tensor * src0, - const struct htp_tensor * src1, - struct htp_tensor * dst, - const int32_t * op_params, - struct htp_spad * src0_spad, - struct htp_spad * src1_spad, - struct htp_spad * dst_spad, - uint32_t nth, - uint32_t ith, - uint32_t src0_nrows_per_thread, - dma_queue * dma_queue) { +struct htp_act_context { + struct htp_ops_context * octx; + + // Precomputed values + const uint8_t * data_src0; + const uint8_t * data_src1; + uint8_t * data_dst; + + size_t src0_row_size; + size_t src1_row_size; + size_t dst_row_size; + + size_t src0_row_size_aligned; + size_t src1_row_size_aligned; + size_t dst_row_size_aligned; + + size_t src0_spad_half_size; + size_t src1_spad_half_size; + size_t dst_spad_half_size; + + uint32_t block; + uint32_t src0_nrows; + uint32_t src0_nrows_per_thread; + int nc; +}; + +static void glu_swiglu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { + struct htp_act_context * actx = (struct htp_act_context *) data; + const struct htp_tensor * src0 = &actx->octx->src0; + const struct htp_tensor * src1 = &actx->octx->src1; + const struct htp_tensor * dst = &actx->octx->dst; htp_act_preamble3; - size_t src0_row_size = nb01; - size_t src1_row_size = nb11; - size_t dst_row_size = nb1; - - - - const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows + size_t src0_row_size = actx->src0_row_size; + size_t src1_row_size = actx->src1_row_size; + size_t dst_row_size = actx->dst_row_size; + const uint32_t src0_nrows = actx->src0_nrows; + const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); @@ -101,43 +119,34 @@ static void glu_swiglu_f32_per_thread(const struct htp_tensor * src0, uint64_t t1, t2; t1 = HAP_perf_get_qtimer_count(); - const uint8_t * restrict data_src0 = (const uint8_t *) src0->data; - const uint8_t * restrict data_src1 = (const uint8_t *) src1->data; - uint8_t * restrict data_dst = (uint8_t *) dst->data; + const uint8_t * restrict data_src0 = actx->data_src0; + const uint8_t * restrict data_src1 = actx->data_src1; + uint8_t * restrict data_dst = actx->data_dst; - const bool src1_valid = src1->ne[0]; - const int nc = (src1_valid) ? ne00 : ne00 / 2; - if (!src1_valid) { - const int32_t swapped = op_params[1]; - data_src1 = data_src0; - src1_row_size = src0_row_size; + const int nc = actx->nc; - const size_t nc_in_bytes = nc * SIZEOF_FP32; - data_src0 += swapped ? nc_in_bytes : 0; - data_src1 += swapped ? 0 : nc_in_bytes; - } + const size_t src0_row_size_aligned = actx->src0_row_size_aligned; + const size_t src1_row_size_aligned = actx->src1_row_size_aligned; + const size_t dst_row_size_aligned = actx->dst_row_size_aligned; - const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); - const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN); - const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN); + uint8_t * restrict src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); + uint8_t * restrict src1_spad_data = actx->octx->src1_spad.data + (ith * actx->octx->src1_spad.size_per_thread); + uint8_t * restrict dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); - uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread); - uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_spad->size_per_thread); - uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_spad->size_per_thread); + size_t src0_spad_half_size = actx->src0_spad_half_size; + size_t src1_spad_half_size = actx->src1_spad_half_size; + size_t dst_spad_half_size = actx->dst_spad_half_size; - // While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0 - size_t src0_spad_half_size = src0_spad->size_per_thread / 2; - size_t src1_spad_half_size = src1_spad->size_per_thread / 2; - size_t dst_spad_half_size = dst_spad->size_per_thread / 2; - - const int BLOCK = src0_spad_half_size / src0_row_size_aligned; // How many rows can we process in one block + const int BLOCK = actx->block; if (BLOCK == 0) { FARF(ERROR, "swiglu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - src0_spad->size_per_thread, src0_row_size_aligned); + actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); return; } + dma_queue * dma_queue = actx->octx->ctx->dma[ith]; + // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); @@ -196,27 +205,22 @@ static void glu_swiglu_f32_per_thread(const struct htp_tensor * src0, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); } -static void glu_swiglu_oai_f32_per_thread(const struct htp_tensor * src0, - const struct htp_tensor * src1, - struct htp_tensor * dst, - const int32_t * op_params, - struct htp_spad * src0_spad, - struct htp_spad * src1_spad, - struct htp_spad * dst_spad, - uint32_t nth, - uint32_t ith, - uint32_t src0_nrows_per_thread, - dma_queue * dma_queue) { +static void glu_swiglu_oai_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { + struct htp_act_context * actx = (struct htp_act_context *) data; + const struct htp_tensor * src0 = &actx->octx->src0; + const struct htp_tensor * src1 = &actx->octx->src1; + const struct htp_tensor * dst = &actx->octx->dst; htp_act_preamble3; uint64_t t1, t2; t1 = HAP_perf_get_qtimer_count(); - size_t src0_row_size = nb01; - size_t src1_row_size = nb11; - size_t dst_row_size = nb1; + size_t src0_row_size = actx->src0_row_size; + size_t src1_row_size = actx->src1_row_size; + size_t dst_row_size = actx->dst_row_size; - const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows + const uint32_t src0_nrows = actx->src0_nrows; + const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); @@ -226,45 +230,36 @@ static void glu_swiglu_oai_f32_per_thread(const struct htp_tensor * src0, return; } - const uint8_t * restrict data_src0 = (const uint8_t *) src0->data; - const uint8_t * restrict data_src1 = (const uint8_t *) src1->data; - uint8_t * restrict data_dst = (uint8_t *) dst->data; + const uint8_t * restrict data_src0 = actx->data_src0; + const uint8_t * restrict data_src1 = actx->data_src1; + uint8_t * restrict data_dst = actx->data_dst; - const bool src1_valid = src1->ne[0]; - const int nc = (src1_valid) ? ne00 : ne00 / 2; - if (!src1_valid) { - const int32_t swapped = op_params[1]; - data_src1 = data_src0; - src1_row_size = src0_row_size; + const int nc = actx->nc; - const size_t nc_in_bytes = nc * SIZEOF_FP32; - data_src0 += swapped ? nc_in_bytes : 0; - data_src1 += swapped ? 0 : nc_in_bytes; - } + const size_t src0_row_size_aligned = actx->src0_row_size_aligned; + const size_t src1_row_size_aligned = actx->src1_row_size_aligned; + const size_t dst_row_size_aligned = actx->dst_row_size_aligned; - const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); - const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN); - const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN); + uint8_t * restrict src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); + uint8_t * restrict src1_spad_data = actx->octx->src1_spad.data + (ith * actx->octx->src1_spad.size_per_thread); + uint8_t * restrict dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); - uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread); - uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_spad->size_per_thread); - uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_spad->size_per_thread); + size_t src0_spad_half_size = actx->src0_spad_half_size; + size_t src1_spad_half_size = actx->src1_spad_half_size; + size_t dst_spad_half_size = actx->dst_spad_half_size; - // While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0 - size_t src0_spad_half_size = src0_spad->size_per_thread / 2; - size_t src1_spad_half_size = src1_spad->size_per_thread / 2; - size_t dst_spad_half_size = dst_spad->size_per_thread / 2; - - const int BLOCK = src0_spad_half_size / src0_row_size_aligned; // How many rows can we process in one block + const int BLOCK = actx->block; if (BLOCK == 0) { FARF(ERROR, "swiglu-oai-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least " "%zu\n", - src0_spad->size_per_thread, src0_row_size_aligned); + actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); return; } - const float alpha = ((const float *) (op_params))[2]; - const float limit = ((const float *) (op_params))[3]; + const float alpha = ((const float *) (actx->octx->op_params))[2]; + const float limit = ((const float *) (actx->octx->op_params))[3]; + + dma_queue * dma_queue = actx->octx->ctx->dma[ith]; // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { @@ -335,26 +330,22 @@ static void glu_swiglu_oai_f32_per_thread(const struct htp_tensor * src0, } -static void unary_gelu_f32_per_thread(const struct htp_tensor * src0, - struct htp_tensor * dst, - const int32_t * op_params, - struct htp_spad * src0_spad, - struct htp_spad * dst_spad, - uint32_t nth, - uint32_t ith, - uint32_t src0_nrows_per_thread, - dma_queue * dma_queue) { +static void unary_gelu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { + struct htp_act_context * actx = (struct htp_act_context *) data; + const struct htp_tensor * src0 = &actx->octx->src0; + const struct htp_tensor * dst = &actx->octx->dst; htp_act_preamble2; uint64_t t1, t2; t1 = HAP_perf_get_qtimer_count(); - const size_t src0_row_size = nb01; - const size_t dst_row_size = nb1; - const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); - const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN); + const size_t src0_row_size = actx->src0_row_size; + const size_t dst_row_size = actx->dst_row_size; + const size_t src0_row_size_aligned = actx->src0_row_size_aligned; + const size_t dst_row_size_aligned = actx->dst_row_size_aligned; - const uint32_t src0_nrows = ne01 * ne02 * ne03; + const uint32_t src0_nrows = actx->src0_nrows; + const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); @@ -364,25 +355,29 @@ static void unary_gelu_f32_per_thread(const struct htp_tensor * src0, return; } - const uint8_t * data_src0 = (const uint8_t *) src0->data; - uint8_t * data_dst = (uint8_t *) dst->data; + const uint8_t * data_src0 = actx->data_src0; + uint8_t * data_dst = actx->data_dst; - uint8_t * src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread); - uint8_t * dst_spad_data = dst_spad->data + (ith * dst_spad->size_per_thread); + // nc/ne0 matches. + const int ne0_val = actx->nc; // == dst->ne[0] - // While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0 - size_t src0_spad_half_size = src0_spad->size_per_thread / 2; - size_t dst_spad_half_size = dst_spad->size_per_thread / 2; + uint8_t * src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); + uint8_t * dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); + + size_t src0_spad_half_size = actx->src0_spad_half_size; + size_t dst_spad_half_size = actx->dst_spad_half_size; // In gelu = x*sigmoid(x*1.702) - const int BLOCK = src0_spad_half_size / src0_row_size_aligned; // How many rows can we process in one block + const int BLOCK = actx->block; if (BLOCK == 0) { FARF(ERROR, "gelu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - src0_spad->size_per_thread, src0_row_size_aligned); + actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); return; } + dma_queue * dma_queue = actx->octx->ctx->dma[ith]; + // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); @@ -408,9 +403,9 @@ static void unary_gelu_f32_per_thread(const struct htp_tensor * src0, float* dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); // gelu = x * sigmoid(1.702 * x) // current implementation - hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (float) 1.702, ne0); - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0); - hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0); + hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (float) 1.702, ne0_val); + hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0_val); + hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0_val); } dma_queue_push_vtcm_to_ddr(dma_queue, @@ -435,34 +430,23 @@ static void unary_gelu_f32_per_thread(const struct htp_tensor * src0, ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); } -static void unary_gelu_f32(unsigned int n, unsigned int i, void * data) { - struct htp_ops_context * octx = (struct htp_ops_context *) data; - unary_gelu_f32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i, - octx->src0_nrows_per_thread, octx->ctx->dma[i]); -} - - -static void unary_silu_f32_per_thread(const struct htp_tensor * src0, - struct htp_tensor * dst, - const int32_t * op_params, - struct htp_spad * src0_spad, - struct htp_spad * dst_spad, - uint32_t nth, - uint32_t ith, - uint32_t src0_nrows_per_thread, - dma_queue * dma_queue) { +static void unary_silu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { + struct htp_act_context * actx = (struct htp_act_context *) data; + const struct htp_tensor * src0 = &actx->octx->src0; + const struct htp_tensor * dst = &actx->octx->dst; htp_act_preamble2; uint64_t t1, t2; t1 = HAP_perf_get_qtimer_count(); - const size_t src0_row_size = nb01; - const size_t dst_row_size = nb1; - const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); - const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN); + const size_t src0_row_size = actx->src0_row_size; + const size_t dst_row_size = actx->dst_row_size; + const size_t src0_row_size_aligned = actx->src0_row_size_aligned; + const size_t dst_row_size_aligned = actx->dst_row_size_aligned; - const uint32_t src0_nrows = ne01 * ne02 * ne03; + const uint32_t src0_nrows = actx->src0_nrows; + const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); @@ -472,24 +456,27 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0, return; } - const uint8_t * data_src0 = (const uint8_t *) src0->data; - uint8_t * data_dst = (uint8_t *) dst->data; + const uint8_t * data_src0 = actx->data_src0; + uint8_t * data_dst = actx->data_dst; - uint8_t * src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread); - uint8_t * dst_spad_data = dst_spad->data + (ith * dst_spad->size_per_thread); + const int ne0_val = actx->nc; // == dst->ne[0] - // While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0 - size_t src0_spad_half_size = src0_spad->size_per_thread / 2; - size_t dst_spad_half_size = dst_spad->size_per_thread / 2; + uint8_t * src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); + uint8_t * dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); - const int BLOCK = src0_spad_half_size / src0_row_size_aligned; // How many rows can we process in one block + size_t src0_spad_half_size = actx->src0_spad_half_size; + size_t dst_spad_half_size = actx->dst_spad_half_size; + + const int BLOCK = actx->block; if (BLOCK == 0) { FARF(ERROR, "silu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - src0_spad->size_per_thread, src0_row_size_aligned); + actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); return; } + dma_queue * dma_queue = actx->octx->ctx->dma[ith]; + // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); @@ -515,8 +502,8 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0, float* dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); // silu = x * sigmoid(x) - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, ne0); - hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0); + hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, ne0_val); + hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0_val); } dma_queue_push_vtcm_to_ddr(dma_queue, @@ -544,27 +531,22 @@ static void unary_silu_f32_per_thread(const struct htp_tensor * src0, static const float GELU_COEF_A = 0.044715f; static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; -static void glu_geglu_f32_per_thread(const struct htp_tensor * src0, - const struct htp_tensor * src1, - struct htp_tensor * dst, - const int32_t * op_params, - struct htp_spad * src0_spad, - struct htp_spad * src1_spad, - struct htp_spad * dst_spad, - uint32_t nth, - uint32_t ith, - uint32_t src0_nrows_per_thread, - dma_queue * dma_queue) { +static void glu_geglu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { + struct htp_act_context * actx = (struct htp_act_context *) data; + const struct htp_tensor * src0 = &actx->octx->src0; + const struct htp_tensor * src1 = &actx->octx->src1; + const struct htp_tensor * dst = &actx->octx->dst; htp_act_preamble3; - size_t src0_row_size = nb01; - size_t src1_row_size = nb11; - size_t dst_row_size = nb1; + size_t src0_row_size = actx->src0_row_size; + size_t src1_row_size = actx->src1_row_size; + size_t dst_row_size = actx->dst_row_size; uint64_t t1, t2; t1 = HAP_perf_get_qtimer_count(); - const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows + const uint32_t src0_nrows = actx->src0_nrows; + const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); @@ -574,43 +556,34 @@ static void glu_geglu_f32_per_thread(const struct htp_tensor * src0, return; } - const uint8_t * restrict data_src0 = (const uint8_t *) src0->data; - const uint8_t * restrict data_src1 = (const uint8_t *) src1->data; - uint8_t * restrict data_dst = (uint8_t *) dst->data; + const uint8_t * restrict data_src0 = actx->data_src0; + const uint8_t * restrict data_src1 = actx->data_src1; + uint8_t * restrict data_dst = actx->data_dst; - const bool src1_valid = src1->ne[0]; - const int nc = (src1_valid) ? ne00 : ne00 / 2; - if (!src1_valid) { - const int32_t swapped = op_params[1]; - data_src1 = data_src0; - src1_row_size = src0_row_size; + const int nc = actx->nc; - const size_t nc_in_bytes = nc * SIZEOF_FP32; - data_src0 += swapped ? nc_in_bytes : 0; - data_src1 += swapped ? 0 : nc_in_bytes; - } + const size_t src0_row_size_aligned = actx->src0_row_size_aligned; + const size_t src1_row_size_aligned = actx->src1_row_size_aligned; + const size_t dst_row_size_aligned = actx->dst_row_size_aligned; - const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); - const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN); - const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN); + uint8_t * restrict src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); + uint8_t * restrict src1_spad_data = actx->octx->src1_spad.data + (ith * actx->octx->src1_spad.size_per_thread); + uint8_t * restrict dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); - uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread); - uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_spad->size_per_thread); - uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_spad->size_per_thread); + size_t src0_spad_half_size = actx->src0_spad_half_size; + size_t src1_spad_half_size = actx->src1_spad_half_size; + size_t dst_spad_half_size = actx->dst_spad_half_size; - // While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0 - size_t src0_spad_half_size = src0_spad->size_per_thread / 2; - size_t src1_spad_half_size = src1_spad->size_per_thread / 2; - size_t dst_spad_half_size = dst_spad->size_per_thread / 2; - - const int BLOCK = src0_spad_half_size / src0_row_size_aligned; // How many rows can we process in one block + const int BLOCK = actx->block; if (BLOCK == 0) { FARF(ERROR, "geglu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - src0_spad->size_per_thread, src0_row_size_aligned); + actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); return; } + dma_queue * dma_queue = actx->octx->ctx->dma[ith]; + // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); @@ -678,33 +651,7 @@ static void glu_geglu_f32_per_thread(const struct htp_tensor * src0, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); } -static void unary_silu_f32(unsigned int n, unsigned int i, void * data) { - struct htp_ops_context * octx = (struct htp_ops_context *) data; - unary_silu_f32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i, - octx->src0_nrows_per_thread, octx->ctx->dma[i]); -} - -static void glu_swiglu_f32(unsigned int n, unsigned int i, void * data) { - struct htp_ops_context * octx = (struct htp_ops_context *) data; - glu_swiglu_f32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad, - &octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]); -} - -static void glu_swiglu_oai_f32(unsigned int n, unsigned int i, void * data) { - struct htp_ops_context * octx = (struct htp_ops_context *) data; - glu_swiglu_oai_f32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad, - &octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]); -} - -static void glu_geglu_f32(unsigned int n, unsigned int i, void * data) { - struct htp_ops_context * octx = (struct htp_ops_context *) data; - glu_geglu_f32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad, - &octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]); -} - static int execute_op_activations_f32(struct htp_ops_context * octx) { - int err = HTP_STATUS_OK; - const struct htp_tensor * src0 = &octx->src0; const struct htp_tensor * src1 = &octx->src1; struct htp_tensor * dst = &octx->dst; @@ -719,26 +666,26 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { switch (octx->op) { case HTP_OP_UNARY_SILU: - act_op_func = unary_silu_f32; + act_op_func = (worker_callback_t)unary_silu_f32_per_thread; op_type = "silu-f32"; break; case HTP_OP_GLU_SWIGLU: - act_op_func = glu_swiglu_f32; + act_op_func = (worker_callback_t)glu_swiglu_f32_per_thread; op_type = "swiglu-f32"; break; case HTP_OP_GLU_SWIGLU_OAI: - act_op_func = glu_swiglu_oai_f32; + act_op_func = (worker_callback_t)glu_swiglu_oai_f32_per_thread; op_type = "swiglu-oai-f32"; break; case HTP_OP_UNARY_GELU: - act_op_func = unary_gelu_f32; + act_op_func = (worker_callback_t)unary_gelu_f32_per_thread; op_type = "gelu-f32"; break; case HTP_OP_GLU_GEGLU: - act_op_func = glu_geglu_f32; + act_op_func = (worker_callback_t)glu_geglu_f32_per_thread; op_type = "geglu-f32"; break; default: @@ -746,8 +693,8 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { return HTP_STATUS_NO_SUPPORT; } - const uint32_t n_threads = octx->n_threads; const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; + const uint32_t n_threads = MIN(octx->n_threads, src0_nrows); size_t src0_row_size = src0->nb[1]; size_t src1_row_size = src1->nb[1]; // zero bytes if src1 is not used @@ -797,13 +744,56 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size); } - if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { - uint32_t n_jobs = MIN(n_threads, src0_nrows); - octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs; - worker_pool_run_func(octx->ctx->worker_pool, act_op_func, octx, n_jobs); + if ((octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { + return HTP_STATUS_OK; } - return err; + // Prepare context + struct htp_act_context actx; + actx.octx = octx; + + actx.src0_nrows_per_thread = (src0_nrows + n_threads - 1) / n_threads; + + actx.src0_row_size = src0_row_size; + actx.src1_row_size = src1_row_size; + actx.dst_row_size = dst_row_size; + + actx.src0_row_size_aligned = src0_row_size_aligned; + actx.src1_row_size_aligned = src1_row_size_aligned; + actx.dst_row_size_aligned = dst_row_size_aligned; + + actx.src0_spad_half_size = octx->src0_spad.size_per_thread / 2; + actx.src1_spad_half_size = octx->src1_spad.size_per_thread / 2; + actx.dst_spad_half_size = octx->dst_spad.size_per_thread / 2; + + actx.block = actx.src0_spad_half_size / actx.src0_row_size_aligned; + actx.src0_nrows = src0_nrows; + + actx.nc = dst->ne[0]; + + // Pointers and GLU logic + const uint8_t * data_src0 = (const uint8_t *) src0->data; + const uint8_t * data_src1 = (const uint8_t *) src1->data; + + if (!src1_valid && (octx->op == HTP_OP_GLU_SWIGLU || octx->op == HTP_OP_GLU_SWIGLU_OAI || octx->op == HTP_OP_GLU_GEGLU)) { + const int32_t swapped = octx->op_params[1]; + data_src1 = data_src0; + actx.src1_row_size = actx.src0_row_size; + + size_t nc_in_bytes = actx.nc * SIZEOF_FP32; + if (swapped) { + data_src0 += nc_in_bytes; + } else { + data_src1 += nc_in_bytes; + } + } + + actx.data_src0 = data_src0; + actx.data_src1 = data_src1; + actx.data_dst = (uint8_t *) dst->data; + + worker_pool_run_func(octx->ctx->worker_pool, act_op_func, &actx, n_threads); + return HTP_STATUS_OK; } int op_activations(struct htp_ops_context * octx) { diff --git a/ggml/src/ggml-hexagon/htp/argsort-ops.c b/ggml/src/ggml-hexagon/htp/argsort-ops.c index a4cee980be..170220e8f8 100644 --- a/ggml/src/ggml-hexagon/htp/argsort-ops.c +++ b/ggml/src/ggml-hexagon/htp/argsort-ops.c @@ -241,6 +241,9 @@ int op_argsort(struct htp_ops_context * octx) { return HTP_STATUS_NO_SUPPORT; } + const uint32_t total_rows = octx->src0.ne[1] * octx->src0.ne[2] * octx->src0.ne[3]; + const uint32_t n_threads = MIN(total_rows, octx->n_threads); + // Allocate scratchpad // We need 1 row of float + 1 row of int32 per thread. uint32_t ne00 = octx->src0.ne[0]; @@ -251,7 +254,7 @@ int op_argsort(struct htp_ops_context * octx) { // Make sure we round up to 256 for alignment requirements spad_per_thread = hex_round_up(spad_per_thread, 256); - size_t total_spad_size = spad_per_thread * octx->n_threads; + size_t total_spad_size = spad_per_thread * n_threads; if (octx->ctx->vtcm_size < total_spad_size) { FARF(ERROR, "argsort: VTCM size too small. Needed %zu, have %zu", total_spad_size, octx->ctx->vtcm_size); @@ -267,15 +270,12 @@ int op_argsort(struct htp_ops_context * octx) { octx->dst.ne[0], octx->dst.ne[1], octx->dst.ne[2], octx->dst.ne[3], octx->src0.data, octx->dst.data); - uint32_t total_rows = octx->src0.ne[1] * octx->src0.ne[2] * octx->src0.ne[3]; - uint32_t n_jobs = MIN(total_rows, octx->n_threads); - struct htp_argsort_context actx; actx.octx = octx; - actx.nrows_per_thread = (total_rows + n_jobs - 1) / n_jobs; + actx.nrows_per_thread = (total_rows + n_threads - 1) / n_threads; // Run jobs - worker_pool_run_func(octx->ctx->worker_pool, htp_argsort_f32, &actx, n_jobs); + worker_pool_run_func(octx->ctx->worker_pool, htp_argsort_f32, &actx, n_threads); return HTP_STATUS_OK; } diff --git a/ggml/src/ggml-hexagon/htp/binary-ops.c b/ggml/src/ggml-hexagon/htp/binary-ops.c index 00dbcf8798..ec90f22de5 100644 --- a/ggml/src/ggml-hexagon/htp/binary-ops.c +++ b/ggml/src/ggml-hexagon/htp/binary-ops.c @@ -95,43 +95,87 @@ static inline uint32_t calc_block_size(struct htp_binary_context * bctx, uint32_ } // Macro for scalar op switch -#define COMPUTE_SCALAR_OP(DST, SRC, VAL, N) \ - switch (octx->op) { \ - case HTP_OP_ADD: hvx_add_scalar_f32_aa(DST, SRC, VAL, N); break; \ - case HTP_OP_SUB: hvx_sub_scalar_f32_aa(DST, SRC, VAL, N); break; \ - case HTP_OP_MUL: hvx_mul_scalar_f32_aa(DST, SRC, VAL, N); break; \ - case HTP_OP_DIV: hvx_mul_scalar_f32_aa(DST, SRC, 1.0f / (VAL), N); break; \ - default: break; \ +#define COMPUTE_SCALAR_OP(DST, SRC, VAL, TYPE, N) \ + if(TYPE == HTP_TYPE_F32) { \ + switch (octx->op) { \ + case HTP_OP_ADD: hvx_add_scalar_f32_aa(DST, SRC, *(float *)VAL, N); break; \ + case HTP_OP_SUB: hvx_sub_scalar_f32_aa(DST, SRC, *(float *)VAL, N); break; \ + case HTP_OP_MUL: hvx_mul_scalar_f32_aa(DST, SRC, *(float *)VAL, N); break; \ + case HTP_OP_DIV: hvx_mul_scalar_f32_aa(DST, SRC, 1.0f / (*(float *)VAL), N); break; \ + default: break; \ + } \ + } \ + else { \ + switch (octx->op) { \ + case HTP_OP_ADD: hvx_add_scalar_f16_aa(DST, SRC, *(_Float16 *)VAL, N); break; \ + case HTP_OP_SUB: hvx_sub_scalar_f16_aa(DST, SRC, *(_Float16 *)VAL, N); break; \ + case HTP_OP_MUL: hvx_mul_scalar_f16_aa(DST, SRC, *(_Float16 *)VAL, N); break; \ + case HTP_OP_DIV: hvx_div_scalar_f16_aa(DST, SRC, *(_Float16 *)VAL, N); break; \ + default: break; \ + } \ } // Macro for vector op switch (All Aligned) -#define COMPUTE_VECTOR_OP_AAA(DST, SRC0, SRC1, N) \ - switch (octx->op) { \ - case HTP_OP_ADD: hvx_add_f32_aaa(DST, SRC0, SRC1, N); break; \ - case HTP_OP_SUB: hvx_sub_f32_aaa(DST, SRC0, SRC1, N); break; \ - case HTP_OP_MUL: hvx_mul_f32_aaa(DST, SRC0, SRC1, N); break; \ - case HTP_OP_DIV: hvx_div_f32_aaa(DST, SRC0, SRC1, N); break; \ - default: break; \ +#define COMPUTE_VECTOR_OP_AAA(DST, SRC0, SRC1, TYPE, N) \ + if(TYPE == HTP_TYPE_F32) { \ + switch (octx->op) { \ + case HTP_OP_ADD: hvx_add_f32_aaa(DST, SRC0, SRC1, N); break; \ + case HTP_OP_SUB: hvx_sub_f32_aaa(DST, SRC0, SRC1, N); break; \ + case HTP_OP_MUL: hvx_mul_f32_aaa(DST, SRC0, SRC1, N); break; \ + case HTP_OP_DIV: hvx_div_f32_aaa(DST, SRC0, SRC1, N); break; \ + default: break; \ + } \ + } \ + else { \ + switch (octx->op) { \ + case HTP_OP_ADD: hvx_add_f16_aaa(DST, SRC0, SRC1, N); break; \ + case HTP_OP_SUB: hvx_sub_f16_aaa(DST, SRC0, SRC1, N); break; \ + case HTP_OP_MUL: hvx_mul_f16_aaa(DST, SRC0, SRC1, N); break; \ + case HTP_OP_DIV: hvx_div_f16_aaa(DST, SRC0, SRC1, N); break; \ + default: break; \ + } \ } // Macro for vector op switch (Dst Aligned, Src0 Aligned, Src1 Unaligned) -#define COMPUTE_VECTOR_OP_AAU(DST, SRC0, SRC1, N) \ - switch (octx->op) { \ - case HTP_OP_ADD: hvx_add_f32_aau(DST, SRC0, SRC1, N); break; \ - case HTP_OP_SUB: hvx_sub_f32_aau(DST, SRC0, SRC1, N); break; \ - case HTP_OP_MUL: hvx_mul_f32_aau(DST, SRC0, SRC1, N); break; \ - case HTP_OP_DIV: hvx_div_f32_aau(DST, SRC0, SRC1, N); break; \ - default: break; \ +#define COMPUTE_VECTOR_OP_AAU(DST, SRC0, SRC1, TYPE, N) \ + if(TYPE == HTP_TYPE_F32) { \ + switch (octx->op) { \ + case HTP_OP_ADD: hvx_add_f32_aau(DST, SRC0, SRC1, N); break; \ + case HTP_OP_SUB: hvx_sub_f32_aau(DST, SRC0, SRC1, N); break; \ + case HTP_OP_MUL: hvx_mul_f32_aau(DST, SRC0, SRC1, N); break; \ + case HTP_OP_DIV: hvx_div_f32_aau(DST, SRC0, SRC1, N); break; \ + default: break; \ + } \ + } \ + else { \ + switch (octx->op) { \ + case HTP_OP_ADD: hvx_add_f16_aau(DST, SRC0, SRC1, N); break; \ + case HTP_OP_SUB: hvx_sub_f16_aau(DST, SRC0, SRC1, N); break; \ + case HTP_OP_MUL: hvx_mul_f16_aau(DST, SRC0, SRC1, N); break; \ + case HTP_OP_DIV: hvx_div_f16_aau(DST, SRC0, SRC1, N); break; \ + default: break; \ + } \ } // Macro for vector op switch (All Unaligned - generic loop used in element repeat) -#define COMPUTE_VECTOR_OP_UUU(DST, SRC0, SRC1, N) \ - switch (octx->op) { \ - case HTP_OP_ADD: hvx_add_f32_uuu(DST, SRC0, SRC1, N); break; \ - case HTP_OP_SUB: hvx_sub_f32_uuu(DST, SRC0, SRC1, N); break; \ - case HTP_OP_MUL: hvx_mul_f32_uuu(DST, SRC0, SRC1, N); break; \ - case HTP_OP_DIV: hvx_div_f32_uuu(DST, SRC0, SRC1, N); break; \ - default: break; \ +#define COMPUTE_VECTOR_OP_UUU(DST, SRC0, SRC1, TYPE, N) \ + if(TYPE == HTP_TYPE_F32) { \ + switch (octx->op) { \ + case HTP_OP_ADD: hvx_add_f32_uuu(DST, SRC0, SRC1, N); break; \ + case HTP_OP_SUB: hvx_sub_f32_uuu(DST, SRC0, SRC1, N); break; \ + case HTP_OP_MUL: hvx_mul_f32_uuu(DST, SRC0, SRC1, N); break; \ + case HTP_OP_DIV: hvx_div_f32_uuu(DST, SRC0, SRC1, N); break; \ + default: break; \ + } \ + } \ + else { \ + switch (octx->op) { \ + case HTP_OP_ADD: hvx_add_f16_uuu(DST, SRC0, SRC1, N); break; \ + case HTP_OP_SUB: hvx_sub_f16_uuu(DST, SRC0, SRC1, N); break; \ + case HTP_OP_MUL: hvx_mul_f16_uuu(DST, SRC0, SRC1, N); break; \ + case HTP_OP_DIV: hvx_div_f16_uuu(DST, SRC0, SRC1, N); break; \ + default: break; \ + } \ } // 1. Scalar src1 (ne10 == 1) @@ -140,6 +184,8 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) { struct htp_ops_context * octx = bctx->octx; htp_binary_preamble; + const uint32_t src0_type = octx->src0.type; + const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16); const uint32_t total_rows = ne01 * ne02 * ne03; const uint32_t start_row = bctx->nrows_per_thread * ith; const uint32_t end_row = MIN(start_row + bctx->nrows_per_thread, total_rows); @@ -170,7 +216,7 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) { uint8_t * d_spad = dst_spad_base + spad_idx * dst_spad_half; dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0); - dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size); ir_prefetch += current_block_size; spad_idx ^= 1; } @@ -199,13 +245,12 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) { for (uint32_t r = 0; r < current_block_size; r++) { uint8_t * r_src0 = s0_spad + r * bctx->src0_row_size_aligned; uint8_t * r_dst = d_spad + r * bctx->dst_row_size_aligned; - float val = *(float *)src1_ptr; + COMPUTE_SCALAR_OP(r_dst, r_src0, src1_ptr, src0_type, ne00); src1_ptr += s1_stride; - COMPUTE_SCALAR_OP(r_dst, r_src0, val, ne00); } uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1; - dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, current_block_size); if (ir_prefetch < end_row) { uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02); @@ -216,7 +261,7 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) { p01 = prem - p02 * ne01; uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01; - dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, row_size_bytes, next_block_size); ir_prefetch += next_block_size; } ir += current_block_size; @@ -230,6 +275,8 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi struct htp_ops_context * octx = bctx->octx; htp_binary_preamble; + const uint32_t src0_type = octx->src0.type; + const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16); const uint32_t total_rows = ne01 * ne02 * ne03; const uint32_t start_row = bctx->nrows_per_thread * ith; const uint32_t end_row = MIN(start_row + bctx->nrows_per_thread, total_rows); @@ -268,8 +315,8 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi uint8_t * d_spad = dst_spad_base + spad_idx * dst_spad_half; dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0); - dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size); - dma_queue_push(q, dma_make_ptr(s1_spad, src1_base), bctx->src1_row_size_aligned, bctx->src1_dma_stride, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size); + dma_queue_push(q, dma_make_ptr(s1_spad, src1_base), bctx->src1_row_size_aligned, bctx->src1_dma_stride, row_size_bytes, current_block_size); ir_prefetch += current_block_size; spad_idx ^= 1; } @@ -284,7 +331,7 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi uint8_t * r_src0 = s0_spad + r * bctx->src0_row_size_aligned; uint8_t * r_src1 = s1_spad + r * bctx->src1_row_size_aligned; uint8_t * r_dst = d_spad + r * bctx->dst_row_size_aligned; - COMPUTE_VECTOR_OP_AAA(r_dst, r_src0, r_src1, ne00); + COMPUTE_VECTOR_OP_AAA(r_dst, r_src0, r_src1, src0_type, ne00); } uint32_t i03, i02, i01, rem; @@ -293,7 +340,7 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi i02 = fastdiv(rem, &bctx->dim1_div); i01 = rem - i02 * ne01; uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1; - dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, current_block_size); if (ir_prefetch < end_row) { uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02); @@ -310,8 +357,8 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01; uint8_t * s1_next = (uint8_t *)src1->data + p13 * nb13 + p12 * nb12 + p11 * nb11; - dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size); - dma_queue_push(q, dma_make_ptr(s1_spad, s1_next), bctx->src1_row_size_aligned, bctx->src1_dma_stride, ne00 * sizeof(float), next_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, row_size_bytes, next_block_size); + dma_queue_push(q, dma_make_ptr(s1_spad, s1_next), bctx->src1_row_size_aligned, bctx->src1_dma_stride, row_size_bytes, next_block_size); ir_prefetch += next_block_size; } @@ -326,6 +373,8 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith, struct htp_ops_context * octx = bctx->octx; htp_binary_preamble; + const uint32_t src0_type = octx->src0.type; + const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16); const uint32_t total_rows = ne01 * ne02 * ne03; const uint32_t start_row = bctx->nrows_per_thread * ith; const uint32_t end_row = MIN(start_row + bctx->nrows_per_thread, total_rows); @@ -359,7 +408,7 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith, uint8_t * d_spad = dst_spad_base + spad_idx * dst_spad_half; dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0); - dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size); ir_prefetch += current_block_size; spad_idx ^= 1; } @@ -373,7 +422,7 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith, uint8_t * r_src0 = s0_spad + r * bctx->src0_row_size_aligned; uint8_t * r_src1 = (uint8_t *)s1_ptr; // Constant uint8_t * r_dst = d_spad + r * bctx->dst_row_size_aligned; - COMPUTE_VECTOR_OP_AAA(r_dst, r_src0, r_src1, ne00); + COMPUTE_VECTOR_OP_AAA(r_dst, r_src0, r_src1, src0_type, ne00); } uint32_t i03, i02, i01, rem; @@ -382,7 +431,7 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith, i02 = fastdiv(rem, &bctx->dim1_div); i01 = rem - i02 * ne01; uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1; - dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, current_block_size); if (ir_prefetch < end_row) { uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02); @@ -392,7 +441,7 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith, p02 = fastdiv(prem, &bctx->dim1_div); p01 = prem - p02 * ne01; uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01; - dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, row_size_bytes, next_block_size); ir_prefetch += next_block_size; } ir += current_block_size; @@ -406,6 +455,8 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void * struct htp_ops_context * octx = bctx->octx; htp_binary_preamble; + const uint32_t src0_type = octx->src0.type; + const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16); const uint32_t total_rows = ne01 * ne02 * ne03; const uint32_t start_row = bctx->nrows_per_thread * ith; const uint32_t end_row = MIN(start_row + bctx->nrows_per_thread, total_rows); @@ -435,7 +486,7 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void * uint8_t * d_spad = dst_spad_base + spad_idx * dst_spad_half; dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0); - dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size); ir_prefetch += current_block_size; spad_idx ^= 1; } @@ -462,11 +513,11 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void * uint8_t * r_dst = d_spad + r * bctx->dst_row_size_aligned; // Read src1 from DDR (unaligned) - COMPUTE_VECTOR_OP_AAU(r_dst, r_src0, r_src1, ne00); + COMPUTE_VECTOR_OP_AAU(r_dst, r_src0, r_src1, src0_type, ne00); } uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1; - dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, current_block_size); if (ir_prefetch < end_row) { uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02); @@ -476,7 +527,7 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void * p02 = fastdiv(prem, &bctx->dim1_div); p01 = prem - p02 * ne01; uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01; - dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, row_size_bytes, next_block_size); ir_prefetch += next_block_size; } ir += current_block_size; @@ -490,6 +541,9 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void * struct htp_ops_context * octx = bctx->octx; htp_binary_preamble; + const uint32_t src0_type = octx->src0.type; + const uint32_t elem_size_bytes = (src0_type == HTP_TYPE_F32) ? sizeof(float) : sizeof(_Float16); + const uint32_t row_size_bytes = ne00 * elem_size_bytes;; const uint32_t total_rows = ne01 * ne02 * ne03; const uint32_t start_row = bctx->nrows_per_thread * ith; const uint32_t end_row = MIN(start_row + bctx->nrows_per_thread, total_rows); @@ -519,7 +573,7 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void * uint8_t * d_spad = dst_spad_base + spad_idx * dst_spad_half; dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0); - dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size); ir_prefetch += current_block_size; spad_idx ^= 1; } @@ -549,12 +603,12 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void * for (uint32_t c = 0; c < ne00; c += ne10) { uint32_t len = MIN(ne10, ne00 - c); // Use UUU for speed and simplicity - COMPUTE_VECTOR_OP_UUU(r_dst + c * sizeof(float), r_src0 + c * sizeof(float), r_src1_row, len); + COMPUTE_VECTOR_OP_UUU(r_dst + c * elem_size_bytes, r_src0 + c * elem_size_bytes, r_src1_row, src0_type, len); } } uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1; - dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), current_block_size); + dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, current_block_size); if (ir_prefetch < end_row) { uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02); @@ -564,7 +618,7 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void * p02 = fastdiv(prem, &bctx->dim1_div); p01 = prem - p02 * ne01; uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01; - dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size); + dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, row_size_bytes, next_block_size); ir_prefetch += next_block_size; } ir += current_block_size; @@ -672,18 +726,20 @@ static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) { dma_queue_flush(q); } -static int execute_op_binary_f32(struct htp_ops_context * octx) { +static int execute_op_binary(struct htp_ops_context * octx) { const struct htp_tensor * src0 = &octx->src0; const struct htp_tensor * src1 = &octx->src1; struct htp_tensor * dst = &octx->dst; - const uint32_t n_threads = octx->n_threads; const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; + const uint32_t n_threads = MIN(octx->n_threads, src0_nrows); // Use packed row sizes for VTCM allocation - const size_t src0_row_size = src0->ne[0] * sizeof(float); - const size_t src1_row_size = src1->ne[0] * sizeof(float); - const size_t dst_row_size = dst->ne[0] * sizeof(float); + const uint32_t src0_type = octx->src0.type; + const size_t elem_size = (src0_type == HTP_TYPE_F32) ? sizeof(float) : sizeof(_Float16); + const size_t src0_row_size = src0->ne[0] * elem_size; + const size_t src1_row_size = src1->ne[0] * elem_size; + const size_t dst_row_size = dst->ne[0] * elem_size; // Align to VLEN const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); @@ -694,7 +750,7 @@ static int execute_op_binary_f32(struct htp_ops_context * octx) { bool is_scalar = !is_add_id && (src1->ne[0] == 1); // Determine which kernel we will use to alloc memory and dispatch - bool use_vector_same = !is_add_id && !is_scalar && src1->ne[0] == src0->ne[0] && + bool use_vector_same = !is_add_id && !is_scalar && ((src0->nb[1] % VLEN) == 0) && (src1->ne[0] == src0->ne[0]) && (src1->ne[1] == src0->ne[1] || src1->ne[1] == 1) && (src1->ne[2] == src0->ne[2] || src1->ne[2] == 1) && (src1->ne[3] == src0->ne[3] || src1->ne[3] == 1); @@ -726,7 +782,7 @@ static int execute_op_binary_f32(struct htp_ops_context * octx) { } if (rows_per_buffer < 1) { - FARF(ERROR, "binary-f32: VTCM too small\n"); + FARF(ERROR, "binary: VTCM too small\n"); return HTP_STATUS_VTCM_TOO_SMALL; } @@ -761,16 +817,14 @@ static int execute_op_binary_f32(struct htp_ops_context * octx) { return HTP_STATUS_OK; } - uint32_t n_jobs = MIN(n_threads, src0_nrows); - dma_queue * q = octx->ctx->dma[0]; if (is_row_bcast) { - dma_queue_push(q, dma_make_ptr(octx->src1_spad.data, (const void *) src1->data), src1_row_size_aligned, 0, src1->ne[0] * sizeof(float), 1); + dma_queue_push(q, dma_make_ptr(octx->src1_spad.data, (const void *) src1->data), src1_row_size_aligned, 0, src1->ne[0] * elem_size, 1); } struct htp_binary_context bctx; bctx.octx = octx; - bctx.nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs; + bctx.nrows_per_thread = (src0_nrows + n_threads - 1) / n_threads; bctx.block_max = rows_per_buffer; bctx.src0_row_size_aligned = src0_row_size_aligned; bctx.src1_row_size_aligned = src1_row_size_aligned; @@ -814,14 +868,24 @@ static int execute_op_binary_f32(struct htp_ops_context * octx) { dma_queue_pop(q); } - worker_pool_run_func(octx->ctx->worker_pool, worker_func, &bctx, n_jobs); + worker_pool_run_func(octx->ctx->worker_pool, worker_func, &bctx, n_threads); return HTP_STATUS_OK; } int op_binary(struct htp_ops_context * octx) { - if (octx->src0.type == HTP_TYPE_F32) { - return execute_op_binary_f32(octx); + + // Does not support permutations of src1 + const struct htp_tensor * src1 = &octx->src1; + if (src1->nb[1] < src1->nb[0]) { + return HTP_STATUS_NO_SUPPORT; } + + const uint32_t src0_type = octx->src0.type; + if ((src0_type == HTP_TYPE_F32) || (src0_type == HTP_TYPE_F16)) { + return execute_op_binary(octx); + } + return HTP_STATUS_NO_SUPPORT; } + diff --git a/ggml/src/ggml-hexagon/htp/cpy-ops.c b/ggml/src/ggml-hexagon/htp/cpy-ops.c index 559ca18378..a40d866b9c 100644 --- a/ggml/src/ggml-hexagon/htp/cpy-ops.c +++ b/ggml/src/ggml-hexagon/htp/cpy-ops.c @@ -202,6 +202,8 @@ static void cpy_work_func(unsigned int n, unsigned int i, void *data) { int op_cpy(struct htp_ops_context * octx) { cpy_preamble; + const uint32_t n_threads = MIN(nr, octx->n_threads); + struct htp_copy_context ct; ct.octx = octx; @@ -227,8 +229,7 @@ int op_cpy(struct htp_ops_context * octx) { const bool transposed = (nb00 > nb01) || (nb0 > nb1); const bool sameshape = !transposed && (ne00 == ne0 && ne01 == ne1 && ne02 == ne2 && ne03 == ne3); - const uint32_t n_jobs = MIN(nr, octx->n_threads); - ct.src0_nrows_per_thread = (nr + n_jobs - 1) / n_jobs; + ct.src0_nrows_per_thread = (nr + n_threads - 1) / n_threads; if (sametype && sameshape) { ct.copy = cpy_thread_sametype_sameshape; @@ -245,7 +246,7 @@ int op_cpy(struct htp_ops_context * octx) { return HTP_STATUS_NO_SUPPORT; } - worker_pool_run_func(octx->ctx->worker_pool, cpy_work_func, &ct, n_jobs); + worker_pool_run_func(octx->ctx->worker_pool, cpy_work_func, &ct, n_threads); return HTP_STATUS_OK; } diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c index 74c777d4c3..6dc978dd68 100644 --- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c +++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c @@ -10,6 +10,7 @@ #include "hex-dma.h" #include "hvx-utils.h" +#include "hvx-dump.h" #define GGML_COMMON_DECL_C #include "ggml-common.h" @@ -17,6 +18,16 @@ #include "htp-msg.h" #include "htp-ops.h" +// Must be multiple of 32 +#define FLASH_ATTN_BLOCK_SIZE (32 * 2) + +// This is a bit of a hack because the compiler is strugling to properly inline +// the default hvx_vec_f32_to_f16 with output into the local array. +static void __attribute__((noinline)) hvx_vec_f32_to_f16_a(void *ptr, HVX_Vector v0, HVX_Vector v1) +{ + *(HVX_Vector *) ptr = hvx_vec_f32_to_f16(v0, v1); +} + // Dot product of two F16 vectors, accumulating to float static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict x, const void * restrict y, unsigned int n, float s) { const HVX_Vector * restrict vx = (const HVX_Vector * restrict) x; // fp16 @@ -25,175 +36,184 @@ static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors uint32_t nloe = n % VLEN_FP16; // leftover elements - HVX_Vector rsum = Q6_V_vsplat_R(0); + HVX_VectorPair rsum_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0)); uint32_t i = 0; #pragma unroll(4) for (i = 0; i < nvec; i++) { - HVX_Vector y_hf = vy[i]; - HVX_Vector x_hf = vx[i]; - - HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf); - - rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf)), rsum)); + rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, vx[i], vy[i]); } if (nloe) { - // Load x (fp16) and zero-out unused elements HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2); HVX_Vector y_hf = Q6_V_vand_QV(bmask, vy[i]); HVX_Vector x_hf = Q6_V_vand_QV(bmask, vx[i]); - HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf); - - rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf)), rsum)); + rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x_hf, y_hf); } - rsum = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32(rsum)); - hvx_vec_store_u(r, 4, Q6_Vsf_equals_Vqf32(rsum)); + HVX_Vector rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum_p), Q6_V_hi_W(rsum_p))); + rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32(rsum))); + hvx_vec_store_u(r, 4, rsum); } -static inline void hvx_dot_f16_f16_aa_rx2(float * restrict r, - const void * restrict y, - const void * restrict x0, - const void * restrict x1, - unsigned int n, - float s) { - const HVX_Vector * restrict vx0 = (const HVX_Vector * restrict) x0; // fp16 - const HVX_Vector * restrict vx1 = (const HVX_Vector * restrict) x1; // fp16 - const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp16 +static inline HVX_Vector hvx_dot_f16_f16_aa_rx4(const void * restrict y, + const uint8_t * restrict x, + const size_t stride_x, + const size_t nvec, + const size_t nloe) { + const HVX_Vector * restrict vx0 = (const HVX_Vector * restrict) x; // fp16 + const HVX_Vector * restrict vx1 = (const HVX_Vector * restrict) (x + stride_x); // fp16 + const HVX_Vector * restrict vx2 = (const HVX_Vector * restrict) (x + stride_x * 2); // fp16 + const HVX_Vector * restrict vx3 = (const HVX_Vector * restrict) (x + stride_x * 3); // fp16 + const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp16 - uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors - uint32_t nloe = n % VLEN_FP16; // leftover elements - - HVX_Vector rsum0 = Q6_V_vsplat_R(0); - HVX_Vector rsum1 = Q6_V_vsplat_R(0); + HVX_VectorPair rsum0_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0)); + HVX_VectorPair rsum1_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0)); + HVX_VectorPair rsum2_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0)); + HVX_VectorPair rsum3_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0)); uint32_t i = 0; - #pragma unroll(4) for (i = 0; i < nvec; i++) { HVX_Vector y_hf = vy[i]; HVX_Vector x0_hf = vx0[i]; HVX_Vector x1_hf = vx1[i]; + HVX_Vector x2_hf = vx2[i]; + HVX_Vector x3_hf = vx3[i]; - HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf); - HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf); - - rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf)), rsum0)); - rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf)), rsum1)); + rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0_hf, y_hf); + rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1_hf, y_hf); + rsum2_p = hvx_vec_mpyacc_f32_f16(rsum2_p, x2_hf, y_hf); + rsum3_p = hvx_vec_mpyacc_f32_f16(rsum3_p, x3_hf, y_hf); } if (nloe) { // Load x (fp16) and zero-out unused elements HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2); - HVX_Vector x0_hf = Q6_V_vand_QV(bmask, vx0[i]); - HVX_Vector x1_hf = Q6_V_vand_QV(bmask, vx1[i]); - HVX_Vector y_hf = Q6_V_vand_QV(bmask, vy[i]); + HVX_Vector y_hf = Q6_V_vand_QV(bmask, vy[i]); + HVX_Vector x0_hf = Q6_V_vand_QV(bmask, vx0[i]); + HVX_Vector x1_hf = Q6_V_vand_QV(bmask, vx1[i]); + HVX_Vector x2_hf = Q6_V_vand_QV(bmask, vx2[i]); + HVX_Vector x3_hf = Q6_V_vand_QV(bmask, vx3[i]); - HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf); - HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf); - - rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf)), rsum0)); - rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf)), rsum1)); + rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0_hf, y_hf); + rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1_hf, y_hf); + rsum2_p = hvx_vec_mpyacc_f32_f16(rsum2_p, x2_hf, y_hf); + rsum3_p = hvx_vec_mpyacc_f32_f16(rsum3_p, x3_hf, y_hf); } - HVX_Vector rsum = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32x2(rsum0, rsum1)); - hvx_vec_store_u(r, 8, Q6_Vsf_equals_Vqf32(rsum)); + HVX_Vector rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum0_p), Q6_V_hi_W(rsum0_p))); + HVX_Vector rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum1_p), Q6_V_hi_W(rsum1_p))); + HVX_Vector rsum2 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum2_p), Q6_V_hi_W(rsum2_p))); + HVX_Vector rsum3 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum3_p), Q6_V_hi_W(rsum3_p))); + + HVX_Vector_x4 rsum0123 = { .v = { rsum0, rsum1, rsum2, rsum3 } }; + return hvx_vec_reduce_sum_f32x4(rsum0123); } -// MAD: y (F32) += x (F16) * s (F32) -static inline void hvx_mad_f32_f16_aa(float * restrict y, const void * restrict x, int n, float s) { - const HVX_Vector * restrict ptr_x = (const HVX_Vector *) x; - HVX_Vector * restrict ptr_y = (HVX_Vector *) y; +static inline HVX_Vector hvx_dot_f16_f16_aa_rx32(const void * restrict y, + const uint8_t * restrict x, + const size_t stride_x, + const size_t n, + float s) { + + const size_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors + const size_t nloe = n % VLEN_FP16; // leftover elements + + HVX_Vector sums; // initialize at j = 0 + const size_t stride_x_4 = stride_x * 4; + for (uint32_t j = 0; j < VLEN_FP32; j += 4) { + HVX_Vector sums_x4 = hvx_dot_f16_f16_aa_rx4(y, x, stride_x, nvec, nloe); + HVX_VectorPred pred = Q6_Q_vsetq_R(j * SIZEOF_FP32); + sums = Q6_V_vmux_QVV(pred, sums, sums_x4); + x += stride_x_4; + } + + sums = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), sums); + return Q6_Vsf_equals_Vqf32(sums); +} + +// MAD: y (F32) += x (F16) * s (F16) +static inline void hvx_mad_f32_f16_aa(float * restrict y, const void * restrict x, const __fp16 * restrict s, int n) { + const HVX_Vector * restrict vx0 = (const HVX_Vector *) x; + + HVX_VectorPair * restrict vy_p = (HVX_VectorPair *) y; + HVX_Vector * restrict vy = (HVX_Vector *) y; uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors uint32_t nloe = n % VLEN_FP16; // leftover elements - HVX_Vector S = hvx_vec_splat_f16(s); + HVX_Vector S0 = hvx_vec_splat_f16(*s); uint32_t i = 0; - #pragma unroll(4) + + #pragma unroll(2) for (i = 0; i < nvec; ++i) { - // Multiply x * s -> pair of F32 vectors - HVX_VectorPair xs_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x[i]), S); - ptr_y[i*2] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_V_lo_W(xs_p), ptr_y[i*2])); - ptr_y[i*2+1] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_V_hi_W(xs_p), ptr_y[i*2+1])); + vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0); } if (nloe) { - HVX_VectorPair xs_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x[i]), S); + HVX_VectorPair xy_p = vy_p[i]; + xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0); - HVX_Vector xs = Q6_V_lo_W(xs_p); - i = 2 * i; // index for ptr_y + HVX_Vector xy = Q6_V_lo_W(xy_p); + i = 2 * i; // index for vy - if (nloe >= 32) { - ptr_y[i] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs, ptr_y[i])); - nloe -= 32; ++i; xs = Q6_V_hi_W(xs_p); + if (nloe >= VLEN_FP32) { + vy[i] = xy; + nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p); } if (nloe) { - HVX_Vector xy = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs, ptr_y[i])); - hvx_vec_store_a(&ptr_y[i], nloe * 4, xy); + hvx_vec_store_a(&vy[i], nloe * 4, xy); } } } -// MAD: y (F32) += x0 (F16) * s0 (F32) + x1 (F16) * s1 (F32) -static inline void hvx_mad_f32_f16_aa_rx2(float * restrict y, - const void * restrict x0, - const void * restrict x1, - float s0, - float s1, - int n) { - const HVX_Vector * restrict ptr_x0 = (const HVX_Vector *) x0; - const HVX_Vector * restrict ptr_x1 = (const HVX_Vector *) x1; - HVX_Vector * restrict ptr_y = (HVX_Vector *) y; +// MAD: y (F32) += x0 (F16) * s0 (F16) + x1 (F16) * s1 (F16) +static inline void hvx_mad_f32_f16_aa_rx2(float * restrict y, const void * restrict x0, const void * restrict x1, + const __fp16 * restrict s0, const __fp16 * restrict s1, int n) { + const HVX_Vector * restrict vx0 = (const HVX_Vector *) x0; + const HVX_Vector * restrict vx1 = (const HVX_Vector *) x1; + + HVX_VectorPair * restrict vy_p = (HVX_VectorPair *) y; + HVX_Vector * restrict vy = (HVX_Vector *) y; uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors uint32_t nloe = n % VLEN_FP16; // leftover elements - HVX_Vector S0 = hvx_vec_splat_f16(s0); - HVX_Vector S1 = hvx_vec_splat_f16(s1); + HVX_Vector S0 = hvx_vec_splat_f16(*s0); + HVX_Vector S1 = hvx_vec_splat_f16(*s1); uint32_t i = 0; + #pragma unroll(2) for (i = 0; i < nvec; ++i) { - // Multiply x * s -> pair of F32 vectors - HVX_VectorPair xs0_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x0[i]), S0); - HVX_VectorPair xs1_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x1[i]), S1); - - HVX_Vector xs_p_lo = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xs0_p), Q6_V_lo_W(xs1_p)); - HVX_Vector xs_p_hi = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_hi_W(xs0_p), Q6_V_hi_W(xs1_p)); - - ptr_y[i * 2] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs_p_lo, ptr_y[i * 2])); - ptr_y[i * 2 + 1] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs_p_hi, ptr_y[i * 2 + 1])); + vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0); + vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx1[i]), S1); } if (nloe) { - HVX_VectorPair xs0_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x0[i]), S0); - HVX_VectorPair xs1_p = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(ptr_x1[i]), S1); + HVX_VectorPair xy_p = vy_p[i]; + xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0); + xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx1[i]), S1); - HVX_Vector xs_p_lo = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xs0_p), Q6_V_lo_W(xs1_p)); - HVX_Vector xs = xs_p_lo; - i = 2 * i; // index for ptr_y + HVX_Vector xy = Q6_V_lo_W(xy_p); + i = 2 * i; // index for vy - if (nloe >= 32) { - ptr_y[i] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs, ptr_y[i])); - nloe -= 32; ++i; - xs = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_hi_W(xs0_p), Q6_V_hi_W(xs1_p)); + if (nloe >= VLEN_FP32) { + vy[i] = xy; + nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p); } if (nloe) { - HVX_Vector xy = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(xs, ptr_y[i])); - hvx_vec_store_a(&ptr_y[i], nloe * 4, xy); + hvx_vec_store_a(&vy[i], nloe * 4, xy); } } } -#define FLASH_ATTN_BLOCK_SIZE 128 - struct htp_fa_context { const struct htp_ops_context * octx; @@ -226,7 +246,12 @@ struct htp_fa_context { size_t size_v_block; size_t size_m_block; + uint32_t qrows; + uint32_t qrows_per_thread; + bool is_q_fp32; + + uint64_t t_start; }; static inline void hvx_scale_vec_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const int n, HVX_Vector vs) { @@ -296,9 +321,8 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * const uint32_t nb3 = dst->nb[3]; // total rows in q - const uint32_t nr = neq1*neq2*neq3; - - const uint32_t dr = (nr + nth - 1) / nth; + const uint32_t nr = factx->qrows; + const uint32_t dr = factx->qrows_per_thread; const uint32_t ir0 = dr * ith; const uint32_t ir1 = MIN(ir0 + dr, nr); @@ -337,15 +361,8 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * const uint8_t * q_row_ptr = (const uint8_t *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3); dma_queue_push(dma, dma_make_ptr(spad_q, q_row_ptr), factx->size_q_row_padded, nbq1, size_q_row, 1); - const uint32_t h = iq2; // head index - const float slope = (factx->max_bias > 0.0f) ? (h < factx->n_head_log2 ? powf(factx->m0, h + 1) : powf(factx->m1, 2*(h - factx->n_head_log2) + 1)) : 1.0f; - - HVX_Vector S_vec = hvx_vec_splat_f32(0.0f); - HVX_Vector M_vec = hvx_vec_splat_f32(-INFINITY); - - // Clear accumulator - hvx_splat_f32_a(spad_a, 0, DV); - float * VKQ32 = (float *) spad_a; + // FARF(HIGH, "fa %u: prefetch Q: ir %u iq1 %u iq2 %u iq3 %u q_row_ptr %p size %u : usec %u", ith, ir, iq1, iq2, iq3, q_row_ptr, size_q_row, + // (unsigned)HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - factx->t_start)); const __fp16 * mp_base = NULL; if (mask) { @@ -376,8 +393,23 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * // Mask is 1D contiguous for this row dma_queue_push(dma, dma_make_ptr(m_dst, m_src), current_block_size * 2, current_block_size * 2, current_block_size * 2, 1); } + + // FARF(HIGH, "fa %u: prefetch KVM: ir %u ib %u iq1 %u iq2 %u iq3 %u : size_k_row %u size_v_row %u bs %u: usec %u", + // ith, ir, ib, iq1, iq2, iq3, + // size_k_row, size_v_row, current_block_size, + // (unsigned)HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - factx->t_start)); } + const uint32_t h = iq2; // head index + const float slope = (factx->max_bias > 0.0f) ? (h < factx->n_head_log2 ? powf(factx->m0, h + 1) : powf(factx->m1, 2*(h - factx->n_head_log2) + 1)) : 1.0f; + + HVX_Vector S_vec = hvx_vec_splat_f32(0.0f); + HVX_Vector M_vec = hvx_vec_splat_f32(-INFINITY); + + // Clear accumulator + hvx_splat_f32_a(spad_a, 0, DV); + float * VKQ32 = (float *) (spad_a + 0); + uint8_t * q_ptr_vtcm = dma_queue_pop(dma).dst; if (factx->is_q_fp32) { hvx_copy_f16_f32_aa(q_ptr_vtcm, q_ptr_vtcm, DK); // inplace convert f32 to f16 @@ -393,23 +425,19 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * uint8_t * v_base = dma_queue_pop(dma).dst; // V __fp16 * m_base = mask ? dma_queue_pop(dma).dst : NULL; // M + // FARF(HIGH, "fa %u: process: ir %u ib %u : iq1 %u iq2 %u iq3 %u q_ptr_vtcm %p : usec %u", + // ith, ir, ib, iq1, iq2, iq3, q_ptr_vtcm, + // (unsigned)HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - factx->t_start)); + // Inner loop processing the block from VTCM uint32_t ic = 0; - // Process in blocks of 32 (VLEN_FP32) - static_assert(FLASH_ATTN_BLOCK_SIZE / VLEN_FP32 <= 4, "FLASH_ATTN_BLOCK_SIZE changed, fix HVX_Vector_x4 usage"); - HVX_Vector_x4 scores_x4; + // Process in sub-blocks of 32 (VLEN_FP32) + HVX_Vector sb_scores[FLASH_ATTN_BLOCK_SIZE / VLEN_FP32]; HVX_Vector v_max = hvx_vec_splat_f32(-INFINITY); for (uint32_t iv = 0; ic + VLEN_FP32 <= current_block_size; ic += VLEN_FP32, ++iv) { // 1. Compute scores - float __attribute__((aligned(VLEN))) scores_arr[VLEN_FP32]; - for (uint32_t j = 0; j < VLEN_FP32; j += 2) { - const uint32_t cur_ic = ic + j; - const uint8_t * k_ptr = k_base + cur_ic * factx->size_k_row_padded; - hvx_dot_f16_f16_aa_rx2(&scores_arr[j], q_ptr_vtcm, k_ptr, k_ptr + factx->size_k_row_padded, DK, factx->scale); - } - - HVX_Vector scores = *(HVX_Vector *) scores_arr; + HVX_Vector scores = hvx_dot_f16_f16_aa_rx32(q_ptr_vtcm, k_base + ic * factx->size_k_row_padded, factx->size_k_row_padded, DK, factx->scale); // 2. Softcap if (factx->logit_softcap != 0.0f) { @@ -428,35 +456,35 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * scores = Q6_Vsf_equals_Vqf32(scores); } - scores_x4.v[iv] = scores; + sb_scores[iv] = scores; v_max = hvx_vec_reduce_max2_f32(scores, v_max); // All lanes have block max } { // 4. Online Softmax Update HVX_Vector M_new_vec = Q6_Vsf_vmax_VsfVsf(v_max, M_vec); - HVX_Vector diff_vec = Q6_Vqf32_vsub_VsfVsf(M_vec, M_new_vec); - HVX_Vector ms_vec = hvx_vec_exp_f32(Q6_Vsf_equals_Vqf32(diff_vec)); + HVX_Vector diff_vec = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(M_vec, M_new_vec)); + HVX_Vector ms_vec = hvx_vec_exp_f32(diff_vec); M_vec = M_new_vec; hvx_scale_vec_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms_vec); HVX_Vector p_sum_vec = hvx_vec_splat_f32(0.0f); for (uint32_t ic2 = 0, iv = 0; ic2 + VLEN_FP32 <= current_block_size; ic2 += VLEN_FP32, ++iv) { - HVX_Vector scores = scores_x4.v[iv]; + HVX_Vector scores = sb_scores[iv]; HVX_Vector scores_shifted = Q6_Vqf32_vsub_VsfVsf(scores, M_vec); HVX_Vector P = hvx_vec_exp_f32(Q6_Vsf_equals_Vqf32(scores_shifted)); p_sum_vec = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(p_sum_vec, P)); // 5. Accumulate V - float __attribute__((aligned(VLEN))) p_arr[VLEN_FP32]; - *(HVX_Vector *) p_arr = P; + __fp16 __attribute__((aligned(VLEN))) p_arr[VLEN_FP16]; + hvx_vec_f32_to_f16_a(p_arr, P, hvx_vec_splat_f32(0)); for (uint32_t j = 0; j < VLEN_FP32; j += 2) { const uint32_t cur_ic = ic2 + j; const uint8_t * v_ptr = v_base + cur_ic * factx->size_v_row_padded; - hvx_mad_f32_f16_aa_rx2(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, p_arr[j], p_arr[j + 1], DV); + hvx_mad_f32_f16_aa_rx2(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, (p_arr + j), (p_arr + j + 1), DV); } } @@ -464,47 +492,50 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * S_vec = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(S_vec, ms_vec)), p_sum_vec)); } - // Sync scalars for leftover/next block if needed - float M = hvx_vec_get_f32(M_vec); - float S = hvx_vec_get_f32(S_vec); + if (ic < current_block_size) { + // Sync scalars for leftover/next block if needed + float M = hvx_vec_get_f32(M_vec); + float S = hvx_vec_get_f32(S_vec); - // Leftover - for (; ic < current_block_size; ++ic) { - float s_val; - const uint8_t * k_ptr = k_base + ic * factx->size_k_row_padded; - hvx_dot_f16_f16_aa(&s_val, q_ptr_vtcm, k_ptr, DK, factx->scale); - if (factx->logit_softcap != 0.0f) { - s_val = factx->logit_softcap * tanhf(s_val); + // Leftover + for (; ic < current_block_size; ++ic) { + float s_val; + const uint8_t * k_ptr = k_base + ic * factx->size_k_row_padded; + hvx_dot_f16_f16_aa(&s_val, q_ptr_vtcm, k_ptr, DK, factx->scale); + if (factx->logit_softcap != 0.0f) { + s_val = factx->logit_softcap * tanhf(s_val); + } + + if (mask) { + const float m_val = m_base[ic]; + s_val += slope * m_val; + } + + const float Mold = M; + __fp16 vs = 1.0f; + + if (s_val > M) { + M = s_val; + HVX_Vector diff_vec = hvx_vec_splat_f32(Mold - M); + HVX_Vector ms_vec = hvx_vec_exp_f32(diff_vec); + hvx_scale_vec_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms_vec); + + float ms = hvx_vec_get_f32(ms_vec); + S = S * ms + vs; + } else { + HVX_Vector diff_vec = hvx_vec_splat_f32(s_val - M); + vs = hvx_vec_get_f32(hvx_vec_exp_f32(diff_vec)); + S += vs; + } + + const uint8_t * v_ptr = v_base + ic * factx->size_v_row_padded; + + hvx_mad_f32_f16_aa(VKQ32, v_ptr, &vs, DV); } - if (mask) { - const float m_val = m_base[ic]; - s_val += slope * m_val; - } - - const float Mold = M; - float vs = 1.0f; - - if (s_val > M) { - M = s_val; - HVX_Vector diff_vec = hvx_vec_splat_f32(Mold - M); - HVX_Vector ms_vec = hvx_vec_exp_f32(diff_vec); - hvx_scale_vec_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms_vec); - - float ms = hvx_vec_get_f32(ms_vec); - S = S * ms + vs; - } else { - HVX_Vector diff_vec = hvx_vec_splat_f32(s_val - M); - vs = hvx_vec_get_f32(hvx_vec_exp_f32(diff_vec)); - S += vs; - } - - const uint8_t * v_ptr = v_base + ic * factx->size_v_row_padded; - - hvx_mad_f32_f16_aa(VKQ32, v_ptr, DV, vs); + M_vec = hvx_vec_splat_f32(M); + S_vec = hvx_vec_splat_f32(S); } - M_vec = hvx_vec_splat_f32(M); - S_vec = hvx_vec_splat_f32(S); // Issue DMA for next+1 block (if exists) if (ib + 2 < factx->n_blocks) { @@ -525,6 +556,11 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * const uint8_t * m_src = (const uint8_t *) (mp_base + next_ic_start); dma_queue_push(dma, dma_make_ptr(m_base, m_src), next_block_size * 2, next_block_size * 2, next_block_size * 2, 1); } + + // FARF(HIGH, "fa %u: prefetch KVM: ir %u ib %u : iq1 %u iq2 %u iq3 %u : size_k_row %u size_v_row %u bs %u: usec %u", + // ith, ir, next_ib, iq1, iq2, iq3, + // size_k_row, size_v_row, next_block_size, + // (unsigned)HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - factx->t_start)); } } @@ -586,6 +622,8 @@ int op_flash_attn_ext(struct htp_ops_context * octx) { struct htp_fa_context factx; factx.octx = octx; + factx.t_start = HAP_perf_get_qtimer_count(); + factx.src0_div21 = init_fastdiv_values(q->ne[2] * q->ne[1]); factx.src0_div1 = init_fastdiv_values(q->ne[1]); @@ -632,6 +670,15 @@ int op_flash_attn_ext(struct htp_ops_context * octx) { factx.m0 = powf(2.0f, -(max_bias ) / factx.n_head_log2); factx.m1 = powf(2.0f, -(max_bias / 2.0f) / factx.n_head_log2); + // total rows in q + const uint32_t neq0 = q->ne[0]; + const uint32_t neq1 = q->ne[1]; + const uint32_t neq2 = q->ne[2]; + const uint32_t neq3 = q->ne[3]; + + factx.qrows = neq1*neq2*neq3; + factx.qrows_per_thread = (factx.qrows + octx->n_threads - 1) / octx->n_threads; + size_t size_vkq_acc = hex_round_up(v->ne[0] * sizeof(float), 128); // VKQ32 octx->src0_spad.size_per_thread = size_q_block * 1; diff --git a/ggml/src/ggml-hexagon/htp/get-rows-ops.c b/ggml/src/ggml-hexagon/htp/get-rows-ops.c index a657cd2dcf..047d2850aa 100644 --- a/ggml/src/ggml-hexagon/htp/get-rows-ops.c +++ b/ggml/src/ggml-hexagon/htp/get-rows-ops.c @@ -15,6 +15,13 @@ #include "htp-ops.h" #include "hvx-utils.h" +struct get_rows_context { + struct htp_ops_context * octx; + uint32_t src1_nrows_per_thread; + struct fastdiv_values get_rows_div_ne10; + struct fastdiv_values get_rows_div_ne10_ne11; +}; + #define get_rows_preamble \ const uint32_t ne00 = octx->src0.ne[0]; \ const uint32_t ne01 = octx->src0.ne[1]; \ @@ -39,20 +46,22 @@ \ const uint32_t nr = ne10 * ne11 * ne12; -static int get_rows_thread_f32_f32(struct htp_ops_context * octx, const int nth, const int ith) { +static void get_rows_thread_f32_f32(unsigned int nth, unsigned int ith, void *data) { + struct get_rows_context * grctx = (struct get_rows_context *)data; + struct htp_ops_context * octx = grctx->octx; get_rows_preamble; // parallelize by src1 elements (which correspond to dst rows) - const uint32_t dr = octx->src1_nrows_per_thread; + const uint32_t dr = grctx->src1_nrows_per_thread; const uint32_t ir0 = dr * ith; const uint32_t ir1 = (ir0 + dr < nr) ? (ir0 + dr) : nr; const bool is_i32 = (octx->src1.type == HTP_TYPE_I32); for (uint32_t i = ir0; i < ir1; ++i) { - const uint32_t i12 = fastdiv(i, &octx->get_rows_div_ne10_ne11); + const uint32_t i12 = fastdiv(i, &grctx->get_rows_div_ne10_ne11); const uint32_t rem = i - i12 * ne11 * ne10; - const uint32_t i11 = fastdiv(rem, &octx->get_rows_div_ne10); + const uint32_t i11 = fastdiv(rem, &grctx->get_rows_div_ne10); const uint32_t i10 = rem - i11 * ne10; const uintptr_t src1_addr = octx->src1.data + i10*nb10 + i11*nb11 + i12*nb12; @@ -68,17 +77,13 @@ static int get_rows_thread_f32_f32(struct htp_ops_context * octx, const int nth, const uintptr_t dst_ptr = octx->dst.data + i10*nb1 + i11*nb2 + i12*nb3; hvx_copy_f32_uu((uint8_t *)dst_ptr, (const uint8_t *)src0_ptr, ne00); } - - return HTP_STATUS_OK; -} - -static void get_rows_work_f32_f32(unsigned int n, unsigned int i, void *data) { - get_rows_thread_f32_f32((struct htp_ops_context *) data, n, i); } int op_get_rows(struct htp_ops_context * octx) { get_rows_preamble; + const uint32_t n_threads = MIN(nr, octx->n_threads); + if (octx->src0.type != HTP_TYPE_F32) { return HTP_STATUS_NO_SUPPORT; } @@ -95,12 +100,13 @@ int op_get_rows(struct htp_ops_context * octx) { return HTP_STATUS_OK; } - octx->get_rows_div_ne10 = init_fastdiv_values(octx->src1.ne[0]); - octx->get_rows_div_ne10_ne11 = init_fastdiv_values(octx->src1.ne[0] * octx->src1.ne[1]); + struct get_rows_context grctx; + grctx.octx = octx; + grctx.get_rows_div_ne10 = init_fastdiv_values(octx->src1.ne[0]); + grctx.get_rows_div_ne10_ne11 = init_fastdiv_values(octx->src1.ne[0] * octx->src1.ne[1]); - const uint32_t n_jobs = MIN(nr, octx->n_threads); - octx->src1_nrows_per_thread = (nr + n_jobs - 1) / n_jobs; + grctx.src1_nrows_per_thread = (nr + n_threads - 1) / n_threads; - worker_pool_run_func(octx->ctx->worker_pool, get_rows_work_f32_f32, octx, n_jobs); + worker_pool_run_func(octx->ctx->worker_pool, get_rows_thread_f32_f32, &grctx, n_threads); return HTP_STATUS_OK; } diff --git a/ggml/src/ggml-hexagon/htp/hex-dma.h b/ggml/src/ggml-hexagon/htp/hex-dma.h index d1ddb0ecbf..350ab9d966 100644 --- a/ggml/src/ggml-hexagon/htp/hex-dma.h +++ b/ggml/src/ggml-hexagon/htp/hex-dma.h @@ -102,7 +102,7 @@ static inline bool dma_queue_push(dma_queue * q, dmlink(q->tail, desc); q->tail = desc; - // FARF(ERROR, "dma-push: i %u len %u dst %p src %p\n", q->push_idx, len, dst, src); + // FARF(ERROR, "dma-push: i %u width %u nrows %d dst %p src %p\n", q->push_idx, width, nrows, dptr.dst, dptr.src); q->push_idx = (q->push_idx + 1) & q->idx_mask; return true; } @@ -144,11 +144,37 @@ static inline dma_ptr dma_queue_pop(dma_queue * q) { dptr = q->dptr[q->pop_idx]; - // FARF(ERROR, "dma-pop: i %u dst %p\n", q->pop_idx, dst); + // FARF(ERROR, "dma-pop: i %u dst %p src %p\n", q->pop_idx, dptr.dst, dptr.src); q->pop_idx = (q->pop_idx + 1) & q->idx_mask; return dptr; } +static inline dma_ptr dma_queue_pop_nowait(dma_queue * q) { + dma_ptr dptr = { NULL }; + + if (q->push_idx == q->pop_idx) { + return dptr; + } + + dptr = q->dptr[q->pop_idx]; + + // FARF(ERROR, "dma-pop-nowait: i %u dst %p src %p\n", q->pop_idx, dptr.dst, dptr.src); + q->pop_idx = (q->pop_idx + 1) & q->idx_mask; + return dptr; +} + +static inline bool dma_queue_empty(dma_queue * q) { + return q->push_idx == q->pop_idx; +} + +static inline uint32_t dma_queue_depth(dma_queue * q) { + return (q->push_idx - q->pop_idx) & q->idx_mask; +} + +static inline uint32_t dma_queue_capacity(dma_queue * q) { + return q->capacity; +} + #ifdef __cplusplus } // extern "C" #endif diff --git a/ggml/src/ggml-hexagon/htp/htp-msg.h b/ggml/src/ggml-hexagon/htp/htp-msg.h index 25403bb112..52dcc36d8f 100644 --- a/ggml/src/ggml-hexagon/htp/htp-msg.h +++ b/ggml/src/ggml-hexagon/htp/htp-msg.h @@ -68,6 +68,7 @@ enum htp_op { HTP_OP_SQR, HTP_OP_SQRT, HTP_OP_SUM_ROWS, + HTP_OP_SSM_CONV, INVALID }; diff --git a/ggml/src/ggml-hexagon/htp/htp-ops.h b/ggml/src/ggml-hexagon/htp/htp-ops.h index f1ad24dbfa..2ef20936f1 100644 --- a/ggml/src/ggml-hexagon/htp/htp-ops.h +++ b/ggml/src/ggml-hexagon/htp/htp-ops.h @@ -41,35 +41,6 @@ struct htp_ops_context { worker_pool_context_t * wpool; // worker pool uint32_t n_threads; // num threads - uint32_t src0_nrows_per_thread; - uint32_t src1_nrows_per_thread; - - struct fastdiv_values src0_div1; // fastdiv values for ne1 - struct fastdiv_values src0_div2; // fastdiv values for ne2 - struct fastdiv_values src0_div3; // fastdiv values for ne3 - struct fastdiv_values src0_div21; // fastdiv values for ne2 * ne1 - - struct fastdiv_values src1_div1; // fastdiv values for ne1 - struct fastdiv_values src1_div2; // fastdiv values for ne2 - struct fastdiv_values src1_div3; // fastdiv values for ne3 - struct fastdiv_values src1_div21; // fastdiv values for ne2 * ne1 - - struct fastdiv_values src3_div1; // fastdiv values for ne1 - struct fastdiv_values src3_div2; // fastdiv values for ne2 - struct fastdiv_values src3_div3; // fastdiv values for ne3 - struct fastdiv_values src3_div21; // fastdiv values for ne2 * ne1 - - struct fastdiv_values broadcast_rk2; - struct fastdiv_values broadcast_rk3; - struct fastdiv_values broadcast_rv2; - struct fastdiv_values broadcast_rv3; - - struct fastdiv_values set_rows_div_ne12; // fastdiv values for ne12 - struct fastdiv_values set_rows_div_ne11; // fastdiv values for ne11 - - struct fastdiv_values get_rows_div_ne10; // fastdiv values for ne10 - struct fastdiv_values get_rows_div_ne10_ne11; // fastdiv values for ne10 * ne11 - uint32_t flags; }; @@ -87,5 +58,6 @@ int op_set_rows(struct htp_ops_context * octx); int op_get_rows(struct htp_ops_context * octx); int op_cpy(struct htp_ops_context * octx); int op_argsort(struct htp_ops_context * octx); +int op_ssm_conv(struct htp_ops_context * octx); #endif /* HTP_OPS_H */ diff --git a/ggml/src/ggml-hexagon/htp/hvx-arith.h b/ggml/src/ggml-hexagon/htp/hvx-arith.h index 2577cdd041..82e3416970 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-arith.h +++ b/ggml/src/ggml-hexagon/htp/hvx-arith.h @@ -13,14 +13,15 @@ // Binary operations (add, mul, sub) // -#define hvx_arith_loop_body(dst_type, src0_type, src1_type, vec_store, vec_op) \ +#define UNUSED(x) (void)(x) + +#define hvx_arith_loop_body(dst_type, src0_type, src1_type, elem_size, vec_store, vec_op) \ do { \ dst_type * restrict vdst = (dst_type *) dst; \ src0_type * restrict vsrc0 = (src0_type *) src0; \ src1_type * restrict vsrc1 = (src1_type *) src1; \ \ - const uint32_t elem_size = sizeof(float); \ - const uint32_t epv = 128 / elem_size; \ + const uint32_t epv = 128 / (elem_size); \ const uint32_t nvec = n / epv; \ const uint32_t nloe = n % epv; \ \ @@ -32,62 +33,74 @@ } \ if (nloe) { \ HVX_Vector v = vec_op(vsrc0[i], vsrc1[i]); \ - vec_store((void *) &vdst[i], nloe * elem_size, v); \ + vec_store((void *) &vdst[i], nloe * (elem_size), v); \ } \ } while(0) #if __HVX_ARCH__ < 79 -#define HVX_OP_ADD(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b)) -#define HVX_OP_SUB(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(a, b)) -#define HVX_OP_MUL(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b)) + +#define HVX_OP_ADD_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b)) +#define HVX_OP_SUB_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(a, b)) +#define HVX_OP_MUL_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b)) + #else -#define HVX_OP_ADD(a, b) Q6_Vsf_vadd_VsfVsf(a, b) -#define HVX_OP_SUB(a, b) Q6_Vsf_vsub_VsfVsf(a, b) -#define HVX_OP_MUL(a, b) Q6_Vsf_vmpy_VsfVsf(a, b) + +#define HVX_OP_ADD_F32(a, b) Q6_Vsf_vadd_VsfVsf(a, b) +#define HVX_OP_SUB_F32(a, b) Q6_Vsf_vsub_VsfVsf(a, b) +#define HVX_OP_MUL_F32(a, b) Q6_Vsf_vmpy_VsfVsf(a, b) + #endif +#define HVX_OP_ADD_F16(a, b) hvx_vec_add_f16_f16(a, b) +#define HVX_OP_SUB_F16(a, b) hvx_vec_sub_f16_f16(a, b) +#define HVX_OP_MUL_F16(a, b) hvx_vec_mul_f16_f16(a, b) + // Generic macro to define alignment permutations for an op -#define DEFINE_HVX_BINARY_OP_VARIANTS(OP_NAME, OP_MACRO) \ +#define DEFINE_HVX_BINARY_OP_VARIANTS(OP_NAME, OP_MACRO, ELEM_TYPE) \ static inline void OP_NAME##_aaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ assert((uintptr_t) dst % 128 == 0); \ assert((uintptr_t) src0 % 128 == 0); \ assert((uintptr_t) src1 % 128 == 0); \ - hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a, OP_MACRO); \ + hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, sizeof(ELEM_TYPE), hvx_vec_store_a, OP_MACRO); \ } \ static inline void OP_NAME##_aau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ assert((uintptr_t) dst % 128 == 0); \ assert((uintptr_t) src0 % 128 == 0); \ - hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a, OP_MACRO); \ + hvx_arith_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, sizeof(ELEM_TYPE), hvx_vec_store_a, OP_MACRO); \ } \ static inline void OP_NAME##_aua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ assert((uintptr_t) dst % 128 == 0); \ assert((uintptr_t) src1 % 128 == 0); \ - hvx_arith_loop_body(HVX_Vector, HVX_UVector, HVX_Vector, hvx_vec_store_a, OP_MACRO); \ + hvx_arith_loop_body(HVX_Vector, HVX_UVector, HVX_Vector, sizeof(ELEM_TYPE), hvx_vec_store_a, OP_MACRO); \ } \ static inline void OP_NAME##_auu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ assert((uintptr_t) dst % 128 == 0); \ - hvx_arith_loop_body(HVX_Vector, HVX_UVector, HVX_UVector, hvx_vec_store_a, OP_MACRO); \ + hvx_arith_loop_body(HVX_Vector, HVX_UVector, HVX_UVector, sizeof(ELEM_TYPE), hvx_vec_store_a, OP_MACRO); \ } \ static inline void OP_NAME##_uaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ assert((uintptr_t) src0 % 128 == 0); \ assert((uintptr_t) src1 % 128 == 0); \ - hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u, OP_MACRO); \ + hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, sizeof(ELEM_TYPE), hvx_vec_store_u, OP_MACRO); \ } \ static inline void OP_NAME##_uau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ assert((uintptr_t) src0 % 128 == 0); \ - hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_UVector, hvx_vec_store_u, OP_MACRO); \ + hvx_arith_loop_body(HVX_UVector, HVX_Vector, HVX_UVector, sizeof(ELEM_TYPE), hvx_vec_store_u, OP_MACRO); \ } \ static inline void OP_NAME##_uua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ assert((uintptr_t) src1 % 128 == 0); \ - hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_Vector, hvx_vec_store_u, OP_MACRO); \ + hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_Vector, sizeof(ELEM_TYPE), hvx_vec_store_u, OP_MACRO); \ } \ static inline void OP_NAME##_uuu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ - hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u, OP_MACRO); \ + hvx_arith_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, sizeof(ELEM_TYPE), hvx_vec_store_u, OP_MACRO); \ } \ -DEFINE_HVX_BINARY_OP_VARIANTS(hvx_add_f32, HVX_OP_ADD) -DEFINE_HVX_BINARY_OP_VARIANTS(hvx_sub_f32, HVX_OP_SUB) -DEFINE_HVX_BINARY_OP_VARIANTS(hvx_mul_f32, HVX_OP_MUL) +DEFINE_HVX_BINARY_OP_VARIANTS(hvx_add_f32, HVX_OP_ADD_F32, float) +DEFINE_HVX_BINARY_OP_VARIANTS(hvx_sub_f32, HVX_OP_SUB_F32, float) +DEFINE_HVX_BINARY_OP_VARIANTS(hvx_mul_f32, HVX_OP_MUL_F32, float) + +DEFINE_HVX_BINARY_OP_VARIANTS(hvx_add_f16, HVX_OP_ADD_F16, _Float16) +DEFINE_HVX_BINARY_OP_VARIANTS(hvx_sub_f16, HVX_OP_SUB_F16, _Float16) +DEFINE_HVX_BINARY_OP_VARIANTS(hvx_mul_f16, HVX_OP_MUL_F16, _Float16) // Dispatcher logic #define HVX_BINARY_DISPATCHER(OP_NAME) \ @@ -115,6 +128,10 @@ HVX_BINARY_DISPATCHER(hvx_add_f32) HVX_BINARY_DISPATCHER(hvx_sub_f32) HVX_BINARY_DISPATCHER(hvx_mul_f32) +HVX_BINARY_DISPATCHER(hvx_add_f16) +HVX_BINARY_DISPATCHER(hvx_sub_f16) +HVX_BINARY_DISPATCHER(hvx_mul_f16) + // Mul-Mul Optimized static inline void hvx_mul_mul_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint8_t * restrict src2, const uint32_t num_elems) { assert((unsigned long) dst % 128 == 0); @@ -136,26 +153,25 @@ static inline void hvx_mul_mul_f32_aa(uint8_t * restrict dst, const uint8_t * re _Pragma("unroll(4)") for (; i < nvec; i++) { - HVX_Vector v1 = HVX_OP_MUL(vsrc0[i], vsrc1[i]); + HVX_Vector v1 = HVX_OP_MUL_F32(vsrc0[i], vsrc1[i]); vdst[i] = HVX_OP_MUL(v1, vsrc2[i]); } if (nloe) { - HVX_Vector v1 = HVX_OP_MUL(vsrc0[i], vsrc1[i]); - HVX_Vector v2 = HVX_OP_MUL(v1, vsrc2[i]); + HVX_Vector v1 = HVX_OP_MUL_F32(vsrc0[i], vsrc1[i]); + HVX_Vector v2 = HVX_OP_MUL_F32(v1, vsrc2[i]); hvx_vec_store_a((void *) &vdst[i], nloe * elem_size, v2); } } // Scalar Operations -#define hvx_scalar_loop_body(dst_type, src_type, vec_store, scalar_op_macro) \ +#define hvx_scalar_loop_body(dst_type, src_type, elem_size, vec_store, scalar_op_macro) \ do { \ dst_type * restrict vdst = (dst_type *) dst; \ src_type * restrict vsrc = (src_type *) src; \ \ - const uint32_t elem_size = sizeof(float); \ - const uint32_t epv = 128 / elem_size; \ + const uint32_t epv = 128 / (elem_size); \ const uint32_t nvec = n / epv; \ const uint32_t nloe = n % epv; \ \ @@ -169,138 +185,88 @@ static inline void hvx_mul_mul_f32_aa(uint8_t * restrict dst, const uint8_t * re if (nloe) { \ HVX_Vector v = vsrc[i]; \ v = scalar_op_macro(v); \ - vec_store((void *) &vdst[i], nloe * elem_size, v); \ + vec_store((void *) &vdst[i], nloe * (elem_size), v); \ } \ } while(0) -#define HVX_OP_ADD_SCALAR(v) \ +#define HVX_OP_ADD_SCALAR_F32(v) \ ({ \ const HVX_VectorPred pred_inf = Q6_Q_vcmp_eq_VwVw(inf, v); \ - HVX_Vector out = HVX_OP_ADD(v, val_vec); \ + HVX_Vector out = HVX_OP_ADD_F32(v, val_vec); \ Q6_V_vmux_QVV(pred_inf, inf, out); \ }) -#define HVX_OP_MUL_SCALAR(v) HVX_OP_MUL(v, val_vec) -#define HVX_OP_SUB_SCALAR(v) HVX_OP_SUB(v, val_vec) +#define HVX_OP_MUL_SCALAR_F32(v) HVX_OP_MUL_F32(v, val_vec) +#define HVX_OP_SUB_SCALAR_F32(v) HVX_OP_SUB_F32(v, val_vec) -// Add Scalar Variants +#define HVX_OP_ADD_SCALAR_F16(v) \ + ({ \ + const HVX_VectorPred pred_inf = Q6_Q_vcmp_eq_VhVh(inf, v); \ + HVX_Vector out = HVX_OP_ADD_F16(v, val_vec); \ + Q6_V_vmux_QVV(pred_inf, inf, out); \ + }) -static inline void hvx_add_scalar_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - const HVX_Vector inf = hvx_vec_splat_f32(INFINITY); - assert((unsigned long) dst % 128 == 0); - assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_ADD_SCALAR); +#define HVX_OP_MUL_SCALAR_F16(v) HVX_OP_MUL_F16(v, val_vec) +#define HVX_OP_SUB_SCALAR_F16(v) HVX_OP_SUB_F16(v, val_vec) + +// Scalar Variants + +// Generic macro to define alignment permutations for an op +#define DEFINE_HVX_BINARY_SCALAR_OP_VARIANTS(OP_NAME, OP_MACRO, SPLAT_MACRO, ELEM_TYPE) \ +static inline void OP_NAME##_aa(uint8_t * restrict dst, const uint8_t * restrict src, const ELEM_TYPE val, uint32_t n) { \ + const HVX_Vector val_vec = SPLAT_MACRO(val); \ + const HVX_Vector inf = SPLAT_MACRO((ELEM_TYPE)INFINITY); UNUSED(inf); \ + assert((uintptr_t) dst % 128 == 0); \ + assert((uintptr_t) src % 128 == 0); \ + hvx_scalar_loop_body(HVX_Vector, HVX_Vector, sizeof(ELEM_TYPE), hvx_vec_store_a, OP_MACRO); \ +} \ +static inline void OP_NAME##_au(uint8_t * restrict dst, const uint8_t * restrict src, const ELEM_TYPE val, uint32_t n) { \ + const HVX_Vector val_vec = SPLAT_MACRO(val); \ + const HVX_Vector inf = SPLAT_MACRO((ELEM_TYPE)INFINITY); UNUSED(inf); \ + assert((uintptr_t) dst % 128 == 0); \ + hvx_scalar_loop_body(HVX_Vector, HVX_UVector, sizeof(ELEM_TYPE), hvx_vec_store_a, OP_MACRO); \ +} \ +static inline void OP_NAME##_ua(uint8_t * restrict dst, const uint8_t * restrict src, const ELEM_TYPE val, uint32_t n) { \ + const HVX_Vector val_vec = SPLAT_MACRO(val); \ + const HVX_Vector inf = SPLAT_MACRO((ELEM_TYPE)INFINITY); UNUSED(inf); \ + assert((uintptr_t) src % 128 == 0); \ + hvx_scalar_loop_body(HVX_UVector, HVX_Vector, sizeof(ELEM_TYPE), hvx_vec_store_u, OP_MACRO); \ +} \ +static inline void OP_NAME##_uu(uint8_t * restrict dst, const uint8_t * restrict src, const ELEM_TYPE val, uint32_t n) { \ + const HVX_Vector val_vec = SPLAT_MACRO(val); \ + const HVX_Vector inf = SPLAT_MACRO((ELEM_TYPE)INFINITY); UNUSED(inf); \ + hvx_scalar_loop_body(HVX_UVector, HVX_UVector, sizeof(ELEM_TYPE), hvx_vec_store_u, OP_MACRO); \ +} \ + +DEFINE_HVX_BINARY_SCALAR_OP_VARIANTS(hvx_add_scalar_f32, HVX_OP_ADD_SCALAR_F32, hvx_vec_splat_f32, float) +DEFINE_HVX_BINARY_SCALAR_OP_VARIANTS(hvx_sub_scalar_f32, HVX_OP_SUB_SCALAR_F32, hvx_vec_splat_f32, float) +DEFINE_HVX_BINARY_SCALAR_OP_VARIANTS(hvx_mul_scalar_f32, HVX_OP_MUL_SCALAR_F32, hvx_vec_splat_f32, float) + +DEFINE_HVX_BINARY_SCALAR_OP_VARIANTS(hvx_add_scalar_f16, HVX_OP_ADD_SCALAR_F16, hvx_vec_splat_f16, _Float16) +DEFINE_HVX_BINARY_SCALAR_OP_VARIANTS(hvx_sub_scalar_f16, HVX_OP_SUB_SCALAR_F16, hvx_vec_splat_f16, _Float16) +DEFINE_HVX_BINARY_SCALAR_OP_VARIANTS(hvx_mul_scalar_f16, HVX_OP_MUL_SCALAR_F16, hvx_vec_splat_f16, _Float16) + +// Dispatcher logic +#define HVX_BINARY_SCALAR_DISPATCHER(OP_NAME, ELEM_TYPE) \ +static inline void OP_NAME(uint8_t * restrict dst, const uint8_t * restrict src, const ELEM_TYPE val, const uint32_t num_elems) { \ + if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) { \ + OP_NAME##_aa(dst, src, val, num_elems); \ + } else if (hex_is_aligned((void *) dst, 128)) { \ + OP_NAME##_au(dst, src, val, num_elems); \ + } else if (hex_is_aligned((void *) src, 128)) { \ + OP_NAME##_ua(dst, src, val, num_elems); \ + } else { \ + OP_NAME##_uu(dst, src, val, num_elems); \ + } \ } -static inline void hvx_add_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - const HVX_Vector inf = hvx_vec_splat_f32(INFINITY); - assert((unsigned long) dst % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_ADD_SCALAR); -} +HVX_BINARY_SCALAR_DISPATCHER(hvx_add_scalar_f32, float) +HVX_BINARY_SCALAR_DISPATCHER(hvx_sub_scalar_f32, float) +HVX_BINARY_SCALAR_DISPATCHER(hvx_mul_scalar_f32, float) -static inline void hvx_add_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - const HVX_Vector inf = hvx_vec_splat_f32(INFINITY); - assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_ADD_SCALAR); -} - -static inline void hvx_add_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - static const float kInf = INFINITY; - const HVX_Vector inf = hvx_vec_splat_f32(kInf); - hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_ADD_SCALAR); -} - -// Sub Scalar Variants - -static inline void hvx_sub_scalar_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - assert((unsigned long) dst % 128 == 0); - assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_SUB_SCALAR); -} - -static inline void hvx_sub_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - assert((unsigned long) dst % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_SUB_SCALAR); -} - -static inline void hvx_sub_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_SUB_SCALAR); -} - -static inline void hvx_sub_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_SUB_SCALAR); -} - -// Mul Scalar Variants - -static inline void hvx_mul_scalar_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - assert((unsigned long) dst % 128 == 0); - assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_MUL_SCALAR); -} - -static inline void hvx_mul_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - assert((unsigned long) dst % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_MUL_SCALAR); -} - -static inline void hvx_mul_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_MUL_SCALAR); -} - -static inline void hvx_mul_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { - const HVX_Vector val_vec = hvx_vec_splat_f32(val); - hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_MUL_SCALAR); -} - -static inline void hvx_add_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float val, const int num_elems) { - if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) { - hvx_add_scalar_f32_aa(dst, src, val, num_elems); - } else if (hex_is_aligned((void *) dst, 128)) { - hvx_add_scalar_f32_au(dst, src, val, num_elems); - } else if (hex_is_aligned((void *) src, 128)) { - hvx_add_scalar_f32_ua(dst, src, val, num_elems); - } else { - hvx_add_scalar_f32_uu(dst, src, val, num_elems); - } -} - -static inline void hvx_mul_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float val, const int num_elems) { - if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) { - hvx_mul_scalar_f32_aa(dst, src, val, num_elems); - } else if (hex_is_aligned((void *) dst, 128)) { - hvx_mul_scalar_f32_au(dst, src, val, num_elems); - } else if (hex_is_aligned((void *) src, 128)) { - hvx_mul_scalar_f32_ua(dst, src, val, num_elems); - } else { - hvx_mul_scalar_f32_uu(dst, src, val, num_elems); - } -} - -static inline void hvx_sub_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float val, const int num_elems) { - if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) { - hvx_sub_scalar_f32_aa(dst, src, val, num_elems); - } else if (hex_is_aligned((void *) dst, 128)) { - hvx_sub_scalar_f32_au(dst, src, val, num_elems); - } else if (hex_is_aligned((void *) src, 128)) { - hvx_sub_scalar_f32_ua(dst, src, val, num_elems); - } else { - hvx_sub_scalar_f32_uu(dst, src, val, num_elems); - } -} +HVX_BINARY_SCALAR_DISPATCHER(hvx_add_scalar_f16, _Float16) +HVX_BINARY_SCALAR_DISPATCHER(hvx_sub_scalar_f16, _Float16) +HVX_BINARY_SCALAR_DISPATCHER(hvx_mul_scalar_f16, _Float16) // MIN Scalar variants @@ -310,24 +276,24 @@ static inline void hvx_min_scalar_f32_aa(uint8_t * restrict dst, const uint8_t * const HVX_Vector val_vec = hvx_vec_splat_f32(val); assert((unsigned long) dst % 128 == 0); assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_MIN_SCALAR); + hvx_scalar_loop_body(HVX_Vector, HVX_Vector, sizeof(float), hvx_vec_store_a, HVX_OP_MIN_SCALAR); } static inline void hvx_min_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { const HVX_Vector val_vec = hvx_vec_splat_f32(val); assert((unsigned long) dst % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_MIN_SCALAR); + hvx_scalar_loop_body(HVX_Vector, HVX_UVector, sizeof(float), hvx_vec_store_a, HVX_OP_MIN_SCALAR); } static inline void hvx_min_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { const HVX_Vector val_vec = hvx_vec_splat_f32(val); assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_MIN_SCALAR); + hvx_scalar_loop_body(HVX_UVector, HVX_Vector, sizeof(float), hvx_vec_store_u, HVX_OP_MIN_SCALAR); } static inline void hvx_min_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float val, uint32_t n) { const HVX_Vector val_vec = hvx_vec_splat_f32(val); - hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_MIN_SCALAR); + hvx_scalar_loop_body(HVX_UVector, HVX_UVector, sizeof(float), hvx_vec_store_u, HVX_OP_MIN_SCALAR); } static inline void hvx_min_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float val, const int num_elems) { @@ -357,27 +323,27 @@ static inline void hvx_clamp_scalar_f32_aa(uint8_t * restrict dst, const uint8_t const HVX_Vector max_vec = hvx_vec_splat_f32(max); assert((unsigned long) dst % 128 == 0); assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a, HVX_OP_CLAMP_SCALAR); + hvx_scalar_loop_body(HVX_Vector, HVX_Vector, sizeof(float), hvx_vec_store_a, HVX_OP_CLAMP_SCALAR); } static inline void hvx_clamp_scalar_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, const float min, const float max, uint32_t n) { const HVX_Vector min_vec = hvx_vec_splat_f32(min); const HVX_Vector max_vec = hvx_vec_splat_f32(max); assert((unsigned long) dst % 128 == 0); - hvx_scalar_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a, HVX_OP_CLAMP_SCALAR); + hvx_scalar_loop_body(HVX_Vector, HVX_UVector, sizeof(float), hvx_vec_store_a, HVX_OP_CLAMP_SCALAR); } static inline void hvx_clamp_scalar_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, const float min, const float max, uint32_t n) { const HVX_Vector min_vec = hvx_vec_splat_f32(min); const HVX_Vector max_vec = hvx_vec_splat_f32(max); assert((unsigned long) src % 128 == 0); - hvx_scalar_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u, HVX_OP_CLAMP_SCALAR); + hvx_scalar_loop_body(HVX_UVector, HVX_Vector, sizeof(float), hvx_vec_store_u, HVX_OP_CLAMP_SCALAR); } static inline void hvx_clamp_scalar_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, const float min, const float max, uint32_t n) { const HVX_Vector min_vec = hvx_vec_splat_f32(min); const HVX_Vector max_vec = hvx_vec_splat_f32(max); - hvx_scalar_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u, HVX_OP_CLAMP_SCALAR); + hvx_scalar_loop_body(HVX_UVector, HVX_UVector, sizeof(float), hvx_vec_store_u, HVX_OP_CLAMP_SCALAR); } static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t * restrict src, const float min, const float max, const int num_elems) { @@ -396,7 +362,7 @@ static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t * // Square // -#define hvx_sqr_loop_body(dst_type, src_type, vec_store) \ +#define hvx_sqr_f32_loop_body(dst_type, src_type, vec_store) \ do { \ dst_type * restrict vdst = (dst_type *) dst; \ src_type * restrict vsrc = (src_type *) src; \ @@ -410,10 +376,10 @@ static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t * \ _Pragma("unroll(4)") \ for (; i < nvec; i++) { \ - vdst[i] = HVX_OP_MUL(vsrc[i], vsrc[i]); \ + vdst[i] = HVX_OP_MUL_F32(vsrc[i], vsrc[i]); \ } \ if (nloe) { \ - HVX_Vector v = HVX_OP_MUL(vsrc[i], vsrc[i]); \ + HVX_Vector v = HVX_OP_MUL_F32(vsrc[i], vsrc[i]); \ vec_store((void *) &vdst[i], nloe * elem_size, v); \ } \ } while(0) @@ -421,21 +387,21 @@ static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t * static inline void hvx_sqr_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { assert((unsigned long) dst % 128 == 0); assert((unsigned long) src % 128 == 0); - hvx_sqr_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a); + hvx_sqr_f32_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a); } static inline void hvx_sqr_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { assert((unsigned long) dst % 128 == 0); - hvx_sqr_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a); + hvx_sqr_f32_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a); } static inline void hvx_sqr_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { assert((unsigned long) src % 128 == 0); - hvx_sqr_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u); + hvx_sqr_f32_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u); } static inline void hvx_sqr_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { - hvx_sqr_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u); + hvx_sqr_f32_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u); } static inline void hvx_sqr_f32(uint8_t * restrict dst, const uint8_t * restrict src, const uint32_t num_elems) { @@ -454,17 +420,24 @@ static inline void hvx_sqr_f32(uint8_t * restrict dst, const uint8_t * restrict } } -#undef HVX_OP_ADD -#undef HVX_OP_SUB -#undef HVX_OP_MUL +#undef HVX_OP_ADD_F32 +#undef HVX_OP_SUB_F32 +#undef HVX_OP_MUL_F32 +#undef HVX_OP_ADD_F16 +#undef HVX_OP_SUB_F16 +#undef HVX_OP_MUL_F16 #undef hvx_arith_loop_body -#undef HVX_OP_ADD_SCALAR -#undef HVX_OP_SUB_SCALAR -#undef HVX_OP_MUL_SCALAR +#undef HVX_OP_ADD_SCALAR_F32 +#undef HVX_OP_SUB_SCALAR_F32 +#undef HVX_OP_MUL_SCALAR_F32 +#undef HVX_OP_ADD_SCALAR_F16 +#undef HVX_OP_SUB_SCALAR_F16 +#undef HVX_OP_MUL_SCALAR_F16 #undef hvx_scalar_loop_body #undef HVX_OP_MIN_SCALAR #undef HVX_OP_CLAMP_SCALAR #undef DEFINE_HVX_BINARY_OP_VARIANTS #undef HVX_BINARY_DISPATCHER +#undef UNUSED #endif // HVX_ARITH_H diff --git a/ggml/src/ggml-hexagon/htp/hvx-base.h b/ggml/src/ggml-hexagon/htp/hvx-base.h index 12a1b7f128..578ca288fb 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-base.h +++ b/ggml/src/ggml-hexagon/htp/hvx-base.h @@ -38,7 +38,7 @@ static inline HVX_Vector hvx_vec_splat_f32(float v) { return Q6_V_vsplat_R(u.i); } -static inline HVX_Vector hvx_vec_splat_f16(float v) { +static inline HVX_Vector hvx_vec_splat_f16(_Float16 v) { union { __fp16 f; uint16_t i; } u = { .f = v }; return Q6_Vh_vsplat_R(u.i); } @@ -170,4 +170,71 @@ static inline HVX_Vector hvx_vec_i16_from_hf_rnd_sat(HVX_Vector vin) { return Q6_Vh_vround_VwVw_sat(vsf_1, vsf_0); } +#if __HVX_ARCH__ < 79 + +static inline HVX_VectorPair hvx_vec_mpyacc_f32_f16(HVX_VectorPair acc, HVX_Vector x, HVX_Vector y) +{ + HVX_VectorPair m = Q6_Wqf32_vmpy_VhfVhf(x, y); + HVX_Vector a0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_V_lo_W(m), Q6_V_lo_W(acc))); + HVX_Vector a1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(Q6_V_hi_W(m), Q6_V_hi_W(acc))); + return Q6_W_vcombine_VV(a1, a0); +} + +#else + +static inline HVX_VectorPair hvx_vec_mpyacc_f32_f16(HVX_VectorPair acc, HVX_Vector x, HVX_Vector y) +{ + return Q6_Wsf_vmpyacc_WsfVhfVhf(acc, x, y); +} + +#endif + +#if __HVX_ARCH__ < 79 + +static inline HVX_Vector hvx_vec_add_f16_f16(HVX_Vector a, HVX_Vector b) +{ + const HVX_Vector negone = Q6_Vh_vsplat_R(0xBC00); // -1.0 in IEEE FP16 + const HVX_Vector one = Q6_Vh_vsplat_R(0x3C00); // 1.0 in IEEE FP16 + HVX_VectorPair a_p = Q6_Wqf32_vmpy_VhfVhf(a, one); + HVX_VectorPair b_p = Q6_Wqf32_vmpy_VhfVhf(b, negone); + HVX_Vector a0 = Q6_Vqf32_vsub_Vqf32Vqf32(Q6_V_lo_W(a_p), Q6_V_lo_W(b_p)); + HVX_Vector a1 = Q6_Vqf32_vsub_Vqf32Vqf32(Q6_V_hi_W(a_p), Q6_V_hi_W(b_p)); + return Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(a1, a0)); +} + +static inline HVX_Vector hvx_vec_sub_f16_f16(HVX_Vector a, HVX_Vector b) +{ + const HVX_Vector negone = Q6_Vh_vsplat_R(0xBC00); // -1.0 in IEEE FP16 + const HVX_Vector one = Q6_Vh_vsplat_R(0x3C00); // 1.0 in IEEE FP16 + HVX_VectorPair a_p = Q6_Wqf32_vmpy_VhfVhf(a, one); + HVX_VectorPair b_p = Q6_Wqf32_vmpy_VhfVhf(b, negone); + HVX_Vector a0 = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(a_p), Q6_V_lo_W(b_p)); + HVX_Vector a1 = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_hi_W(a_p), Q6_V_hi_W(b_p)); + return Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(a1, a0)); +} + +static inline HVX_Vector hvx_vec_mul_f16_f16(HVX_Vector a, HVX_Vector b) +{ + return Q6_Vhf_equals_Wqf32(Q6_Wqf32_vmpy_VhfVhf(a, b)); +} + +#else + +static inline HVX_Vector hvx_vec_add_f16_f16(HVX_Vector a, HVX_Vector b) +{ + return Q6_Vhf_vadd_VhfVhf(a, b); +} + +static inline HVX_Vector hvx_vec_sub_f16_f16(HVX_Vector a, HVX_Vector b) +{ + return Q6_Vhf_vsub_VhfVhf(a, b); +} + +static inline HVX_Vector hvx_vec_mul_f16_f16(HVX_Vector a, HVX_Vector b) +{ + return Q6_Vhf_vmpy_VhfVhf(a, b); +} + +#endif // __HVX_ARCH__ < 79 + #endif /* HVX_BASE_H */ diff --git a/ggml/src/ggml-hexagon/htp/hvx-copy.h b/ggml/src/ggml-hexagon/htp/hvx-copy.h index ae0dbed030..851482e01b 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-copy.h +++ b/ggml/src/ggml-hexagon/htp/hvx-copy.h @@ -42,11 +42,11 @@ static inline void hvx_splat_f32_u(uint8_t * restrict dst, float v, uint32_t n) hvx_splat_u(dst, hvx_vec_splat_f32(v), n, sizeof(float)); } -static inline void hvx_splat_f16_a(uint8_t * restrict dst, float v, uint32_t n) { +static inline void hvx_splat_f16_a(uint8_t * restrict dst, _Float16 v, uint32_t n) { hvx_splat_u(dst, hvx_vec_splat_f16(v), n, sizeof(__fp16)); } -static inline void hvx_splat_f16_u(uint8_t * restrict dst, float v, uint32_t n) { +static inline void hvx_splat_f16_u(uint8_t * restrict dst, _Float16 v, uint32_t n) { hvx_splat_u(dst, hvx_vec_splat_f16(v), n, sizeof(__fp16)); } diff --git a/ggml/src/ggml-hexagon/htp/hvx-div.h b/ggml/src/ggml-hexagon/htp/hvx-div.h index 7dae012e0e..05cefea039 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-div.h +++ b/ggml/src/ggml-hexagon/htp/hvx-div.h @@ -15,11 +15,144 @@ #include "hvx-arith.h" #if __HVX_ARCH__ < 79 -#define HVX_OP_MUL(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b)) +#define HVX_OP_MUL_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b)) #else -#define HVX_OP_MUL(a, b) Q6_Vsf_vmpy_VsfVsf(a, b) +#define HVX_OP_MUL_F32(a, b) Q6_Vsf_vmpy_VsfVsf(a, b) #endif +// Compute div by scaler in f32. Requires first by expanding fp32 to fp16 and converting the result back to fp32. +static inline HVX_Vector hvx_div_mul_f16_const_using_f32(HVX_Vector vec1_hf, HVX_Vector vec2_sf_const, HVX_Vector vec_hf_one_1_0) { +#if __HVX_ARCH__ < 79 + HVX_VectorPair src_to_f32 = Q6_Wqf32_vmpy_VhfVhf(vec1_hf, vec_hf_one_1_0); + HVX_Vector src_to_f32_0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(src_to_f32)); + HVX_Vector src_to_f32_1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(src_to_f32)); +#else + HVX_VectorPair src_to_f32 = Q6_Wsf_vmpy_VhfVhf(vec1_hf, vec_hf_one_1_0); + HVX_Vector src_to_f32_0 = Q6_V_lo_W(src_to_f32); + HVX_Vector src_to_f32_1 = Q6_V_hi_W(src_to_f32); +#endif + + HVX_Vector div_f32_0 = HVX_OP_MUL_F32(src_to_f32_0, vec2_sf_const); + HVX_Vector div_f32_1 = HVX_OP_MUL_F32(src_to_f32_1, vec2_sf_const); + +#if __HVX_ARCH__ < 79 + HVX_Vector res = hvx_vec_f32_to_f16(div_f32_0, div_f32_1); +#else + HVX_Vector res = Q6_Vhf_vcvt_VsfVsf(div_f32_0, div_f32_1); +#endif + return res; +} + +#define hvx_div_scaler_f16_loop_body(dst_type, src_type, vec_store) \ + do { \ + dst_type * restrict vdst = (dst_type *) dst; \ + src_type * restrict vsrc = (src_type *) src; \ + HVX_Vector hf_one = Q6_Vh_vsplat_R(0x3C00); \ + \ + const uint32_t nvec = n / VLEN_FP16; \ + const uint32_t nloe = n % VLEN_FP16; \ + \ + uint32_t i = 0; \ + \ + _Pragma("unroll(4)") \ + for (; i < nvec; i++) { \ + HVX_Vector res = hvx_div_mul_f16_const_using_f32(vsrc[i], val_vec_f32, hf_one); \ + vdst[i] = res; \ + } \ + if (nloe) { \ + HVX_Vector res = hvx_div_mul_f16_const_using_f32(vsrc[i], val_vec_f32, hf_one); \ + vec_store((void *) &vdst[i], nloe * SIZEOF_FP16, res); \ + } \ + } while(0) + +static inline void hvx_div_scalar_f16_aa(uint8_t * restrict dst, const uint8_t * restrict src, const _Float16 val, uint32_t n) { + const HVX_Vector val_vec_f32 = hvx_vec_splat_f32(1.0f/((float)val)); + assert((uintptr_t) dst % 128 == 0); + assert((uintptr_t) src % 128 == 0); + hvx_div_scaler_f16_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a); +} +static inline void hvx_div_scalar_f16_au(uint8_t * restrict dst, const uint8_t * restrict src, const _Float16 val, uint32_t n) { + const HVX_Vector val_vec_f32 = hvx_vec_splat_f32(1.0f/((float)val)); + assert((uintptr_t) dst % 128 == 0); + hvx_div_scaler_f16_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a); +} +static inline void hvx_div_scalar_f16_ua(uint8_t * restrict dst, const uint8_t * restrict src, const _Float16 val, uint32_t n) { + const HVX_Vector val_vec_f32 = hvx_vec_splat_f32(1.0f/((float)val)); + assert((uintptr_t) src % 128 == 0); + hvx_div_scaler_f16_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u); +} +static inline void hvx_div_scalar_f16_uu(uint8_t * restrict dst, const uint8_t * restrict src, const _Float16 val, uint32_t n) { + const HVX_Vector val_vec_f32 = hvx_vec_splat_f32(1.0f/((float)val)); + hvx_div_scaler_f16_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u); +} + +// Compute div by using hvx_vec_inverse_f32_guard. Requires first by exapnding fp32 to fp16 and convert the result back to fp32. +static inline HVX_Vector hvx_vec_div_f16_using_f32(HVX_Vector vec1, HVX_Vector vec2, HVX_Vector f32_nan_inf_mask, HVX_Vector vec_hf_one_1_0) { +#if __HVX_ARCH__ < 79 + // Convert first input to fp32 + HVX_VectorPair vec1_to_f32 = Q6_Wqf32_vmpy_VhfVhf(vec1, vec_hf_one_1_0); // *1.0 + HVX_Vector vec1_to_f32_0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vec1_to_f32)); + HVX_Vector vec1_to_f32_1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vec1_to_f32)); + + // Convert second input to fp32 + HVX_VectorPair vec2_to_f32 = Q6_Wqf32_vmpy_VhfVhf(vec2, vec_hf_one_1_0); // *1.0 + HVX_Vector vec2_to_f32_0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vec2_to_f32)); + HVX_Vector vec2_to_f32_1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vec2_to_f32)); +#else + // Convert first input to fp32 + HVX_VectorPair vec1_to_f32 = Q6_Wsf_vmpy_VhfVhf(vec1, vec_hf_one_1_0); // *1.0 + HVX_Vector vec1_to_f32_0 = Q6_V_lo_W(vec1_to_f32); + HVX_Vector vec1_to_f32_1 = Q6_V_hi_W(vec1_to_f32); + + // Convert second input to fp32 + HVX_VectorPair vec2_to_f32 = Q6_Wsf_vmpy_VhfVhf(vec2, vec_hf_one_1_0); // *1.0 + HVX_Vector vec2_to_f32_0 = Q6_V_lo_W(vec2_to_f32); + HVX_Vector vec2_to_f32_1 = Q6_V_hi_W(vec2_to_f32); +#endif + + // Inverse second input in fp32 + HVX_Vector vec2_inv_f32_0 = hvx_vec_inverse_f32_guard(vec2_to_f32_0, f32_nan_inf_mask); + HVX_Vector vec2_inv_f32_1 = hvx_vec_inverse_f32_guard(vec2_to_f32_1, f32_nan_inf_mask); + + // Multiply first input by inverse of second, in fp32 + HVX_Vector div_f32_0 = HVX_OP_MUL_F32(vec1_to_f32_0, vec2_inv_f32_0); + HVX_Vector div_f32_1 = HVX_OP_MUL_F32(vec1_to_f32_1, vec2_inv_f32_1); + + // Convert back to fp16 +#if __HVX_ARCH__ < 79 + HVX_Vector recip = hvx_vec_f32_to_f16(div_f32_0, div_f32_1); +#else + HVX_Vector recip = Q6_Vhf_vcvt_VsfVsf(div_f32_0, div_f32_1); +#endif + + return recip; +} + +#define hvx_div_f16_loop_body(dst_type, src0_type, src1_type, vec_store) \ + do { \ + dst_type * restrict vdst = (dst_type *) dst; \ + src0_type * restrict vsrc0 = (src0_type *) src0; \ + src1_type * restrict vsrc1 = (src1_type *) src1; \ + \ + const HVX_Vector nan_inf_mask = Q6_V_vsplat_R(0x7f800000); \ + const HVX_Vector hf_one = Q6_Vh_vsplat_R(0x3C00); \ + \ + const uint32_t nvec = n / VLEN_FP16; \ + const uint32_t nloe = n % VLEN_FP16; \ + \ + uint32_t i = 0; \ + \ + _Pragma("unroll(4)") \ + for (; i < nvec; i++) { \ + HVX_Vector res = hvx_vec_div_f16_using_f32(vsrc0[i], vsrc1[i], nan_inf_mask, hf_one); \ + vdst[i] = res; \ + } \ + if (nloe) { \ + HVX_Vector res = hvx_vec_div_f16_using_f32(vsrc0[i], vsrc1[i], nan_inf_mask, hf_one); \ + vec_store((void *) &vdst[i], nloe * SIZEOF_FP16, res); \ + } \ + } while(0) + #define hvx_div_f32_loop_body(dst_type, src0_type, src1_type, vec_store) \ do { \ dst_type * restrict vdst = (dst_type *) dst; \ @@ -36,81 +169,83 @@ _Pragma("unroll(4)") \ for (; i < nvec; i++) { \ HVX_Vector inv_src1 = hvx_vec_inverse_f32_guard(vsrc1[i], nan_inf_mask); \ - HVX_Vector res = HVX_OP_MUL(vsrc0[i], inv_src1); \ + HVX_Vector res = HVX_OP_MUL_F32(vsrc0[i], inv_src1); \ vdst[i] = res; \ } \ if (nloe) { \ HVX_Vector inv_src1 = hvx_vec_inverse_f32_guard(vsrc1[i], nan_inf_mask); \ - HVX_Vector res = HVX_OP_MUL(vsrc0[i], inv_src1); \ + HVX_Vector res = HVX_OP_MUL_F32(vsrc0[i], inv_src1); \ vec_store((void *) &vdst[i], nloe * SIZEOF_FP32, res); \ } \ } while(0) -// 3-letter suffix variants -static inline void hvx_div_f32_aaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { - assert((uintptr_t) dst % 128 == 0); - assert((uintptr_t) src0 % 128 == 0); - assert((uintptr_t) src1 % 128 == 0); - hvx_div_f32_loop_body(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a); +// Generic macro to define alignment permutations for an op +#define DEFINE_HVX_DIV_OP_VARIANTS(OP_NAME, OP_LOOP_BODY) \ +static inline void OP_NAME##_aaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ + assert((uintptr_t) dst % 128 == 0); \ + assert((uintptr_t) src0 % 128 == 0); \ + assert((uintptr_t) src1 % 128 == 0); \ + OP_LOOP_BODY(HVX_Vector, HVX_Vector, HVX_Vector, hvx_vec_store_a); \ +} \ +static inline void OP_NAME##_aau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ + assert((uintptr_t) dst % 128 == 0); \ + assert((uintptr_t) src0 % 128 == 0); \ + OP_LOOP_BODY(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a); \ +} \ +static inline void OP_NAME##_aua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ + assert((uintptr_t) dst % 128 == 0); \ + assert((uintptr_t) src1 % 128 == 0); \ + OP_LOOP_BODY(HVX_Vector, HVX_UVector, HVX_Vector, hvx_vec_store_a); \ +} \ +static inline void OP_NAME##_auu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ + assert((uintptr_t) dst % 128 == 0); \ + OP_LOOP_BODY(HVX_Vector, HVX_UVector, HVX_UVector, hvx_vec_store_a); \ +} \ +static inline void OP_NAME##_uaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ + assert((uintptr_t) src0 % 128 == 0); \ + assert((uintptr_t) src1 % 128 == 0); \ + OP_LOOP_BODY(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u); \ +} \ +static inline void OP_NAME##_uau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ + assert((uintptr_t) src0 % 128 == 0); \ + OP_LOOP_BODY(HVX_UVector, HVX_Vector, HVX_UVector, hvx_vec_store_u); \ +} \ +static inline void OP_NAME##_uua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ + assert((uintptr_t) src1 % 128 == 0); \ + OP_LOOP_BODY(HVX_UVector, HVX_UVector, HVX_Vector, hvx_vec_store_u); \ +} \ +static inline void OP_NAME##_uuu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { \ + OP_LOOP_BODY(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u); \ +} \ + +// Dispatcher logic +#define HVX_DIV_DISPATCHER(OP_NAME) \ +static inline void OP_NAME(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) { \ + if (hex_is_aligned((void *) dst, 128)) { \ + if (hex_is_aligned((void *) src0, 128)) { \ + if (hex_is_aligned((void *) src1, 128)) OP_NAME##_aaa(dst, src0, src1, num_elems); \ + else OP_NAME##_aau(dst, src0, src1, num_elems); \ + } else { \ + if (hex_is_aligned((void *) src1, 128)) OP_NAME##_aua(dst, src0, src1, num_elems); \ + else OP_NAME##_auu(dst, src0, src1, num_elems); \ + } \ + } else { \ + if (hex_is_aligned((void *) src0, 128)) { \ + if (hex_is_aligned((void *) src1, 128)) OP_NAME##_uaa(dst, src0, src1, num_elems); \ + else OP_NAME##_uau(dst, src0, src1, num_elems); \ + } else { \ + if (hex_is_aligned((void *) src1, 128)) OP_NAME##_uua(dst, src0, src1, num_elems); \ + else OP_NAME##_uuu(dst, src0, src1, num_elems); \ + } \ + } \ } -static inline void hvx_div_f32_aau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { - assert((uintptr_t) dst % 128 == 0); - assert((uintptr_t) src0 % 128 == 0); - hvx_div_f32_loop_body(HVX_Vector, HVX_Vector, HVX_UVector, hvx_vec_store_a); -} +DEFINE_HVX_DIV_OP_VARIANTS(hvx_div_f32, hvx_div_f32_loop_body) +DEFINE_HVX_DIV_OP_VARIANTS(hvx_div_f16, hvx_div_f16_loop_body) -static inline void hvx_div_f32_aua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { - assert((uintptr_t) dst % 128 == 0); - assert((uintptr_t) src1 % 128 == 0); - hvx_div_f32_loop_body(HVX_Vector, HVX_UVector, HVX_Vector, hvx_vec_store_a); -} +HVX_DIV_DISPATCHER(hvx_div_f32) +HVX_DIV_DISPATCHER(hvx_div_f16) -static inline void hvx_div_f32_auu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { - assert((uintptr_t) dst % 128 == 0); - hvx_div_f32_loop_body(HVX_Vector, HVX_UVector, HVX_UVector, hvx_vec_store_a); -} - -static inline void hvx_div_f32_uaa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { - assert((uintptr_t) src0 % 128 == 0); - assert((uintptr_t) src1 % 128 == 0); - hvx_div_f32_loop_body(HVX_UVector, HVX_Vector, HVX_Vector, hvx_vec_store_u); -} - -static inline void hvx_div_f32_uau(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { - assert((uintptr_t) src0 % 128 == 0); - hvx_div_f32_loop_body(HVX_UVector, HVX_Vector, HVX_UVector, hvx_vec_store_u); -} - -static inline void hvx_div_f32_uua(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { - assert((uintptr_t) src1 % 128 == 0); - hvx_div_f32_loop_body(HVX_UVector, HVX_UVector, HVX_Vector, hvx_vec_store_u); -} - -static inline void hvx_div_f32_uuu(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { - hvx_div_f32_loop_body(HVX_UVector, HVX_UVector, HVX_UVector, hvx_vec_store_u); -} - -static inline void hvx_div_f32(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, const uint32_t num_elems) { - if (hex_is_aligned((void *) dst, 128)) { - if (hex_is_aligned((void *) src0, 128)) { - if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_aaa(dst, src0, src1, num_elems); - else hvx_div_f32_aau(dst, src0, src1, num_elems); - } else { - if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_aua(dst, src0, src1, num_elems); - else hvx_div_f32_auu(dst, src0, src1, num_elems); - } - } else { - if (hex_is_aligned((void *) src0, 128)) { - if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_uaa(dst, src0, src1, num_elems); - else hvx_div_f32_uau(dst, src0, src1, num_elems); - } else { - if (hex_is_aligned((void *) src1, 128)) hvx_div_f32_uua(dst, src0, src1, num_elems); - else hvx_div_f32_uuu(dst, src0, src1, num_elems); - } - } -} - -#undef HVX_OP_MUL +#undef HVX_OP_MUL_F32 #endif // HVX_DIV_H diff --git a/ggml/src/ggml-hexagon/htp/hvx-inverse.h b/ggml/src/ggml-hexagon/htp/hvx-inverse.h index 49f3efabbc..f2054f45ba 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-inverse.h +++ b/ggml/src/ggml-hexagon/htp/hvx-inverse.h @@ -67,7 +67,7 @@ static inline HVX_Vector hvx_vec_inverse_f16(HVX_Vector vals) { HVX_Vector vcl0 = Q6_Vuh_vcl0_Vuh(rm); //count leading zeros - // Get mantissa for 16-bit represenation + // Get mantissa for 16-bit representation HVX_Vector mant_recip = Q6_V_vand_VV(Q6_Vh_vasr_VhR(Q6_Vh_vasl_VhVh(rm, vcl0), 5), Q6_Vh_vsplat_R(0x03FF)); //Compute Reciprocal Exponent @@ -137,40 +137,74 @@ static inline HVX_Vector hvx_vec_inverse_f32_guard(HVX_Vector v_sf, HVX_Vector n } \ } while(0) -static inline void hvx_inverse_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { - assert((unsigned long) dst % 128 == 0); - assert((unsigned long) src % 128 == 0); - hvx_inverse_f32_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a); +static inline HVX_Vector hvx_vec_inverse_f16_guard(HVX_Vector v_sf, HVX_Vector nan_inf_mask) { + HVX_Vector out = hvx_vec_inverse_f16(v_sf); + + HVX_Vector masked_out = Q6_V_vand_VV(out, nan_inf_mask); + const HVX_VectorPred pred = Q6_Q_vcmp_eq_VhVh(nan_inf_mask, masked_out); + + return Q6_V_vmux_QVV(pred, Q6_V_vzero(), out); } -static inline void hvx_inverse_f32_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { - assert((unsigned long) dst % 128 == 0); - hvx_inverse_f32_loop_body(HVX_Vector, HVX_UVector, hvx_vec_store_a); +#define hvx_inverse_f16_loop_body(dst_type, src_type, vec_store) \ + do { \ + dst_type * restrict vdst = (dst_type *) dst; \ + src_type * restrict vsrc = (src_type *) src; \ + \ + const HVX_Vector nan_inf_mask = Q6_Vh_vsplat_R(0x7c00); \ + \ + const uint32_t nvec = n / VLEN_FP16; \ + const uint32_t nloe = n % VLEN_FP16; \ + \ + uint32_t i = 0; \ + \ + _Pragma("unroll(4)") \ + for (; i < nvec; i++) { \ + vdst[i] = hvx_vec_inverse_f16_guard(vsrc[i], nan_inf_mask); \ + } \ + if (nloe) { \ + HVX_Vector v = hvx_vec_inverse_f16_guard(vsrc[i], nan_inf_mask); \ + vec_store((void *) &vdst[i], nloe * SIZEOF_FP16, v); \ + } \ + } while(0) + +// Generic macro to define alignment permutations for an op +#define DEFINE_HVX_INV_OP_VARIANTS(OP_NAME, OP_LOOP_BODY) \ +static inline void OP_NAME##_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { \ + assert((uintptr_t) dst % 128 == 0); \ + assert((uintptr_t) src % 128 == 0); \ + OP_LOOP_BODY(HVX_Vector, HVX_Vector, hvx_vec_store_a); \ +} \ +static inline void OP_NAME##_au(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { \ + assert((uintptr_t) dst % 128 == 0); \ + OP_LOOP_BODY(HVX_Vector, HVX_UVector, hvx_vec_store_a); \ +} \ +static inline void OP_NAME##_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { \ + assert((uintptr_t) src % 128 == 0); \ + OP_LOOP_BODY(HVX_UVector, HVX_Vector, hvx_vec_store_u); \ +} \ +static inline void OP_NAME##_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { \ + OP_LOOP_BODY(HVX_UVector, HVX_UVector, hvx_vec_store_u); \ +} \ + +// Dispatcher logic +#define HVX_INV_DISPATCHER(OP_NAME) \ +static inline void OP_NAME(uint8_t * restrict dst, const uint8_t * restrict src, const uint32_t num_elems) { \ + if (hex_is_aligned((void *) dst, 128) && hex_is_aligned((void *) src, 128)) { \ + OP_NAME##_aa(dst, src, num_elems); \ + } else if (hex_is_aligned((void *) dst, 128)) { \ + OP_NAME##_au(dst, src, num_elems); \ + } else if (hex_is_aligned((void *) src, 128)) { \ + OP_NAME##_ua(dst, src, num_elems); \ + } else { \ + OP_NAME##_uu(dst, src, num_elems); \ + } \ } -static inline void hvx_inverse_f32_ua(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { - assert((unsigned long) src % 128 == 0); - hvx_inverse_f32_loop_body(HVX_UVector, HVX_Vector, hvx_vec_store_u); -} +DEFINE_HVX_INV_OP_VARIANTS(hvx_inverse_f32, hvx_inverse_f32_loop_body) +DEFINE_HVX_INV_OP_VARIANTS(hvx_inverse_f16, hvx_inverse_f16_loop_body) -static inline void hvx_inverse_f32_uu(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) { - hvx_inverse_f32_loop_body(HVX_UVector, HVX_UVector, hvx_vec_store_u); -} - -static inline void hvx_inverse_f32(uint8_t * restrict dst, uint8_t * restrict src, const int num_elems) { - if ((unsigned long) dst % 128 == 0) { - if ((unsigned long) src % 128 == 0) { - hvx_inverse_f32_aa(dst, src, num_elems); - } else { - hvx_inverse_f32_au(dst, src, num_elems); - } - } else { - if ((unsigned long) src % 128 == 0) { - hvx_inverse_f32_ua(dst, src, num_elems); - } else { - hvx_inverse_f32_uu(dst, src, num_elems); - } - } -} +HVX_INV_DISPATCHER(hvx_inverse_f32) +HVX_INV_DISPATCHER(hvx_inverse_f16) #endif // HVX_INVERSE_H diff --git a/ggml/src/ggml-hexagon/htp/hvx-reduce.h b/ggml/src/ggml-hexagon/htp/hvx-reduce.h index 1ca7c05d98..3c0073ef6d 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-reduce.h +++ b/ggml/src/ggml-hexagon/htp/hvx-reduce.h @@ -46,6 +46,21 @@ static inline HVX_Vector hvx_vec_reduce_sum_qf32(HVX_Vector in) { #if __HVX_ARCH__ > 75 +static inline HVX_Vector hvx_vec_reduce_sum_f32x4(HVX_Vector_x4 in) { + HVX_VectorPair sum_p01 = Q6_W_vshuff_VVR(in.v[1], in.v[0], 4); + HVX_VectorPair sum_p23 = Q6_W_vshuff_VVR(in.v[3], in.v[2], 4); + HVX_Vector sum_sf01 = Q6_Vsf_vadd_VsfVsf(Q6_V_lo_W(sum_p01), Q6_V_hi_W(sum_p01)); + HVX_Vector sum_sf23 = Q6_Vsf_vadd_VsfVsf(Q6_V_lo_W(sum_p23), Q6_V_hi_W(sum_p23)); + + HVX_VectorPair sum_p0123 = Q6_W_vshuff_VVR(sum_sf23, sum_sf01, 8); + HVX_Vector sum_sf = Q6_Vsf_vadd_VsfVsf(Q6_V_lo_W(sum_p0123), Q6_V_hi_W(sum_p0123)); + + sum_sf = Q6_Vsf_vadd_VsfVsf(sum_sf, Q6_V_vror_VR(sum_sf, VLEN / 2)); + sum_sf = Q6_Vsf_vadd_VsfVsf(sum_sf, Q6_V_vror_VR(sum_sf, VLEN / 4)); + sum_sf = Q6_Vsf_vadd_VsfVsf(sum_sf, Q6_V_vror_VR(sum_sf, VLEN / 8)); + return sum_sf; +} + static inline HVX_Vector hvx_vec_reduce_sum_f32x2(HVX_Vector in0, HVX_Vector in1) { HVX_VectorPair sump = Q6_W_vshuff_VVR(in1, in0, 4); HVX_Vector sum_sf = Q6_Vsf_vadd_VsfVsf(Q6_V_lo_W(sump), Q6_V_hi_W(sump)); @@ -72,6 +87,21 @@ static inline HVX_Vector hvx_vec_reduce_sum_n_f32(HVX_Vector in, unsigned int n) #else +static inline HVX_Vector hvx_vec_reduce_sum_f32x4(HVX_Vector_x4 in) { + HVX_VectorPair sum_p01 = Q6_W_vshuff_VVR(in.v[1], in.v[0], 4); + HVX_VectorPair sum_p23 = Q6_W_vshuff_VVR(in.v[3], in.v[2], 4); + HVX_Vector sum_qf01 = Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(sum_p01), Q6_V_hi_W(sum_p01)); + HVX_Vector sum_qf23 = Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(sum_p23), Q6_V_hi_W(sum_p23)); + + HVX_VectorPair sum_p0123 = Q6_W_vshuff_VVR(Q6_Vsf_equals_Vqf32(sum_qf23), Q6_Vsf_equals_Vqf32(sum_qf01), 8); + HVX_Vector sum_qf = Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(sum_p0123), Q6_V_hi_W(sum_p0123)); + + sum_qf = Q6_Vqf32_vadd_Vqf32Vsf(sum_qf, Q6_V_vror_VR(Q6_Vsf_equals_Vqf32(sum_qf), VLEN / 2)); + sum_qf = Q6_Vqf32_vadd_Vqf32Vsf(sum_qf, Q6_V_vror_VR(Q6_Vsf_equals_Vqf32(sum_qf), VLEN / 4)); + sum_qf = Q6_Vqf32_vadd_Vqf32Vsf(sum_qf, Q6_V_vror_VR(Q6_Vsf_equals_Vqf32(sum_qf), VLEN / 8)); + return Q6_Vsf_equals_Vqf32(sum_qf); +} + static inline HVX_Vector hvx_vec_reduce_sum_f32x2(HVX_Vector in0, HVX_Vector in1) { HVX_VectorPair sump = Q6_W_vshuff_VVR(in1, in0, 4); HVX_Vector sum_qf = Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(sump), Q6_V_hi_W(sump)); diff --git a/ggml/src/ggml-hexagon/htp/hvx-utils.h b/ggml/src/ggml-hexagon/htp/hvx-utils.h index a518ad3733..0834379879 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-utils.h +++ b/ggml/src/ggml-hexagon/htp/hvx-utils.h @@ -15,4 +15,12 @@ #include "hvx-div.h" #include "hvx-base.h" +#ifndef GATHER_TYPE +# if defined(__hexagon__) +# define GATHER_TYPE(_a) (intptr_t) _a +# else +# define GATHER_TYPE(_a) (HVX_Vector *) _a +# endif +#endif + #endif /* HVX_UTILS_H */ diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c index 92a1422896..3f99dbb32c 100644 --- a/ggml/src/ggml-hexagon/htp/main.c +++ b/ggml/src/ggml-hexagon/htp/main.c @@ -757,6 +757,47 @@ static void proc_sum_rows_req(struct htp_context * ctx, struct htp_general_req * send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof); } +static void proc_ssm_conv_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs) { + struct dspqueue_buffer rsp_bufs[HTP_MAX_PACKET_BUFFERS]; + + // We've written to the output buffer, we'd also need to flush it + rsp_bufs[0].fd = bufs[2].fd; + rsp_bufs[0].ptr = bufs[2].ptr; + rsp_bufs[0].offset = bufs[2].offset; + rsp_bufs[0].size = bufs[2].size; + rsp_bufs[0].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush HTP + DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate CPU + + // Setup OP context + struct htp_ops_context octx = { 0 }; + octx.ctx = ctx; + octx.src0 = req->src0; + octx.src1 = req->src1; + octx.dst = req->dst; + octx.flags = req->flags; + octx.op = req->op; + + memcpy(octx.op_params, req->op_params, sizeof(octx.op_params)); + + // Update data pointers + octx.src0.data = (uint32_t) bufs[0].ptr; + octx.src1.data = (uint32_t) bufs[1].ptr; + octx.dst.data = (uint32_t) bufs[2].ptr; + octx.n_threads = ctx->n_threads; + + struct profile_data prof; + profile_start(&prof); + + uint32_t rsp_status = HTP_STATUS_INTERNAL_ERR; + if (vtcm_acquire(ctx) == AEE_SUCCESS) { + rsp_status = op_ssm_conv(&octx); + vtcm_release(ctx); + } + + profile_stop(&prof); + send_htp_rsp(ctx, req->op, rsp_status, rsp_bufs, 1, &prof); +} + static void proc_activations_req(struct htp_context * ctx, struct htp_general_req * req, struct dspqueue_buffer * bufs, @@ -1142,6 +1183,14 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) { proc_argsort_req(ctx, &req, bufs); break; + case HTP_OP_SSM_CONV: + if (n_bufs != 3) { + FARF(ERROR, "Bad ssm-conv-req buffer list"); + continue; + } + proc_ssm_conv_req(ctx, &req, bufs); + break; + default: FARF(ERROR, "Unknown Op %u", req.op); break; diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c index c360abe8da..73aaba79eb 100644 --- a/ggml/src/ggml-hexagon/htp/matmul-ops.c +++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c @@ -49,62 +49,6 @@ struct htp_matmul_context { struct fastdiv_values mm_div_r3; }; -// vdelta control to replicate first 4x fp32 values across lanes -static const uint8_t __attribute__((aligned(128))) repl_4x_f32[128] = { - 0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10, - 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20, - 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10, 0x10, 0x04, - 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x40, 0x40, 0x40, 0x40, - 0x44, 0x44, 0x44, 0x44, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, - 0x04, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20, 0x20, 0x20, 0x04, 0x04, - 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10, 0x10, -}; - -// vdelta control to replicate and interleave first 8x fp32 values across lanes -static const uint8_t __attribute__((aligned(128))) repl_interleave_8x_f32[128] = { - 0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x00, 0x00, 0x00, - 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20, - 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20, 0x20, 0x20, 0x04, - 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x40, 0x40, 0x40, 0x40, - 0x44, 0x44, 0x44, 0x44, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x40, 0x40, 0x40, 0x40, 0x44, 0x44, 0x44, - 0x44, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20, 0x20, 0x20, 0x04, 0x04, - 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20, 0x20, 0x20, -}; - -// vdelta control to replicate first fp32 value across all elements -static const uint8_t __attribute__((aligned(128))) repl_1x_f32[128] = { - 0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10, - 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x20, 0x20, 0x20, 0x20, 0x04, 0x04, - 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, - 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, - 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, - 0x04, 0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, 0x10, 0x10, - 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04, -}; - -// vdelta control to replicate first fp16 value across all elements -static const uint8_t __attribute__((aligned(128))) repl_1x_f16[128] = { - 0x00, 0x00, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x10, 0x10, 0x02, - 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x20, 0x20, 0x02, 0x02, 0x04, 0x04, - 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, - 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x40, 0x40, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, - 0x04, 0x04, 0x02, 0x02, 0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, - 0x02, 0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x10, 0x10, - 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, -}; - -// vdelta control to replicate first fp16 value across all elements -static const uint8_t __attribute__((aligned(128))) repl_2x_f16[128] = { - 0x00, 0x00, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, - 0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, - 0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, - 0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, - 0x00, 0x00, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, - 0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, - 0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, - 0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, -}; - // vdelta control to expand first 32 e8m0 values into 32 uint32 elements static const uint8_t __attribute__((aligned(128))) expand_x32_e8m0[128] = { 0x00, 0x00, 0x00, 0x00, 0x01, 0x04, 0x00, 0x00, 0x02, 0x00, 0x08, 0x08, 0x01, 0x02, 0x00, 0x04, 0x04, 0x00, 0x00, @@ -133,7 +77,7 @@ static inline size_t q8x4x2_row_size(uint32_t ne) { return hex_round_up(ne + nb * 8 * sizeof(__fp16), 128); } -static inline HVX_Vector_x8 hvx_vec_load_q4x4x8(const uint8_t * restrict ptr) { +static inline HVX_Vector_x8 hvx_vec_load_q4x4x8_full(const uint8_t * restrict ptr) { const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr; HVX_Vector v0_1 = vptr[0]; // first 256 elements (128 bytes) @@ -144,9 +88,9 @@ static inline HVX_Vector_x8 hvx_vec_load_q4x4x8(const uint8_t * restrict ptr) { const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F); const HVX_Vector i8 = Q6_Vb_vsplat_R(8); - HVX_Vector v0 = Q6_V_vand_VV(v0_1, mask_h4); // & 0x0F - HVX_Vector v1 = Q6_Vub_vlsr_VubR(v0_1, 4); // >> 4 - HVX_Vector v2 = Q6_V_vand_VV(v2_3, mask_h4); // & 0x0F + HVX_Vector v0 = Q6_V_vand_VV(v0_1, mask_h4); // & 0x0F : first 128 elements + HVX_Vector v1 = Q6_Vub_vlsr_VubR(v0_1, 4); // >> 4 : second 128 elements + HVX_Vector v2 = Q6_V_vand_VV(v2_3, mask_h4); // & 0x0F ... HVX_Vector v3 = Q6_Vub_vlsr_VubR(v2_3, 4); // >> 4 HVX_Vector v4 = Q6_V_vand_VV(v4_5, mask_h4); // & 0x0F HVX_Vector v5 = Q6_Vub_vlsr_VubR(v4_5, 4); // >> 4 @@ -167,7 +111,41 @@ static inline HVX_Vector_x8 hvx_vec_load_q4x4x8(const uint8_t * restrict ptr) { return r; } -static inline HVX_Vector_x8 hvx_vec_load_mxfp4x4x8(const uint8_t * restrict ptr) { +static HVX_Vector_x8 hvx_vec_load_q4x4x8_partial(const uint8_t * restrict ptr, uint32_t n) { + const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr; + + const uint32_t qk = QK_Q4_0x4x2; // 256 + const uint32_t nb = n / qk; + const uint32_t nloe = n % qk; + + const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F); + const HVX_Vector i8 = Q6_Vb_vsplat_R(8); + + HVX_Vector_x8 r; + uint32_t i = 0; + + #pragma unroll(2) + for (i=0; i < nb; i++) { + HVX_Vector v = vptr[i]; // 256 elements (128 bytes) + HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4); // & 0x0F : first 128 elements + HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4); // >> 4 : second 128 elements + r.v[i*2+0] = Q6_Vb_vsub_VbVb(v0, i8); + r.v[i*2+1] = Q6_Vb_vsub_VbVb(v1, i8); + } + + if (nloe) { + HVX_Vector v = vptr[i]; // 256 elements (128 bytes) + HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4); // & 0x0F : even 128 elements + HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4); // >> 4 : odd 128 elements + HVX_VectorPair v0_1_p = Q6_W_vshuff_VVR(v1, v0, -1); // zip even:odd:... + r.v[i*2+0] = Q6_Vb_vsub_VbVb(Q6_V_lo_W(v0_1_p), i8); + r.v[i*2+1] = Q6_Vb_vsub_VbVb(Q6_V_hi_W(v0_1_p), i8); + } + + return r; +} + +static inline HVX_Vector_x8 hvx_vec_load_mxfp4x4x8_full(const uint8_t * restrict ptr) { const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr; HVX_Vector v0_1 = vptr[0]; // first 256 elements (128 bytes) @@ -200,7 +178,41 @@ static inline HVX_Vector_x8 hvx_vec_load_mxfp4x4x8(const uint8_t * restrict ptr) return r; } -static inline HVX_Vector_x8 hvx_vec_load_q8x4x8(const uint8_t * restrict ptr) { +static inline HVX_Vector_x8 hvx_vec_load_mxfp4x4x8_partial(const uint8_t * restrict ptr, uint32_t n) { + const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr; + + const uint32_t qk = QK_Q4_0x4x2; // 256 + const uint32_t nb = n / qk; + const uint32_t nloe = n % qk; + + const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F); + const HVX_Vector lut = *(const HVX_Vector *) kvalues_mxfp4_lut; + + HVX_Vector_x8 r; + uint32_t i = 0; + + #pragma unroll(2) + for (i=0; i < nb; i++) { + HVX_Vector v = vptr[i]; // 256 elements (128 bytes) + HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4); // & 0x0F : first 128 elements + HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4); // >> 4 : second 128 elements + r.v[i*2+0] = Q6_Vb_vlut32_VbVbI(v0, lut, 0); + r.v[i*2+1] = Q6_Vb_vlut32_VbVbI(v1, lut, 0); + } + + if (nloe) { + HVX_Vector v = vptr[i]; // 256 elements (128 bytes) + HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4); // & 0x0F : even 128 elements + HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4); // >> 4 : odd 128 elements + HVX_VectorPair v0_1_p = Q6_W_vshuff_VVR(v1, v0, -1); // zip even:odd:... + r.v[i*2+0] = Q6_Vb_vlut32_VbVbI(Q6_V_lo_W(v0_1_p), lut, 0); + r.v[i*2+1] = Q6_Vb_vlut32_VbVbI(Q6_V_hi_W(v0_1_p), lut, 0); + } + + return r; +} + +static inline HVX_Vector_x8 hvx_vec_load_q8x4x8_full(const uint8_t * restrict ptr) { const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr; HVX_Vector v0 = vptr[0]; // first 128 vals @@ -216,6 +228,10 @@ static inline HVX_Vector_x8 hvx_vec_load_q8x4x8(const uint8_t * restrict ptr) { return r; } +static inline HVX_Vector_x8 hvx_vec_load_q8x4x8_partial(const uint8_t * restrict ptr, uint32_t nloe) { + return hvx_vec_load_q8x4x8_full(ptr); +} + // Reduce multiply 1024 x 1024 int8 elements (32x q4/8 blocks in 8x HVX vectors). // Accumulate each block into a single int32 value. // Return a single HVX vector with 32x int32 accumulators. @@ -223,14 +239,14 @@ static inline HVX_Vector_x8 hvx_vec_load_q8x4x8(const uint8_t * restrict ptr) { // if() checks are optimized out at compile time -- make sure to pass N as a constexpr. static inline HVX_Vector hvx_vec_rmpy_x8_n(HVX_Vector_x8 x, HVX_Vector_x8 y, unsigned int n) { - HVX_Vector r0 = Q6_V_vsplat_R(0); - HVX_Vector r1 = Q6_V_vsplat_R(0); - HVX_Vector r2 = Q6_V_vsplat_R(0); - HVX_Vector r3 = Q6_V_vsplat_R(0); - HVX_Vector r4 = Q6_V_vsplat_R(0); - HVX_Vector r5 = Q6_V_vsplat_R(0); - HVX_Vector r6 = Q6_V_vsplat_R(0); - HVX_Vector r7 = Q6_V_vsplat_R(0); + HVX_Vector r0 = Q6_V_vzero(); + HVX_Vector r1 = Q6_V_vzero(); + HVX_Vector r2 = Q6_V_vzero(); + HVX_Vector r3 = Q6_V_vzero(); + HVX_Vector r4 = Q6_V_vzero(); + HVX_Vector r5 = Q6_V_vzero(); + HVX_Vector r6 = Q6_V_vzero(); + HVX_Vector r7 = Q6_V_vzero(); HVX_VectorPair p3; HVX_VectorPair p2; @@ -269,15 +285,42 @@ static inline HVX_Vector hvx_vec_rmpy_x8_n(HVX_Vector_x8 x, HVX_Vector_x8 y, uns } static inline HVX_Vector hvx_vec_rmpy_x8_full(HVX_Vector_x8 x, HVX_Vector_x8 y) { - return hvx_vec_rmpy_x8_n(x, y, 1024); + HVX_Vector r0 = Q6_Vw_vrmpy_VbVb(x.v[0], y.v[0]); + HVX_Vector r1 = Q6_Vw_vrmpy_VbVb(x.v[1], y.v[1]); + HVX_Vector r2 = Q6_Vw_vrmpy_VbVb(x.v[2], y.v[2]); + HVX_Vector r3 = Q6_Vw_vrmpy_VbVb(x.v[3], y.v[3]); + HVX_Vector r4 = Q6_Vw_vrmpy_VbVb(x.v[4], y.v[4]); + HVX_Vector r5 = Q6_Vw_vrmpy_VbVb(x.v[5], y.v[5]); + HVX_Vector r6 = Q6_Vw_vrmpy_VbVb(x.v[6], y.v[6]); + HVX_Vector r7 = Q6_Vw_vrmpy_VbVb(x.v[7], y.v[7]); + + HVX_VectorPair p0 = Q6_W_vdeal_VVR(r1, r0, -4); + HVX_VectorPair p1 = Q6_W_vdeal_VVR(r3, r2, -4); + HVX_VectorPair p2 = Q6_W_vdeal_VVR(r5, r4, -4); + HVX_VectorPair p3 = Q6_W_vdeal_VVR(r7, r6, -4); + + r0 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p0), Q6_V_hi_W(p0)); + r1 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p1), Q6_V_hi_W(p1)); + r2 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p2), Q6_V_hi_W(p2)); + r3 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p3), Q6_V_hi_W(p3)); + + p0 = Q6_W_vdeal_VVR(r1, r0, -4); + p1 = Q6_W_vdeal_VVR(r3, r2, -4); + + r0 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p0), Q6_V_hi_W(p0)); + r1 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p1), Q6_V_hi_W(p1)); + + p0 = Q6_W_vdeal_VVR(r1, r0, -4); + r0 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p0), Q6_V_hi_W(p0)); + + return r0; } -// Handle most common cases of tensors not multiple of 1024. -static inline HVX_Vector hvx_vec_rmpy_x8_nloe(HVX_Vector_x8 x, HVX_Vector_x8 y, unsigned int n) { - if (n <= 256) { return hvx_vec_rmpy_x8_n(x, y, 256); }; - if (n <= 512) { return hvx_vec_rmpy_x8_n(x, y, 512); }; - if (n <= 768) { return hvx_vec_rmpy_x8_n(x, y, 768); }; - return hvx_vec_rmpy_x8_n(x, y, 1024); +static inline HVX_Vector hvx_vec_rmpy_x8_partial(HVX_Vector_x8 x, HVX_Vector_x8 y, unsigned int n) { + if (n >= 512) + return hvx_vec_rmpy_x8_full(x, y); + + return hvx_vec_rmpy_x8_partial(x, y, 512); } static void vec_dot_q4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const void * restrict vx0, const void * restrict vy0) { @@ -302,7 +345,7 @@ static void vec_dot_q4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const vo const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size); // then scales // Row sum (sf) - HVX_Vector r0_sum = Q6_V_vsplat_R(0); + HVX_Vector r0_sum = Q6_V_vzero(); // Multiply and accumulate into int32. // Compute combined scale (fp32). @@ -313,12 +356,12 @@ static void vec_dot_q4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const vo uint32_t i = 0; for (; i < nb; i++) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size); + HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_full(r0_x_q + i * x_qblk_size); HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q)); - HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); + HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d))); @@ -328,19 +371,19 @@ static void vec_dot_q4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const vo r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); } - // Process leftovers, we still load full 4x4x2 block but zero out unused scales/blocks + // Process leftovers if (nloe) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q + i * y_qblk_size, nloe); + HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_partial(r0_x_q + i * x_qblk_size, nloe); - HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy_q, nloe)); + HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe)); - HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); + HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d))); - // Zero out unused scales + // Zero out unused elements HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); r0_ia = Q6_V_vand_QV(bmask, r0_ia); @@ -382,8 +425,8 @@ static void vec_dot_q4x4x2_q8x4x2_2x1(const int n, float * restrict s0, const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size); // then scales // Row sum (sf) - HVX_Vector r0_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_sum = Q6_V_vsplat_R(0); + HVX_Vector r0_sum = Q6_V_vzero(); + HVX_Vector r1_sum = Q6_V_vzero(); // Multiply and accumulate into int32. // Compute combined scale (fp32). @@ -394,14 +437,14 @@ static void vec_dot_q4x4x2_q8x4x2_2x1(const int n, float * restrict s0, uint32_t i = 0; for (; i < nb; i++) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size); + HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_full(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8_full(r1_x_q + i * x_qblk_size); HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q)); HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q)); - HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); + HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size)); @@ -415,23 +458,23 @@ static void vec_dot_q4x4x2_q8x4x2_2x1(const int n, float * restrict s0, r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum)); } - // Process leftovers, we still load full 4x4x2 block but zero out unused scales/blocks + // Process leftovers if (nloe) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q + i * y_qblk_size, nloe); + HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_partial(r0_x_q + i * x_qblk_size, nloe); + HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8_partial(r1_x_q + i * x_qblk_size, nloe); - HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy_q, nloe)); - HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy_q, nloe)); + HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe)); + HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe)); - HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); + HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size)); HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d))); HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d))); - // Zero out unused scales + // Zero out unused elements HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); r1_dd = Q6_V_vand_QV(bmask, r1_dd); @@ -479,10 +522,10 @@ static void vec_dot_q4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size; // then scales // Row sums (sf) - 4 accumulators for 2×2 tile - HVX_Vector r0_c0_sum = Q6_V_vsplat_R(0); - HVX_Vector r0_c1_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_c0_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_c1_sum = Q6_V_vsplat_R(0); + HVX_Vector r0_c0_sum = Q6_V_vzero(); + HVX_Vector r0_c1_sum = Q6_V_vzero(); + HVX_Vector r1_c0_sum = Q6_V_vzero(); + HVX_Vector r1_c1_sum = Q6_V_vzero(); const uint32_t nb = n / qk; // num full blocks const uint32_t nloe = n % qk; // num leftover elements @@ -490,12 +533,12 @@ static void vec_dot_q4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * uint32_t i = 0; for (; i < nb; i++) { // Load src1 columns (reused across both src0 rows) - HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size); - HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size); + HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_full(y0_q + i * y_qblk_size); + HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_full(y1_q + i * y_qblk_size); // Load src0 rows (reused across both src1 columns) - HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_full(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8_full(r1_x_q + i * x_qblk_size); // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1 HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q)); @@ -504,8 +547,8 @@ static void vec_dot_q4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q)); // Load scales - HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size)); - HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size)); + HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size)); + HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size)); @@ -529,18 +572,18 @@ static void vec_dot_q4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * // Process leftovers if (nloe) { - HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size); - HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_partial(y0_q + i * y_qblk_size, nloe); + HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_partial(y1_q + i * y_qblk_size, nloe); + HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_partial(r0_x_q + i * x_qblk_size, nloe); + HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8_partial(r1_x_q + i * x_qblk_size, nloe); - HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy0_q, nloe)); - HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy1_q, nloe)); - HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy0_q, nloe)); - HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy1_q, nloe)); + HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy0_q, nloe)); + HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy1_q, nloe)); + HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy0_q, nloe)); + HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy1_q, nloe)); - HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size)); - HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size)); + HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size)); + HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size)); @@ -601,7 +644,7 @@ static void vec_dot_q8x4x2_q8x4x2_1x1(const int n, float * restrict s0, const vo const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size); // then scales // Row sum (sf) - HVX_Vector r0_sum = Q6_V_vsplat_R(0); + HVX_Vector r0_sum = Q6_V_vzero(); // Multiply and accumulate into int32. // Compute combined scale (fp32). @@ -612,12 +655,12 @@ static void vec_dot_q8x4x2_q8x4x2_1x1(const int n, float * restrict s0, const vo uint32_t i = 0; for (; i < nb; i++) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size); + HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_full(r0_x_q + i * x_qblk_size); HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q)); - HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); + HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d))); @@ -627,19 +670,19 @@ static void vec_dot_q8x4x2_q8x4x2_1x1(const int n, float * restrict s0, const vo r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum)); } - // Process leftovers, we still load full 4x4x2 block but zero out unused scales/blocks + // Process leftovers if (nloe) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q + i * y_qblk_size, nloe); + HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_partial(r0_x_q + i * x_qblk_size, nloe); - HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy_q, nloe)); + HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe)); - HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); + HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d))); - // Zero out unused scales + // Zero out unused elements HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); r0_ia = Q6_V_vand_QV(bmask, r0_ia); @@ -681,8 +724,8 @@ static void vec_dot_q8x4x2_q8x4x2_2x1(const int n, float * restrict s0, const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size); // then scales // Row sum (qf32) - HVX_Vector r0_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_sum = Q6_V_vsplat_R(0); + HVX_Vector r0_sum = Q6_V_vzero(); + HVX_Vector r1_sum = Q6_V_vzero(); // Multiply and accumulate into int32. // Compute combined scale (fp32). @@ -693,14 +736,14 @@ static void vec_dot_q8x4x2_q8x4x2_2x1(const int n, float * restrict s0, uint32_t i = 0; for (; i < nb; i++) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size); + HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_full(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8_full(r1_x_q + i * x_qblk_size); HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q)); HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q)); - HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); + HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size)); @@ -714,14 +757,14 @@ static void vec_dot_q8x4x2_q8x4x2_2x1(const int n, float * restrict s0, r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum)); } - // Process leftovers, we still load full 4x4x2 block but zero out unused scales/blocks + // Process leftovers if (nloe) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q + i * y_qblk_size, nloe); + HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_partial(r0_x_q + i * x_qblk_size, nloe); + HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8_partial(r1_x_q + i * x_qblk_size, nloe); - HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy_q, nloe)); - HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy_q, nloe)); + HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe)); + HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe)); HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); @@ -730,7 +773,7 @@ static void vec_dot_q8x4x2_q8x4x2_2x1(const int n, float * restrict s0, HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d))); HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d))); - // Zero out unused scales + // Zero out unused elements HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_dd = Q6_V_vand_QV(bmask, r0_dd); r1_dd = Q6_V_vand_QV(bmask, r1_dd); @@ -778,10 +821,10 @@ static void vec_dot_q8x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size; // then scales // Row sums (sf) - 4 accumulators for 2×2 tile - HVX_Vector r0_c0_sum = Q6_V_vsplat_R(0); - HVX_Vector r0_c1_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_c0_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_c1_sum = Q6_V_vsplat_R(0); + HVX_Vector r0_c0_sum = Q6_V_vzero(); + HVX_Vector r0_c1_sum = Q6_V_vzero(); + HVX_Vector r1_c0_sum = Q6_V_vzero(); + HVX_Vector r1_c1_sum = Q6_V_vzero(); const uint32_t nb = n / qk; // num full blocks const uint32_t nloe = n % qk; // num leftover elements @@ -789,12 +832,12 @@ static void vec_dot_q8x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * uint32_t i = 0; for (; i < nb; i++) { // Load src1 columns (reused across both src0 rows) - HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size); - HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size); + HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_full(y0_q + i * y_qblk_size); + HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_full(y1_q + i * y_qblk_size); // Load src0 rows (reused across both src1 columns) - HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_full(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8_full(r1_x_q + i * x_qblk_size); // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1 HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q)); @@ -803,8 +846,8 @@ static void vec_dot_q8x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q)); // Load scales - HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size)); - HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size)); + HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size)); + HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size)); @@ -828,18 +871,18 @@ static void vec_dot_q8x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * // Process leftovers if (nloe) { - HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size); - HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_partial(y0_q + i * y_qblk_size, nloe); + HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_partial(y1_q + i * y_qblk_size, nloe); + HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_partial(r0_x_q + i * x_qblk_size, nloe); + HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8_partial(r1_x_q + i * x_qblk_size, nloe); - HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy0_q, nloe)); - HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy1_q, nloe)); - HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy0_q, nloe)); - HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy1_q, nloe)); + HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy0_q, nloe)); + HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy1_q, nloe)); + HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy0_q, nloe)); + HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy1_q, nloe)); - HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size)); - HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size)); + HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size)); + HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size)); HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size)); HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size)); @@ -848,7 +891,7 @@ static void vec_dot_q8x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d))); HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d))); - // Zero out unused scales + // Zero out unused elements HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8); r0_c0_dd = Q6_V_vand_QV(bmask, r0_c0_dd); r0_c1_dd = Q6_V_vand_QV(bmask, r0_c1_dd); @@ -900,7 +943,7 @@ static void vec_dot_mxfp4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size); // then scales // Row sum (sf) - HVX_Vector r0_sum = Q6_V_vsplat_R(0); + HVX_Vector r0_sum = Q6_V_vzero(); // Multiply and accumulate into int32. // Compute combined scale (fp32). @@ -911,8 +954,8 @@ static void vec_dot_mxfp4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const uint32_t i = 0; for (; i < nb; i++) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full( y_q + i * y_qblk_size); + HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_full(r0_x_q + i * x_qblk_size); HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q)); @@ -943,12 +986,12 @@ static void vec_dot_mxfp4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const // Process leftovers if (nloe) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial( y_q + i * y_qblk_size, nloe); + HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_partial(r0_x_q + i * x_qblk_size, nloe); - HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q)); + HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe)); - HVX_Vector vy_d = *(const HVX_UVector *) (y_d + i * y_dblk_size); + HVX_Vector vy_d = *(const HVX_UVector *) (y_d + i * y_dblk_size); HVX_Vector r0_d = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size); // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving @@ -1010,8 +1053,8 @@ static void vec_dot_mxfp4x4x2_q8x4x2_2x1(const int n, float * restrict s0, const uint8_t * restrict y_d = ((const uint8_t *) vy0) + y_qrow_size; // then scales // Row sum (sf) - HVX_Vector r0_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_sum = Q6_V_vsplat_R(0); + HVX_Vector r0_sum = Q6_V_vzero(); + HVX_Vector r1_sum = Q6_V_vzero(); // Multiply and accumulate into int32. // Compute combined scale (fp32). @@ -1022,9 +1065,9 @@ static void vec_dot_mxfp4x4x2_q8x4x2_2x1(const int n, float * restrict s0, uint32_t i = 0; for (; i < nb; i++) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full( y_q + i * y_qblk_size); + HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_full(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8_full(r1_x_q + i * x_qblk_size); HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q)); HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q)); @@ -1063,14 +1106,14 @@ static void vec_dot_mxfp4x4x2_q8x4x2_2x1(const int n, float * restrict s0, // Process leftovers if (nloe) { - HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8(y_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial( y_q + i * y_qblk_size, nloe); + HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_partial(r0_x_q + i * x_qblk_size, nloe); + HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8_partial(r1_x_q + i * x_qblk_size, nloe); HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q)); HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q)); - HVX_Vector vy_d = *(const HVX_UVector *) (y_d + i * y_dblk_size); + HVX_Vector vy_d = *(const HVX_UVector *) (y_d + i * y_dblk_size); HVX_Vector r0_d = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size); HVX_Vector r1_d = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size); @@ -1143,10 +1186,10 @@ static void vec_dot_mxfp4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size; // then scales // Row sums (sf) - 4 accumulators for 2×2 tile - HVX_Vector r0_c0_sum = Q6_V_vsplat_R(0); - HVX_Vector r0_c1_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_c0_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_c1_sum = Q6_V_vsplat_R(0); + HVX_Vector r0_c0_sum = Q6_V_vzero(); + HVX_Vector r0_c1_sum = Q6_V_vzero(); + HVX_Vector r1_c0_sum = Q6_V_vzero(); + HVX_Vector r1_c1_sum = Q6_V_vzero(); const uint32_t nb = n / qk; // num full blocks const uint32_t nloe = n % qk; // num leftover elements @@ -1154,12 +1197,12 @@ static void vec_dot_mxfp4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float uint32_t i = 0; for (; i < nb; i++) { // Load src1 columns (reused across both src0 rows) - HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size); - HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size); + HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_full(y0_q + i * y_qblk_size); + HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_full(y1_q + i * y_qblk_size); // Load src0 rows (reused across both src1 columns) - HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_full(r0_x_q + i * x_qblk_size); + HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8_full(r1_x_q + i * x_qblk_size); // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1 HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q)); @@ -1213,15 +1256,15 @@ static void vec_dot_mxfp4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float // Process leftovers if (nloe) { - HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8(y0_q + i * y_qblk_size); - HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8(y1_q + i * y_qblk_size); - HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8(r0_x_q + i * x_qblk_size); - HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8(r1_x_q + i * x_qblk_size); + HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_partial( y0_q + i * y_qblk_size, nloe); + HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_partial( y1_q + i * y_qblk_size, nloe); + HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_partial(r0_x_q + i * x_qblk_size, nloe); + HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8_partial(r1_x_q + i * x_qblk_size, nloe); - HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy0_q, nloe)); - HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r0_q, vy1_q, nloe)); - HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy0_q, nloe)); - HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_nloe(r1_q, vy1_q, nloe)); + HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy0_q, nloe)); + HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy1_q, nloe)); + HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy0_q, nloe)); + HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy1_q, nloe)); HVX_Vector vy0_d = *(const HVX_UVector *) (y0_d + i * y_dblk_size); HVX_Vector vy1_d = *(const HVX_UVector *) (y1_d + i * y_dblk_size); @@ -1290,27 +1333,24 @@ static void vec_dot_f16_f16_aa_1x1(const int n, float * restrict s, const void * uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors uint32_t nloe = n % VLEN_FP16; // leftover elements - HVX_Vector rsum = Q6_V_vsplat_R(0); + HVX_VectorPair rsum_p = Q6_W_vzero(); uint32_t i = 0; #pragma unroll(4) for (i = 0; i < nvec; i++) { - HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x[i], y[i]); - rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf))); + rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x[i], y[i]); } if (nloe) { HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2); HVX_Vector x_hf = Q6_V_vand_QV(bmask, x[i]); HVX_Vector y_hf = Q6_V_vand_QV(bmask, y[i]); - - HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf); - rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf), Q6_V_hi_W(xy_qf))); + rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x_hf, y_hf); } - rsum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(rsum)); - hvx_vec_store_u(&s[0], 4, rsum); + HVX_Vector rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum_p), Q6_V_hi_W(rsum_p))); + hvx_vec_store_u(s, 4, hvx_vec_reduce_sum_f32(rsum)); } static void vec_dot_f16_f16_aa_2x1(const int n, float * restrict s0, @@ -1323,35 +1363,30 @@ static void vec_dot_f16_f16_aa_2x1(const int n, float * restrict s0, uint32_t nvec = n / VLEN_FP16; uint32_t nloe = n % VLEN_FP16; - HVX_Vector rsum0 = Q6_V_vsplat_R(0); - HVX_Vector rsum1 = Q6_V_vsplat_R(0); + HVX_VectorPair rsum0_p = Q6_W_vzero(); + HVX_VectorPair rsum1_p = Q6_W_vzero(); uint32_t i = 0; #pragma unroll(2) for (i = 0; i < nvec; i++) { HVX_Vector y_hf = y[i]; - HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0[i], y_hf); - HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1[i], y_hf); - - rsum0 = Q6_Vqf32_vadd_Vqf32Vqf32(rsum0, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf))); - rsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(rsum1, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf))); + rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0[i], y_hf); + rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1[i], y_hf); } if (nloe) { HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2); + HVX_Vector y_hf = Q6_V_vand_QV(bmask, y[i]); HVX_Vector x0_hf = Q6_V_vand_QV(bmask, x0[i]); HVX_Vector x1_hf = Q6_V_vand_QV(bmask, x1[i]); - HVX_Vector y_hf = Q6_V_vand_QV(bmask, y[i]); - - HVX_VectorPair xy0_qf = Q6_Wqf32_vmpy_VhfVhf(x0_hf, y_hf); - HVX_VectorPair xy1_qf = Q6_Wqf32_vmpy_VhfVhf(x1_hf, y_hf); - - rsum0 = Q6_Vqf32_vadd_Vqf32Vqf32(rsum0, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy0_qf), Q6_V_hi_W(xy0_qf))); - rsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(rsum1, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy1_qf), Q6_V_hi_W(xy1_qf))); + rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0_hf, y_hf); + rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1_hf, y_hf); } - HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(Q6_Vsf_equals_Vqf32(rsum0), Q6_Vsf_equals_Vqf32(rsum1)); + HVX_Vector rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum0_p), Q6_V_hi_W(rsum0_p))); + HVX_Vector rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum1_p), Q6_V_hi_W(rsum1_p))); + HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(rsum0, rsum1); hvx_vec_store_u(s0, 8, rsum); } @@ -1367,10 +1402,10 @@ static void vec_dot_f16_f16_aa_2x2(const int n, float * restrict s0, float * res uint32_t nloe = n % VLEN_FP16; // Row sums (sf) - 4 accumulators for 2×2 tile - HVX_Vector r0_c0_sum = Q6_V_vsplat_R(0); - HVX_Vector r0_c1_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_c0_sum = Q6_V_vsplat_R(0); - HVX_Vector r1_c1_sum = Q6_V_vsplat_R(0); + HVX_VectorPair r0_c0_sum_p = Q6_W_vzero(); + HVX_VectorPair r0_c1_sum_p = Q6_W_vzero(); + HVX_VectorPair r1_c0_sum_p = Q6_W_vzero(); + HVX_VectorPair r1_c1_sum_p = Q6_W_vzero(); uint32_t i = 0; @@ -1382,20 +1417,10 @@ static void vec_dot_f16_f16_aa_2x2(const int n, float * restrict s0, float * res HVX_Vector c1_hf = y1[i]; // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1 - HVX_VectorPair r0_c0_qf_p = Q6_Wqf32_vmpy_VhfVhf(r0_hf, c0_hf); - HVX_VectorPair r0_c1_qf_p = Q6_Wqf32_vmpy_VhfVhf(r0_hf, c1_hf); - HVX_VectorPair r1_c0_qf_p = Q6_Wqf32_vmpy_VhfVhf(r1_hf, c0_hf); - HVX_VectorPair r1_c1_qf_p = Q6_Wqf32_vmpy_VhfVhf(r1_hf, c1_hf); - - HVX_Vector r0_c0_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r0_c0_qf_p), Q6_V_hi_W(r0_c0_qf_p)); - HVX_Vector r0_c1_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r0_c1_qf_p), Q6_V_hi_W(r0_c1_qf_p)); - HVX_Vector r1_c0_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r1_c0_qf_p), Q6_V_hi_W(r1_c0_qf_p)); - HVX_Vector r1_c1_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r1_c1_qf_p), Q6_V_hi_W(r1_c1_qf_p)); - - r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_qf, r0_c0_sum)); - r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_qf, r0_c1_sum)); - r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_qf, r1_c0_sum)); - r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_qf, r1_c1_sum)); + r0_c0_sum_p = hvx_vec_mpyacc_f32_f16(r0_c0_sum_p, r0_hf, c0_hf); + r0_c1_sum_p = hvx_vec_mpyacc_f32_f16(r0_c1_sum_p, r0_hf, c1_hf); + r1_c0_sum_p = hvx_vec_mpyacc_f32_f16(r1_c0_sum_p, r1_hf, c0_hf); + r1_c1_sum_p = hvx_vec_mpyacc_f32_f16(r1_c1_sum_p, r1_hf, c1_hf); } if (nloe) { @@ -1406,23 +1431,17 @@ static void vec_dot_f16_f16_aa_2x2(const int n, float * restrict s0, float * res HVX_Vector c0_hf = Q6_V_vand_QV(bmask, y0[i]); HVX_Vector c1_hf = Q6_V_vand_QV(bmask, y1[i]); - HVX_VectorPair r0_c0_qf_p = Q6_Wqf32_vmpy_VhfVhf(r0_hf, c0_hf); - HVX_VectorPair r0_c1_qf_p = Q6_Wqf32_vmpy_VhfVhf(r0_hf, c1_hf); - HVX_VectorPair r1_c0_qf_p = Q6_Wqf32_vmpy_VhfVhf(r1_hf, c0_hf); - HVX_VectorPair r1_c1_qf_p = Q6_Wqf32_vmpy_VhfVhf(r1_hf, c1_hf); - - HVX_Vector r0_c0_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r0_c0_qf_p), Q6_V_hi_W(r0_c0_qf_p)); - HVX_Vector r0_c1_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r0_c1_qf_p), Q6_V_hi_W(r0_c1_qf_p)); - HVX_Vector r1_c0_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r1_c0_qf_p), Q6_V_hi_W(r1_c0_qf_p)); - HVX_Vector r1_c1_qf = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(r1_c1_qf_p), Q6_V_hi_W(r1_c1_qf_p)); - - r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_qf, r0_c0_sum)); - r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_qf, r0_c1_sum)); - r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_qf, r1_c0_sum)); - r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_qf, r1_c1_sum)); - + r0_c0_sum_p = hvx_vec_mpyacc_f32_f16(r0_c0_sum_p, r0_hf, c0_hf); + r0_c1_sum_p = hvx_vec_mpyacc_f32_f16(r0_c1_sum_p, r0_hf, c1_hf); + r1_c0_sum_p = hvx_vec_mpyacc_f32_f16(r1_c0_sum_p, r1_hf, c0_hf); + r1_c1_sum_p = hvx_vec_mpyacc_f32_f16(r1_c1_sum_p, r1_hf, c1_hf); } + HVX_Vector r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r0_c0_sum_p), Q6_V_hi_W(r0_c0_sum_p))); + HVX_Vector r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r0_c1_sum_p), Q6_V_hi_W(r0_c1_sum_p))); + HVX_Vector r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r1_c0_sum_p), Q6_V_hi_W(r1_c0_sum_p))); + HVX_Vector r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r1_c1_sum_p), Q6_V_hi_W(r1_c1_sum_p))); + // Reduce and store results HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum); HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum); @@ -1438,7 +1457,7 @@ static void vec_dot_f16_f16_uu_1x1(const int n, float * restrict s, const void * uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors uint32_t nloe = n % VLEN_FP16; // leftover elements - HVX_Vector rsum = Q6_V_vsplat_R(0); + HVX_Vector rsum = Q6_V_vzero(); uint32_t i = 0; @@ -1468,9 +1487,9 @@ static void vec_dot_f16_f32_uu_1x1(const int n, float * restrict s, const void * uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors uint32_t nloe = n % VLEN_FP16; // leftover elements - const HVX_Vector zero = Q6_V_vsplat_R(0); + const HVX_Vector zero = Q6_V_vzero(); - HVX_Vector rsum = Q6_V_vsplat_R(0); + HVX_Vector rsum = Q6_V_vzero(); uint32_t i = 0; @@ -2053,7 +2072,7 @@ static inline void quantize_block_f32_q8x1(float * restrict x, uint8_t * restric assert((unsigned long) y_q % 128 == 0); HVX_Vector * vx = (HVX_Vector *) x; - HVX_Vector zero = Q6_V_vsplat_R(0); + HVX_Vector zero = Q6_V_vzero(); // Use reduce max fp32 to find max(abs(e)) first HVX_Vector vmax0_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[0])); @@ -2067,10 +2086,10 @@ static inline void quantize_block_f32_q8x1(float * restrict x, uint8_t * restric HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero); // 32 elements // Convert to QF32 - HVX_Vector vmax0_qf = Q6_Vqf32_vsub_VsfVsf(vmax0_sf, zero); - HVX_Vector vmax1_qf = Q6_Vqf32_vsub_VsfVsf(vmax1_sf, zero); - HVX_Vector vmax2_qf = Q6_Vqf32_vsub_VsfVsf(vmax2_sf, zero); - HVX_Vector vmax3_qf = Q6_Vqf32_vsub_VsfVsf(vmax3_sf, zero); + HVX_Vector vmax0_qf = Q6_Vqf32_vsub_VsfVsf(vmax0_sf, zero); // replicated over all lanes + HVX_Vector vmax1_qf = Q6_Vqf32_vsub_VsfVsf(vmax1_sf, zero); // replicated over all lanes + HVX_Vector vmax2_qf = Q6_Vqf32_vsub_VsfVsf(vmax2_sf, zero); // replicated over all lanes + HVX_Vector vmax3_qf = Q6_Vqf32_vsub_VsfVsf(vmax3_sf, zero); // replicated over all lanes // Combine and convert to fp16 HVX_Vector vmax01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax1_qf, vmax0_qf))); @@ -2080,11 +2099,6 @@ static inline void quantize_block_f32_q8x1(float * restrict x, uint8_t * restric HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf))); HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf))); - // Replicate first fp16 scale across all lanes - HVX_Vector ctrl = *(const HVX_Vector *) repl_2x_f16; - vmax01_hf = Q6_V_vdelta_VV(vmax01_hf, ctrl); - vmax23_hf = Q6_V_vdelta_VV(vmax23_hf, ctrl); - HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008)); // 1.0 / 127.0 HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008)); // 1.0 / 127.0 HVX_Vector vd01_hf = Q6_Vhf_equals_Vqf16(vd01_qf16); @@ -2119,7 +2133,7 @@ static inline void quantize_block_f32_q8x2(float * restrict x, uint8_t * restric HVX_Vector * vx = (HVX_Vector *) x; // Load and convert into QF32 - HVX_Vector zero = Q6_V_vsplat_R(0); + HVX_Vector zero = Q6_V_vzero(); HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero); // 32 elements HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero); // 32 elements HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero); // 32 elements @@ -2130,13 +2144,8 @@ static inline void quantize_block_f32_q8x2(float * restrict x, uint8_t * restric HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf))); // Compute max and scale - HVX_Vector vmax01_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx01_hf)); - HVX_Vector vmax23_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx23_hf)); - - // Replicate first fp16 scale across all lanes - HVX_Vector ctrl = *(const HVX_Vector *) repl_1x_f16; - vmax01_hf = Q6_V_vdelta_VV(vmax01_hf, ctrl); - vmax23_hf = Q6_V_vdelta_VV(vmax23_hf, ctrl); + HVX_Vector vmax01_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx01_hf)); // replicated over all lanes + HVX_Vector vmax23_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx23_hf)); // replicated over all lanes HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008)); // 1.0 / 127.0 HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008)); // 1.0 / 127.0 @@ -2167,7 +2176,7 @@ static inline void quantize_block_f32_q8x4(float * restrict x, uint8_t * restric HVX_Vector * vx = (HVX_Vector *) x; // Load and convert into QF32 - HVX_Vector zero = Q6_V_vsplat_R(0); + HVX_Vector zero = Q6_V_vzero(); HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero); // 32 elements HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero); // 32 elements HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero); // 32 elements @@ -2179,11 +2188,7 @@ static inline void quantize_block_f32_q8x4(float * restrict x, uint8_t * restric // Compute max and scale HVX_Vector vmax_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx01_hf)); - vmax_hf = hvx_vec_reduce_max2_f16(hvx_vec_abs_f16(vx23_hf), vmax_hf); - - // Replicate first fp16 scale across all lanes - HVX_Vector ctrl = *(const HVX_Vector *) repl_1x_f16; - vmax_hf = Q6_V_vdelta_VV(vmax_hf, ctrl); + vmax_hf = hvx_vec_reduce_max2_f16(hvx_vec_abs_f16(vx23_hf), vmax_hf); // replicated over all lanes HVX_Vector vd_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax_hf, Q6_Vh_vsplat_R(0x2008)); // 1.0 / 127.0 HVX_Vector vd_hf = Q6_Vhf_equals_Vqf16(vd_qf16); diff --git a/ggml/src/ggml-hexagon/htp/rope-ops.c b/ggml/src/ggml-hexagon/htp/rope-ops.c index 943ca5c952..be9469538f 100644 --- a/ggml/src/ggml-hexagon/htp/rope-ops.c +++ b/ggml/src/ggml-hexagon/htp/rope-ops.c @@ -10,6 +10,7 @@ #include "hex-dma.h" #include "hvx-utils.h" +#include "hex-fastdiv.h" #define GGML_COMMON_DECL_C #include "ggml-common.h" @@ -17,10 +18,13 @@ #include "htp-msg.h" #include "htp-ops.h" -// Redefined the types GGML_ROPE_TYPE_NORMAL & GGML_ROPE_TYPE_NEOX as we cant include ggml.h +// Redefined the types GGML_ROPE_TYPE_NORMAL & GGML_ROPE_TYPE_NEOX as we can't include ggml.h #define HTP_ROPE_TYPE_NORMAL 0 #define HTP_ROPE_TYPE_NEOX 2 +#define HTP_ROPE_SPAD_NROWS 16 +#define HTP_ROPE_SPAD_BLOCK (HTP_ROPE_SPAD_NROWS/2) + #define htp_rope_preamble \ const uint32_t ne00 = src0->ne[0]; \ const uint32_t ne01 = src0->ne[1]; \ @@ -42,7 +46,7 @@ const uint32_t nb2 = dst->nb[2]; \ const uint32_t nb3 = dst->nb[3]; -struct rope_th_ctx { +struct htp_rope_context { int32_t n_dims; int32_t mode; int32_t n_ctx_orig; @@ -57,7 +61,19 @@ struct rope_th_ctx { float theta_scale; float corr_dims[2]; + uint32_t src0_nrows_per_thread; + size_t spad_stride; + struct htp_ops_context * octx; + + size_t src0_row_size; + size_t dst_row_size; + size_t src0_row_size_aligned; + size_t dst_row_size_aligned; + size_t theta_cache_offset; + uint32_t src0_nrows; + + uint64_t t_start; }; static float rope_yarn_ramp(const float low, const float high, const int i0) { @@ -117,64 +133,23 @@ static void rope_corr_dims(int n_dims, dims[1] = MIN(n_dims - 1, end); } -static void init_rope_ctx(struct rope_th_ctx * rope_ctx, struct htp_ops_context * octx) { - memset(rope_ctx, 0, sizeof(struct rope_th_ctx)); +static inline void hvx_rope_neox_f32_aa(float * restrict dst, const float * restrict src0, uint32_t ne, const float * restrict theta_cache) { + const HVX_Vector * restrict vsrc = (const HVX_Vector *) src0; + const HVX_Vector * restrict vtheta = (const HVX_Vector *) theta_cache; + HVX_Vector * restrict vdst = (HVX_Vector *) dst; - const int32_t * op_params = &octx->op_params[0]; + uint32_t nvec = (ne / (VLEN_FP32 * 2) * 2); // 2 vecs per loop, step of 2 - rope_ctx->n_dims = ((const int32_t *) op_params)[1]; - rope_ctx->mode = ((const int32_t *) op_params)[2]; - rope_ctx->n_ctx_orig = ((const int32_t *) op_params)[4]; + uint32_t he = ne / 2; // half_dims offset in elements + uint32_t hv = he / VLEN_FP32; // half_dims offset in vectors - memcpy(&rope_ctx->freq_base, (int32_t *) op_params + 5, sizeof(float)); - memcpy(&rope_ctx->freq_scale, (int32_t *) op_params + 6, sizeof(float)); - memcpy(&rope_ctx->ext_factor, (int32_t *) op_params + 7, sizeof(float)); - memcpy(&rope_ctx->attn_factor, (int32_t *) op_params + 8, sizeof(float)); - memcpy(&rope_ctx->beta_fast, (int32_t *) op_params + 9, sizeof(float)); - memcpy(&rope_ctx->beta_slow, (int32_t *) op_params + 10, sizeof(float)); - memcpy(&rope_ctx->sections, (int32_t *) op_params + 11, sizeof(int) * 4); + #pragma unroll(2) + for (uint32_t i = 0; i < nvec; i += 2) { + HVX_Vector v0 = vsrc[i/2+0]; + HVX_Vector v1 = vsrc[i/2+hv]; - rope_ctx->theta_scale = powf(rope_ctx->freq_base, -2.0f / rope_ctx->n_dims); - - rope_corr_dims(rope_ctx->n_dims, rope_ctx->n_ctx_orig, rope_ctx->freq_base, rope_ctx->beta_fast, - rope_ctx->beta_slow, rope_ctx->corr_dims); - - rope_ctx->octx = octx; - FARF(HIGH, "rope-f32 n_dims:%d, ext_factor:%.6f, theta_scale:%.6f, attn_factor:%.6f\n", rope_ctx->n_dims, - rope_ctx->ext_factor, rope_ctx->theta_scale, rope_ctx->attn_factor); -} - -static void hvx_calc_rope_neox_f32(const float * restrict src0, - float * restrict dst, - const int num_elems, - const float * restrict theta_cache) { - // for (int i = 0; i < num_elems; i += 2) { - //const float cos_theta = theta_cache[i + 0]; - //const float sin_theta = theta_cache[i + 1]; - - //const float x0 = src[0]; - //const float x1 = src[num_elems/2]; - - //dst[0] = x0*cos_theta - x1*sin_theta; - //dst[num_elems/2] = x0*sin_theta + x1*cos_theta; - - //src += 1; - //dst += 1; - // } - - const uint8_t * restrict src0_curr = (const uint8_t *) src0; - const uint8_t * restrict theta_curr = (const uint8_t *) theta_cache; - uint8_t * restrict dst_curr = (uint8_t *) dst; - - int step_of_1 = num_elems >> 6; // 6 because we process two vectors at once - int half_size = (sizeof(float) * (num_elems / 2)); - - for (int i = 0; i < step_of_1; i++) { - HVX_Vector v0 = *(HVX_Vector *) src0_curr; - HVX_Vector v1 = *(HVX_Vector *) (src0_curr + half_size); - - HVX_Vector v2 = *(HVX_Vector *) theta_curr; - HVX_Vector v3 = *(HVX_Vector *) (theta_curr + VLEN); + HVX_Vector v2 = vtheta[i+0]; + HVX_Vector v3 = vtheta[i+1]; HVX_VectorPair vcos_sin = Q6_W_vdeal_VVR(v3, v2, -4); // vcos_sin[0] = cos_theta, vcos_sin[1] = sin_theta @@ -186,45 +161,34 @@ static void hvx_calc_rope_neox_f32(const float * restrict src0, HVX_Vector v4 = Q6_Vqf32_vsub_Vqf32Vqf32(vx0_c, vx1_s); HVX_Vector v5 = Q6_Vqf32_vadd_Vqf32Vqf32(vx0_s, vx1_c); - *(HVX_Vector *) dst_curr = Q6_Vsf_equals_Vqf32(v4); - *(HVX_Vector *) (dst_curr + half_size) = Q6_Vsf_equals_Vqf32(v5); + vdst[i/2+0] = Q6_Vsf_equals_Vqf32(v4); + vdst[i/2+hv] = Q6_Vsf_equals_Vqf32(v5); + } - src0_curr += VLEN; - theta_curr += 2 * VLEN; - dst_curr += VLEN; + for (uint32_t i = nvec * VLEN_FP32; i < ne; i += 2) { + const float cos_theta = theta_cache[i+0]; + const float sin_theta = theta_cache[i+1]; + float x0 = src0[i/2]; + float x1 = src0[i/2 + he]; + dst[i/2] = x0 * cos_theta - x1 * sin_theta; + dst[i/2 + he] = x0 * sin_theta + x1 * cos_theta; } } -static void hvx_calc_rope_f32(const float * restrict src0, - float * restrict dst, - const int num_elems, - const float * restrict theta_cache) { - // for (int i = 0; i < num_elems; i += 2) { - //const float cos_theta = theta_cache[i + 0]; - //const float sin_theta = theta_cache[i + 1]; +static inline void hvx_rope_f32_aa(float * restrict dst, const float * restrict src0, uint32_t ne, const float * restrict theta_cache) { + const HVX_Vector * restrict vsrc = (const HVX_Vector *) src0; + const HVX_Vector * restrict vtheta = (const HVX_Vector *) theta_cache; + HVX_Vector * restrict vdst = (HVX_Vector *) dst; - //const float x0 = src[0]; - //const float x1 = src[1]; + uint32_t nvec = (ne / (VLEN_FP32 * 2)) * 2; // 2 vecs per loop, step of two - //dst[0] = x0*cos_theta - x1*sin_theta; - //dst[1] = x0*sin_theta + x1*cos_theta; + #pragma unroll(2) + for (uint32_t i = 0; i < nvec; i+=2) { + HVX_Vector v0 = vsrc[i+0]; + HVX_Vector v1 = vsrc[i+1]; - //src += 2; - //dst += 2; - // } - - const uint8_t * restrict src0_curr = (const uint8_t *) src0; - const uint8_t * restrict theta_curr = (const uint8_t *) theta_cache; - uint8_t * restrict dst_curr = (uint8_t *) dst; - - int step_of_1 = num_elems >> 6; // 6 because we process two vectors at once - - for (int i = 0; i < step_of_1; i++) { - HVX_Vector v0 = *(HVX_Vector *) src0_curr; - HVX_Vector v1 = *(HVX_Vector *) (src0_curr + VLEN); - - HVX_Vector v2 = *(HVX_Vector *) theta_curr; - HVX_Vector v3 = *(HVX_Vector *) (theta_curr + VLEN); + HVX_Vector v2 = vtheta[i+0]; + HVX_Vector v3 = vtheta[i+1]; HVX_VectorPair vx0_x1 = Q6_W_vdeal_VVR(v1, v0, -4); // vx0_x1[0] = x0, vx0_x1[1] = x1 HVX_VectorPair vcos_sin = Q6_W_vdeal_VVR(v3, v2, -4); // vcos_sin[0] = cos_theta, vcos_sin[1] = sin_theta @@ -239,116 +203,65 @@ static void hvx_calc_rope_f32(const float * restrict src0, HVX_VectorPair vstore = Q6_W_vshuff_VVR(Q6_Vsf_equals_Vqf32(v5), Q6_Vsf_equals_Vqf32(v4), -4); - *(HVX_Vector *) dst_curr = Q6_V_lo_W(vstore); - *(HVX_Vector *) (dst_curr + VLEN) = Q6_V_hi_W(vstore); + vdst[i+0] = Q6_V_lo_W(vstore); + vdst[i+1] = Q6_V_hi_W(vstore); + } - src0_curr += 2 * VLEN; - theta_curr += 2 * VLEN; - dst_curr += 2 * VLEN; + for (uint32_t i = nvec * VLEN_FP32; i < ne; i += 2) { + const float cos_theta = theta_cache[i+0]; + const float sin_theta = theta_cache[i+1]; + float x0 = src0[i+0]; + float x1 = src0[i+1]; + dst[i+0] = x0 * cos_theta - x1 * sin_theta; + dst[i+1] = x0 * sin_theta + x1 * cos_theta; } } -static void rope_hex_f32(struct rope_th_ctx * rope_ctx, - const uint32_t ir0, - const uint32_t ir1, - int nth, - int ith, - const int opt_path) { - struct htp_ops_context * octx = rope_ctx->octx; +static void inline rope_basic_f32(struct htp_rope_context * rctx, uint8_t * restrict dst, uint8_t * restrict src, + uint32_t nr, uint32_t ne0, const float * restrict theta_cache) { + #pragma unroll(4) + for (uint32_t i = 0; i < nr; i++) { + float * d = (float *) (dst + i * rctx->dst_row_size_aligned); + float * s = (float *) (src + i * rctx->src0_row_size_aligned); + + hvx_rope_f32_aa(d, s, rctx->n_dims, theta_cache); + + // fill the remain channels with data from src tensor + if (rctx->n_dims < ne0) { + hvx_copy_f32_uu((uint8_t *)(d + rctx->n_dims), (uint8_t *)(s + rctx->n_dims), ne0 - rctx->n_dims); + } + } +} + +static void inline rope_neox_f32(struct htp_rope_context * rctx, uint8_t * restrict dst, uint8_t * restrict src, + uint32_t nr, uint32_t ne0, const float * restrict theta_cache) { + #pragma unroll(4) + for (uint32_t i = 0; i < nr; i++) { + float * d = (float *) (dst + i * rctx->dst_row_size_aligned); + float * s = (float *) (src + i * rctx->src0_row_size_aligned); + + hvx_rope_neox_f32_aa(d, s, rctx->n_dims, theta_cache); + + // fill the remain channels with data from src tensor + if (rctx->n_dims < ne0) { + hvx_copy_f32_uu((uint8_t *)(d + rctx->n_dims), (uint8_t *)(s + rctx->n_dims), ne0 - rctx->n_dims); + } + } +} + +static void rope_job_f32(unsigned int nth, unsigned int ith, void * data) { + struct htp_rope_context * rctx = (struct htp_rope_context *) data; + struct htp_ops_context * octx = rctx->octx; const struct htp_tensor * src0 = &octx->src0; const struct htp_tensor * src1 = &octx->src1; const struct htp_tensor * src2 = &octx->src2; struct htp_tensor * dst = &octx->dst; - const int32_t mode = rope_ctx->mode; - const bool is_neox = mode & HTP_ROPE_TYPE_NEOX; - htp_rope_preamble; - const int32_t * pos = (const int32_t *) src1->data; - - float * wp0 = (float *) (octx->src0_spad.data + (ith * nb01)); - - const float * freq_factors = NULL; - if (src2 != NULL) { - freq_factors = (const float *) src2->data; - } - - const uint32_t i1_end = MIN(ir1, ne1); - const int32_t half_dims = rope_ctx->n_dims / 2; - const size_t remain_bytes = (ne0 - rope_ctx->n_dims) * sizeof(float); - for (uint32_t i3 = 0; i3 < ne3; i3++) { // batch - for (uint32_t i2 = 0; i2 < ne2; i2++) { // seq-len - const int32_t p = pos[i2]; - - rope_cache_init(p, rope_ctx->freq_scale, freq_factors, rope_ctx->corr_dims, ne0, rope_ctx->ext_factor, - rope_ctx->attn_factor, wp0, rope_ctx->theta_scale); - - for (uint32_t i1 = ir0; i1 < i1_end; i1++) { // attn-heads - const float * src = (float *) ((char *) src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01); - float * dst_data = (float *) ((char *) dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1); - - const float * src_loc = src; - float * dst_data_loc = dst_data; - - if (1 == opt_path) { - if (is_neox) { - hvx_calc_rope_neox_f32(src_loc, dst_data_loc, rope_ctx->n_dims, wp0); - } else { - hvx_calc_rope_f32(src_loc, dst_data_loc, rope_ctx->n_dims, wp0); - } - - src_loc += rope_ctx->n_dims; - dst_data_loc += rope_ctx->n_dims; - } else { - for (uint32_t i0 = 0; i0 < rope_ctx->n_dims; i0 += 2) { - const float cos_theta = wp0[i0 + 0]; - const float sin_theta = wp0[i0 + 1]; - - if (is_neox) { - const float x0 = src_loc[0]; - const float x1 = src_loc[half_dims]; - - dst_data_loc[0] = x0 * cos_theta - x1 * sin_theta; - dst_data_loc[half_dims] = x0 * sin_theta + x1 * cos_theta; - - src_loc += 1; - dst_data_loc += 1; - } else { - const float x0 = src_loc[0]; - const float x1 = src_loc[1]; - - dst_data_loc[0] = x0 * cos_theta - x1 * sin_theta; - dst_data_loc[1] = x0 * sin_theta + x1 * cos_theta; - - src_loc += 2; - dst_data_loc += 2; - } - } - - src_loc += (is_neox ? half_dims : 0); - dst_data_loc += (is_neox ? half_dims : 0); - } - - // TODO: use simd to speed up the remaining elements copy - memcpy(dst_data_loc, src_loc, remain_bytes); - } - } - } -} - -static void rope_job_f32_per_thread(struct rope_th_ctx * rope_ctx, int nth, int ith) { - struct htp_ops_context * octx = rope_ctx->octx; - - const struct htp_tensor * src0 = &octx->src0; - const struct htp_tensor * src1 = &octx->src1; - struct htp_tensor * dst = &octx->dst; - - htp_rope_preamble; - - const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows - const uint32_t src0_nrows_per_thread = octx->src0_nrows_per_thread; + const uint32_t src0_nrows = rctx->src0_nrows; + const uint32_t src0_nrows_per_thread = rctx->src0_nrows_per_thread; const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); @@ -358,32 +271,114 @@ static void rope_job_f32_per_thread(struct rope_th_ctx * rope_ctx, int nth, int return; } - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); + uint64_t tt = HAP_perf_get_qtimer_count(); - int is_aligned = 1; - int opt_path = 0; - if ((0 == hex_is_aligned((void *) src0->data, VLEN)) || (0 == hex_is_aligned((void *) src1->data, VLEN)) || - (0 == hex_is_aligned((void *) dst->data, VLEN))) { - FARF(HIGH, "rope-f32: unaligned addresses in rope op, possibly slower execution\n"); - is_aligned = 0; - } - if ((1 == is_aligned) && !(nb01 & (VLEN - 1))) { - opt_path = 1; + const int32_t mode = rctx->mode; + const bool is_neox = mode & HTP_ROPE_TYPE_NEOX; + + // VTCM setup + uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread); + float * theta_cache = (float *) (src0_spad_base); + src0_spad_base = src0_spad_base + rctx->theta_cache_offset; + uint8_t * dst_spad_base = octx->dst_spad.data + (ith * octx->dst_spad.size_per_thread); + + dma_queue * dma_queue = octx->ctx->dma[ith]; + const int32_t * pos = (const int32_t *) src1->data; + const float * freq_factors = src2->data ? (const float *) src2->data : NULL; + + uint32_t ir = 0; + uint32_t prev_i2 = (uint32_t) -1; + + for (uint32_t i3 = 0; i3 < ne3; i3++) { // batch + for (uint32_t i2 = 0; i2 < ne2; i2++) { // seq-len + for (uint32_t i1 = 0; i1 < ne1; ) { // attn-heads + if (ir < src0_start_row) { ir++; i1++; continue; } + if (ir >= src0_end_row) goto done; + + // Rows in this block + const uint32_t nrows = MIN(src0_end_row - ir, ne1 - i1); + + // Depth before prefetch + uint32_t dma_depth = dma_queue_depth(dma_queue); + + // FARF(HIGH, "rope-block %u: ir %u n-rows %u dma-depth %u : usec %u", ith, ir, nrows, dma_depth, + // (unsigned) HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - rctx->t_start)); + + // Prefetch loop + for (uint32_t pnr = 0, pr = 0; pr < nrows && pr < HTP_ROPE_SPAD_NROWS; pr += pnr) { + pnr = MIN(nrows - pr, HTP_ROPE_SPAD_BLOCK); + + uint32_t pi1 = i1 + pr; + uint32_t pir = ir + pr; + + // Dummy DMA transaction for sequencing (interleaving dst,src,dst,...) + dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr((void *) dst->data, dst_spad_base + pr * rctx->dst_row_size_aligned), 0, 0, 0); + + const uint8_t * src_addr = (const uint8_t *) src0->data + i3 * nb03 + i2 * nb02 + pi1 * nb01; + uint8_t * src_spad = src0_spad_base + pr * rctx->src0_row_size_aligned; + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src_spad, src_addr), + rctx->src0_row_size_aligned, rctx->src0_row_size, pnr); + + // FARF(HIGH, "rope-prefetch %u: pr %u i1 %u i2 %u i3 %u src-spad %p src-addr %p pnr %u", ith, pir, pi1, i2, i3, src_spad, src_addr, pnr); + } + + // Update theta cache + if (i2 != prev_i2) { + prev_i2 = i2; + + const int32_t p = pos[i2]; + rope_cache_init(p, rctx->freq_scale, freq_factors, rctx->corr_dims, ne0, rctx->ext_factor, rctx->attn_factor, theta_cache, rctx->theta_scale); + + // FARF(HIGH, "rope-theta %u: ir %u i1 %u i2 %u i3 %u cache %p : usec %u", ith, ir, i1, i2, i3, theta_cache, + // (unsigned) HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - rctx->t_start)); + } + + // Skip DMA transactions from prev block (if any) + // No need to wait for these since the DMA is setup for in-order processing + for (uint32_t d=0; d < dma_depth; d++) { dma_queue_pop_nowait(dma_queue); } + + // Compute loop + for (uint32_t cnr = 0, cr = 0; cr < nrows; cr += cnr, ir += cnr, i1 += cnr) { + // Number of rows to compute + cnr = MIN(nrows - cr, HTP_ROPE_SPAD_BLOCK); + + uint8_t * dst_spad = (uint8_t *) dma_queue_pop(dma_queue).src; + uint8_t * src_spad = (uint8_t *) dma_queue_pop(dma_queue).dst; + + // FARF(HIGH, "rope-compute %u: ir %u i1 %u i2 %u i3 %u src-spad %p cnr %u : usec %u", ith, ir, i1, i2, i3, src_spad, cnr, + // (unsigned) HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - rctx->t_start)); + + if (is_neox) { + rope_neox_f32(rctx, dst_spad, src_spad, cnr, ne0, theta_cache); + } else { + rope_basic_f32(rctx, dst_spad, src_spad, cnr, ne0, theta_cache); + } + + uint8_t * dst_addr = (uint8_t *) dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1; + dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(dst_addr, dst_spad), rctx->dst_row_size, rctx->dst_row_size_aligned, cnr); + + // Prefetch more rows (if any) + if ((cr + HTP_ROPE_SPAD_NROWS) < nrows) { + uint32_t pnr = MIN(nrows - (cr + HTP_ROPE_SPAD_NROWS), HTP_ROPE_SPAD_BLOCK); + uint32_t pi1 = i1 + HTP_ROPE_SPAD_NROWS; + uint32_t pir = ir + HTP_ROPE_SPAD_NROWS; + + const uint8_t * src_addr = (const uint8_t *) src0->data + i3 * nb03 + i2 * nb02 + pi1 * nb01; + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src_spad, src_addr), + rctx->src0_row_size_aligned, rctx->src0_row_size, pnr); + + // FARF(HIGH, "rope-prefetch %u: pr %u i1 %u i2 %u i3 %u src-spad %p src-addr %p pnr %u", ith, pir, pi1, i2, i3, src_spad, src_addr, pnr); + } + } + } + } } - rope_hex_f32(rope_ctx, src0_start_row, src0_end_row, nth, ith, opt_path); +done: + dma_queue_flush(dma_queue); + tt = HAP_perf_get_qtimer_count() - tt; - t2 = HAP_perf_get_qtimer_count(); - - FARF(HIGH, "rope-f32: %d/%d/%d: (%u:%u) usec %u\n", ith, nth, opt_path, src0_start_row, src0_end_row, - (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); -} - -static void rope_job_dispatcher_f32(unsigned int n, unsigned int i, void * data) { - struct rope_th_ctx * rope_ctx = (struct rope_th_ctx *) data; - - rope_job_f32_per_thread(rope_ctx, n, i); + FARF(HIGH, "rope-f32: %d/%d: (%u:%u) usec %u\n", ith, nth, src0_start_row, src0_end_row, (unsigned) HAP_perf_qtimer_count_to_us(tt)); } static int execute_op_rope_f32(struct htp_ops_context * octx) { @@ -394,17 +389,10 @@ static int execute_op_rope_f32(struct htp_ops_context * octx) { const struct htp_tensor * src2 = &octx->src2; struct htp_tensor * dst = &octx->dst; - worker_callback_t op_func; - const char * op_type = NULL; - - struct rope_th_ctx rope_ctx; + const char * op_type = "rope-f32"; switch (octx->op) { case HTP_OP_ROPE: - op_func = rope_job_dispatcher_f32; - op_type = "rope-f32"; - - init_rope_ctx(&rope_ctx, octx); break; default: @@ -412,52 +400,81 @@ static int execute_op_rope_f32(struct htp_ops_context * octx) { return HTP_STATUS_NO_SUPPORT; } - const uint32_t n_threads = octx->n_threads; + const uint32_t ne0 = dst->ne[0]; + const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; + const uint32_t n_threads = MIN(octx->n_threads, src0_nrows); const size_t src0_row_size = src0->nb[1]; - const size_t src1_row_size = src0_row_size; const size_t dst_row_size = dst->nb[1]; - // VTCM scratchpads for all tensors - // N rows per thread, padded to HVX vector size - octx->dst_spad.size = hex_round_up(dst_row_size, 128) * n_threads; - octx->src0_spad.size = hex_round_up(src0_row_size, 128) * n_threads; - octx->src1_spad.size = hex_round_up(src1_row_size, 128) * n_threads; + // Aligned row sizes for VTCM + const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); + const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN); + const size_t theta_cache_size_aligned = hex_round_up(src0->ne[0] * sizeof(float), 128); - size_t spad_size = octx->src0_spad.size + octx->src1_spad.size + octx->dst_spad.size; + // Calculate spad sizes per thread + size_t src0_spad_per_thread = theta_cache_size_aligned + HTP_ROPE_SPAD_NROWS * src0_row_size_aligned; + size_t dst_spad_per_thread = HTP_ROPE_SPAD_NROWS * dst_row_size_aligned; + size_t spad_per_thread = src0_spad_per_thread + dst_spad_per_thread; - if (src2->ne[0]) { - FARF(HIGH, - "%s: %ux%ux%ux%u (x %ux%ux%ux%u x %ux%ux%ux%u) -> %ux%ux%ux%u : src0-spad-size %u src1-spad-size %u " - "dst-spad-size %u\n", - op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], - src1->ne[3], src2->ne[0], src2->ne[1], src2->ne[2], src2->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], - dst->ne[3], octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size); - } else { - FARF(HIGH, - "%s: %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", - op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], - src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], octx->src0_spad.size, octx->src1_spad.size, - octx->dst_spad.size); - } - - // Make sure the reserved vtcm size is sufficient - if (octx->ctx->vtcm_size < spad_size) { - FARF(ERROR, "%s : current VTCM reservation %zu is too small, needed %zu\n", op_type, octx->ctx->vtcm_size, - spad_size); + // Check if we fit in VTCM + size_t total_vtcm_needed = spad_per_thread * n_threads; + if (octx->ctx->vtcm_size < total_vtcm_needed) { + FARF(ERROR, "%s : current VTCM reservation %zu is too small, needed %zu\n", op_type, octx->ctx->vtcm_size, total_vtcm_needed); return HTP_STATUS_VTCM_TOO_SMALL; } - octx->src0_spad.data = octx->ctx->vtcm_base; - octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; - octx->dst_spad.data = octx->src1_spad.data + octx->src1_spad.size; + // Assign sizes + octx->src0_spad.size_per_thread = src0_spad_per_thread; + octx->dst_spad.size_per_thread = dst_spad_per_thread; + octx->src0_spad.size = n_threads * src0_spad_per_thread; + octx->dst_spad.size = n_threads * dst_spad_per_thread; + octx->src1_spad.size = 0; - uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; + // Assign pointers + octx->src0_spad.data = octx->ctx->vtcm_base; + octx->src1_spad.data = NULL; + octx->dst_spad.data = octx->src0_spad.data + octx->src0_spad.size; + + // Fill context + struct htp_rope_context rctx; + memset(&rctx, 0, sizeof(struct htp_rope_context)); + + rctx.t_start = HAP_perf_get_qtimer_count(); + + rctx.octx = octx; + + const int32_t * op_params = &octx->op_params[0]; + rctx.n_dims = ((const int32_t *) op_params)[1]; + rctx.mode = ((const int32_t *) op_params)[2]; + rctx.n_ctx_orig = ((const int32_t *) op_params)[4]; + + memcpy(&rctx.freq_base, (int32_t *) op_params + 5, sizeof(float)); + memcpy(&rctx.freq_scale, (int32_t *) op_params + 6, sizeof(float)); + memcpy(&rctx.ext_factor, (int32_t *) op_params + 7, sizeof(float)); + memcpy(&rctx.attn_factor, (int32_t *) op_params + 8, sizeof(float)); + memcpy(&rctx.beta_fast, (int32_t *) op_params + 9, sizeof(float)); + memcpy(&rctx.beta_slow, (int32_t *) op_params + 10, sizeof(float)); + memcpy(&rctx.sections, (int32_t *) op_params + 11, sizeof(int) * 4); + + rctx.theta_scale = powf(rctx.freq_base, -2.0f / rctx.n_dims); + + rope_corr_dims(rctx.n_dims, rctx.n_ctx_orig, rctx.freq_base, rctx.beta_fast, rctx.beta_slow, rctx.corr_dims); + + rctx.src0_row_size = src0_row_size; + rctx.dst_row_size = dst_row_size; + rctx.src0_row_size_aligned = src0_row_size_aligned; + rctx.dst_row_size_aligned = dst_row_size_aligned; + rctx.theta_cache_offset = theta_cache_size_aligned; + + rctx.src0_nrows = src0_nrows; + rctx.src0_nrows_per_thread = (src0_nrows + n_threads - 1) / n_threads; + + FARF(HIGH, "rope-f32 n-rows %u n-dims %d ne0 %u ext-factor %.6f theta-scale %.6f attn-factor %.6f\n", rctx.src0_nrows, rctx.n_dims, ne0, + rctx.ext_factor, rctx.theta_scale, rctx.attn_factor); if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { - uint32_t n_jobs = MIN(n_threads, src0_nrows); - octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs; - worker_pool_run_func(octx->ctx->worker_pool, op_func, &rope_ctx, n_jobs); + worker_pool_run_func(octx->ctx->worker_pool, rope_job_f32, &rctx, n_threads); } return err; diff --git a/ggml/src/ggml-hexagon/htp/set-rows-ops.c b/ggml/src/ggml-hexagon/htp/set-rows-ops.c index 904484da9d..4b6967749f 100644 --- a/ggml/src/ggml-hexagon/htp/set-rows-ops.c +++ b/ggml/src/ggml-hexagon/htp/set-rows-ops.c @@ -43,11 +43,21 @@ \ const uint32_t nr = ne01; -static int set_rows_thread_f32_f32(struct htp_ops_context * octx, const int nth, const int ith) { +struct htp_set_rows_context { + struct htp_ops_context * octx; + struct fastdiv_values div_ne12; + struct fastdiv_values div_ne11; + uint32_t src0_nrows_per_thread; +}; + +static void set_rows_thread_f32_f32(unsigned int nth, unsigned int ith, void *data) { + struct htp_set_rows_context * srctx = (struct htp_set_rows_context *)data; + struct htp_ops_context * octx = srctx->octx; + set_rows_preamble; // parallelize by rows of src0 - const uint32_t dr = octx->src0_nrows_per_thread; + const uint32_t dr = srctx->src0_nrows_per_thread; const uint32_t ir0 = dr * ith; const uint32_t ir1 = (ir0 + dr < nr) ? (ir0 + dr) : nr; @@ -56,8 +66,8 @@ static int set_rows_thread_f32_f32(struct htp_ops_context * octx, const int nth, for (uint32_t i03 = 0; i03 < ne03; ++i03) { for (uint32_t i02 = 0; i02 < ne02; ++i02) { for (uint32_t i = ir0; i < ir1; ++i) { - const uint32_t i12 = fastmodulo(i03, ne12, &octx->set_rows_div_ne12); - const uint32_t i11 = fastmodulo(i02, ne11, &octx->set_rows_div_ne11); + const uint32_t i12 = fastmodulo(i03, ne12, &srctx->div_ne12); + const uint32_t i11 = fastmodulo(i02, ne11, &srctx->div_ne11); const uint32_t i10 = i; const uintptr_t src1_addr = octx->src1.data + i10*nb10 + i11*nb11 + i12*nb12; @@ -76,15 +86,16 @@ static int set_rows_thread_f32_f32(struct htp_ops_context * octx, const int nth, } } } - - return HTP_STATUS_OK; } -static int set_rows_thread_f16_f32(struct htp_ops_context * octx, const int nth, const int ith) { +static void set_rows_thread_f16_f32(unsigned int nth, unsigned int ith, void *data) { + struct htp_set_rows_context * srctx = (struct htp_set_rows_context *)data; + struct htp_ops_context * octx = srctx->octx; + set_rows_preamble; // parallelize by rows of src0 - const uint32_t dr = octx->src0_nrows_per_thread; + const uint32_t dr = srctx->src0_nrows_per_thread; const uint32_t ir0 = dr * ith; const uint32_t ir1 = (ir0 + dr < nr) ? (ir0 + dr) : nr; @@ -93,8 +104,8 @@ static int set_rows_thread_f16_f32(struct htp_ops_context * octx, const int nth, for (uint32_t i03 = 0; i03 < ne03; ++i03) { for (uint32_t i02 = 0; i02 < ne02; ++i02) { for (uint32_t i = ir0; i < ir1; ++i) { - const uint32_t i12 = fastmodulo(i03, ne12, &octx->set_rows_div_ne12); - const uint32_t i11 = fastmodulo(i02, ne11, &octx->set_rows_div_ne11); + const uint32_t i12 = fastmodulo(i03, ne12, &srctx->div_ne12); + const uint32_t i11 = fastmodulo(i02, ne11, &srctx->div_ne11); const uint32_t i10 = i; const uintptr_t src1_addr = octx->src1.data + i10*nb10 + i11*nb11 + i12*nb12; @@ -112,21 +123,13 @@ static int set_rows_thread_f16_f32(struct htp_ops_context * octx, const int nth, } } } - - return HTP_STATUS_OK; -} - -static void set_rows_work_f16_f32(unsigned int n, unsigned int i, void *data) { - set_rows_thread_f16_f32((struct htp_ops_context *) data, n, i); -} - -static void set_rows_work_f32_f32(unsigned int n, unsigned int i, void *data) { - set_rows_thread_f32_f32((struct htp_ops_context *) data, n, i); } int op_set_rows(struct htp_ops_context * octx) { set_rows_preamble; + const uint32_t n_threads = MIN(nr, octx->n_threads); + if (octx->src0.type != HTP_TYPE_F32) { return HTP_STATUS_NO_SUPPORT; } @@ -143,18 +146,19 @@ int op_set_rows(struct htp_ops_context * octx) { return HTP_STATUS_OK; } - octx->set_rows_div_ne12 = init_fastdiv_values(ne12); - octx->set_rows_div_ne11 = init_fastdiv_values(ne11); + struct htp_set_rows_context srctx; + srctx.octx = octx; + srctx.div_ne12 = init_fastdiv_values(ne12); + srctx.div_ne11 = init_fastdiv_values(ne11); - const uint32_t n_jobs = MIN(nr, octx->n_threads); - octx->src0_nrows_per_thread = (nr + n_jobs - 1) / n_jobs; + srctx.src0_nrows_per_thread = (nr + n_threads - 1) / n_threads; switch(octx->dst.type) { case HTP_TYPE_F32: - worker_pool_run_func(octx->ctx->worker_pool, set_rows_work_f32_f32, octx, n_jobs); + worker_pool_run_func(octx->ctx->worker_pool, set_rows_thread_f32_f32, &srctx, n_threads); break; case HTP_TYPE_F16: - worker_pool_run_func(octx->ctx->worker_pool, set_rows_work_f16_f32, octx, n_jobs); + worker_pool_run_func(octx->ctx->worker_pool, set_rows_thread_f16_f32, &srctx, n_threads); break; default: return HTP_STATUS_NO_SUPPORT; diff --git a/ggml/src/ggml-hexagon/htp/softmax-ops.c b/ggml/src/ggml-hexagon/htp/softmax-ops.c index e91a16d947..8dae7f1ed5 100644 --- a/ggml/src/ggml-hexagon/htp/softmax-ops.c +++ b/ggml/src/ggml-hexagon/htp/softmax-ops.c @@ -10,6 +10,7 @@ #include "hex-dma.h" #include "hvx-utils.h" +#include "hex-fastdiv.h" #define GGML_COMMON_DECL_C #include "ggml-common.h" @@ -48,7 +49,7 @@ const uint32_t nb2 = dst->nb[2]; \ const uint32_t nb3 = dst->nb[3]; -struct softmax_th_ctx { +struct htp_softmax_context { bool use_f16; bool use_src1; uint32_t n_head; @@ -59,28 +60,48 @@ struct softmax_th_ctx { float m0; float m1; + uint32_t src0_nrows_per_thread; + struct fastdiv_values fastdiv_ne01; + struct fastdiv_values fastdiv_ne02; + struct fastdiv_values fastdiv_ne12; // For mask broadcasting + struct fastdiv_values fastdiv_ne13; // For mask broadcasting + size_t spad_stride; + struct htp_ops_context * octx; }; -static void init_softmax_ctx(struct softmax_th_ctx * softmax_ctx, struct htp_ops_context * octx) { +static void init_softmax_ctx(struct htp_softmax_context * smctx, struct htp_ops_context * octx) { const struct htp_tensor * src0 = &octx->src0; const struct htp_tensor * src1 = &octx->src1; - memset(softmax_ctx, 0, sizeof(struct softmax_th_ctx)); + memset(smctx, 0, sizeof(struct htp_softmax_context)); - memcpy(&softmax_ctx->scale, (float *) octx->op_params, sizeof(float)); - memcpy(&softmax_ctx->max_bias, (float *) octx->op_params + 1, sizeof(float)); + memcpy(&smctx->scale, (float *) octx->op_params, sizeof(float)); + memcpy(&smctx->max_bias, (float *) octx->op_params + 1, sizeof(float)); - softmax_ctx->n_head = src0->ne[2]; - softmax_ctx->n_head_log2 = 1u << (uint32_t) floor(log2(softmax_ctx->n_head)); + smctx->n_head = src0->ne[2]; + smctx->n_head_log2 = 1u << (uint32_t) floor(log2(smctx->n_head)); - softmax_ctx->m0 = powf(2.0f, -(softmax_ctx->max_bias) / softmax_ctx->n_head_log2); - softmax_ctx->m1 = powf(2.0f, -(softmax_ctx->max_bias / 2.0f) / softmax_ctx->n_head_log2); + smctx->m0 = powf(2.0f, -(smctx->max_bias) / smctx->n_head_log2); + smctx->m1 = powf(2.0f, -(smctx->max_bias / 2.0f) / smctx->n_head_log2); - softmax_ctx->use_src1 = (src1->ne[0] != 0); - softmax_ctx->use_f16 = (src1->ne[0] != 0) && (src1->type == HTP_TYPE_F16); + smctx->use_src1 = (src1->ne[0] != 0); + smctx->use_f16 = (src1->ne[0] != 0) && (src1->type == HTP_TYPE_F16); - softmax_ctx->octx = octx; + smctx->octx = octx; + + // Initialize fastdiv values + const uint32_t ne01 = src0->ne[1]; + const uint32_t ne02 = src0->ne[2]; + + if (ne01 > 0) smctx->fastdiv_ne01 = init_fastdiv_values(ne01); + if (ne02 > 0) smctx->fastdiv_ne02 = init_fastdiv_values(ne02); + + const uint32_t ne12 = (src1->ne[0]) ? src1->ne[2] : 1; + const uint32_t ne13 = (src1->ne[0]) ? src1->ne[3] : 1; + + if (ne12 > 0) smctx->fastdiv_ne12 = init_fastdiv_values(ne12); + if (ne13 > 0) smctx->fastdiv_ne13 = init_fastdiv_values(ne13); } static void hvx_fast_softmax_prep_f32(const uint8_t * restrict src, @@ -139,8 +160,7 @@ static void hvx_fast_softmax_f32(const uint8_t * restrict src, max_vec = Q6_Vsf_vmax_VsfVsf(max_vec, v1); } - HVX_Vector v = hvx_vec_reduce_max_f32(max_vec); - max_vec = hvx_vec_repl4(v); + max_vec = hvx_vec_reduce_max_f32(max_vec); // replicated over all lanes #pragma unroll(4) for (int i = 0; i < step_of_1; i++) { @@ -154,8 +174,7 @@ static void hvx_fast_softmax_f32(const uint8_t * restrict src, v_pad[i] = v3; } - v = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_vec)); - sum_vec = hvx_vec_repl4(v); + sum_vec = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_vec)); // replicated over all lanes HVX_VectorPred pos_sum = Q6_Q_vcmp_gt_VwVw(sum_vec, zero_v); HVX_Vector v4 = hvx_vec_inverse_f32(sum_vec); @@ -183,83 +202,9 @@ static float hvx_softmax_f32(const uint8_t * restrict src, return sum; } -static void softmax_htp_f32(int nth, int ith, struct softmax_th_ctx * softmax_ctx, int opt_path) { - struct htp_ops_context * octx = softmax_ctx->octx; - - const struct htp_tensor * src0 = &octx->src0; - const struct htp_tensor * src1 = &octx->src1; - const struct htp_tensor * dst = &octx->dst; - - htp_softmax_preamble3; - - uint8_t * src0_spad_data = octx->src0_spad.data + (ith * nb01); - uint8_t * src1_spad_data = octx->src1_spad.data + (ith * nb01); - uint8_t * dst_spad_data = octx->dst_spad.data + (ith * nb1); - - float * wp0 = (float *) src0_spad_data; - float * wp1 = (float *) src1_spad_data; - float * wp2 = (float *) dst_spad_data; - - for (uint32_t i03 = 0; i03 < ne03; i03++) { - for (uint32_t i02 = 0; i02 < ne02; i02++) { - for (uint32_t i01 = ith; i01 < ne01; i01 += nth) { - const uint32_t i11 = i01; - const uint32_t i12 = i02 % ne12; - const uint32_t i13 = i03 % ne13; - - // ALiBi - const uint32_t h = i02; // head - - const float slope = (softmax_ctx->max_bias > 0.0f) ? - h < softmax_ctx->n_head_log2 ? - powf(softmax_ctx->m0, h + 1) : - powf(softmax_ctx->m1, 2 * (h - softmax_ctx->n_head_log2) + 1) : - 1.0f; - - float * sp = (float *) ((char *) octx->src0.data + i01 * nb01 + i02 * nb02 + i03 * nb03); - float * dp = (float *) ((char *) octx->dst.data + i01 * nb1 + i02 * nb2 + i03 * nb3); - - // broadcast the mask across rows - __fp16 * mp_f16 = (softmax_ctx->use_src1) ? - (__fp16 *) ((char *) octx->src1.data + i11 * nb11 + i12 * nb12 + i13 * nb13) : - NULL; - float * mp_f32 = (softmax_ctx->use_src1) ? - (float *) ((char *) octx->src1.data + i11 * nb11 + i12 * nb12 + i13 * nb13) : - NULL; - - if ((1 == opt_path) && (mp_f32) && !(softmax_ctx->use_f16)) { - hvx_fast_softmax_prep_f32((const uint8_t *) sp, (uint8_t *) wp0, ne00, softmax_ctx->scale, - (const uint8_t *) mp_f32, slope); - } else { - hvx_scale_f32((uint8_t *) wp0, (const uint8_t *) sp, ne00, softmax_ctx->scale); - if (mp_f32) { - if (softmax_ctx->use_f16) { - for (int i = 0; i < ne00; ++i) { - wp0[i] += slope * (float) mp_f16[i]; - } - } else { - for (int i = 0; i < ne00; ++i) { - wp0[i] += slope * mp_f32[i]; - } - } - } - } - - if (1 == opt_path) { - hvx_fast_softmax_f32((const uint8_t *) wp0, (uint8_t *) dp, (uint8_t *) wp1, ne00); - } else { - float max = hvx_reduce_max_f32((const uint8_t *) wp0, ne00); - float sum = hvx_softmax_f32((const uint8_t *) wp0, (uint8_t *) wp2, (uint8_t *) wp1, ne00, max); - sum = sum > 0.0 ? (1.0 / sum) : 1; - hvx_scale_f32((uint8_t *) dp, (const uint8_t *) wp2, ne00, sum); - } - } - } - } -} - -static void softmax_job_f32_per_thread(struct softmax_th_ctx * softmax_ctx, int nth, int ith) { - struct htp_ops_context * octx = softmax_ctx->octx; +static void softmax_job_f32(unsigned int nth, unsigned int ith, void * data) { + struct htp_softmax_context * smctx = (struct htp_softmax_context *) data; + struct htp_ops_context * octx = smctx->octx; const struct htp_tensor * src0 = &octx->src0; const struct htp_tensor * src1 = &octx->src1; @@ -268,7 +213,7 @@ static void softmax_job_f32_per_thread(struct softmax_th_ctx * softmax_ctx, int htp_softmax_preamble3; const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows - const uint32_t src0_nrows_per_thread = octx->src0_nrows_per_thread; + const uint32_t src0_nrows_per_thread = smctx->src0_nrows_per_thread; const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); @@ -291,20 +236,103 @@ static void softmax_job_f32_per_thread(struct softmax_th_ctx * softmax_ctx, int opt_path = 1; } - softmax_htp_f32(nth, ith, softmax_ctx, opt_path); + uint8_t * src0_spad_data = octx->src0_spad.data + (ith * smctx->spad_stride); + uint8_t * src1_spad_data = octx->src1_spad.data + (ith * smctx->spad_stride); + uint8_t * dst_spad_data = octx->dst_spad.data + (ith * smctx->spad_stride); + + float * wp0 = (float *) src0_spad_data; + float * wp1 = (float *) src1_spad_data; + float * wp2 = (float *) dst_spad_data; + + uint32_t prev_i2 = (uint32_t)-1; + float slope = 1.0f; + + for (uint32_t r = src0_start_row; r < src0_end_row; ++r) { + uint32_t i1 = fastmodulo(r, ne01, &smctx->fastdiv_ne01); + uint32_t r_div_ne01 = fastdiv(r, &smctx->fastdiv_ne01); + uint32_t i2 = fastmodulo(r_div_ne01, ne02, &smctx->fastdiv_ne02); + uint32_t i3 = fastdiv(r_div_ne01, &smctx->fastdiv_ne02); + + // Map to original logic indices + // i01 = i1 + // i02 = i2 + // i03 = i3 + + const uint32_t i11 = i1; + // const uint32_t i12 = i2 % ne12; + // const uint32_t i13 = i3 % ne13; + + uint32_t i12, i13; + if (ne12 == ne02) { + i12 = i2; + } else { + i12 = fastmodulo(i2, ne12, &smctx->fastdiv_ne12); + } + + if (ne13 == ne03) { + i13 = i3; + } else { + i13 = fastmodulo(i3, ne13, &smctx->fastdiv_ne13); + } + + // ALiBi + if (i2 != prev_i2) { + const uint32_t h = i2; // head + + slope = (smctx->max_bias > 0.0f) ? + h < smctx->n_head_log2 ? + powf(smctx->m0, h + 1) : + powf(smctx->m1, 2 * (h - smctx->n_head_log2) + 1) : + 1.0f; + prev_i2 = i2; + } + + float * sp = (float *) ((char *) octx->src0.data + i1 * nb01 + i2 * nb02 + i3 * nb03); + float * dp = (float *) ((char *) octx->dst.data + i1 * nb1 + i2 * nb2 + i3 * nb3); + + // broadcast the mask across rows + __fp16 * mp_f16 = (smctx->use_src1) ? + (__fp16 *) ((char *) octx->src1.data + i11 * nb11 + i12 * nb12 + i13 * nb13) : + NULL; + float * mp_f32 = (smctx->use_src1) ? + (float *) ((char *) octx->src1.data + i11 * nb11 + i12 * nb12 + i13 * nb13) : + NULL; + + if ((1 == opt_path) && (mp_f32) && !(smctx->use_f16)) { + hvx_fast_softmax_prep_f32((const uint8_t *) sp, (uint8_t *) wp0, ne00, smctx->scale, + (const uint8_t *) mp_f32, slope); + } else { + hvx_scale_f32((uint8_t *) wp0, (const uint8_t *) sp, ne00, smctx->scale); + if (mp_f32) { + if (smctx->use_f16) { + for (int i = 0; i < ne00; ++i) { + wp0[i] += slope * (float) mp_f16[i]; + } + } else { + for (int i = 0; i < ne00; ++i) { + wp0[i] += slope * mp_f32[i]; + } + } + } + } + + if (1 == opt_path) { + hvx_fast_softmax_f32((const uint8_t *) wp0, (uint8_t *) dp, (uint8_t *) wp1, ne00); + } else { + float max = hvx_reduce_max_f32((const uint8_t *) wp0, ne00); + float sum = hvx_softmax_f32((const uint8_t *) wp0, (uint8_t *) wp2, (uint8_t *) wp1, ne00, max); + sum = sum > 0.0 ? (1.0 / sum) : 1; + hvx_scale_f32((uint8_t *) dp, (const uint8_t *) wp2, ne00, sum); + } + } t2 = HAP_perf_get_qtimer_count(); FARF(HIGH, "softmax-f32 %d/%d/%d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, - softmax_ctx->use_f16, opt_path, ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, + smctx->use_f16, opt_path, ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); } -static void softmax_job_dispatcher_f32(unsigned int n, unsigned int i, void * p_data) { - struct softmax_th_ctx * p_softmax_ctx = (struct softmax_th_ctx *) p_data; - softmax_job_f32_per_thread(p_softmax_ctx, n, i); -} - static int execute_op_softmax_f32(struct htp_ops_context * octx) { int err = HTP_STATUS_OK; @@ -312,17 +340,12 @@ static int execute_op_softmax_f32(struct htp_ops_context * octx) { const struct htp_tensor * src1 = &octx->src1; struct htp_tensor * dst = &octx->dst; - worker_callback_t op_func; - const char * op_type = NULL; - - struct softmax_th_ctx softmax_ctx; + struct htp_softmax_context smctx; + const char * op_type = "softmax-f32"; switch (octx->op) { case HTP_OP_SOFTMAX: - op_func = softmax_job_dispatcher_f32; - op_type = "softmax-f32"; - - init_softmax_ctx(&softmax_ctx, octx); + init_softmax_ctx(&smctx, octx); break; default: @@ -330,7 +353,8 @@ static int execute_op_softmax_f32(struct htp_ops_context * octx) { return HTP_STATUS_NO_SUPPORT; } - const uint32_t n_threads = octx->n_threads; + const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; + const uint32_t n_threads = MIN(octx->n_threads, src0_nrows); const size_t src0_row_size = src0->nb[1]; const size_t src1_row_size = src0_row_size; @@ -342,6 +366,9 @@ static int execute_op_softmax_f32(struct htp_ops_context * octx) { octx->src0_spad.size = hex_round_up(src0_row_size, 128) * n_threads; octx->src1_spad.size = hex_round_up(src1_row_size, 128) * n_threads; + // Use stride for calculating offset + smctx.spad_stride = hex_round_up(src0_row_size, 128); + size_t spad_size = octx->src0_spad.size + octx->src1_spad.size + octx->dst_spad.size; if (src1->ne[0]) { @@ -367,12 +394,9 @@ static int execute_op_softmax_f32(struct htp_ops_context * octx) { octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; octx->dst_spad.data = octx->src1_spad.data + octx->src1_spad.size; - uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; - if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { - uint32_t n_jobs = MIN(n_threads, src0_nrows); - octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs; - worker_pool_run_func(octx->ctx->worker_pool, op_func, &softmax_ctx, n_jobs); + smctx.src0_nrows_per_thread = (src0_nrows + n_threads - 1) / n_threads; + worker_pool_run_func(octx->ctx->worker_pool, softmax_job_f32, &smctx, n_threads); } return err; diff --git a/ggml/src/ggml-hexagon/htp/ssm-conv.c b/ggml/src/ggml-hexagon/htp/ssm-conv.c new file mode 100644 index 0000000000..b3c1ef9572 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/ssm-conv.c @@ -0,0 +1,339 @@ +#pragma clang diagnostic ignored "-Wunused-variable" +#pragma clang diagnostic ignored "-Wunused-function" +#pragma clang diagnostic ignored "-Wunused-but-set-variable" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#define GGML_COMMON_DECL_C +#include "ggml-common.h" +#include "htp-ctx.h" +#include "hex-dma.h" +#include "htp-msg.h" +#include "htp-ops.h" +#include "hvx-utils.h" + +#define htp_ssm_conv_tensors_preamble \ + struct htp_tensor * restrict src0 = &octx->src0; \ + struct htp_tensor * restrict src1 = &octx->src1; \ + struct htp_tensor * restrict dst = &octx->dst; \ + struct htp_spad * restrict src0_spad = &octx->src0_spad; \ + struct htp_spad * restrict src1_spad = &octx->src1_spad; \ + struct htp_spad * restrict dst_spad = &octx->dst_spad; \ + \ + const uint32_t ne00 = src0->ne[0]; \ + const uint32_t ne01 = src0->ne[1]; \ + const uint32_t ne02 = src0->ne[2]; \ + const uint32_t ne03 = src0->ne[3]; \ + \ + const uint32_t ne10 = src1->ne[0]; \ + const uint32_t ne11 = src1->ne[1]; \ + const uint32_t ne12 = src1->ne[2]; \ + const uint32_t ne13 = src1->ne[3]; \ + \ + const uint32_t ne0 = dst->ne[0]; \ + const uint32_t ne1 = dst->ne[1]; \ + const uint32_t ne2 = dst->ne[2]; \ + const uint32_t ne3 = dst->ne[3]; \ + \ + const uint32_t nb00 = src0->nb[0]; \ + const uint32_t nb01 = src0->nb[1]; \ + const uint32_t nb02 = src0->nb[2]; \ + const uint32_t nb03 = src0->nb[3]; \ + \ + const uint32_t nb10 = src1->nb[0]; \ + const uint32_t nb11 = src1->nb[1]; \ + const uint32_t nb12 = src1->nb[2]; \ + const uint32_t nb13 = src1->nb[3]; \ + \ + const uint32_t nb0 = dst->nb[0]; \ + const uint32_t nb1 = dst->nb[1]; \ + const uint32_t nb2 = dst->nb[2]; \ + const uint32_t nb3 = dst->nb[3]; + +struct htp_ssm_conv_context { + struct htp_ops_context * octx; + uint32_t nrows_per_thread; + uint64_t t_start; +}; + +#define htp_ssm_conv_preamble \ + struct htp_ssm_conv_context * scctx = (struct htp_ssm_conv_context *) data; \ + struct htp_ops_context * octx = scctx->octx; \ + htp_ssm_conv_tensors_preamble; \ + dma_queue * dma_queue = octx->ctx->dma[ith]; + +// Scalar FP32 SSM_CONV implementation +static void ssm_conv_thread_f32_f32(unsigned int nth, unsigned int ith, void *data) { + htp_ssm_conv_preamble; + + uint64_t t1, t2; + t1 = HAP_perf_get_qtimer_count(); + + const uint32_t d_conv = src1->ne[0]; + const uint32_t d_inner = src0->ne[1]; + const uint32_t n_t = dst->ne[1]; + const uint32_t n_s = dst->ne[2]; + + const uint32_t src0_stride_inner = src0->nb[1] / sizeof(float); // stride for inner dimension + const uint32_t src0_stride_seq = src0->nb[2] / sizeof(float); // stride for sequence dimension + const uint32_t src1_stride_inner = src1->nb[1] / sizeof(float); // stride for inner dimension + const uint32_t dst_stride_token = dst->nb[1] / sizeof(float); // stride for token dimension + const uint32_t dst_stride_seq = dst->nb[2] / sizeof(float); // stride for sequence dimension + + const float * src0_data = (const float *) src0->data; + const float * src1_data = (const float *) src1->data; + float * dst_data = (float *) dst->data; + + // Calculate row range for this thread + const uint32_t d_inner_per_thread = scctx->nrows_per_thread; + const uint32_t d_inner_start = d_inner_per_thread * ith; + const uint32_t d_inner_end = MIN(d_inner_start + d_inner_per_thread, d_inner); + + // No work for this thread + if (d_inner_start >= d_inner_end) { + return; + } + + for (uint32_t i3 = 0; i3 < n_s; ++i3) { + for (uint32_t i2 = 0; i2 < n_t; ++i2) { + for (uint32_t i1 = d_inner_start; i1 < d_inner_end; ++i1) { + float sumf = 0.0f; + + for (uint32_t i0 = 0; i0 < d_conv; ++i0) { + const uint32_t src0_idx = (i2 + i0) + i1 * src0_stride_inner + i3 * src0_stride_seq; + const uint32_t src1_idx = i0 + i1 * src1_stride_inner; + + sumf += src0_data[src0_idx] * src1_data[src1_idx]; + } + + const uint32_t dst_idx = i1 + i2 * dst_stride_token + i3 * dst_stride_seq; + dst_data[dst_idx] = sumf; + } + } + } + + t2 = HAP_perf_get_qtimer_count(); + + FARF(HIGH, "ssm-conv-f32 %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", + ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], d_inner_start, d_inner_end, + src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], dst->ne[0], dst->ne[1], + dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); +} + +// HVX FP32 SSM_CONV implementation - vectorizes across d_inner dimension +static void ssm_conv_thread_f32_f32_hvx(unsigned int nth, unsigned int ith, void *data) { + htp_ssm_conv_preamble; + + uint64_t t1, t2; + t1 = HAP_perf_get_qtimer_count(); + + const int nc = src1->ne[0]; // d_conv + const int ncs = src0->ne[0]; // d_conv - 1 + n_t + + const uint32_t d_conv = src1->ne[0]; + const uint32_t d_inner = src0->ne[1]; + const uint32_t n_t = dst->ne[1]; + const uint32_t n_s = dst->ne[2]; + + const float * src0_data = (const float *) src0->data; + const float * src1_data = (const float *) src1->data; + float * dst_data = (float *) dst->data; + + // Calculate row range for this thread + const int dr = scctx->nrows_per_thread; + const uint32_t ir0 = dr * ith; + const uint32_t ir1 = MIN(ir0 + dr, d_inner); + const int ir = ir1 - ir0; + + if (ir0 >= ir1) { + return; // No work for this thread + } + + // src0 and src1 gather offsets + uint32_t __attribute__((aligned(VLEN))) src0_offsets[VLEN_FP32] = { 0 }; + uint32_t __attribute__((aligned(VLEN))) src1_offsets[VLEN_FP32] = { 0 }; + + for (uint32_t i = 0; i < VLEN_FP32; ++i) { + src0_offsets[i] = i * (ncs) * sizeof(float); + src1_offsets[i] = i * (d_conv) * sizeof(float); + } + + const uint32_t src0_gather_len = VLEN * ncs; + const uint32_t src1_gather_len = VLEN * d_conv; + + // gather scratchpads + HVX_Vector * src0_vec = (HVX_Vector *) (octx->ctx->vtcm_base + ith * VLEN*2 + 0); + HVX_Vector * src1_vec = (HVX_Vector *) (octx->ctx->vtcm_base + ith * VLEN*2 + VLEN); + + float * data_src0 = (float *) ((char *) src0->data + ir0 * src0->nb[1]); + float * data_src1 = (float *) ((char *) src1->data + ir0 * src1->nb[1]); + + uint8_t * spad_src0 = octx->src0_spad.data + ith * octx->src0_spad.size_per_thread; + uint8_t * spad_src1 = octx->src1_spad.data + ith * octx->src1_spad.size_per_thread; + + // copy src1 workload to VTCM + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src1, data_src1), nb11, nb11, ir); + + // FARF(HIGH, "ssm-conv-src1-fetch %d: ir0 %u size %u\n", ith, ir0, nb11 * ir); + + for (uint32_t i3 = 0; i3 < n_s; ++i3) { + float * src0_data_ptr = (float *) ((char *) data_src0 + i3 * (src0->nb[2])); + + // copy src0 workload to VTCM + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0, src0_data_ptr), nb01, nb01, ir); + + // FARF(HIGH, "ssm-conv-src0-fetch %d: ir0 %u i3 %u size %u\n", ith, ir0, i3, nb01 * ir); + + dma_queue_flush(dma_queue); + + for (uint32_t i2 = 0; i2 < n_t; ++i2) { + float * dst_ptr = (float *) ((char *) dst->data + ir0 * (dst->nb[0]) + i2 * (dst->nb[1]) + i3 * (dst->nb[2])); + + const uint32_t nvec = ir / VLEN_FP32; + const uint32_t nloe = ir % VLEN_FP32; + uint32_t i1 = 0; + + for (uint32_t vi1 = 0; vi1 < nvec; vi1++) { + HVX_Vector acc_vec = Q6_V_vsplat_R(0); + + for (uint32_t i0 = 0; i0 < d_conv; ++i0) { + Q6_vgather_ARMVw(src0_vec, GATHER_TYPE(spad_src0 + (i0 + i1 * ncs) * sizeof(float) + i2 * (src0->nb[0])), + src0_gather_len, (*(const HVX_Vector *) src0_offsets)); + Q6_vgather_ARMVw(src1_vec, GATHER_TYPE(spad_src1 + (i0 + i1 * nc) * sizeof(float)), + src1_gather_len, (*(const HVX_Vector *) src1_offsets)); + + HVX_Vector prod = Q6_Vqf32_vmpy_VsfVsf(*(const HVX_Vector *) src0_vec, *(const HVX_Vector *) src1_vec); + acc_vec = Q6_Vqf32_vadd_Vqf32Vqf32(acc_vec, prod); + } + + *(HVX_UVector *) (dst_ptr + i1) = Q6_Vsf_equals_Vqf32(acc_vec); + i1 += VLEN_FP32; + } + + if (nloe) { + HVX_Vector acc_vec = Q6_V_vsplat_R(0); + + for (uint32_t i0 = 0; i0 < d_conv; ++i0) { + Q6_vgather_ARMVw(src0_vec, GATHER_TYPE(spad_src0 + (i0 + i1 * ncs) * sizeof(float) + i2 * (src0->nb[0])), + src0_gather_len, (*(const HVX_Vector *) src0_offsets)); + Q6_vgather_ARMVw(src1_vec, GATHER_TYPE(spad_src1 + (i0 + i1 * nc) * sizeof(float)), + src1_gather_len, (*(const HVX_Vector *) src1_offsets)); + + HVX_Vector prod = Q6_Vqf32_vmpy_VsfVsf(*(const HVX_Vector *) src0_vec, *(const HVX_Vector *) src1_vec); + acc_vec = Q6_Vqf32_vadd_Vqf32Vqf32(acc_vec, prod); + } + + hvx_vec_store_u(dst_ptr + i1, (ir - i1) * 4, Q6_Vsf_equals_Vqf32(acc_vec)); + } + } + } + + t2 = HAP_perf_get_qtimer_count(); + + FARF(HIGH, "ssm-conv-f32-hvx %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", + ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], ir0, ir1, + src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], dst->ne[0], dst->ne[1], + dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); +} + +int op_ssm_conv_f32(struct htp_ops_context * octx) { + htp_ssm_conv_tensors_preamble; + + if (src0->type != HTP_TYPE_F32 || src1->type != HTP_TYPE_F32 || dst->type != HTP_TYPE_F32) { + FARF(ERROR, "ssm_conv: only (F32 x F32 -> F32) OPs supported"); + return HTP_STATUS_NO_SUPPORT; + } + + struct htp_ssm_conv_context scctx = { 0 }; + scctx.octx = octx; + + const uint32_t d_conv = src1->ne[0]; + const uint32_t d_inner = src0->ne[1]; + const uint32_t n_t = dst->ne[1]; // tokens per sequence + const uint32_t n_s = dst->ne[2]; // number of sequences in the batch + + const uint32_t n_threads = MIN(octx->n_threads, d_inner); + + if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { + uint32_t use_hvx = 0; + if (d_inner >= VLEN_FP32 && d_inner % VLEN_FP32 == 0) { + int is_aligned = hex_is_aligned((void *) src0->data, VLEN) && + hex_is_aligned((void *) src1->data, VLEN) && + hex_is_aligned((void *) dst->data, VLEN); + + if (is_aligned) { + use_hvx = 1; + } + } + + if (use_hvx) { + scctx.nrows_per_thread = (d_inner + n_threads - 1) / n_threads; // d_inner chunks per thread + scctx.nrows_per_thread += (scctx.nrows_per_thread & 1); // round up to even + + octx->src0_spad.size_per_thread = hex_round_up(scctx.nrows_per_thread * nb01, 256); + octx->src1_spad.size_per_thread = hex_round_up(scctx.nrows_per_thread * nb11, 256); + octx->dst_spad.size_per_thread = hex_round_up(scctx.nrows_per_thread * sizeof(float), 256); + + octx->src0_spad.size = octx->src0_spad.size_per_thread * n_threads; + octx->src1_spad.size = octx->src1_spad.size_per_thread * n_threads; + octx->dst_spad.size = octx->dst_spad.size_per_thread * n_threads; + + // Compute gather scratchpad size for src0 and src1 + const size_t gather_spad_size = n_threads * VLEN * 2; + + octx->src0_spad.data = octx->ctx->vtcm_base + gather_spad_size; + octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; + octx->dst_spad.data = octx->src1_spad.data + octx->src1_spad.size; + + FARF(HIGH, "ssm_conv-f32: gather-spad:%zu spad-per-thread:(%u:%u:%u) spad-sizes:(%u:%u:%u) spad-data:(%p:%p:%p)\n", + gather_spad_size, octx->src0_spad.size_per_thread, octx->src1_spad.size_per_thread, + octx->dst_spad.size_per_thread, octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size, + octx->src0_spad.data, octx->src1_spad.data, octx->dst_spad.data); + + const size_t total_spad_size = + gather_spad_size + octx->src0_spad.size + octx->src1_spad.size + octx->dst_spad.size; + + if (total_spad_size > octx->ctx->vtcm_size) { + FARF(HIGH, "ssm_conv-f32: HVX scratchpad size %zu exceeds VTCM size %zu", total_spad_size, + octx->ctx->vtcm_size); + use_hvx = 0; + } + } + + FARF(HIGH, "ssm-conv-f32: (%ux%ux%ux%u) x (%ux%ux%ux%u) -> (%ux%ux%ux%u) : use_hvx %d\n", src0->ne[0], + src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], dst->ne[0], + dst->ne[1], dst->ne[2], dst->ne[3], use_hvx); + + if (use_hvx) { + worker_pool_run_func(octx->ctx->worker_pool, ssm_conv_thread_f32_f32_hvx, &scctx, n_threads); + } else { + worker_pool_run_func(octx->ctx->worker_pool, ssm_conv_thread_f32_f32, &scctx, n_threads); + } + } + + return HTP_STATUS_OK; +} + +int op_ssm_conv(struct htp_ops_context * octx) { + int err = HTP_STATUS_OK; + struct htp_tensor * dst = &octx->dst; + + switch (dst->type) { + case HTP_TYPE_F32: + err = op_ssm_conv_f32(octx); + break; + default: + err = HTP_STATUS_NO_SUPPORT; + break; + } + + return err; +} diff --git a/ggml/src/ggml-hexagon/htp/sum-rows-ops.c b/ggml/src/ggml-hexagon/htp/sum-rows-ops.c index 62e45da2b3..352650b689 100644 --- a/ggml/src/ggml-hexagon/htp/sum-rows-ops.c +++ b/ggml/src/ggml-hexagon/htp/sum-rows-ops.c @@ -17,7 +17,6 @@ #include "htp-msg.h" #include "htp-ops.h" - #define sum_rows_preamble \ struct htp_tensor *src0 = &octx->src0;\ struct htp_tensor *dst = &octx->dst; \ @@ -42,53 +41,54 @@ const uint32_t nb2 = dst->nb[2]; \ const uint32_t nb3 = dst->nb[3]; \ -static int sum_rows_thread_f32(struct htp_ops_context * octx, const int nth, const int ith) { - sum_rows_preamble; +struct sum_rows_context { + const uint8_t * src_data; + uint8_t * dst_data; + uint32_t ne00; + size_t src_stride; + size_t dst_stride; + uint32_t rows_per_thread; + uint32_t total_rows; + bool opt_path; +}; - const uint32_t src0_nrows_per_thread = octx->src0_nrows_per_thread; - const size_t src0_row_size = nb01; - const size_t dst_row_size = nb1; +static void sum_rows_thread_f32(unsigned int nth, unsigned int ith, void *data) { + const struct sum_rows_context * smctx = (const struct sum_rows_context *) data; - const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows + const uint32_t rows_per_thread = smctx->rows_per_thread; + const uint32_t total_rows = smctx->total_rows; - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); + const uint32_t start_row = rows_per_thread * ith; + const uint32_t end_row = MIN(start_row + rows_per_thread, total_rows); - // no work for this thread - if (src0_start_row >= src0_end_row) { - return HTP_STATUS_OK; + if (start_row >= end_row) { + return; } - int opt_path = 0; - if ((0 == hex_is_aligned((void *) src0->data, VLEN)) && !(nb01 & (VLEN - 1))) { - opt_path = 1; - } + const size_t src_stride = smctx->src_stride; + const size_t dst_stride = smctx->dst_stride; + const uint32_t ne00 = smctx->ne00; + const bool opt_path = smctx->opt_path; - const uint8_t * restrict data_src = (const uint8_t *) src0->data; - uint8_t * restrict data_dst = (uint8_t *) dst->data; + const float * restrict src_th = (const float *) (smctx->src_data + (start_row * src_stride)); + float * restrict dst_th = (float *) (smctx->dst_data + (start_row * dst_stride)); - const float * restrict src_th = (float *) (data_src + (src0_start_row * src0_row_size)); - float * restrict dst_th = (float *) (data_dst + (src0_start_row * dst_row_size)); + // Calculate actual number of rows for this thread + const uint32_t n_rows = end_row - start_row; - for (uint32_t ir = 0; ir < src0_nrows_per_thread; ir++) { - const float * restrict src_local = src_th + (ir * ne00); + for (uint32_t ir = 0; ir < n_rows; ir++) { + const float * restrict src_local = src_th + (ir * (src_stride / sizeof(float))); - if (ir + 1 < src0_nrows_per_thread) { - hex_l2fetch(src_local + ne00, src0_row_size, src0_row_size, 1); + if (ir + 1 < n_rows) { + hex_l2fetch(src_local + (src_stride / sizeof(float)), src_stride, src_stride, 1); } - if (1 == opt_path) { + if (opt_path) { dst_th[ir] = hvx_reduce_sum_f32_a((const uint8_t *) src_local, ne00); } else { dst_th[ir] = hvx_reduce_sum_f32((const uint8_t *) src_local, ne00); } } - - return HTP_STATUS_OK; -} - -static void sum_rows_work_f32(unsigned int n, unsigned int i, void *data) { - sum_rows_thread_f32((struct htp_ops_context *) data, n, i); } int op_sum_rows(struct htp_ops_context * octx) { @@ -102,14 +102,27 @@ int op_sum_rows(struct htp_ops_context * octx) { return HTP_STATUS_OK; } - const int n_threads = octx->n_threads; const uint32_t src0_nrows = ne01 * ne02 * ne03; + const uint32_t n_threads = MIN(octx->n_threads, src0_nrows); + const uint32_t rows_per_thread = (src0_nrows + n_threads - 1) / n_threads; - uint32_t n_jobs = MIN(n_threads, src0_nrows); - octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs; + bool opt_path = false; + if ((0 == hex_is_aligned((void *) src0->data, VLEN)) && !(nb01 & (VLEN - 1))) { + opt_path = true; + } - worker_pool_run_func(octx->ctx->worker_pool, sum_rows_work_f32, octx, n_jobs); + struct sum_rows_context smctx = { + .src_data = (const uint8_t *) src0->data, + .dst_data = (uint8_t *) dst->data, + .ne00 = ne00, + .src_stride = nb01, + .dst_stride = nb1, + .rows_per_thread = rows_per_thread, + .total_rows = src0_nrows, + .opt_path = opt_path, + }; + + worker_pool_run_func(octx->ctx->worker_pool, sum_rows_thread_f32, &smctx, n_threads); return HTP_STATUS_OK; } - diff --git a/ggml/src/ggml-hexagon/htp/unary-ops.c b/ggml/src/ggml-hexagon/htp/unary-ops.c index ce879bf037..5bbd5040d3 100644 --- a/ggml/src/ggml-hexagon/htp/unary-ops.c +++ b/ggml/src/ggml-hexagon/htp/unary-ops.c @@ -17,6 +17,28 @@ #include "htp-msg.h" #include "htp-ops.h" +struct htp_unary_context { + struct htp_ops_context * octx; + + // Precomputed values + const uint8_t * data_src0; + uint8_t * data_dst; + + size_t src0_row_size; + size_t dst_row_size; + + size_t src0_row_size_aligned; + size_t dst_row_size_aligned; + + size_t src0_spad_half_size; + size_t dst_spad_half_size; + + uint32_t block; + uint32_t src0_nrows; + uint32_t src0_nrows_per_thread; + uint32_t nc; +}; + #define htp_unary_preamble \ const uint32_t ne00 = src->ne[0]; \ const uint32_t ne01 = src->ne[1]; \ @@ -57,8 +79,7 @@ static void hvx_fast_rms_norm_f32(const uint8_t * restrict src, sum_v = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, v2); } - HVX_Vector reduced_sum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_v)); - sum_v = hvx_vec_repl4(reduced_sum); + sum_v = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_v)); // replicated over all lanes HVX_Vector t_v = hvx_vec_splat_f32((float) num_elems); HVX_Vector denom_v = hvx_vec_inverse_f32(t_v); @@ -75,128 +96,95 @@ static void hvx_fast_rms_norm_f32(const uint8_t * restrict src, } } -static void scale_htp_f32(const float * restrict src, - float * restrict dst, - uint8_t * restrict spad, - const uint32_t num_rows, - const uint32_t row_elems, - const size_t row_size, - int32_t * op_params, - int opt_path) { +static void scale_f32(const float * restrict src, + float * restrict dst, + uint8_t * restrict spad, + const uint32_t num_rows, + const uint32_t row_elems, + const size_t row_size, + int32_t * op_params) { float scale = 0.f; float bias = 0.f; memcpy(&scale, &op_params[0], sizeof(float)); memcpy(&bias, &op_params[1], sizeof(float)); for (uint32_t ir = 0; ir < num_rows; ir++) { - const float * restrict src_local = src + (ir * row_elems); - float * restrict dst_local = dst + (ir * row_elems); + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * row_size); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * row_size); - if (ir + 1 < num_rows) { - hex_l2fetch(src_local + row_elems, row_size, row_size, 1); - } - - hvx_scale_offset_f32((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems, scale, bias); + hvx_scale_offset_f32_aa((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems, scale, bias); } } -static void rms_norm_htp_f32(const float * restrict src, - float * restrict dst, - uint8_t * restrict spad, - const uint32_t num_rows, - const uint32_t row_elems, - const size_t row_size, - int32_t * op_params, - int opt_path) { +static void rms_norm_f32(const float * restrict src, + float * restrict dst, + uint8_t * restrict spad, + const uint32_t num_rows, + const uint32_t row_elems, + const size_t row_size, + int32_t * op_params) { float epsilon = 0.f; memcpy(&epsilon, op_params, sizeof(float)); for (uint32_t ir = 0; ir < num_rows; ir++) { - const float * restrict src_local = src + (ir * row_elems); - float * restrict dst_local = dst + (ir * row_elems); + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * row_size); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * row_size); - if (ir + 1 < num_rows) { - hex_l2fetch(src_local + row_elems, row_size, row_size, 1); - } - - if (1 == opt_path) { - hvx_fast_rms_norm_f32((const uint8_t *) src_local, (uint8_t *) dst_local, spad, row_elems, epsilon); - } else { - float sum = hvx_sum_of_squares_f32((const uint8_t *) src_local, row_elems); - - const float mean = sum / row_elems; - const float scale = 1.0f / sqrtf(mean + epsilon); - - hvx_scale_f32((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems, scale); - } + hvx_fast_rms_norm_f32((const uint8_t *) src_local, (uint8_t *) dst_local, spad, row_elems, epsilon); } } -static void sqr_htp_f32(const float * restrict src, - float * restrict dst, - uint8_t * restrict spad, - const uint32_t num_rows, - const uint32_t row_elems, - const size_t row_size, - int32_t * op_params, - int opt_path) { +static void sqr_f32(const float * restrict src, + float * restrict dst, + uint8_t * restrict spad, + const uint32_t num_rows, + const uint32_t row_elems, + const size_t row_size, + int32_t * op_params) { for (uint32_t ir = 0; ir < num_rows; ir++) { - const float * restrict src_local = src + (ir * row_elems); - float * restrict dst_local = dst + (ir * row_elems); + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * row_size); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * row_size); - if (ir + 1 < num_rows) { - hex_l2fetch(src_local + row_elems, row_size, row_size, 1); - } - - if (1 == opt_path) { - hvx_sqr_f32_aa((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems); - } else { - hvx_sqr_f32((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems); - } + hvx_sqr_f32_aa((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems); } } -static void sqrt_htp_f32(const float * restrict src, - float * restrict dst, - uint8_t * restrict spad, - const uint32_t num_rows, - const uint32_t row_elems, - const size_t row_size, - int32_t * op_params, - int opt_path) { +static void sqrt_f32(const float * restrict src, + float * restrict dst, + uint8_t * restrict spad, + const uint32_t num_rows, + const uint32_t row_elems, + const size_t row_size, + int32_t * op_params) { for (uint32_t ir = 0; ir < num_rows; ir++) { - const float * restrict src_local = src + (ir * row_elems); - float * restrict dst_local = dst + (ir * row_elems); + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * row_size); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * row_size); - if (ir + 1 < num_rows) { - hex_l2fetch(src_local + row_elems, row_size, row_size, 1); - } - - if (1 == opt_path) { - hvx_sqrt_f32_aa((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems); - } else { - hvx_sqrt_f32((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems); - } + hvx_sqrt_f32_aa((uint8_t *) dst_local, (const uint8_t *) src_local, row_elems); } } -static void unary_job_f32_per_thread(const struct htp_tensor * src, - struct htp_tensor * dst, - uint8_t * spad, - int htp_op, - int32_t * op_params, - uint32_t nth, - uint32_t ith, - uint32_t src0_nrows_per_thread) { +static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { + const struct htp_unary_context * uctx = (const struct htp_unary_context *) data; + struct htp_ops_context * octx = uctx->octx; + const struct htp_tensor * src = &octx->src0; + const struct htp_tensor * dst = &octx->dst; + htp_unary_preamble; - const size_t src0_row_size = nb01; - const size_t dst_row_size = nb1; + int htp_op = octx->op; + int32_t * op_params = octx->op_params; + uint32_t src0_nrows_per_thread = uctx->src0_nrows_per_thread; - const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows + const size_t src0_row_size = uctx->src0_row_size; + const size_t dst_row_size = uctx->dst_row_size; + const size_t src0_row_size_aligned = uctx->src0_row_size_aligned; + const size_t dst_row_size_aligned = uctx->dst_row_size_aligned; + + const uint32_t src0_nrows = uctx->src0_nrows; const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); @@ -208,79 +196,104 @@ static void unary_job_f32_per_thread(const struct htp_tensor * src, uint64_t t1, t2; t1 = HAP_perf_get_qtimer_count(); - int is_aligned = 1; - int opt_path = 0; - if ((0 == hex_is_aligned((void *) src->data, VLEN)) || (0 == hex_is_aligned((void *) dst->data, VLEN))) { - is_aligned = 0; - } - if ((1 == is_aligned) && !(nb01 & (VLEN - 1))) { - opt_path = 1; + const uint8_t * restrict data_src = uctx->data_src0; + uint8_t * restrict data_dst = uctx->data_dst; + + uint8_t * src0_spad_data = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread); + uint8_t * dst_spad_data = octx->dst_spad.data + (ith * octx->dst_spad.size_per_thread); + + size_t src0_spad_half_size = uctx->src0_spad_half_size; + size_t dst_spad_half_size = uctx->dst_spad_half_size; + + const int BLOCK = uctx->block; + if (BLOCK == 0) { + FARF(ERROR, "unary-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", + octx->src0_spad.size_per_thread, src0_row_size_aligned); + return; } - const uint8_t * restrict data_src = (const uint8_t *) src->data; - uint8_t * restrict data_dst = (uint8_t *) dst->data; + dma_queue * dma_queue = octx->ctx->dma[ith]; - const float * restrict src_th = (float *) (data_src + (src0_start_row * src0_row_size)); - float * restrict dst_th = (float *) (data_dst + (src0_start_row * dst_row_size)); - uint8_t * restrict spad_th = (uint8_t *) spad + (ith * nb01); + for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { + const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - switch (htp_op) { - case HTP_OP_RMS_NORM: - rms_norm_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path); - break; - case HTP_OP_SCALE: - scale_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path); - break; - case HTP_OP_SQR: - sqr_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path); - break; - case HTP_OP_SQRT: - sqrt_htp_f32(src_th, dst_th, spad_th, src0_end_row - src0_start_row, ne0, nb1, op_params, opt_path); - break; + // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) + dma_queue_push_vtcm_to_ddr(dma_queue, + dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), + dst_row_size, dst_row_size_aligned, 0); - default: - break; + dma_queue_push_ddr_to_vtcm(dma_queue, + dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src + (ir * src0_row_size)), + src0_row_size_aligned, src0_row_size, block_size); } + for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { + const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); + + float * dst_spad = (float *) dma_queue_pop(dma_queue).src; + float * src0_spad = (float *) dma_queue_pop(dma_queue).dst; + + // Process block in VTCM + switch (htp_op) { + case HTP_OP_RMS_NORM: + rms_norm_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params); + break; + case HTP_OP_SCALE: + scale_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params); + break; + case HTP_OP_SQR: + sqr_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params); + break; + case HTP_OP_SQRT: + sqrt_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params); + break; + default: + break; + } + + dma_queue_push_vtcm_to_ddr(dma_queue, + dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), + dst_row_size, dst_row_size_aligned, block_size); + + // prefetch N+2 loop iteration if any + const uint32_t pref_block = (ir + BLOCK * 2); + if (pref_block < src0_end_row) { + const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); + dma_queue_push_ddr_to_vtcm(dma_queue, + dma_make_ptr(src0_spad, data_src + (pref_block * src0_row_size)), + src0_row_size_aligned, src0_row_size, pref_block_size); + } + } + + dma_queue_flush(dma_queue); + t2 = HAP_perf_get_qtimer_count(); - FARF(HIGH, "unary-f32 %d/%d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, opt_path, src->ne[0], + FARF(HIGH, "unary-f32 %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, src->ne[0], src->ne[1], src->ne[2], src->ne[3], src0_start_row, src0_end_row, dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); } -static void unary_job_dispatcher_f32(unsigned int n, unsigned int i, void * data) { - struct htp_ops_context * octx = (struct htp_ops_context *) data; - - unary_job_f32_per_thread(&octx->src0, &octx->dst, octx->src0_spad.data, octx->op, octx->op_params, n, i, - octx->src0_nrows_per_thread); -} - static int execute_op_unary_f32(struct htp_ops_context * octx) { int err = HTP_STATUS_OK; const struct htp_tensor * src0 = &octx->src0; struct htp_tensor * dst = &octx->dst; - worker_callback_t unary_op_func; - const char * op_type = NULL; + const char * op_type = NULL; switch (octx->op) { case HTP_OP_RMS_NORM: - unary_op_func = unary_job_dispatcher_f32; - op_type = "rmsnorm-f32"; + op_type = "rmsnorm-f32"; break; case HTP_OP_SCALE: - unary_op_func = unary_job_dispatcher_f32; - op_type = "scale-f32"; + op_type = "scale-f32"; break; case HTP_OP_SQR: - unary_op_func = unary_job_dispatcher_f32; - op_type = "sqr-f32"; + op_type = "sqr-f32"; break; case HTP_OP_SQRT: - unary_op_func = unary_job_dispatcher_f32; - op_type = "sqrt-f32"; + op_type = "sqrt-f32"; break; default: @@ -288,38 +301,65 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) { return HTP_STATUS_NO_SUPPORT; } - const int n_threads = octx->n_threads; const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; + const uint32_t n_threads = MIN(octx->n_threads, src0_nrows); const size_t src0_row_size = src0->nb[1]; const size_t dst_row_size = dst->nb[1]; - // VTCM scratchpads for all tensors - octx->dst_spad.size = hex_round_up(dst_row_size, 128) * n_threads; - octx->src0_spad.size = hex_round_up(src0_row_size, 128) * n_threads; + const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); + const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN); - size_t spad_size = octx->src0_spad.size + octx->dst_spad.size; + // VTCM scratchpads for all tensors + // N rows per thread, padded to HVX vector size + // Double buffering requires 2x size per buffer + + size_t spad_size_per_row = 2 * (src0_row_size_aligned + dst_row_size_aligned); + size_t vtcm_row_per_thread = (octx->ctx->vtcm_size)/ (n_threads * spad_size_per_row); + + // Make sure the reserved vtcm size is sufficient + if (vtcm_row_per_thread == 0) { + FARF(ERROR, "unary-%s : current VTCM reservation %zu is too small, needed %zu\n", op_type, octx->ctx->vtcm_size, + spad_size_per_row * n_threads); + return HTP_STATUS_VTCM_TOO_SMALL; + } + + octx->src0_spad.size_per_thread = src0_row_size_aligned * vtcm_row_per_thread * 2; + octx->dst_spad.size_per_thread = dst_row_size_aligned * vtcm_row_per_thread * 2; + + octx->src0_spad.size = n_threads * octx->src0_spad.size_per_thread; + octx->dst_spad.size = n_threads * octx->dst_spad.size_per_thread; + + octx->src0_spad.data = octx->ctx->vtcm_base; + octx->dst_spad.data = octx->src0_spad.data + octx->src0_spad.size; FARF(HIGH, "%s: (%ux%ux%ux%u) -> (%ux%ux%ux%u) : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size); - // Make sure the reserved vtcm size is sufficient - if (octx->ctx->vtcm_size < spad_size) { - FARF(ERROR, "unary-%s : current VTCM reservation %zu is too small, needed %zu\n", op_type, octx->ctx->vtcm_size, - spad_size); - return HTP_STATUS_VTCM_TOO_SMALL; - } - - octx->src0_spad.data = octx->ctx->vtcm_base; - octx->dst_spad.data = octx->src0_spad.data + octx->src0_spad.size; - if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { - uint32_t n_jobs = MIN(n_threads, src0_nrows); + struct htp_unary_context uctx = { + .octx = octx, + .src0_nrows_per_thread = (src0_nrows + n_threads - 1) / n_threads, + .src0_nrows = src0_nrows, - octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs; + .data_src0 = (const uint8_t *)src0->data, + .data_dst = (uint8_t *)dst->data, - worker_pool_run_func(octx->ctx->worker_pool, unary_op_func, octx, n_jobs); + .src0_row_size = src0_row_size, + .dst_row_size = dst_row_size, + + .src0_row_size_aligned = src0_row_size_aligned, + .dst_row_size_aligned = dst_row_size_aligned, + + .src0_spad_half_size = octx->src0_spad.size_per_thread / 2, + .dst_spad_half_size = octx->dst_spad.size_per_thread / 2, + + .block = (octx->src0_spad.size_per_thread / 2) / src0_row_size_aligned, + .nc = src0->ne[0], + }; + + worker_pool_run_func(octx->ctx->worker_pool, unary_job_f32_per_thread, &uctx, n_threads); } return err; diff --git a/ggml/src/ggml-hexagon/htp/worker-pool.c b/ggml/src/ggml-hexagon/htp/worker-pool.c index 894815f46a..172e28908e 100644 --- a/ggml/src/ggml-hexagon/htp/worker-pool.c +++ b/ggml/src/ggml-hexagon/htp/worker-pool.c @@ -56,7 +56,7 @@ static void worker_pool_main(void * context) { unsigned int n = atomic_load(&pool->n_jobs); unsigned int i = atomic_fetch_add(&pool->next_job, 1); if (i >= n) { - // Spurios wakeup + // Spurious wakeup continue; } diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt index 80037d2436..b44ed0f721 100644 --- a/ggml/src/ggml-hip/CMakeLists.txt +++ b/ggml/src/ggml-hip/CMakeLists.txt @@ -11,6 +11,10 @@ endif() list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}) list(APPEND CMAKE_PREFIX_PATH "${ROCM_PATH}/lib64/cmake") +if (NOT DEFINED CMAKE_HIP_FLAGS_DEBUG) + set(CMAKE_HIP_FLAGS_DEBUG "-g -O2") +endif() + # CMake on Windows doesn't support the HIP language yet if (WIN32) set(CXX_IS_HIPCC TRUE) diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h index e3714b38a6..9256865595 100644 --- a/ggml/src/ggml-impl.h +++ b/ggml/src/ggml-impl.h @@ -491,6 +491,61 @@ static inline float ggml_e8m0_to_fp32_half(uint8_t x) { #define GGML_E8M0_TO_FP32(x) ggml_e8m0_to_fp32(x) #define GGML_E8M0_TO_FP32_HALF(x) ggml_e8m0_to_fp32_half(x) +// UE4M3: unsigned, 4 exp bits (bias=7), 3 mantissa bits +// Returns value * 0.5 to match kvalues_mxfp4 convention (kvalues = 2 * E2M1_float) +static inline float ggml_ue4m3_to_fp32(uint8_t x) { + if (x == 0 || x == 0x7F) { + return 0.0f; + } + int exp = (x >> 3) & 0xF; + int man = x & 0x7; + float raw; + if (exp == 0) { + raw = ldexpf((float) man, -9); + } else { + raw = ldexpf(1.0f + (float) man / 8.0f, exp - 7); + } + return raw * 0.5f; +} + +static inline uint8_t ggml_fp32_to_ue4m3(float x) { + if (!(x > 0.0f)) { + return 0; + } + if (x > 448.0f) { + x = 448.0f; + } + uint32_t bits; + memcpy(&bits, &x, 4); + int fp32_exp = ((bits >> 23) & 0xFF) - 127; + int fp32_man = (bits >> 20) & 0x7; + int ue4m3_exp = fp32_exp + 7; + if (ue4m3_exp <= 0) { + // subnormal: value = man * 2^-9, man = round(x * 2^9) + int man = (int) (x * 512.0f + 0.5f); + if (man > 7) { + man = 7; + } + if (man < 1) { + return 0; + } + return (uint8_t) man; + } + if (ue4m3_exp >= 15) { + return 0x7E; + } + int round_bit = (bits >> 19) & 1; + int ue4m3_man = fp32_man + round_bit; + if (ue4m3_man > 7) { + ue4m3_man = 0; + ue4m3_exp++; + if (ue4m3_exp >= 15) { + return 0x7E; + } + } + return (uint8_t) ((ue4m3_exp << 3) | ue4m3_man); +} + /** * Converts brain16 to float32. * diff --git a/ggml/src/ggml-metal/ggml-metal-context.m b/ggml/src/ggml-metal/ggml-metal-context.m index 5d3a8ce412..32d97cd5d0 100644 --- a/ggml/src/ggml-metal/ggml-metal-context.m +++ b/ggml/src/ggml-metal/ggml-metal-context.m @@ -47,7 +47,7 @@ struct ggml_metal { uint64_t fuse_cnt[GGML_OP_COUNT]; // capture state - bool capture_next_compute; + int capture_compute; bool capture_started; id capture_scope; @@ -75,6 +75,10 @@ struct ggml_metal { // abort ggml_metal_graph_compute if callback returns true ggml_abort_callback abort_callback; void * abort_callback_data; + + // error state - set when a command buffer fails during synchronize + // once set, graph_compute will return GGML_STATUS_FAILED until the backend is recreated + bool has_error; }; ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) { @@ -154,10 +158,19 @@ ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) { GGML_LOG_INFO("%s: use concurrency = %s\n", __func__, res->use_concurrency ? "true" : "false"); GGML_LOG_INFO("%s: use graph optimize = %s\n", __func__, res->use_graph_optimize ? "true" : "false"); - res->capture_next_compute = false; + res->capture_compute = 0; res->capture_started = false; res->capture_scope = nil; + { + const char * val = getenv("GGML_METAL_CAPTURE_COMPUTE"); + if (val) { + res->capture_compute = atoi(val); + } + } + + res->has_error = false; + res->gf = nil; res->encode_async = nil; for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) { @@ -246,7 +259,8 @@ void ggml_metal_synchronize(ggml_metal_t ctx) { if (status == MTLCommandBufferStatusError) { GGML_LOG_ERROR("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]); } - GGML_ABORT("fatal error"); + ctx->has_error = true; + return; } } } @@ -262,7 +276,15 @@ void ggml_metal_synchronize(ggml_metal_t ctx) { if (status == MTLCommandBufferStatusError) { GGML_LOG_ERROR("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]); } - GGML_ABORT("fatal error"); + + // release this and all remaining command buffers before returning + for (size_t j = i; j < ctx->cmd_bufs_ext.count; ++j) { + [ctx->cmd_bufs_ext[j] release]; + } + [ctx->cmd_bufs_ext removeAllObjects]; + + ctx->has_error = true; + return; } [cmd_buf release]; @@ -414,6 +436,11 @@ bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, con } enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * gf) { + if (ctx->has_error) { + GGML_LOG_ERROR("%s: backend is in error state from a previous command buffer failure - recreate the backend to recover\n", __func__); + return GGML_STATUS_FAILED; + } + // number of nodes encoded by the main thread (empirically determined) const int n_main = MAX(64, 0.1*gf->n_nodes); @@ -438,9 +465,13 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * ctx->n_nodes_per_cb = (ctx->n_nodes_1 + ctx->n_cb - 1) / ctx->n_cb; - const bool use_capture = ctx->capture_next_compute; + if (ctx->capture_compute >= 0) { + ctx->capture_compute--; + } + + const bool use_capture = ctx->capture_compute == 0; if (use_capture) { - ctx->capture_next_compute = false; + ctx->capture_compute = -1; // make sure all previous computations have finished before starting the capture if (ctx->cmd_buf_last) { @@ -449,6 +480,10 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * } if (!ctx->capture_started) { + NSString * path = [NSString stringWithFormat:@"/tmp/perf-metal-%d.gputrace", getpid()]; + + GGML_LOG_WARN("%s: capturing graph in %s\n", __func__, [path UTF8String]); + // create capture scope id device = ggml_metal_device_get_obj(ctx->dev); ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:device]; @@ -456,7 +491,7 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new]; descriptor.captureObject = ctx->capture_scope; descriptor.destination = MTLCaptureDestinationGPUTraceDocument; - descriptor.outputURL = [NSURL fileURLWithPath:[NSString stringWithFormat:@"/tmp/perf-metal.gputrace"]]; + descriptor.outputURL = [NSURL fileURLWithPath:path]; NSError * error = nil; if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) { @@ -519,7 +554,7 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * // enter here only when capturing in order to wait for all computation to finish // otherwise, we leave the graph to compute asynchronously - if (!use_capture && ctx->capture_started) { + if (use_capture && ctx->capture_started) { // wait for completion and check status of each command buffer // needed to detect if the device ran out-of-memory for example (#1881) { @@ -571,6 +606,8 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * [ctx->capture_scope endScope]; [[MTLCaptureManager sharedCaptureManager] stopCapture]; + + ctx->capture_started = false; } } @@ -663,7 +700,7 @@ void ggml_metal_set_n_cb(ggml_metal_t ctx, int n_cb) { idx_end, ctx->use_fusion, ctx->use_concurrency, - ctx->capture_next_compute, + ctx->capture_compute, ctx->debug_graph, ctx->debug_fusion); @@ -698,5 +735,5 @@ bool ggml_metal_supports_family(ggml_metal_t ctx, int family) { } void ggml_metal_capture_next_compute(ggml_metal_t ctx) { - ctx->capture_next_compute = true; + ctx->capture_compute = 1; } diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index 06f3d80459..72ad876d5e 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -577,6 +577,41 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rwkv(ggml_metal_ return res; } +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_gated_delta_net(ggml_metal_library_t lib, const ggml_tensor * op) { + char base[256]; + char name[256]; + + // v is src[2], dimensions: S_v = ne[0], H = ne[1] + const int ne20 = op->src[2]->ne[0]; // S_v + const int ne21 = op->src[2]->ne[1]; // H + const int ne30 = op->src[3]->ne[0]; // G + + const int nsg = op->src[2]->ne[0]/32; + + GGML_ASSERT(op->src[5]->type == GGML_TYPE_F32); + GGML_ASSERT(op->ne[0] == ne20 * ne21); + GGML_ASSERT(ne20 % 32 == 0); + + snprintf(base, 256, "kernel_gated_delta_net_%s_%d", ggml_type_name(op->src[0]->type), nsg); + snprintf(name, 256, "%s_ne20=%d_ne30=%d", base, ne20, ne30); + + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); + if (!res.pipeline) { + ggml_metal_cv_t cv = ggml_metal_cv_init(); + + ggml_metal_cv_set_int16(cv, ne20, FC_GATED_DELTA_NET + 0); + ggml_metal_cv_set_int16(cv, ne30, FC_GATED_DELTA_NET + 1); + + res = ggml_metal_library_compile_pipeline(lib, base, name, cv); + + ggml_metal_cv_free(cv); + } + + res.nsg = nsg; + + return res; +} + ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_solve_tri(ggml_metal_library_t lib, const ggml_tensor * op) { char base[256]; char name[256]; @@ -1435,10 +1470,11 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin(ggml_metal_l const bool is_c4 = (op->src[0]->ne[0] % 4 == 0) && (op->src[1]->ne[0] % 4 == 0); + const bool is_cb = op->src[0]->ne[0] != op->src[1]->ne[0]; const bool is_rb = ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && (ggml_nrows(op->src[1]) == 1) && ggml_nelements(op) < 65536; snprintf(base, 256, "kernel_bin_fuse_%s_%s_%s%s", t0_str, t1_str, t_str, is_c4 ? "_4" : ""); - snprintf(name, 256, "%s_op=%d_nf=%d_rb=%d", base, op_num, n_fuse, is_rb); + snprintf(name, 256, "%s_op=%d_nf=%d_rb=%d_cb=%d", base, op_num, n_fuse, is_rb, is_cb); ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); if (!res.pipeline) { @@ -1447,6 +1483,7 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin(ggml_metal_l ggml_metal_cv_set_int16(cv, op_num, FC_BIN + 0); ggml_metal_cv_set_int16(cv, n_fuse, FC_BIN + 1); ggml_metal_cv_set_bool (cv, is_rb, FC_BIN + 2); + ggml_metal_cv_set_bool (cv, is_cb, FC_BIN + 3); res = ggml_metal_library_compile_pipeline(lib, base, name, cv); @@ -1717,12 +1754,29 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_upscale(ggml_met char base[256]; char name[256]; - snprintf(base, 256, "kernel_upscale_%s", ggml_type_name(op->src[0]->type)); - snprintf(name, 256, "%s", base); + const int32_t mode_flags = ggml_get_op_params_i32(op, 0); + const ggml_scale_mode mode = (ggml_scale_mode) (mode_flags & 0xFF); + + const bool antialias = (mode_flags & GGML_SCALE_FLAG_ANTIALIAS); + + if (mode == GGML_SCALE_MODE_BILINEAR) { + snprintf(base, 256, "kernel_upscale_bilinear_%s", ggml_type_name(op->src[0]->type)); + } else if (mode == GGML_SCALE_MODE_BICUBIC) { + snprintf(base, 256, "kernel_upscale_bicubic_%s", ggml_type_name(op->src[0]->type)); + } else { + snprintf(base, 256, "kernel_upscale_nearest_%s", ggml_type_name(op->src[0]->type)); + } + snprintf(name, 256, "%s_aa=%d", base, antialias); ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); if (!res.pipeline) { - res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + ggml_metal_cv_t cv = ggml_metal_cv_init(); + + ggml_metal_cv_set_bool(cv, antialias, FC_UPSCALE + 0); + + res = ggml_metal_library_compile_pipeline(lib, base, name, cv); + + ggml_metal_cv_free(cv); } return res; diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 93d7f6a216..fd2b3ddeb5 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -125,6 +125,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv_batched (ggml_metal_library_t lib, const struct ggml_tensor * op, int ssm_conv_bs); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rwkv (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_gated_delta_net (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_solve_tri (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ext (ggml_metal_library_t lib, enum ggml_type tsrc0, enum ggml_type tsrc1, int nsg, int nxpsg, int r1ptg); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm (ggml_metal_library_t lib, const struct ggml_tensor * op); diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index 3db7f12629..82101f4714 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -1108,7 +1108,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te op->type == GGML_TYPE_F32 && (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32); case GGML_OP_UPSCALE: - return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST && !(op->op_params[0] & GGML_SCALE_FLAG_ANTIALIAS); + return op->src[0]->type == GGML_TYPE_F32; case GGML_OP_POOL_1D: return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; case GGML_OP_POOL_2D: @@ -1142,6 +1142,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te op->src[0]->ne[0] != 128 && op->src[0]->ne[0] != 192 && op->src[0]->ne[0] != 256 && + op->src[0]->ne[0] != 320 && op->src[0]->ne[0] != 576) { return false; } @@ -1155,10 +1156,12 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: return true; + case GGML_OP_GATED_DELTA_NET: + return has_simdgroup_reduction && op->src[2]->ne[0] % 32 == 0; case GGML_OP_SOLVE_TRI: case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: - return has_simdgroup_reduction; + return has_simdgroup_reduction && op->src[0]->type != GGML_TYPE_NVFP4; case GGML_OP_SET: case GGML_OP_CPY: case GGML_OP_DUP: @@ -1216,7 +1219,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te }; } case GGML_OP_GET_ROWS: - return true; + return op->src[0]->type != GGML_TYPE_NVFP4; case GGML_OP_SET_ROWS: { if (op->src[0]->type != GGML_TYPE_F32) { @@ -1281,7 +1284,7 @@ struct ggml_metal_buffer { bool use_residency_sets; // optional MTLResidencySet - // note: cannot use explicity "id" here because it is not available on certain OSes + // note: cannot use explicitly "id" here because it is not available on certain OSes id rset; // pointers to global device diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index 383e0d6e93..53437b23cd 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -35,7 +35,7 @@ #define N_R0_Q4_K 2 #define N_SG_Q4_K 2 -#define N_R0_Q5_K 2 +#define N_R0_Q5_K 1 #define N_SG_Q5_K 2 #define N_R0_Q6_K 2 @@ -83,6 +83,8 @@ #define FC_UNARY 1200 #define FC_BIN 1300 #define FC_SUM_ROWS 1400 +#define FC_UPSCALE 1500 +#define FC_GATED_DELTA_NET 1600 // op-specific constants #define OP_FLASH_ATTN_EXT_NQPSG 8 @@ -792,6 +794,44 @@ typedef struct { uint64_t nb0; } ggml_metal_kargs_ssm_scan; +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne10; + int32_t ne11; + int32_t ne12; + int32_t ne13; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne20; + int32_t ne21; + int32_t ne22; + int32_t ne23; + uint64_t nb20; + uint64_t nb21; + uint64_t nb22; + uint64_t nb23; + int32_t ns02; + int32_t ns12; + int32_t ns22; + int32_t ne0; + int32_t ne1; + int32_t ne2; + int32_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; +} ggml_metal_kargs_gated_delta_net; + typedef struct { int32_t ne00; int32_t ne01; @@ -890,6 +930,7 @@ typedef struct { float sf1; float sf2; float sf3; + float poffs; } ggml_metal_kargs_upscale; typedef struct { diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index 3d5db0b79f..c0bcad392b 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -333,6 +333,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) { { n_fuse = ggml_metal_op_rwkv(ctx, idx); } break; + case GGML_OP_GATED_DELTA_NET: + { + n_fuse = ggml_metal_op_gated_delta_net(ctx, idx); + } break; case GGML_OP_SOLVE_TRI: { n_fuse = ggml_metal_op_solve_tri(ctx, idx); @@ -631,7 +635,7 @@ int ggml_metal_op_acc(ggml_metal_op_t ctx, int idx) { const bool inplace = (bool) ((const int32_t *) op->op_params)[4]; if (!inplace) { - // run a separete kernel to cpy src->dst + // run a separate kernel to cpy src->dst // not sure how to avoid this // TODO: make a simpler cpy_bytes kernel @@ -1562,6 +1566,81 @@ int ggml_metal_op_rwkv(ggml_metal_op_t ctx, int idx) { return 1; } +int ggml_metal_op_gated_delta_net(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + + GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne); + GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb); + GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne); + GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb); + GGML_TENSOR_LOCALS( int32_t, ne2, op->src[2], ne); + GGML_TENSOR_LOCALS(uint64_t, nb2, op->src[2], nb); + GGML_TENSOR_LOCALS( int32_t, ne, op, ne); + GGML_TENSOR_LOCALS(uint64_t, nb, op, nb); + + auto pipeline = ggml_metal_library_get_pipeline_gated_delta_net(lib, op); + + int ida = 0; + + ggml_metal_kargs_gated_delta_net args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne20 =*/ ne20, + /*.ne21 =*/ ne21, + /*.ne22 =*/ ne22, + /*.ne23 =*/ ne23, + /*.nb20 =*/ nb20, + /*.nb21 =*/ nb21, + /*.nb22 =*/ nb22, + /*.nb23 =*/ nb23, + /*.ns02 =*/ (int32_t) (nb02/sizeof(float)), + /*.ns12 =*/ (int32_t) (nb12/sizeof(float)), + /*.ns22 =*/ (int32_t) (nb22/sizeof(float)), + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + }; + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), ida++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), ida++); // q + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), ida++); // k + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[2]), ida++); // v + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[3]), ida++); // gate + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[4]), ida++); // beta + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[5]), ida++); // state + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), ida++); // dst + + const int nsg = pipeline.nsg; + + ggml_metal_encoder_dispatch_threadgroups(enc, op->src[2]->ne[0]/nsg, op->src[2]->ne[1], op->src[2]->ne[3], 32, nsg, 1); + + return 1; +} + int ggml_metal_op_solve_tri(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); @@ -1644,7 +1723,7 @@ int ggml_metal_op_set(ggml_metal_op_t ctx, int idx) { const bool inplace = (bool) ((const int32_t *) op->op_params)[4]; if (!inplace) { - // run a separete kernel to cpy src->dst + // run a separate kernel to cpy src->dst // not sure how to avoid this // TODO: make a simpler cpy_bytes kernel @@ -1963,6 +2042,7 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) { ( op->src[0]->type == GGML_TYPE_F32 || // TODO: helper function op->src[0]->type == GGML_TYPE_F16 || + op->src[0]->type == GGML_TYPE_BF16 || op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_Q4_1 || op->src[0]->type == GGML_TYPE_Q5_0 || @@ -1977,6 +2057,8 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) { op->src[0]->type == GGML_TYPE_Q4_K || op->src[0]->type == GGML_TYPE_Q5_K || op->src[0]->type == GGML_TYPE_Q6_K || + op->src[0]->type == GGML_TYPE_Q2_K || + op->src[0]->type == GGML_TYPE_Q3_K || false) && (ne11 >= 4 && ne11 <= 8) ) ) @@ -2005,7 +2087,7 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) { const int16_t r0ptg = nypsg*nsg; // num src0 rows per threadgroup int16_t r1ptg = 4; // num src1 rows per threadgroup - // note: not sure how optimal are those across all different hardware. there might be someting cleverer + // note: not sure how optimal are those across all different hardware. there might be something cleverer switch (ne11) { case 2: r1ptg = 2; break; @@ -3098,9 +3180,7 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) { ggml_metal_encoder_set_buffer (enc, bid_dst, 3); if (pipeline.cnt) { - const int n = pipeline.c4 ? ggml_nelements(op)/4 : ggml_nelements(op); - - ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1); + ggml_metal_encoder_dispatch_threadgroups(enc, args.ne0, ggml_nrows(op), 1, 1, 1, 1); } else { const int nth_max = MIN(256, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)); @@ -3729,32 +3809,43 @@ int ggml_metal_op_upscale(ggml_metal_op_t ctx, int idx) { GGML_TENSOR_LOCALS( int32_t, ne, op, ne); GGML_TENSOR_LOCALS(uint64_t, nb, op, nb); - const float sf0 = (float)ne0/op->src[0]->ne[0]; - const float sf1 = (float)ne1/op->src[0]->ne[1]; - const float sf2 = (float)ne2/op->src[0]->ne[2]; - const float sf3 = (float)ne3/op->src[0]->ne[3]; + float sf0 = (float)ne0/op->src[0]->ne[0]; + float sf1 = (float)ne1/op->src[0]->ne[1]; + float sf2 = (float)ne2/op->src[0]->ne[2]; + float sf3 = (float)ne3/op->src[0]->ne[3]; + + const int32_t mode_flags = ggml_get_op_params_i32(op, 0); + + float poffs = 0.5f; + + if (mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) { + poffs = 0.0f; + sf0 = ne0 > 1 && ne00 > 1 ? (float)(ne0 - 1) / (ne00 - 1) : sf0; + sf1 = ne1 > 1 && ne01 > 1 ? (float)(ne1 - 1) / (ne01 - 1) : sf1; + } ggml_metal_kargs_upscale args = { - /*.ne00 =*/ ne00, - /*.ne01 =*/ ne01, - /*.ne02 =*/ ne02, - /*.ne03 =*/ ne03, - /*.nb00 =*/ nb00, - /*.nb01 =*/ nb01, - /*.nb02 =*/ nb02, - /*.nb03 =*/ nb03, - /*.ne0 =*/ ne0, - /*.ne1 =*/ ne1, - /*.ne2 =*/ ne2, - /*.ne3 =*/ ne3, - /*.nb0 =*/ nb0, - /*.nb1 =*/ nb1, - /*.nb2 =*/ nb2, - /*.nb3 =*/ nb3, - /*.sf0 =*/ sf0, - /*.sf1 =*/ sf1, - /*.sf2 =*/ sf2, - /*.sf3 =*/ sf3 + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + /*.sf0 =*/ sf0, + /*.sf1 =*/ sf1, + /*.sf2 =*/ sf2, + /*.sf3 =*/ sf3, + /*.poffs =*/ poffs, }; auto pipeline = ggml_metal_library_get_pipeline_upscale(lib, op); diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h index f3e38c7aa9..019f2fec9e 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.h +++ b/ggml/src/ggml-metal/ggml-metal-ops.h @@ -58,6 +58,7 @@ int ggml_metal_op_soft_max (ggml_metal_op_t ctx, int idx); int ggml_metal_op_ssm_conv (ggml_metal_op_t ctx, int idx); int ggml_metal_op_ssm_scan (ggml_metal_op_t ctx, int idx); int ggml_metal_op_rwkv (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_gated_delta_net (ggml_metal_op_t ctx, int idx); int ggml_metal_op_solve_tri (ggml_metal_op_t ctx, int idx); int ggml_metal_op_set (ggml_metal_op_t ctx, int idx); int ggml_metal_op_cpy (ggml_metal_op_t ctx, int idx); diff --git a/ggml/src/ggml-metal/ggml-metal.cpp b/ggml/src/ggml-metal/ggml-metal.cpp index 1c705362fb..9382ce53b3 100644 --- a/ggml/src/ggml-metal/ggml-metal.cpp +++ b/ggml/src/ggml-metal/ggml-metal.cpp @@ -14,7 +14,7 @@ #define GGML_METAL_MAX_DEVICES 16 // number of Metal devices -// note: can be overriden with GGML_METAL_DEVICES env to simulate virtual devices +// note: can be overridden with GGML_METAL_DEVICES env to simulate virtual devices static int g_devices = 1; //////////////////////////////////////////////////////////////////////////////// diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index 6c349aa0c9..b2328605dd 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -1111,6 +1111,7 @@ template [[host_name("kernel_unary_f16_f16_4")]] kernel kernel_unary_t kernel_un constant short FC_bin_op [[function_constant(FC_BIN + 0)]]; constant short FC_bin_f [[function_constant(FC_BIN + 1)]]; constant bool FC_bin_rb [[function_constant(FC_BIN + 2)]]; +constant bool FC_bin_cb [[function_constant(FC_BIN + 3)]]; template kernel void kernel_bin_fuse_impl( @@ -1124,11 +1125,12 @@ kernel void kernel_bin_fuse_impl( #define FC_OP FC_bin_op #define FC_F FC_bin_f #define FC_RB FC_bin_rb +#define FC_CB FC_bin_cb if (FC_RB) { // row broadcast - const uint i0 = tgpig.x; - const uint i1 = i0%args.ne10; + const uint i0 = tgpig.y*args.ne00 + tgpig.x; + const uint i1 = FC_CB ? tgpig.x%args.ne10 : tgpig.x; device const T0 * src0_row = (device const T0 *) (src0); device T * dst_row = (device T *) (dst); @@ -1200,7 +1202,7 @@ kernel void kernel_bin_fuse_impl( device const T1 * src1_ptr = (device const T1 *) (src1 + args.o1[0] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11); for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { - const int i10 = i0%args.ne10; + const int i10 = FC_CB ? i0%args.ne10 : i0; if (FC_OP == 0) { dst_ptr[i0] = src0_ptr[i0] + src1_ptr[i10]; @@ -1225,7 +1227,7 @@ kernel void kernel_bin_fuse_impl( } for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { - const int i10 = i0%args.ne10; + const int i10 = FC_CB ? i0%args.ne10 : i0; T res = src0_ptr[i0]; @@ -1261,6 +1263,7 @@ kernel void kernel_bin_fuse_impl( #undef FC_OP #undef FC_F #undef FC_RB +#undef FC_CB } typedef decltype(kernel_bin_fuse_impl) kernel_bin_fuse_t; @@ -2434,6 +2437,228 @@ kernel void kernel_rwkv_wkv7_f32( } } +constant short FC_gated_delta_net_ne20 [[function_constant(FC_GATED_DELTA_NET + 0)]]; +constant short FC_gated_delta_net_ne30 [[function_constant(FC_GATED_DELTA_NET + 1)]]; + +#if 1 +template +kernel void kernel_gated_delta_net_impl( + constant ggml_metal_kargs_gated_delta_net & args, + device const char * q, + device const char * k, + device const char * v, + device const char * g, + device const char * b, + device const char * s, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { +#define S_v FC_gated_delta_net_ne20 +#define G FC_gated_delta_net_ne30 + + const uint tx = tpitg.x; + const uint ty = tpitg.y; + + const uint i23 = tgpig.z; // B + const uint i21 = tgpig.y; // H + const uint i20 = tgpig.x*NSG + ty; + + const uint i01 = i21 % args.ne01; + const uint i11 = i21 % args.ne11; + + const float scale = 1.0f / sqrt((float)S_v); + + // state is stored transposed: M[i20][is] = S[is][i20], so row i20 is contiguous + device const float * s_ptr = (device const float *) (s) + (i23*args.ne21 + i21)*S_v*S_v + i20*S_v; + + float ls[NSG]; + + FOR_UNROLL (short j = 0; j < NSG; j++) { + const short is = tx*NSG + j; + ls[j] = s_ptr[is]; + } + + device float * dst_attn = (device float *) (dst) + (i23*args.ne22*args.ne21 + i21)*S_v + i20; + + device const float * q_ptr = (device const float *) (q + i23*args.nb03 + i01*args.nb01); + device const float * k_ptr = (device const float *) (k + i23*args.nb13 + i11*args.nb11); + device const float * v_ptr = (device const float *) (v + i23*args.nb23 + i21*args.nb21); + + device const float * b_ptr = (device const float *) (b) + (i23*args.ne22*args.ne21 + i21); + device const float * g_ptr = (device const float *) (g) + (i23*args.ne22*args.ne21 + i21)*G; + + for (short t = 0; t < args.ne22; t++) { + float s_k = 0.0f; + + if (G == 1) { + const float g_exp = exp(g_ptr[0]); + + FOR_UNROLL (short j = 0; j < NSG; j++) { + const short is = tx*NSG + j; + ls[j] *= g_exp; + + s_k += ls[j]*k_ptr[is]; + } + } else { + // KDA + FOR_UNROLL (short j = 0; j < NSG; j++) { + const short is = tx*NSG + j; + ls[j] *= exp(g_ptr[is]); + + s_k += ls[j]*k_ptr[is]; + } + } + + s_k = simd_sum(s_k); + + const float d = (v_ptr[i20] - s_k)*b_ptr[0]; + + float y = 0.0f; + + FOR_UNROLL (short j = 0; j < NSG; j++) { + const short is = tx*NSG + j; + ls[j] += k_ptr[is]*d; + + y += ls[j]*q_ptr[is]; + } + + y = simd_sum(y); + + if (tx == 0) { + dst_attn[t*args.ne21*S_v] = y*scale; + } + + q_ptr += args.ns02; + k_ptr += args.ns12; + v_ptr += args.ns22; + + b_ptr += args.ne21; + g_ptr += args.ne21*G; + } + + device float * dst_state = (device float *) (dst) + args.ne23*args.ne22*args.ne21*S_v + (i23*args.ne21 + i21)*S_v*S_v + i20*S_v; + + FOR_UNROLL (short j = 0; j < NSG; j++) { + const short is = tx*NSG + j; + dst_state[is] = ls[j]; + } + +#undef S_v +#undef G +} + +typedef decltype(kernel_gated_delta_net_impl<4>) kernel_gated_delta_net_t; + +template [[host_name("kernel_gated_delta_net_f32_1")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<1>; +template [[host_name("kernel_gated_delta_net_f32_2")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<2>; +template [[host_name("kernel_gated_delta_net_f32_4")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<4>; + +#else +// a simplified version of the above +// no performance improvement, so keep the above version for now + +template +kernel void kernel_gated_delta_net_impl( + constant ggml_metal_kargs_gated_delta_net & args, + device const char * q, + device const char * k, + device const char * v, + device const char * g, + device const char * b, + device const char * s, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { +#define S_v FC_gated_delta_net_ne20 +#define G FC_gated_delta_net_ne30 + + const uint tx = tpitg.x; + const uint ty = tpitg.y; + + const uint i23 = tgpig.z; // B + const uint i21 = tgpig.y; // H + const uint i20 = tgpig.x*NSG + ty; + + const uint i01 = i21 % args.ne01; + const uint i11 = i21 % args.ne11; + + const float scale = 1.0f / sqrt((float)S_v); + + device const float * s_ptr = (device const float *) (s) + (i23*args.ne21 + i21)*S_v*S_v + i20; + + float lsf[NSG]; + + FOR_UNROLL (short j = 0; j < NSG; j++) { + const short is = tx*NSG + j; + lsf[j] = s_ptr[is*S_v]; + } + + thread T * ls = (thread T *) (lsf); + + device float * dst_attn = (device float *) (dst) + (i23*args.ne22*args.ne21 + i21)*S_v + i20; + + device const float * q_ptr = (device const float *) (q + i23*args.nb03 + i01*args.nb01); + device const float * k_ptr = (device const float *) (k + i23*args.nb13 + i11*args.nb11); + device const float * v_ptr = (device const float *) (v + i23*args.nb23 + i21*args.nb21); + + device const float * b_ptr = (device const float *) (b) + (i23*args.ne22*args.ne21 + i21); + device const float * g_ptr = (device const float *) (g) + (i23*args.ne22*args.ne21 + i21)*G; + + for (short t = 0; t < args.ne22; t++) { + device const T * qt_ptr = (device const T *) (q_ptr); + device const T * kt_ptr = (device const T *) (k_ptr); + device const T * gt_ptr = (device const T *) (g_ptr); + + if (G == 1) { + *ls *= exp(g_ptr[0]); + } else { + // KDA + *ls *= exp(gt_ptr[tx]); + } + + const float s_k = simd_sum(dot(*ls, kt_ptr[tx])); + + const float d = (v_ptr[i20] - s_k)*b_ptr[0]; + + *ls += kt_ptr[tx]*d; + + const float y = simd_sum(dot(*ls, qt_ptr[tx])); + + if (tx == 0) { + *dst_attn = y*scale; + } + + q_ptr += args.ns02; + k_ptr += args.ns12; + v_ptr += args.ns22; + + b_ptr += args.ne21; + g_ptr += args.ne21*G; + + dst_attn += args.ne21*S_v; + } + + device float * dst_state = (device float *) (dst) + args.ne23*args.ne22*args.ne21*S_v + (i23*args.ne21 + i21)*S_v*S_v + i20; + device T * dstt_state = (device T *) (dst_state); + + FOR_UNROLL (short j = 0; j < NSG; j++) { + const short is = tx*NSG + j; + dst_state[is*S_v] = lsf[j]; + } + +#undef S_v +#undef G +} + +typedef decltype(kernel_gated_delta_net_impl) kernel_gated_delta_net_t; + +template [[host_name("kernel_gated_delta_net_f32_1")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl; +template [[host_name("kernel_gated_delta_net_f32_2")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl; +template [[host_name("kernel_gated_delta_net_f32_4")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl; +#endif + constant short FC_solve_tri_nsg [[function_constant(FC_SOLVE_TRI + 0)]]; constant short FC_solve_tri_n [[function_constant(FC_SOLVE_TRI + 1)]]; constant short FC_solve_tri_k [[function_constant(FC_SOLVE_TRI + 2)]]; @@ -2782,7 +3007,7 @@ kernel void kernel_l2_norm_impl( sumf = shmem_f32[tiisg]; sumf = simd_sum(sumf); - const float scale = 1.0f/sqrt(max(sumf, args.eps)); + const float scale = 1.0f/max(sqrt(sumf), args.eps); for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) { y[i00] = x[i00] * scale; @@ -3481,6 +3706,13 @@ template [[host_name("kernel_mul_mv_ext_f16_f32_r1_3")]] kernel mul_mv_ext_q4 template [[host_name("kernel_mul_mv_ext_f16_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, half4, 4, dequantize_f16_t4>; template [[host_name("kernel_mul_mv_ext_f16_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, half4, 4, dequantize_f16_t4>; +#if defined(GGML_METAL_HAS_BF16) +template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, bfloat4, 4, dequantize_bf16_t4>; +template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, bfloat4, 4, dequantize_bf16_t4>; +template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, bfloat4, 4, dequantize_bf16_t4>; +template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, bfloat4, 4, dequantize_bf16_t4>; +#endif + template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_q4_0, 32, dequantize_q4_0_t4>; template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_q4_0, 32, dequantize_q4_0_t4>; template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q4_0, 32, dequantize_q4_0_t4>; @@ -3531,6 +3763,16 @@ template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_3")]] kernel mul_mv_ext_q4x4 template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q6_K, 256, dequantize_q6_K>; template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q6_K, 256, dequantize_q6_K>; +template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_2")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<2, block_q2_K, 256, dequantize_q2_K>; +template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_3")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<3, block_q2_K, 256, dequantize_q2_K>; +template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q2_K, 256, dequantize_q2_K>; +template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q2_K, 256, dequantize_q2_K>; + +template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_2")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<2, block_q3_K, 256, dequantize_q3_K>; +template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_3")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<3, block_q3_K, 256, dequantize_q3_K>; +template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q3_K, 256, dequantize_q3_K>; +template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q3_K, 256, dequantize_q3_K>; + template void kernel_mul_mv_t_t_impl( args_t args, @@ -4218,7 +4460,7 @@ kernel void kernel_im2col( template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col; template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col; -// TODO: obolete -- remove +// TODO: obsolete -- remove //typedef void (im2col_ext_t)( // constant ggml_metal_kargs_im2col & args, // device const float * x, @@ -4530,7 +4772,9 @@ kernel void kernel_conv_transpose_2d( uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]); -kernel void kernel_upscale_f32( +constant bool FC_upscale_aa [[function_constant(FC_UPSCALE + 0)]]; + +kernel void kernel_upscale_nearest_f32( constant ggml_metal_kargs_upscale & args, device const char * src0, device char * dst, @@ -4556,6 +4800,156 @@ kernel void kernel_upscale_f32( } } +static inline float bilinear_tri(float x) { + return MAX(0.0f, 1.0f - fabs(x)); +} + +kernel void kernel_upscale_bilinear_f32( + constant ggml_metal_kargs_upscale & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; + + const int64_t i03 = i3 / args.sf3; + const int64_t i02 = i2 / args.sf2; + + const float f01 = ((float)i1 + args.poffs) / args.sf1 - args.poffs; + const int64_t i01 = MAX(0, MIN(args.ne01 - 1, (int64_t)floor(f01))); + const int64_t i01p = MAX(0, MIN(args.ne01 - 1, i01 + 1)); + const float fd1 = MAX(0.0f, MIN(1.0f, f01 - (float)i01)); + + src0 += i03*args.nb03 + i02*args.nb02; + + device float * dst_ptr = (device float *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1); + + if (FC_upscale_aa) { + const float support0 = MAX(1.0f, 1.0f / args.sf0); + const float invscale0 = 1.0f / support0; + const float support1 = MAX(1.0f, 1.0f / args.sf1); + const float invscale1 = 1.0f / support1; + + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs; + + int64_t x_min = MAX((int64_t)0, (int64_t)floor(f00 - support0 + args.poffs)); + int64_t x_max = MIN(args.ne00, (int64_t)ceil (f00 + support0 + args.poffs)); + + int64_t y_min = MAX((int64_t)0, (int64_t)floor(f01 - support1 + args.poffs)); + int64_t y_max = MIN(args.ne01, (int64_t)ceil (f01 + support1 + args.poffs)); + + float sum = 0.0f; + float wsum = 0.0f; + + for (int64_t sy = y_min; sy < y_max; ++sy) { + const float wy = MAX(0.0f, 1.0f - fabs((float)sy - f01) * invscale1); + for (int64_t sx = x_min; sx < x_max; ++sx) { + const float wx = MAX(0.0f, 1.0f - fabs((float)sx - f00) * invscale0); + const float w = wx * wy; + const device const float * src_ptr = (device const float *)(src0 + sy*args.nb01 + sx*args.nb00); + sum += (*src_ptr) * w; + wsum += w; + } + } + + const float v = (wsum > 0.0f) ? (sum / wsum) : 0.0f; + dst_ptr[i0] = v; + } + } else { + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs; + const int64_t i00 = MAX(0, MIN(args.ne00 - 1, (int64_t)floor(f00))); + const int64_t i00p = MAX(0, MIN(args.ne00 - 1, i00 + 1)); + const float fd0 = MAX(0.0f, MIN(1.0f, f00 - (float)i00)); + + device const float * src00 = (device const float *)(src0 + i01*args.nb01 + i00*args.nb00); + device const float * src10 = (device const float *)(src0 + i01*args.nb01 + i00p*args.nb00); + device const float * src01 = (device const float *)(src0 + i01p*args.nb01 + i00*args.nb00); + device const float * src11 = (device const float *)(src0 + i01p*args.nb01 + i00p*args.nb00); + + const float v = + (*src00) * (1.0f - fd0) * (1.0f - fd1) + + (*src10) * fd0 * (1.0f - fd1) + + (*src01) * (1.0f - fd0) * fd1 + + (*src11) * fd0 * fd1; + + dst_ptr[i0] = v; + } + } +} + +static inline float bicubic_weight1(float x) { + const float a = -0.75f; + return ((a + 2) * x - (a + 3)) * x * x + 1; +} + +static inline float bicubic_weight2(float x) { + const float a = -0.75f; + return ((a * x - 5 * a) * x + 8 * a) * x - 4 * a; +} + +kernel void kernel_upscale_bicubic_f32( + constant ggml_metal_kargs_upscale & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; + + const int64_t i03 = i3 / args.sf3; + const int64_t i02 = i2 / args.sf2; + + const float f01 = ((float)i1 + args.poffs) / args.sf1 - args.poffs; + const int64_t i01 = (int64_t)floor(f01); + const float fd1 = f01 - (float)i01; + + const float w_y0 = bicubic_weight2(fd1 + 1.0f); + const float w_y1 = bicubic_weight1(fd1); + const float w_y2 = bicubic_weight1(1.0f - fd1); + const float w_y3 = bicubic_weight2(2.0f - fd1); + + const device const char * src_slice = src0 + i03 * args.nb03 + i02 * args.nb02; + + device float * dst_ptr = (device float *)(dst + i3 * args.nb3 + i2 * args.nb2 + i1 * args.nb1); + + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs; + const int64_t i00 = (int64_t)floor(f00); + const float fd0 = f00 - (float)i00; + + const float w_x0 = bicubic_weight2(fd0 + 1.0f); + const float w_x1 = bicubic_weight1(fd0); + const float w_x2 = bicubic_weight1(1.0f - fd0); + const float w_x3 = bicubic_weight2(2.0f - fd0); + + float sum = 0.0f; + + for (int dy = -1; dy <= 2; ++dy) { + const int64_t iy = MAX(0, MIN(args.ne01 - 1, i01 + dy)); + const float wy = (dy == -1) ? w_y0 : (dy == 0) ? w_y1 : (dy == 1) ? w_y2 : w_y3; + + for (int dx = -1; dx <= 2; ++dx) { + const int64_t ix = MAX(0, MIN(args.ne00 - 1, i00 + dx)); + const float wx = (dx == -1) ? w_x0 : (dx == 0) ? w_x1 : (dx == 1) ? w_x2 : w_x3; + + const device const float * src_ptr = (device const float *)(src_slice + iy * args.nb01 + ix * args.nb00); + sum += (*src_ptr) * wx * wy; + } + } + + dst_ptr[i0] = sum; + } +} + kernel void kernel_pad_f32( constant ggml_metal_kargs_pad & args, device const char * src0, @@ -5782,6 +6176,7 @@ template [[host_name("kernel_flash_attn_ext_f32_dk128_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_f32_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f32_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f32_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f32_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f32_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f16_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5796,6 +6191,7 @@ template [[host_name("kernel_flash_attn_ext_f16_dk128_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_f16_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f16_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f16_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_f16_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; #if defined(GGML_METAL_HAS_BF16) @@ -5811,6 +6207,7 @@ template [[host_name("kernel_flash_attn_ext_bf16_dk128_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_bf16_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_bf16_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_bf16_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_bf16_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; #endif @@ -5826,6 +6223,7 @@ template [[host_name("kernel_flash_attn_ext_q4_0_dk128_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_q4_0_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_0_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_0_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_0_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_1_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5840,6 +6238,7 @@ template [[host_name("kernel_flash_attn_ext_q4_1_dk128_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_q4_1_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_1_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_1_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q4_1_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_0_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5854,6 +6253,7 @@ template [[host_name("kernel_flash_attn_ext_q5_0_dk128_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_q5_0_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_0_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_0_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_0_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_1_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5868,6 +6268,7 @@ template [[host_name("kernel_flash_attn_ext_q5_1_dk128_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_q5_1_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_1_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_1_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q5_1_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q8_0_dk32_dv32" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; @@ -5882,6 +6283,7 @@ template [[host_name("kernel_flash_attn_ext_q8_0_dk128_dv128")]] kernel flash_at template [[host_name("kernel_flash_attn_ext_q8_0_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q8_0_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q8_0_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; template [[host_name("kernel_flash_attn_ext_q8_0_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext; #undef FA_TYPES @@ -6452,6 +6854,17 @@ template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk256_dv256")]] kernel flas template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk256_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f32_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_f16_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#if defined(GGML_METAL_HAS_BF16) +template [[host_name("kernel_flash_attn_ext_vec_bf16_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#endif +template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; + template [[host_name("kernel_flash_attn_ext_vec_f32_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; template [[host_name("kernel_flash_attn_ext_vec_f16_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; #if defined(GGML_METAL_HAS_BF16) @@ -8912,6 +9325,7 @@ template [[host_name("kernel_mul_mm_id_map0_ne20_6" )]] kernel kernel_mul_mm_id_ template [[host_name("kernel_mul_mm_id_map0_ne20_8" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<8>; template [[host_name("kernel_mul_mm_id_map0_ne20_10")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<10>; template [[host_name("kernel_mul_mm_id_map0_ne20_16")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<16>; +template [[host_name("kernel_mul_mm_id_map0_ne20_22")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<22>; template kernel void kernel_mul_mm_id( diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index f389193691..1f8250934b 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -63,6 +63,7 @@ set(GGML_OPENCL_KERNELS cpy cvt diag_mask_inf + diag div gelu gemv_noshuffle_general @@ -108,10 +109,14 @@ set(GGML_OPENCL_KERNELS mul_mm_q8_0_f32_l4_lm mul_mm_q6_k_f32_l4_lm mul_mm_q8_0_f32_8x4 + gemv_noshuffle_q4_1_f32 + gemm_noshuffle_q4_1_f32 gemv_noshuffle_general_q8_0_f32 mul + neg norm relu + l2_norm rms_norm rope scale @@ -127,11 +132,13 @@ set(GGML_OPENCL_KERNELS ssm_conv sub sum_rows + cumsum transpose concat tsembd upscale tanh + exp expm1 softplus pad diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 3da022ed86..e1dca6b4b4 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -313,7 +313,7 @@ struct ProfilingInfo { cl_ulong cmd_duration_ns; // The time for the kernel to complete - COMPLETE - END cl_ulong cmd_complete_duration_ns; - // Total time to finish the kernel - COMPELTE - QUEUED + // Total time to finish the kernel - COMPLETE - QUEUED cl_ulong cmd_total_duration_ns; // Global and local work sizes. size_t global_size[3]; @@ -416,7 +416,6 @@ struct ggml_backend_opencl_context { cl_program program_add; cl_program program_add_id; cl_program program_clamp; - cl_program program_cpy; cl_program program_cvt; cl_program program_diag_mask_inf; cl_program program_gelu; @@ -498,8 +497,10 @@ struct ggml_backend_opencl_context { kernel_geglu_f16, kernel_reglu_f16, kernel_swiglu_f16, kernel_geglu_erf_f16, kernel_geglu_quick_f16; cl_kernel kernel_norm, kernel_norm_mul_add; cl_kernel kernel_rms_norm, kernel_rms_norm_mul; + cl_kernel kernel_l2_norm_f32; cl_kernel kernel_group_norm, kernel_group_norm_mul_add; cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8; + cl_kernel kernel_diag_f32; cl_kernel kernel_soft_max, kernel_soft_max_4; cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16; std::map, cl_kernel> kernels_flash_attn_f16; @@ -514,7 +515,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_set_rows_f32_i64, kernel_set_rows_f32_i32, kernel_set_rows_f16_i64, kernel_set_rows_f16_i32; cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16; cl_kernel kernel_rope_multi_f32, kernel_rope_multi_f16, kernel_rope_vision_f32, kernel_rope_vision_f16; - cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32; + cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32, kernel_cpy_i32_i32; cl_kernel kernel_mul_mat_f32_f32; cl_kernel kernel_mul_mat_f16_f16; cl_kernel kernel_mul_mat_f16_f32_1row; @@ -531,6 +532,8 @@ struct ggml_backend_opencl_context { cl_kernel kernel_mul_mat_q4_0_f32_8x_flat; cl_kernel kernel_convert_block_q4_0_noshuffle; cl_kernel kernel_restore_block_q4_0_noshuffle; + cl_kernel kernel_convert_block_q4_1_noshuffle; + cl_kernel kernel_restore_block_q4_1_noshuffle; cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K; cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat; cl_kernel kernel_mul_mv_q4_1_f32; @@ -544,10 +547,15 @@ struct ggml_backend_opencl_context { cl_kernel kernel_im2col_f32, kernel_im2col_f16; cl_kernel kernel_argsort_f32_i32; cl_kernel kernel_sum_rows_f32, kernel_sum_rows_f32_4; + cl_kernel kernel_cumsum_blk, kernel_cumsum_add; cl_kernel kernel_repeat_f32; cl_kernel kernel_pad; cl_kernel kernel_tanh_f32, kernel_tanh_f32_4, kernel_tanh_f32_nc; cl_kernel kernel_tanh_f16, kernel_tanh_f16_4, kernel_tanh_f16_nc; + cl_kernel kernel_neg_f32, kernel_neg_f32_4, kernel_neg_f32_nc; + cl_kernel kernel_neg_f16, kernel_neg_f16_4, kernel_neg_f16_nc; + cl_kernel kernel_exp_f32, kernel_exp_f32_4, kernel_exp_f32_nc; + cl_kernel kernel_exp_f16, kernel_exp_f16_4, kernel_exp_f16_nc; cl_kernel kernel_expm1_f32, kernel_expm1_f32_4, kernel_expm1_f32_nc; cl_kernel kernel_expm1_f16, kernel_expm1_f16_4, kernel_expm1_f16_nc; cl_kernel kernel_softplus_f32, kernel_softplus_f32_4, kernel_softplus_f32_nc; @@ -683,7 +691,9 @@ struct ggml_backend_opencl_context { cl_kernel kernel_transpose_32; cl_kernel kernel_transpose_32_16; cl_kernel kernel_transpose_16; + cl_kernel kernel_transpose_8_buf; cl_kernel kernel_transpose_16_buf; + cl_kernel kernel_transpose_32_buf; cl_kernel kernel_transpose_16_4x1; // Gemm and Gemv related programs, kernels, etc @@ -699,6 +709,8 @@ struct ggml_backend_opencl_context { cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096; cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096; cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096; + cl_kernel kernel_gemv_noshuffle_q4_1_f32; + cl_kernel kernel_gemm_noshuffle_q4_1_f32; cl_kernel kernel_mul_mm_q8_0_f32_8x4; cl_kernel CL_mul_mat_vec_q8_0_f32; #endif // GGML_OPENCL_USE_ADRENO_KERNELS @@ -867,13 +879,14 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve #else const std::string kernel_src = read_file("cpy.cl"); #endif - backend_ctx->program_cpy = + cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); - CL_CHECK((backend_ctx->kernel_cpy_f16_f16 = clCreateKernel(backend_ctx->program_cpy, "kernel_cpy_f16_f16", &err), err)); - CL_CHECK((backend_ctx->kernel_cpy_f16_f32 = clCreateKernel(backend_ctx->program_cpy, "kernel_cpy_f16_f32", &err), err)); - CL_CHECK((backend_ctx->kernel_cpy_f32_f16 = clCreateKernel(backend_ctx->program_cpy, "kernel_cpy_f32_f16", &err), err)); - CL_CHECK((backend_ctx->kernel_cpy_f32_f32 = clCreateKernel(backend_ctx->program_cpy, "kernel_cpy_f32_f32", &err), err)); + CL_CHECK((backend_ctx->kernel_cpy_f16_f16 = clCreateKernel(prog, "kernel_cpy_f16_f16", &err), err)); + CL_CHECK((backend_ctx->kernel_cpy_f16_f32 = clCreateKernel(prog, "kernel_cpy_f16_f32", &err), err)); + CL_CHECK((backend_ctx->kernel_cpy_f32_f16 = clCreateKernel(prog, "kernel_cpy_f32_f16", &err), err)); + CL_CHECK((backend_ctx->kernel_cpy_f32_f32 = clCreateKernel(prog, "kernel_cpy_f32_f32", &err), err)); + CL_CHECK((backend_ctx->kernel_cpy_i32_i32 = clCreateKernel(prog, "kernel_cpy_i32_i32", &err), err)); GGML_LOG_CONT("."); } @@ -893,6 +906,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve CL_CHECK((backend_ctx->kernel_restore_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0_noshuffle", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err)); + CL_CHECK((backend_ctx->kernel_convert_block_q4_1_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1_noshuffle", &err), err)); + CL_CHECK((backend_ctx->kernel_restore_block_q4_1_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1_noshuffle", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_q4_1 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q4_1 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err)); @@ -924,6 +939,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve GGML_LOG_CONT("."); } + // diag + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "diag.cl.h" + }; +#else + const std::string kernel_src = read_file("diag.cl"); +#endif + cl_program prog = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + + CL_CHECK((backend_ctx->kernel_diag_f32 = clCreateKernel(prog, "kernel_diag_f32", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + // gelu { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -1555,6 +1587,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve GGML_LOG_CONT("."); } + // l2_norm + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "l2_norm.cl.h" + }; +#else + const std::string kernel_src = read_file("l2_norm.cl"); +#endif + cl_program prog = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + + CL_CHECK((backend_ctx->kernel_l2_norm_f32 = clCreateKernel(prog, "kernel_l2_norm_f32", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + // rope { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -1879,6 +1928,24 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve GGML_LOG_CONT("."); } + // cumsum + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "cumsum.cl.h" + }; +#else + const std::string kernel_src = read_file("cumsum.cl"); +#endif + cl_program prog; + prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + + CL_CHECK((backend_ctx->kernel_cumsum_blk = clCreateKernel(prog, "kernel_cumsum_blk", &err), err)); + CL_CHECK((backend_ctx->kernel_cumsum_add = clCreateKernel(prog, "kernel_cumsum_add", &err), err)); + GGML_LOG_CONT("."); + CL_CHECK(clReleaseProgram(prog)); + } + // sigmoid { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -1971,6 +2038,48 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve GGML_LOG_CONT("."); } + // neg + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "neg.cl.h" + }; +#else + const std::string kernel_src = read_file("neg.cl"); +#endif + cl_program prog = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_neg_f32 = clCreateKernel(prog, "kernel_neg_f32", &err), err)); + CL_CHECK((backend_ctx->kernel_neg_f32_4 = clCreateKernel(prog, "kernel_neg_f32_4", &err), err)); + CL_CHECK((backend_ctx->kernel_neg_f32_nc = clCreateKernel(prog, "kernel_neg_f32_nc", &err), err)); + CL_CHECK((backend_ctx->kernel_neg_f16 = clCreateKernel(prog, "kernel_neg_f16", &err), err)); + CL_CHECK((backend_ctx->kernel_neg_f16_4 = clCreateKernel(prog, "kernel_neg_f16_4", &err), err)); + CL_CHECK((backend_ctx->kernel_neg_f16_nc = clCreateKernel(prog, "kernel_neg_f16_nc", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + + // exp + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "exp.cl.h" + }; +#else + const std::string kernel_src = read_file("exp.cl"); +#endif + cl_program prog = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_exp_f32 = clCreateKernel(prog, "kernel_exp_f32", &err), err)); + CL_CHECK((backend_ctx->kernel_exp_f32_4 = clCreateKernel(prog, "kernel_exp_f32_4", &err), err)); + CL_CHECK((backend_ctx->kernel_exp_f32_nc = clCreateKernel(prog, "kernel_exp_f32_nc", &err), err)); + CL_CHECK((backend_ctx->kernel_exp_f16 = clCreateKernel(prog, "kernel_exp_f16", &err), err)); + CL_CHECK((backend_ctx->kernel_exp_f16_4 = clCreateKernel(prog, "kernel_exp_f16_4", &err), err)); + CL_CHECK((backend_ctx->kernel_exp_f16_nc = clCreateKernel(prog, "kernel_exp_f16_nc", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + // expm1 { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -2258,7 +2367,9 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve CL_CHECK((backend_ctx->kernel_transpose_32_16 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32_16", &err), err)); CL_CHECK((backend_ctx->kernel_transpose_32 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32", &err), err)); CL_CHECK((backend_ctx->kernel_transpose_16 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16", &err), err)); + CL_CHECK((backend_ctx->kernel_transpose_8_buf = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_8_buf", &err), err)); CL_CHECK((backend_ctx->kernel_transpose_16_buf = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16_buf", &err), err)); + CL_CHECK((backend_ctx->kernel_transpose_32_buf = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32_buf", &err), err)); CL_CHECK((backend_ctx->kernel_transpose_16_4x1 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16_4x1", &err), err)); GGML_LOG_CONT("."); } @@ -2378,6 +2489,45 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve GGML_LOG_CONT("."); } + // gemm_noshuffle_q4_1_f32 + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "gemm_noshuffle_q4_1_f32.cl.h" + }; +#else + const std::string kernel_src = read_file("gemm_noshuffle_q4_1_f32.cl"); +#endif + cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_1_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_1_f32", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + + // gemv_noshuffle_q4_1_f32 + { + std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std + + " -cl-mad-enable "; + if (backend_ctx->has_vector_subgroup_broadcast) { + CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT "; + } + +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "gemv_noshuffle_q4_1_f32.cl.h" + }; +#else + const std::string kernel_src = read_file("gemv_noshuffle_q4_1_f32.cl"); +#endif + + cl_program prog = build_program_from_source( + backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + + CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_1_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_1_f32", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + // mul_mm_q8_0_f32_8x4 { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -2413,7 +2563,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve cl_program prog = build_program_from_source( backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); - CL_CHECK((backend_ctx->CL_mul_mat_vec_q8_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle", &err), err)); + CL_CHECK((backend_ctx->CL_mul_mat_vec_q8_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q8_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); } @@ -2506,7 +2656,7 @@ static std::vector ggml_opencl_probe_devices(ggml_backend_r cl_platform_id platform_ids[NPLAT]; if (clGetPlatformIDs(NPLAT, platform_ids, &n_platforms) != CL_SUCCESS) { - GGML_LOG_ERROR("ggml_opencl: plaform IDs not available.\n"); + GGML_LOG_ERROR("ggml_opencl: platform IDs not available.\n"); return found_devices; } @@ -2923,6 +3073,82 @@ static void ggml_cl2_free(ggml_backend_t backend) { } } +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS +static void transpose_2d( + ggml_backend_opencl_context * backend_ctx, + cl_kernel kernel, + cl_mem src, cl_mem dst, size_t size, + cl_int stride, cl_int rows, + bool blocking = true +) { + static ggml_cl_buffer buf; + + cl_event evt; + cl_int err; + + buf.allocate(backend_ctx->context, size); + + cl_mem trans; + cl_buffer_region region; + + region.origin = 0; + region.size = size; + CL_CHECK((trans = clCreateSubBuffer( + buf.buffer, CL_MEM_READ_WRITE, + CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err), err)); + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &src)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &trans)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_int), &stride)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_int), &rows)); + + size_t local_size[3] = {64, 1, 1}; + size_t global_size[3] = {(size_t)stride, (size_t)rows, 1};; + CL_CHECK(clEnqueueNDRangeKernel(backend_ctx->queue, kernel, 3, NULL, + global_size, local_size, 0, NULL, NULL)); + + if (blocking) { + CL_CHECK(clEnqueueCopyBuffer(backend_ctx->queue, trans, dst, 0, 0, size, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + CL_CHECK(clReleaseEvent(evt)); + } else { + CL_CHECK(clEnqueueCopyBuffer(backend_ctx->queue, trans, dst, 0, 0, size, 0, NULL, NULL)); + } + + CL_CHECK(clReleaseMemObject(trans)); +} + +static void transpose_2d_as_8b( + ggml_backend_opencl_context * backend_ctx, + cl_mem src, cl_mem dst, size_t size, + cl_int stride, cl_int rows, + bool blocking = true +) { + transpose_2d(backend_ctx, backend_ctx->kernel_transpose_8_buf, + src, dst, size, stride, rows, blocking); +} + +static void transpose_2d_as_16b( + ggml_backend_opencl_context * backend_ctx, + cl_mem src, cl_mem dst, size_t size, + cl_int stride, cl_int rows, + bool blocking = true +) { + transpose_2d(backend_ctx, backend_ctx->kernel_transpose_16_buf, + src, dst, size, stride, rows, blocking); +} + +static void transpose_2d_as_32b( + ggml_backend_opencl_context * backend_ctx, + cl_mem src, cl_mem dst, size_t size, + cl_int stride, cl_int rows, + bool blocking = true +) { + transpose_2d(backend_ctx, backend_ctx->kernel_transpose_32_buf, + src, dst, size, stride, rows, blocking); +} +#endif // GGML_OPENCL_USE_ADRENO_KERNELS + //------------------------------------------------------------------------------ // Tensor extra management //------------------------------------------------------------------------------ @@ -3214,7 +3440,7 @@ static void ggml_backend_opencl_synchronize(ggml_backend_t backend) { CL_CHECK(clReleaseEvent(evt)); } -// Syncronizes the 'backend_ctx's device with others so that commands +// Synchronizes the 'backend_ctx's device with others so that commands // enqueued to it won't start until commands in the other devices have // completed. static void sync_with_other_backends(ggml_backend_opencl_context * backend_ctx) { @@ -3419,9 +3645,21 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te default: return false; } + case GGML_TYPE_I32: + switch (op->type) { + case GGML_TYPE_I32: + return true; + default: + return false; + } default: return false; } + case GGML_OP_SET: { + return (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_I32) && + op->type == op->src[0]->type && + op->type == op->src[1]->type; + } case GGML_OP_SCALE: return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]); case GGML_OP_ADD: @@ -3455,6 +3693,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te case GGML_UNARY_OP_SIGMOID: return ggml_is_contiguous(op->src[0]); case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_EXP: return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16; case GGML_UNARY_OP_EXPM1: return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16; @@ -3486,6 +3726,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te return true; case GGML_OP_RMS_NORM: return op->ne[0] % 4 == 0 && ggml_is_contiguous_rows(op->src[0]); + case GGML_OP_L2_NORM: + return ggml_is_contiguous_rows(op->src[0]); case GGML_OP_REPEAT: return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; // Assuming F32 for now, can be expanded case GGML_OP_PAD: @@ -3540,6 +3782,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: return true; + case GGML_OP_DIAG: + return true; case GGML_OP_DIAG_MASK_INF: return op->ne[3] == 1; case GGML_OP_ROPE: { @@ -3578,6 +3822,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te return cols <= max_workgroup_size && op->src[0]->type == GGML_TYPE_F32; } case GGML_OP_SUM_ROWS: + case GGML_OP_CUMSUM: + return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]); case GGML_OP_MEAN: return op->src[0]->type == GGML_TYPE_F32; case GGML_OP_FLASH_ATTN_EXT: @@ -3860,7 +4106,7 @@ struct ggml_backend_opencl_buffer_context { // The buffer_context is initially created by ggml_backend_buft_alloc_buffer // before any tensor is initialized (at the beginning of alloc_tensor_range). - // Hence, there is alway a buffer object in this vector. When each tensor is + // Hence, there is always a buffer object in this vector. When each tensor is // being initialized, this original buffer object will be released if both // flattening and small allocation are enabled, and additional buffer // objects will be created in init_tensor to represent flattened quantized @@ -3995,7 +4241,7 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, //GGML_ASSERT(offset == 0); // We create subbuffers from the original tensor buffer for scales and - // quants - i.e., scales and quants are aliases into the buffer obejct + // quants - i.e., scales and quants are aliases into the buffer object // that backs the original tensor. This is a cleaner way to adapt to the // new memory management. // In the old code, we allocate new buffers for scales and quants @@ -4271,7 +4517,15 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); CL_CHECK(err); + #ifdef GGML_OPENCL_USE_ADRENO_KERNELS cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1; + + if (use_adreno_kernels(backend_ctx, tensor)) { + kernel = backend_ctx->kernel_convert_block_q4_1_noshuffle; + } + #else + cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1; + #endif // GGML_OPENCL_USE_ADRENO_KERNELS CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device)); CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q)); CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d)); @@ -4287,6 +4541,22 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, tensor->extra = extra; +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + if (use_adreno_kernels(backend_ctx, tensor)) { + + int M = tensor->ne[1]; + int K = tensor->ne[0]; + + GGML_ASSERT(K % 32 == 0); + + // Transpose q as ushort + transpose_2d_as_16b(backend_ctx, extra->q, extra->q, size_q, K/4, M); + // Transpose d as ushort + transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/32, M); + // Transpose m as ushort + transpose_2d_as_16b(backend_ctx, extra->m, extra->m, size_m, K/32, M); + } +#endif // GGML_OPENCL_USE_ADRENO_KERNELS return; } if (tensor->type == GGML_TYPE_MXFP4) { @@ -4795,6 +5065,53 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, if (tensor->type == GGML_TYPE_Q4_1) { ggml_tensor_extra_cl_q4_1 * extra = (ggml_tensor_extra_cl_q4_1 *)tensor->extra; +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + if (use_adreno_kernels(backend_ctx, tensor)) { + static ggml_cl_buffer buf_trans_q; + static ggml_cl_buffer buf_trans_m; + static ggml_cl_buffer buf_trans_d; + static ggml_cl_buffer buf_unpacked; + + cl_int M = tensor->ne[1]; + cl_int K = tensor->ne[0]; + + GGML_ASSERT(K % ggml_blck_size(tensor->type) == 0); + + size_t size_q = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*ggml_blck_size(tensor->type)/2; + size_t size_d = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t); + size_t size_m = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t); + GGML_ASSERT(size_d + size_q + size_m == ggml_nbytes(tensor) && "Incorrect tensor size"); + + buf_trans_q.allocate(backend_ctx->context, size_q); + buf_trans_m.allocate(backend_ctx->context, size_m); + buf_trans_d.allocate(backend_ctx->context, size_d); + buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor)); + + // transpose q, d, m back + transpose_2d_as_16b(backend_ctx, extra->q, buf_trans_q.buffer, size_q, M, K/4); + transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/32); + transpose_2d_as_16b(backend_ctx, extra->m, buf_trans_m.buffer, size_m, M, K/32); + + cl_uchar mask_0F = 0x0F; + cl_uchar mask_F0 = 0xF0; + + size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1}; + size_t local_work_size[] = {1, 1, 1}; + + cl_kernel kernel = backend_ctx->kernel_restore_block_q4_1_noshuffle; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &buf_trans_q.buffer)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_d.buffer)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &buf_trans_m.buffer)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &buf_unpacked.buffer)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_uchar), &mask_0F)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_F0)); + + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL)); + CL_CHECK(clEnqueueReadBuffer(queue, buf_unpacked.buffer, CL_TRUE, offset, size, data, 0, NULL, NULL)); + return; + } +#endif + cl_int err; cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE, ggml_nbytes(tensor), NULL, &err); @@ -4886,8 +5203,8 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, int ne00 = tensor->ne[0]; int ne01 = tensor->ne[1]; - GGML_ASSERT(tensor->ne[2] == 1); // ??? - GGML_ASSERT(tensor->ne[3] == 1); // ??? + GGML_ASSERT(tensor->ne[2] == 1); + GGML_ASSERT(tensor->ne[3] == 1); CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q)); CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d)); @@ -5069,7 +5386,8 @@ static const char * ggml_backend_opencl_device_get_description(ggml_backend_dev_ } static void ggml_backend_opencl_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - *free = 0; + // no memory to report + *free = 0; *total = 0; GGML_UNUSED(dev); @@ -5478,19 +5796,12 @@ static void ggml_cl_get_rows(ggml_backend_t backend, const ggml_tensor * src0, c GGML_ASSERT(dst); GGML_ASSERT(dst->extra); - const int ne00 = src0->ne[0]; - const cl_ulong nb01 = src0->nb[1]; - const cl_ulong nb02 = src0->nb[2]; - const cl_ulong nb03 = src0->nb[3]; - const int ne10 = src1->ne[0]; - const cl_ulong nb10 = src1->nb[0]; - const int ne11 = src1->ne[1]; - const int ne12 = src1->ne[2]; - const cl_ulong nb11 = src1->nb[1]; - const cl_ulong nb12 = src1->nb[2]; - const cl_ulong nb1 = dst->nb[1]; - const cl_ulong nb2 = dst->nb[2]; - const cl_ulong nb3 = dst->nb[3]; + GGML_TENSOR_LOCALS(int, ne0, src0, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb); + GGML_TENSOR_LOCALS(int, ne1, src1, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb1, src1, nb); + GGML_TENSOR_LOCALS(int, ne, dst, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb, dst, nb); ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; @@ -5536,8 +5847,14 @@ static void ggml_cl_get_rows(ggml_backend_t backend, const ggml_tensor * src0, c CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb2)); CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb3)); - size_t global_work_size[] = {(size_t)ne10*64, (size_t)ne11, (size_t)ne12}; - size_t local_work_size[] = {64, 1, 1}; + int max_workgroup_size = backend_ctx->get_kernel_workgroup_size(kernel); + int nth = 1; + while (nth < ne00 && 2*nth <= max_workgroup_size) { + nth *= 2; + } + + size_t global_work_size[] = {(size_t)ne10*nth, (size_t)ne11, (size_t)ne12}; + size_t local_work_size[] = {(size_t)nth, 1, 1}; backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); } @@ -7277,6 +7594,64 @@ static void ggml_cl_group_norm(ggml_backend_t backend, const ggml_tensor * src0, backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); } +static void ggml_cl_l2_norm(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + UNUSED(src1); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + GGML_TENSOR_LOCALS(int, ne0, src0, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb); + + size_t sgs; + if (backend_ctx->gpu_family == ADRENO) { + sgs = 64; + } else if (backend_ctx->gpu_family == INTEL) { + sgs = 32; + } else { + GGML_ASSERT(false && "Unsupported GPU"); + } + + cl_kernel kernel = backend_ctx->kernel_l2_norm_f32; + + int nth = sgs; + while (nth < ne00 && nth < (int)backend_ctx->get_kernel_workgroup_size(kernel)) { + nth *= 2; + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne01)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne02)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne03)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb01)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb02)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb03)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(float), &eps)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float)*nth/sgs, NULL)); + + size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03}; + size_t local_work_size[] = {(size_t)nth, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); +} + static void ggml_cl_tanh(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -7373,6 +7748,170 @@ static void ggml_cl_tanh(ggml_backend_t backend, const ggml_tensor * src0, const } } +static void ggml_cl_neg(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + UNUSED(src1); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + GGML_TENSOR_LOCALS(int, ne0, src0, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb); + GGML_TENSOR_LOCALS(int, ne, dst, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb, dst, nb); + + cl_kernel kernel; + + if (ggml_is_contiguous(src0)) { + // Handle contiguous input + int n = ggml_nelements(dst); + if (n % 4 == 0) { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_neg_f32_4; + } else { + kernel = backend_ctx->kernel_neg_f16_4; + } + n /= 4; + } else { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_neg_f32; + } else { + kernel = backend_ctx->kernel_neg_f16; + } + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int), &n)); + + size_t global_work_size[] = {(size_t)CEIL_DIV(n, 64)*64, 1, 1}; + size_t local_work_size[] = {64, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + } else { + // Handle non-contiguous input + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_neg_f32_nc; + } else { + kernel = backend_ctx->kernel_neg_f16_nc; + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb00)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &nb01)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb02)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb03)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb0)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb1)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb2)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb3)); + + int nth = 64; + + size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03}; + size_t local_work_size[] = {(size_t)nth, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + } +} + +static void ggml_cl_exp(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + UNUSED(src1); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + GGML_TENSOR_LOCALS(int, ne0, src0, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb); + GGML_TENSOR_LOCALS(int, ne, dst, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb, dst, nb); + + cl_kernel kernel; + + if (ggml_is_contiguous(src0)) { + // Handle contiguous input + int n = ggml_nelements(dst); + if (n % 4 == 0) { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_exp_f32_4; + } else { + kernel = backend_ctx->kernel_exp_f16_4; + } + n /= 4; + } else { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_exp_f32; + } else { + kernel = backend_ctx->kernel_exp_f16; + } + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int), &n)); + + size_t global_work_size[] = {(size_t)CEIL_DIV(n, 64)*64, 1, 1}; + size_t local_work_size[] = {64, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + } else { + // Handle non-contiguous input + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_exp_f32_nc; + } else { + kernel = backend_ctx->kernel_exp_f16_nc; + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb00)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &nb01)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb02)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb03)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb0)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb1)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb2)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb3)); + + int nth = 64; + + size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03}; + size_t local_work_size[] = {(size_t)nth, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + } +} + static void ggml_cl_expm1(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -8371,6 +8910,180 @@ static void ggml_cl_mul_mat_kq_kqv_adreno(ggml_backend_t backend, const ggml_ten CL_CHECK(clReleaseMemObject(D_sub_buffer)); } +static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(src1); + GGML_ASSERT(src1->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra; + + cl_ulong offset1 = extra1->offset + src1->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + const int ne00 = src0->ne[0]; + const int ne01 = src0->ne[1]; + + const int ne1 = dst->ne[1]; + + GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0); + + cl_context context = backend_ctx->context; + cl_kernel kernel; + + cl_int err; + cl_image_format img_fmt; + cl_image_desc img_desc; + cl_buffer_region region; + + int M = ne01; + int N = ne1; + int K = ne00; + + if (ne1 == 1) { + cl_mem q_img = nullptr; + cl_mem b_sub_buf = nullptr; + cl_mem b_img = nullptr; + + // image for q + img_fmt = { CL_R, CL_UNSIGNED_INT32}; + memset(&img_desc, 0, sizeof(img_desc)); + img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc.image_width = M * K / 2 / 4; + img_desc.buffer = extra0_q4_1->q; + CL_CHECK((q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err)); + + // subbuffer for activations + region.origin = offset1; + region.size = K * N * sizeof(float); + CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err), err)); + + // image for activations + img_fmt = {CL_RGBA, CL_FLOAT}; + memset(&img_desc, 0, sizeof(img_desc)); + img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc.image_width = K * N / 4; + img_desc.buffer = b_sub_buf; + CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err)); + + kernel = backend_ctx->kernel_gemv_noshuffle_q4_1_f32; + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_img)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_1->d)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_1->m)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &b_img)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int), &ne01)); + + size_t local_work_size[3] = {64, 4, 1}; + size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, 4, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + + CL_CHECK(clReleaseMemObject(q_img)); + CL_CHECK(clReleaseMemObject(b_sub_buf)); + CL_CHECK(clReleaseMemObject(b_img)); + } else { + cl_mem b_sub_buf = nullptr; + cl_mem b_sub_buf_trans = nullptr; + cl_mem b_img = nullptr; + cl_mem b_img_trans = nullptr; + + // subbuffer for activations + region.origin = offset1; + region.size = K * N * sizeof(float); + CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err), err)); + + // image for activations + img_fmt = {CL_RGBA, CL_FLOAT}; + memset(&img_desc, 0, sizeof(img_desc)); + img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc.image_width = K * N / 4; + img_desc.buffer = b_sub_buf; + CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err)); + + // pad N to multiple of 8 + int extra_elements = N % 8; + int padding = 0; + if (extra_elements > 0){ + padding = 8 - extra_elements; + } + + // subbuffer for transposed activations + region.origin = 0; + region.size = K * (N + padding) * sizeof(float)/2; + backend_ctx->prealloc_act_trans.allocate(context, region.size); + CL_CHECK((b_sub_buf_trans = clCreateSubBuffer(backend_ctx->prealloc_act_trans.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err), err)); + + // image for transposed activations + img_fmt = {CL_RGBA, CL_HALF_FLOAT}; + memset(&img_desc, 0, sizeof(img_desc)); + img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + img_desc.image_width = K * (N + padding) / 4; + img_desc.buffer = b_sub_buf_trans; + CL_CHECK((b_img_trans = clCreateImage(context, 0, &img_fmt, &img_desc, NULL, &err), err)); + + // transpose activations + int height_B = N/4; + if (height_B == 0) { + height_B = 1; + } + int width_B = K/4; + int padded_height_B = (N + padding)/4; + + kernel = backend_ctx->kernel_transpose_32_16; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &b_img)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &b_img_trans)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &height_B)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &width_B)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &padded_height_B)); + + size_t local_work_size_t[2] = { 1, 16 }; + size_t global_work_size_t[2] = { (size_t)width_B, (size_t)padded_height_B }; + backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst); + + // gemm + kernel = backend_ctx->kernel_gemm_noshuffle_q4_1_f32; + int padded_N = N + padding; + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_1->q)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_1->d)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_1->m)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &b_img_trans)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int), &ne01)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int), &padded_N)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_int), &ne1)); + + size_t global_work_size[3] = {(size_t)CEIL_DIV(ne1, 8), (size_t)CEIL_DIV(ne01, 4), 1}; + size_t local_work_size[3] = {1, 128, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + + CL_CHECK(clReleaseMemObject(b_sub_buf)); + CL_CHECK(clReleaseMemObject(b_sub_buf_trans)); + CL_CHECK(clReleaseMemObject(b_img)); + CL_CHECK(clReleaseMemObject(b_img_trans)); + } +#else + GGML_UNUSED(backend); + GGML_UNUSED(src0); + GGML_UNUSED(src1); + GGML_UNUSED(dst); +#endif +} + static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_OPENCL_USE_ADRENO_KERNELS GGML_ASSERT(src0); @@ -8736,6 +9449,16 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co int padding; // <--------------------------------------------> // + // NOTE: Kernels using image1d_buffer_t (e.g., src0_q) would normally require + // a limit check, but q4_0 / q4_1 tensors are very unlikely to exceed that + // limit, so the check is omitted. + + // q4_1 x fp32 + if (src0t == GGML_TYPE_Q4_1 && src1t == GGML_TYPE_F32) { + ggml_cl_mul_mat_q4_1_f32_adreno(backend, src0, src1, dst); + return; + } + // q8_0 x fp32 if (src0t == GGML_TYPE_Q8_0 && src1t == GGML_TYPE_F32 && enable_adreno_trans_weight(backend_ctx, src0)) { @@ -10402,28 +11125,13 @@ static void ggml_cl_cpy(ggml_backend_t backend, const ggml_tensor * src0, const // GGML_OP_DUP and GGML_OP_CONT happen between src0 and dst. UNUSED(dst); - const int ne00 = src0 ? src0->ne[0] : 0; - const int ne01 = src0 ? src0->ne[1] : 0; - const int ne02 = src0 ? src0->ne[2] : 0; - const int ne03 = src0 ? src0->ne[3] : 0; + GGML_TENSOR_LOCALS(int, ne0, src0, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb); + GGML_TENSOR_LOCALS(int, ne1, src1, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb1, src1, nb); - const cl_ulong nb00 = src0 ? src0->nb[0] : 0; - const cl_ulong nb01 = src0 ? src0->nb[1] : 0; - const cl_ulong nb02 = src0 ? src0->nb[2] : 0; - const cl_ulong nb03 = src0 ? src0->nb[3] : 0; - - const int ne10 = src1 ? src1->ne[0] : 0; - const int ne11 = src1 ? src1->ne[1] : 0; - const int ne12 = src1 ? src1->ne[2] : 0; - const int ne13 = src1 ? src1->ne[3] : 0; - - const cl_ulong nb10 = src1 ? src1->nb[0] : 0; - const cl_ulong nb11 = src1 ? src1->nb[1] : 0; - const cl_ulong nb12 = src1 ? src1->nb[2] : 0; - const cl_ulong nb13 = src1 ? src1->nb[3] : 0; - - const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; - const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; + const enum ggml_type src0t = src0->type; + const enum ggml_type src1t = src1->type; ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; @@ -10460,6 +11168,15 @@ static void ggml_cl_cpy(ggml_backend_t backend, const ggml_tensor * src0, const GGML_ASSERT(false && "not implemented"); } break; + case GGML_TYPE_I32: + switch (src1t) { + case GGML_TYPE_I32: + kernel = backend_ctx->kernel_cpy_i32_i32; + break; + default: + GGML_ASSERT(false && "not implemented"); + } + break; default: GGML_ASSERT(false && "not implemented"); } @@ -10498,6 +11215,89 @@ static void ggml_cl_dup(ggml_backend_t backend, const ggml_tensor * src0, const UNUSED(src1); } +static void ggml_cl_set(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(src1); + GGML_ASSERT(src1->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + GGML_ASSERT((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32) && + src1->type == src0->type && dst->type == src0->type); + + GGML_TENSOR_LOCALS(int, ne0, src0, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb); + GGML_TENSOR_LOCALS(int, ne1, src1, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb1, src1, nb); + GGML_TENSOR_LOCALS(int, ne, dst, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb, dst, nb); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + cl_ulong offset1 = extra1->offset + src1->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + const cl_ulong pnb1 = ((const int32_t *)dst->op_params)[0]; + const cl_ulong pnb2 = ((const int32_t *)dst->op_params)[1]; + const cl_ulong pnb3 = ((const int32_t *)dst->op_params)[2]; + const cl_ulong offs = ((const int32_t *)dst->op_params)[3]; + const bool inplace = (bool)((const int32_t *)dst->op_params)[4]; + + cl_kernel kernel = nullptr; + + // for inplace case, dst is a view of src0 and is updated on top of it + // so for non-inplace case, copy src0 to dst first + if (!inplace) { + ggml_cl_cpy(backend, src0, dst, nullptr); + } + + // then copy src1 to dst with specified offset + if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_cpy_f32_f32; + } else if (src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) { + kernel = backend_ctx->kernel_cpy_i32_i32; + } else { + GGML_ASSERT(false && "not implemented"); + } + + offsetd += offs; + cl_ulong nb = ggml_element_size(dst); + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra1->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset1)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne10)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne11)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne12)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne13)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb10)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb11)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb12)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb13)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne10)); + CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne11)); + CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &ne12)); + CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &ne13)); + CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb)); + CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &pnb1)); + CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &pnb2)); + CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &pnb3)); + + int max_local_size = backend_ctx->get_kernel_workgroup_size(kernel); + + const int nth = MIN(max_local_size, ne00); + + size_t global_work_size[] = {(size_t)ne11*nth, (size_t)ne12, (size_t)ne13}; + size_t local_work_size[] = {(size_t)nth, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); +} + static void ggml_cl_diag_mask_inf(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -10560,6 +11360,49 @@ static void ggml_cl_diag_mask_inf(ggml_backend_t backend, const ggml_tensor * sr } } +static void ggml_cl_diag(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + UNUSED(src1); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + GGML_TENSOR_LOCALS(int, ne0, src0, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb); + GGML_TENSOR_LOCALS(int, ne, dst, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb, dst, nb); + + cl_kernel kernel = backend_ctx->kernel_diag_f32; + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &nb01)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb02)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &nb03)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int), &ne0)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb0)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb2)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb3)); + + int nth = 64; + + size_t global_work_size[] = {(size_t)ne1*nth, (size_t)ne2, (size_t)ne3}; + size_t local_work_size[] = {(size_t)nth, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); +} + static void ggml_cl_soft_max(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -11126,6 +11969,118 @@ static void ggml_cl_sum_rows(ggml_backend_t backend, const ggml_tensor * src0, c backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); } +static void ggml_cl_cumsum(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + GGML_UNUSED(src1); + + GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); + GGML_ASSERT(ggml_is_contiguous(src0)); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + GGML_TENSOR_LOCALS(int, ne0, src0, ne); + GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb); + + cl_kernel kernel = backend_ctx->kernel_cumsum_blk; + + int max_workgroup_size = backend_ctx->get_kernel_workgroup_size(kernel); + int nth = 1; + while (nth < ne00 && 2*nth <= max_workgroup_size) { + nth *= 2; + } + + GGML_ASSERT(ne00 <= nth*nth); + + const int net0 = CEIL_DIV(ne00, nth); + const int net1 = ne01; + const int net2 = ne02; + const int net3 = ne03; + + const cl_ulong nbt0 = sizeof(float); + const cl_ulong nbt1 = net0*nbt0; + const cl_ulong nbt2 = net1*nbt1; + const cl_ulong nbt3 = net2*nbt2; + + static ggml_cl_buffer tmp_buffer; + tmp_buffer.allocate(backend_ctx->context, net0*ne01*ne02*ne03*sizeof(float)); + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &tmp_buffer.buffer)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne01)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne02)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne03)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb00)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb01)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb02)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb03)); + CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &net0)); + CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &net1)); + CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &net2)); + + size_t global_work_size[] = { (size_t)(nth*net0*ne01), (size_t)ne02, (size_t)ne03}; + size_t local_work_size[] = { (size_t)nth, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + + if(ne00 > nth) { + // if a single workgroup cannot handle an entire row, each workgroup + // computes a partial sum and stores to dst, tmp_buffer contains the sum + // of the each workgroup; cumsum this buffer and add to the partial sums in dst + cl_ulong offsett = 0; + kernel = backend_ctx->kernel_cumsum_blk; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &tmp_buffer.buffer)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offsett)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &tmp_buffer.buffer)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &tmp_buffer.buffer)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &offsett)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &net0)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne01)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne02)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne03)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nbt0)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nbt1)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nbt2)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nbt3)); + CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &net0)); + CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &net1)); + CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &net2)); + + size_t global_work_size_1[] = { (size_t)net1*nth, (size_t)net2, (size_t)net3}; + size_t local_work_size_1[] = { (size_t)nth, 1, 1}; + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size_1, local_work_size_1, dst); + + kernel = backend_ctx->kernel_cumsum_add; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &tmp_buffer.buffer)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne01)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne02)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne03)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &nbt0)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &nbt1)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &nbt2)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &nbt3)); + + size_t global_work_size_2[] = { (size_t)(nth*net0*ne01), (size_t)ne02, (size_t)ne03}; + size_t local_work_size_2[] = { (size_t)nth, 1, 1}; + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size_2, local_work_size_2, dst); + } +} + static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -11271,6 +12226,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor } func = ggml_cl_cpy; break; + case GGML_OP_SET: + if (!any_on_device) { + return false; + } + func = ggml_cl_set; + break; case GGML_OP_DUP: case GGML_OP_CONT: if (!any_on_device) { @@ -11370,6 +12331,18 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor } func = ggml_cl_tanh; break; + case GGML_UNARY_OP_NEG: + if (!any_on_device) { + return false; + } + func = ggml_cl_neg; + break; + case GGML_UNARY_OP_EXP: + if (!any_on_device) { + return false; + } + func = ggml_cl_exp; + break; case GGML_UNARY_OP_EXPM1: if (!any_on_device) { return false; @@ -11421,6 +12394,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor } func = ggml_cl_rms_norm; break; + case GGML_OP_L2_NORM: + if (!any_on_device) { + return false; + } + func = ggml_cl_l2_norm; + break; case GGML_OP_GROUP_NORM: if (!any_on_device) { return false; @@ -11496,6 +12475,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor } func = ggml_cl_nop; break; + case GGML_OP_DIAG: + if (!any_on_device) { + return false; + } + func = ggml_cl_diag; + break; case GGML_OP_DIAG_MASK_INF: if (!any_on_device) { return false; @@ -11538,6 +12523,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor } func = ggml_cl_sum_rows; break; + case GGML_OP_CUMSUM: + if (!any_on_device) { + return false; + } + func = ggml_cl_cumsum; + break; case GGML_OP_FLASH_ATTN_EXT: if (!any_on_device) { return false; diff --git a/ggml/src/ggml-opencl/kernels/cpy.cl b/ggml/src/ggml-opencl/kernels/cpy.cl index 9369351a60..820aa538a3 100644 --- a/ggml/src/ggml-opencl/kernels/cpy.cl +++ b/ggml/src/ggml-opencl/kernels/cpy.cl @@ -182,3 +182,48 @@ kernel void kernel_cpy_f32_f32( dst_data[i00] = src[0]; } } + +kernel void kernel_cpy_i32_i32( + global int * src0, + ulong offset0, + global int * dst, + ulong offsetd, + int ne00, + int ne01, + int ne02, + int ne03, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + int ne0, + int ne1, + int ne2, + int ne3, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = (global int*)((global char*)src0 + offset0); + dst = (global int*)((global char*)dst + offsetd); + + int i03 = get_group_id(2); + int i02 = get_group_id(1); + int i01 = get_group_id(0); + + int n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + + int i3 = n / (ne2*ne1*ne0); + int i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); + int i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; + int i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0); + + global int * dst_data = (global int *) ((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) { + global const int * src = (global int *)((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + + dst_data[i00] = src[0]; + } +} diff --git a/ggml/src/ggml-opencl/kernels/cumsum.cl b/ggml/src/ggml-opencl/kernels/cumsum.cl new file mode 100644 index 0000000000..edfb74b705 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/cumsum.cl @@ -0,0 +1,139 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +#ifdef cl_intel_required_subgroup_size +#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable +#define INTEL_GPU 1 +#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16))) +#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32))) +#elif defined(cl_qcom_reqd_sub_group_size) +#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable +#define ADRENO_GPU 1 +#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half"))) +#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full"))) +#endif + +// max workgroup size is usually 1024, this covers various subgroups sizes +#define MAX_SUBGROUPS 128 + +#ifdef INTEL_GPU +REQD_SUBGROUP_SIZE_32 +#elif defined (ADRENO_GPU) +REQD_SUBGROUP_SIZE_64 +#endif +kernel void kernel_cumsum_blk( + global char * src0, + ulong offset0, + global char * tmp, + global char * dst, + ulong offsetd, + int ne00, + int ne01, + int ne02, + int ne03, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + uint net0, + uint net1, + uint net2 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + const int nth = get_local_size(0); + const int tid = get_local_id(0); + + const uint sg_size = get_sub_group_size(); + const uint sg_id = get_sub_group_id(); + const uint sg_lid = get_sub_group_local_id(); + + const int ib = i1 / ne01; + const int i00 = ib * nth; + const int i01 = i1 % ne01; + const int i02 = i2; + const int i03 = i3; + + global const float * src0_row = (global const float *)(src0 + i03*nb03 + i02*nb02 + i01*nb01); + global float * tmp_row = (global float *)tmp + net0 * i01 + net0 * net1 * i02 + net0 * net1 * net2 * i03; + global float * dst_row = (global float *)dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + + __local float partial[MAX_SUBGROUPS]; + + float v = 0.0f; + if (i00 + tid < ne00) { + v = src0_row[i00 + tid]; + } + + float s = sub_group_scan_inclusive_add(v); + if (sg_lid == sg_size - 1) { + partial[sg_id] = s; + } + barrier(CLK_LOCAL_MEM_FENCE); + + // NB: subgroup size should be larger than number of subgroups + // assuming max workgroup size of 1024, subgroup size should be >= 32 + if (sg_id == 0) { + float x = 0.0f; + if (sg_lid < get_num_sub_groups()) { + x = partial[sg_lid]; + } + float ex = sub_group_scan_exclusive_add(x); + if (sg_lid < get_num_sub_groups()) { + partial[sg_lid] = ex; + } + } + barrier(CLK_LOCAL_MEM_FENCE); + + s += partial[sg_id]; + + if (i00 + tid < ne00) { + dst_row[i00 + tid] = s; + } + if (ne00 > nth && tid == nth - 1) { + tmp_row[ib] = s; + } +} + +kernel void kernel_cumsum_add( + global char * tmp, + global char * dst, + ulong offsetd, + int ne00, + int ne01, + int ne02, + int ne03, + uint nbt0, + uint nbt1, + uint nbt2, + uint nbt3 +) { + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + const int nth = get_local_size(0); + const int tid = get_local_id(0); + + const int ib = i1 / ne01; + if (ib == 0) { + return; + } + const int i00 = ib * nth; + const int i01 = i1 % ne01; + const int i02 = i2; + const int i03 = i3; + + global float * tmp_row = (global float *)(tmp + nbt1 * i01 + nbt2 * i02 + nbt3 * i03); + global float * dst_row = (global float *)dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + + if (i00 + tid < ne00) { + dst_row[i00 + tid] += tmp_row[ib - 1]; + } +} diff --git a/ggml/src/ggml-opencl/kernels/cvt.cl b/ggml/src/ggml-opencl/kernels/cvt.cl index 2c244ce321..78ef9c177f 100644 --- a/ggml/src/ggml-opencl/kernels/cvt.cl +++ b/ggml/src/ggml-opencl/kernels/cvt.cl @@ -199,6 +199,58 @@ kernel void kernel_restore_block_q4_1( } } +kernel void kernel_convert_block_q4_1_noshuffle( + global struct block_q4_1 * src0, + global uchar * dst_q, + global half * dst_d, + global half * dst_m +) { + global struct block_q4_1 * b = (global struct block_q4_1 *) src0 + get_global_id(0); + global uchar * q = (global uchar *) dst_q + QK4_1/2*get_global_id(0); + global half * d = (global half *) dst_d + get_global_id(0); + global half * m = (global half *) dst_m + get_global_id(0); + + *d = b->d; + *m = b->m; + for (int i = 0; i < QK4_1/4; ++i) { + uchar x0 = b->qs[2*i + 0]; + uchar x1 = b->qs[2*i + 1]; + + q[i + 0 ] = convert_uchar(x0 & 0x0F) | convert_uchar((x1 & 0x0F) << 4); + q[i + QK4_1/4] = convert_uchar((x0 & 0xF0) >> 4) | convert_uchar(x1 & 0xF0); + +#ifdef ADRENO_GPU + if (get_global_id(0) == 65536*4096) { + printf("%04x - %02x\n", *(global ushort*)d, ((x0 & 0xF0) >> 4) | (x1 & 0xF0)); + } +#endif + } +} + +kernel void kernel_restore_block_q4_1_noshuffle( + global uchar * src_q, + global half * src_d, + global half * src_m, + global struct block_q4_1 * dst, + uchar mask_0F, + uchar mask_F0 +) { + global struct block_q4_1 * b = (global struct block_q4_1 *) dst + get_global_id(0); + global uchar * q = (global uchar *) src_q + QK4_1/2*get_global_id(0); + global half * d = (global half *) src_d + get_global_id(0); + global half * m = (global half *) src_m + get_global_id(0); + + b->d = *d; + b->m = *m; + for (int i = 0; i < QK4_1/4; ++i) { + uchar x0 = q[i + 0 ] ; + uchar x1 = q[i + QK4_1/4]; + + b->qs[2*i + 0] = convert_uchar((x0 & mask_0F) | ((x1 & mask_0F) << 4)); + b->qs[2*i + 1] = convert_uchar(((x0 & mask_F0) >> 4) | (x1 & mask_F0)); + } +} + //------------------------------------------------------------------------------ // block_mxfp4 //------------------------------------------------------------------------------ diff --git a/ggml/src/ggml-opencl/kernels/diag.cl b/ggml/src/ggml-opencl/kernels/diag.cl new file mode 100644 index 0000000000..884efa08fd --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/diag.cl @@ -0,0 +1,27 @@ +kernel void kernel_diag_f32( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + ulong nb01, + ulong nb02, + ulong nb03, + int ne0, + ulong nb0, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + int i3 = get_group_id(2); + int i2 = get_group_id(1); + int i1 = get_group_id(0); + + global const float * src0_ptr = (global const float *)(src0 + i2*nb02 + i3*nb03); + global float * dst_ptr = (global float *)(dst + i1*nb01 + i2*nb2 + i3*nb3); + + for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) { + dst_ptr[i0] = i0 == i1 ? src0_ptr[i0] : 0.0f; + } +} diff --git a/ggml/src/ggml-opencl/kernels/exp.cl b/ggml/src/ggml-opencl/kernels/exp.cl new file mode 100644 index 0000000000..a2458b6579 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/exp.cl @@ -0,0 +1,125 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +kernel void kernel_exp_f32( + global const float * src0, + ulong offset0, + global float * dst, + ulong offsetd, + int n +) { + if (get_global_id(0) >= n) { + return; + } + src0 = (global float*)((global char*)src0 + offset0); + dst = (global float*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = exp(src0[get_global_id(0)]); +} + +kernel void kernel_exp_f32_4( + global const float4 * src0, + ulong offset0, + global float4 * dst, + ulong offsetd, + int n +) { + if (get_global_id(0) >= n) { + return; + } + src0 = (global float4*)((global char*)src0 + offset0); + dst = (global float4*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = exp(src0[get_global_id(0)]); +} + +kernel void kernel_exp_f16( + global const half * src0, + ulong offset0, + global half * dst, + ulong offsetd, + int n +) { + if (get_global_id(0) >= n) { + return; + } + src0 = (global half*)((global char*)src0 + offset0); + dst = (global half*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = exp(src0[get_global_id(0)]); +} + +kernel void kernel_exp_f16_4( + global const half4 * src0, + ulong offset0, + global half4 * dst, + ulong offsetd, + int n +) { + if (get_global_id(0) >= n) { + return; + } + src0 = (global half4*)((global char*)src0 + offset0); + dst = (global half4*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = exp(src0[get_global_id(0)]); +} + +kernel void kernel_exp_f32_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const float * x = (global const float *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global float * y = (global float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = exp(*x); + } +} + +kernel void kernel_exp_f16_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const half * x = (global const half *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global half * y = (global half *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = exp(*x); + } +} diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_1_f32.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_1_f32.cl new file mode 100644 index 0000000000..5c4d5cc8e2 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_1_f32.cl @@ -0,0 +1,132 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable +#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable + +#ifdef cl_qcom_reqd_sub_group_size +#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable +#define ADRENO_GPU 1 +#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full"))) +#endif + +#ifdef ADRENO_GPU +REQD_SUBGROUP_SIZE_128 +#endif + +kernel void kernel_gemm_noshuffle_q4_1_f32( + global const ushort * src0_q, + global const half * src0_d, + global const half * src0_m, + read_only image1d_buffer_t src1, + global float * dst, + ulong offsetd, + int m, + int n, + int k, + int n_no_padding +) { + dst = (global float *)((global char *)dst + offsetd); + + int m_4 = m >> 2; + int n_4 = n >> 2; + + int gy = get_global_id(0); + int gx = get_global_id(1); + int gx_2 = gx << 2; + + half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0; + half8 B; + half4 dequantized_weights; + + global const ushort* weight_ptr = src0_q + gx_2; + global const half* scale_ptr = src0_d + gx_2; + global const half* min_ptr = src0_m + gx_2; + + for(int i = 0; i < k; i += 4) { + B.s0123 = read_imageh(src1, gy*2 + (i)*(n_4)); + B.s4567 = read_imageh(src1, gy*2 + (i)*(n_4)+1); + + ushort4 bits4 = vload4(0, weight_ptr + (i/4)*(m)); + + half4 scale = vload4(0, scale_ptr + (i/32)*(m)); + half4 minv = vload4(0, min_ptr + (i/32)*(m)); + + // j=0 + dequantized_weights.s0 = (bits4.s0 & (0x000F)) * scale.s0 + minv.s0; + dequantized_weights.s1 = (bits4.s1 & (0x000F)) * scale.s1 + minv.s1; + dequantized_weights.s2 = (bits4.s2 & (0x000F)) * scale.s2 + minv.s2; + dequantized_weights.s3 = (bits4.s3 & (0x000F)) * scale.s3 + minv.s3; + c0 += B * dequantized_weights.s0; + c1 += B * dequantized_weights.s1; + c2 += B * dequantized_weights.s2; + c3 += B * dequantized_weights.s3; + + // j=1 + B.s0123 = read_imageh(src1, gy*2 + (i+1)*(n_4)); + B.s4567 = read_imageh(src1, gy*2 + (i+1)*(n_4)+1); + dequantized_weights.s0 = ((bits4.s0 & (0x00F0)) >> 4) * scale.s0 + minv.s0; + dequantized_weights.s1 = ((bits4.s1 & (0x00F0)) >> 4) * scale.s1 + minv.s1; + dequantized_weights.s2 = ((bits4.s2 & (0x00F0)) >> 4) * scale.s2 + minv.s2; + dequantized_weights.s3 = ((bits4.s3 & (0x00F0)) >> 4) * scale.s3 + minv.s3; + c0 += B * dequantized_weights.s0; + c1 += B * dequantized_weights.s1; + c2 += B * dequantized_weights.s2; + c3 += B * dequantized_weights.s3; + + // j=2 + B.s0123 = read_imageh(src1, gy*2 + (i+2)*(n_4)); + B.s4567 = read_imageh(src1, gy*2 + (i+2)*(n_4)+1); + dequantized_weights.s0 = ((bits4.s0 & (0x0F00)) >> 8) * scale.s0 + minv.s0; + dequantized_weights.s1 = ((bits4.s1 & (0x0F00)) >> 8) * scale.s1 + minv.s1; + dequantized_weights.s2 = ((bits4.s2 & (0x0F00)) >> 8) * scale.s2 + minv.s2; + dequantized_weights.s3 = ((bits4.s3 & (0x0F00)) >> 8) * scale.s3 + minv.s3; + c0 += B * dequantized_weights.s0; + c1 += B * dequantized_weights.s1; + c2 += B * dequantized_weights.s2; + c3 += B * dequantized_weights.s3; + + // j=3 + B.s0123 = read_imageh(src1, gy*2 + (i+3)*(n_4)); + B.s4567 = read_imageh(src1, gy*2 + (i+3)*(n_4)+1); + dequantized_weights.s0 = ((bits4.s0 & (0xF000)) >> 12) * scale.s0 + minv.s0; + dequantized_weights.s1 = ((bits4.s1 & (0xF000)) >> 12) * scale.s1 + minv.s1; + dequantized_weights.s2 = ((bits4.s2 & (0xF000)) >> 12) * scale.s2 + minv.s2; + dequantized_weights.s3 = ((bits4.s3 & (0xF000)) >> 12) * scale.s3 + minv.s3; + c0 += B * dequantized_weights.s0; + c1 += B * dequantized_weights.s1; + c2 += B * dequantized_weights.s2; + c3 += B * dequantized_weights.s3; + } + + int idx = (gy<<3)*m + (gx<<2); + + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx); + idx += m; + } + if(idx+3 < m*n_no_padding){ + vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx); + } +} diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl index f944ef3a99..9703b693e5 100644 --- a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl +++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_general_q8_0_f32.cl @@ -121,7 +121,7 @@ #ifdef ADRENO_GPU REQD_SUBGROUP_SIZE_64 #endif -__kernel void kernel_gemv_noshuffle( +__kernel void kernel_gemv_noshuffle_q8_0_f32( __read_only image1d_buffer_t src0_q, // quantized A global half * src0_d, // A scales __read_only image1d_buffer_t src1, // B diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_1_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_1_f32.cl new file mode 100644 index 0000000000..fdc1472454 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_1_f32.cl @@ -0,0 +1,283 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable +#pragma OPENCL EXTENSION cl_khr_subgroups : enable + +#ifdef cl_qcom_reqd_sub_group_size +#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable +#define ADRENO_GPU 1 +#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half"))) +#endif + +#define QK4_0 32 +#define NSUBGROUPS 4 +#define SUBGROUP_SIZE 64 + +#define dequantizeBlockAccum_ns_sgbroadcast_1_hi(total_sums, bits4, scale, minv, y) \ + float shared_y; \ + shared_y = sub_group_broadcast(y.s0, 0); \ + total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s1, 0); \ + total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s2, 0); \ + total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s3, 0); \ + total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s4, 0); \ + total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s5, 0); \ + total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s6, 0); \ + total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s7, 0); \ + total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s0, 1); \ + total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s1, 1); \ + total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s2, 1); \ + total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s3, 1); \ + total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s4, 1); \ + total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s5, 1); \ + total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s6, 1); \ + total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s7, 1); \ + total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \ + + +#define dequantizeBlockAccum_ns_sgbroadcast_1_lo(total_sums, bits4, scale, minv, y) \ + shared_y = sub_group_broadcast(y.s0, 2); \ + total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s1, 2); \ + total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s2, 2); \ + total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s3, 2); \ + total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s4, 2); \ + total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s5, 2); \ + total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s6, 2); \ + total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s7, 2); \ + total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s0, 3); \ + total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s1, 3); \ + total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s2, 3); \ + total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s3, 3); \ + total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s4, 3); \ + total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s5, 3); \ + total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s6, 3); \ + total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \ + shared_y = sub_group_broadcast(y.s7, 3); \ + total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \ + total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \ + + +#define dequantizeBlockAccum_ns_sgbroadcast_8_hi(total_sums, bits4, scale, minv, y) \ + float8 shared_y; \ + shared_y = sub_group_broadcast(y, 0); \ + total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 + minv.s0) * shared_y.s0; \ + total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s1; \ + total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s2; \ + total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s3; \ + total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 + minv.s0) * shared_y.s4; \ + total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s5; \ + total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s6; \ + total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s7; \ + total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 + minv.s1) * shared_y.s0; \ + total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s1; \ + total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s2; \ + total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s3; \ + total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 + minv.s1) * shared_y.s4; \ + total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s5; \ + total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s6; \ + total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s7; \ + shared_y = sub_group_broadcast(y, 1); \ + total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 + minv.s0) * shared_y.s0; \ + total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s1; \ + total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s2; \ + total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s3; \ + total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 + minv.s0) * shared_y.s4; \ + total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s5; \ + total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s6; \ + total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s7; \ + total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 + minv.s1) * shared_y.s0; \ + total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s1; \ + total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s2; \ + total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s3; \ + total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 + minv.s1) * shared_y.s4; \ + total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s5; \ + total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s6; \ + total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s7; \ + + +#define dequantizeBlockAccum_ns_sgbroadcast_8_lo(total_sums, bits4, scale, minv, y) \ + shared_y = sub_group_broadcast(y, 2); \ + total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 + minv.s0) * shared_y.s0; \ + total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s1; \ + total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s2; \ + total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s3; \ + total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 + minv.s0) * shared_y.s4; \ + total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s5; \ + total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s6; \ + total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s7; \ + total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 + minv.s1) * shared_y.s0; \ + total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s1; \ + total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s2; \ + total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s3; \ + total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 + minv.s1) * shared_y.s4; \ + total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s5; \ + total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s6; \ + total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s7; \ + shared_y = sub_group_broadcast(y, 3); \ + total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 + minv.s0) * shared_y.s0; \ + total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s1; \ + total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s2; \ + total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s3; \ + total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 + minv.s0) * shared_y.s4; \ + total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s5; \ + total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s6; \ + total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s7; \ + total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 + minv.s1) * shared_y.s0; \ + total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s1; \ + total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s2; \ + total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s3; \ + total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 + minv.s1) * shared_y.s4; \ + total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s5; \ + total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s6; \ + total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s7; \ + +#ifdef ADRENO_GPU +REQD_SUBGROUP_SIZE_64 +#endif +kernel void kernel_gemv_noshuffle_q4_1_f32( + read_only image1d_buffer_t src0_q, + global half2 * src0_d, + global half2 * src0_m, + read_only image1d_buffer_t src1, + global float * dst, + ulong offsetd, + int ne00, + int ne01) +{ + uint groupId = get_local_id(1); + uint gid = get_global_id(0); + ushort slid = get_sub_group_local_id(); + + uint K = ne00; + uint M = ne01; + + uint LINE_STRIDE_A = M / 2; + uint BLOCK_STRIDE_A = NSUBGROUPS * M; + + private uint4 regA; + private half2 regS; + private half2 regM; + private float8 regB; + + private float2 totalSum = (float2)(0.0f); + + // loop along K in block granularity, skip 4 blocks every iter + for (uint k = groupId; k < (K / QK4_0); k += NSUBGROUPS) { + regS = src0_d[gid + k * LINE_STRIDE_A]; // each fiber loads scale of two rows + regM = src0_m[gid + k * LINE_STRIDE_A]; // each fiber loads min of two rows + // first 4 fibers in each wave load 8 B values to its private scope + if (slid < 4) { + regB.s0123 = read_imagef(src1, (slid * 2 + k * 8)); + regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8)); + } + + // load half weights for two blocks in consecutive rows + regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x; + regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x; + regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x; + regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x; +#ifdef VECTOR_SUB_GROUP_BROADCAT + dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regM, regB); +#else + dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regM, regB); +#endif // VECTOR_SUB_GROUP_BROADCAT + + regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x; + regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x; + regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x; + regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x; +#ifdef VECTOR_SUB_GROUP_BROADCAT + dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regM, regB); +#else + dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), regS, regM, regB); +#endif // VECTOR_SUB_GROUP_BROADCAT + } + + // reduction in local memory, assumes #wave=4 + local float2 reduceLM[SUBGROUP_SIZE * 3]; + if (groupId == 1) { + reduceLM[SUBGROUP_SIZE * 0 + slid] = totalSum; + } + if (groupId == 2) { + reduceLM[SUBGROUP_SIZE * 1 + slid] = totalSum; + } + if (groupId == 3) { + reduceLM[SUBGROUP_SIZE * 2 + slid] = totalSum; + } + + barrier(CLK_LOCAL_MEM_FENCE); + + if (groupId == 0) { + totalSum += reduceLM[SUBGROUP_SIZE * 0 + slid]; + } + if (groupId == 0) { + totalSum += reduceLM[SUBGROUP_SIZE * 1 + slid]; + } + if (groupId == 0) { + totalSum += reduceLM[SUBGROUP_SIZE * 2 + slid]; + } + + // 2 outputs per fiber in wave 0 + if (groupId == 0) { + dst = (global float*)((global char*)dst + offsetd); + vstore2(totalSum, 0, &(dst[gid * 2])); + } + +} diff --git a/ggml/src/ggml-opencl/kernels/l2_norm.cl b/ggml/src/ggml-opencl/kernels/l2_norm.cl new file mode 100644 index 0000000000..fb95355a67 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/l2_norm.cl @@ -0,0 +1,71 @@ +#ifdef cl_intel_required_subgroup_size +#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable +#define INTEL_GPU 1 +#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16))) +#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32))) +#elif defined(cl_qcom_reqd_sub_group_size) +#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable +#define ADRENO_GPU 1 +#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half"))) +#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full"))) +#endif + +#ifdef INTEL_GPU +REQD_SUBGROUP_SIZE_32 +#elif defined (ADRENO_GPU) +REQD_SUBGROUP_SIZE_64 +#endif +kernel void kernel_l2_norm_f32( + global void * src0, + ulong offset0, + global float * dst, + ulong offsetd, + int ne00, + int ne01, + int ne02, + int ne03, + ulong nb01, + ulong nb02, + ulong nb03, + float eps, + local float * sum +) { + src0 = (global void*)((global char*)src0 + offset0); + dst = (global float*)((global char*)dst + offsetd); + + int i03 = get_group_id(2); + int i02 = get_group_id(1); + int i01 = get_group_id(0); + + global float * x = (global float *) ((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01); + global float * y = (global float *) (dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); + + float sumf = 0; + + // parallel sum + for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) { + sumf += x[i00] * x[i00]; + } + sumf = sub_group_reduce_add(sumf); + + if (get_sub_group_local_id() == 0) { + sum[get_sub_group_id()] = sumf; + } + + barrier(CLK_LOCAL_MEM_FENCE); + + // broadcast + for (uint i = get_local_size(0) / get_max_sub_group_size() / 2; i > 0; i /= 2) { + if (get_local_id(0) < i) { + sum[get_local_id(0)] += sum[get_local_id(0) + i]; + } + } + + barrier(CLK_LOCAL_MEM_FENCE); + + const float scale = 1.0f/max(sqrt(sum[0]), eps); + + for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) { + y[i00] = x[i00] * scale; + } +} diff --git a/ggml/src/ggml-opencl/kernels/neg.cl b/ggml/src/ggml-opencl/kernels/neg.cl new file mode 100644 index 0000000000..a862d8bc58 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/neg.cl @@ -0,0 +1,125 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +kernel void kernel_neg_f32( + global const float * src0, + ulong offset0, + global float * dst, + ulong offsetd, + int n +) { + if (get_global_id(0) >= n) { + return; + } + src0 = (global float*)((global char*)src0 + offset0); + dst = (global float*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = -src0[get_global_id(0)]; +} + +kernel void kernel_neg_f32_4( + global const float4 * src0, + ulong offset0, + global float4 * dst, + ulong offsetd, + int n +) { + if (get_global_id(0) >= n) { + return; + } + src0 = (global float4*)((global char*)src0 + offset0); + dst = (global float4*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = -src0[get_global_id(0)]; +} + +kernel void kernel_neg_f16( + global const half * src0, + ulong offset0, + global half * dst, + ulong offsetd, + int n +) { + if (get_global_id(0) >= n) { + return; + } + src0 = (global half*)((global char*)src0 + offset0); + dst = (global half*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = -src0[get_global_id(0)]; +} + +kernel void kernel_neg_f16_4( + global const half4 * src0, + ulong offset0, + global half4 * dst, + ulong offsetd, + int n +) { + if (get_global_id(0) >= n) { + return; + } + src0 = (global half4*)((global char*)src0 + offset0); + dst = (global half4*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = -src0[get_global_id(0)]; +} + +kernel void kernel_neg_f32_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const float * x = (global const float *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global float * y = (global float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = -*x; + } +} + +kernel void kernel_neg_f16_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const half * x = (global const half *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global half * y = (global half *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = -*x; + } +} diff --git a/ggml/src/ggml-opencl/kernels/transpose.cl b/ggml/src/ggml-opencl/kernels/transpose.cl index 1279b6531b..ad89bdcbde 100644 --- a/ggml/src/ggml-opencl/kernels/transpose.cl +++ b/ggml/src/ggml-opencl/kernels/transpose.cl @@ -44,6 +44,19 @@ kernel void kernel_transpose_16_4x1( write_imageh(output, i * rows + j, (half4)(temp0, temp1, temp2, temp3)); } +// Transpose treating each element as 8-bit using buffer +kernel void kernel_transpose_8_buf( + global const uchar * input, + global uchar * output, + const int ldi, + const int ldo +) { + const int x = get_global_id(0); + const int y = get_global_id(1); + + output[x*ldo + y] = input[y*ldi + x]; +} + // Transpose treating each element as 16-bit using buffer kernel void kernel_transpose_16_buf( global const ushort * input, @@ -57,6 +70,19 @@ kernel void kernel_transpose_16_buf( output[x*ldo + y] = input[y*ldi + x]; } +// Transpose treating each element as 32-bit using buffer +kernel void kernel_transpose_32_buf( + global const uint * input, + global uint * output, + const int ldi, + const int ldo +) { + const int x = get_global_id(0); + const int y = get_global_id(1); + + output[x*ldo + y] = input[y*ldi + x]; +} + // 32-bit transpose, loading/storing a 4x4 tile of elements kernel void kernel_transpose_32( __read_only image1d_buffer_t input, diff --git a/ggml/src/ggml-openvino/.clang-format b/ggml/src/ggml-openvino/.clang-format new file mode 100644 index 0000000000..a2a24d7d33 --- /dev/null +++ b/ggml/src/ggml-openvino/.clang-format @@ -0,0 +1,154 @@ +--- +# Override root .clang-format +AlignConsecutiveAssignments: false +AlignConsecutiveDeclarations: false +Cpp11BracedListStyle: true +SpacesInContainerLiterals: false +BreakBeforeBraces: Attach +AccessModifierOffset: -4 +IndentCaseBlocks: false +IndentCaseLabels: false + +Language: Cpp +AlignAfterOpenBracket: Align +AlignArrayOfStructures: Left +AlignConsecutiveBitFields: AcrossComments +AlignConsecutiveMacros: AcrossComments +# AlignConsecutiveShortCaseStatements: AcrossComments +AlignEscapedNewlines: Left # LeftWithLastLine +AlignOperands: Align +AlignTrailingComments: + Kind: Always + OverEmptyLines: 1 +AllowAllArgumentsOnNextLine: true +AllowAllParametersOfDeclarationOnNextLine: false +# AllowBreakBeforeNoexceptSpecifier: OnlyWithParen +AllowShortBlocksOnASingleLine: Never +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: Inline +AllowShortIfStatementsOnASingleLine: Never +AllowShortLambdasOnASingleLine: Inline +AllowShortLoopsOnASingleLine: false +AlwaysBreakBeforeMultilineStrings: true +# Treat CUDA keywords/attributes as "attribute macros" and avoid breaking lines inside them +AttributeMacros: + - __host__ + - __device__ + - __global__ + - __forceinline__ + - __launch_bounds__ +BinPackArguments: true +BinPackParameters: false # OnePerLine +BitFieldColonSpacing: Both +# BreakAdjacentStringLiterals: true +BreakAfterAttributes: Never +BreakBeforeBinaryOperators: None +BreakBeforeInlineASMColon: OnlyMultiline +BreakBeforeTernaryOperators: false +# BreakBinaryOperations: Never +BreakConstructorInitializers: AfterColon +# BreakFunctionDefinitionParameters: false +BreakInheritanceList: AfterComma +BreakStringLiterals: true +# BreakTemplateDeclarations: Yes +ColumnLimit: 120 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +DerivePointerAlignment: false +DisableFormat: false +EmptyLineBeforeAccessModifier: Leave +EmptyLineAfterAccessModifier: Never +ExperimentalAutoDetectBinPacking: false +FixNamespaceComments: true +IncludeBlocks: Regroup +IncludeCategories: + - Regex: '".*"' + Priority: 1 + SortPriority: 0 + - Regex: '^<.*\.h>' + Priority: 2 + SortPriority: 0 + - Regex: '^<.*' + Priority: 3 + SortPriority: 0 + - Regex: '.*' + Priority: 4 + SortPriority: 0 +IncludeIsMainRegex: '([-_](test|unittest))?$' +IncludeIsMainSourceRegex: '' +IndentAccessModifiers: false +IndentExternBlock: NoIndent +IndentGotoLabels: false +IndentPPDirectives: AfterHash +IndentWidth: 4 +IndentWrappedFunctionNames: false +InsertBraces: true # NOTE: may lead to incorrect formatting +InsertNewlineAtEOF: true +JavaScriptQuotes: Leave +JavaScriptWrapImports: true +KeepEmptyLinesAtTheStartOfBlocks: false +LambdaBodyIndentation: Signature +LineEnding: LF +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBinPackProtocolList: Auto +ObjCBlockIndentWidth: 4 +ObjCSpaceAfterProperty: true +ObjCSpaceBeforeProtocolList: true +PPIndentWidth: -1 +PackConstructorInitializers: CurrentLine +PenaltyBreakAssignment: 2 +PenaltyBreakBeforeFirstCallParameter: 1 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyBreakTemplateDeclaration: 10 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 200 +PointerAlignment: Middle +QualifierAlignment: Left +#QualifierOrder: ['static', 'inline', 'friend', 'constexpr', 'const', 'volatile', 'type', 'restrict'] +RawStringFormats: + - Language: Cpp + Delimiters: + - cc + - CC + - cpp + - Cpp + - CPP + - 'c++' + - 'C++' + CanonicalDelimiter: '' +ReferenceAlignment: Middle +ReflowComments: false # IndentOnly +SeparateDefinitionBlocks: Always +SortIncludes: CaseInsensitive +SortUsingDeclarations: LexicographicNumeric +SpaceAfterCStyleCast: true +SpaceAfterLogicalNot: false +SpaceAfterTemplateKeyword: true +SpaceBeforeAssignmentOperators: true +SpaceBeforeCpp11BracedList: false +SpaceBeforeCtorInitializerColon: true +SpaceBeforeInheritanceColon: true +SpaceBeforeParens: ControlStatements +SpaceBeforeRangeBasedForLoopColon: true +SpaceInEmptyBlock: false +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 2 +SpacesInAngles: Never +SpacesInLineCommentPrefix: + Minimum: 1 + Maximum: -1 +SpacesInParentheses: false +SpacesInSquareBrackets: false +SpaceBeforeSquareBrackets: false +Standard: c++17 +TabWidth: 4 +UseTab: Never +WhitespaceSensitiveMacros: ['STRINGIZE'] +... diff --git a/ggml/src/ggml-openvino/CMakeLists.txt b/ggml/src/ggml-openvino/CMakeLists.txt new file mode 100644 index 0000000000..175b585661 --- /dev/null +++ b/ggml/src/ggml-openvino/CMakeLists.txt @@ -0,0 +1,22 @@ +find_package(OpenVINO REQUIRED) +find_package(OpenCL REQUIRED) + +include("${OpenVINO_DIR}/../3rdparty/tbb/lib/cmake/TBB/TBBConfig.cmake") + +file(GLOB_RECURSE GGML_HEADERS_OPENVINO "*.h" "*.hpp") +file(GLOB_RECURSE GGML_SOURCES_OPENVINO "*.cpp") + +ggml_add_backend_library(ggml-openvino + ${GGML_SOURCES_OPENVINO} + ${GGML_HEADERS_OPENVINO} +) + +target_link_libraries(ggml-openvino PRIVATE openvino::runtime TBB::tbb OpenCL::OpenCL) + +if (GGML_OPENVINO) + if (CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") + elseif (CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64" OR CMAKE_SYSTEM_PROCESSOR STREQUAL "amd64" OR CMAKE_SYSTEM_PROCESSOR STREQUAL "AMD64") + else() + message(FATAL_ERROR "OpenVINO: OpenVINO toolkit supports x86-64 and arm64 but not ${CMAKE_SYSTEM_PROCESSOR}") + endif() +endif() diff --git a/ggml/src/ggml-openvino/ggml-decoder.cpp b/ggml/src/ggml-openvino/ggml-decoder.cpp new file mode 100644 index 0000000000..0938d2273e --- /dev/null +++ b/ggml/src/ggml-openvino/ggml-decoder.cpp @@ -0,0 +1,975 @@ +#include "ggml-decoder.h" + +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-openvino-extra.h" +#include "ggml-openvino.h" +#include "ggml-quants.h" + +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +GgmlOvDecoder::GgmlOvDecoder(ggml_cgraph * cgraph, + ModelParams & model_params, + ComputeParams & compute_params, + std::map> & model_weights, + bool is_static, + bool is_stateful, + bool is_prefill, + int prefill_chunk_size) : + m_is_static(is_static), + m_is_stateful(is_stateful), + m_is_prefill(is_prefill), + m_naive(false), + m_prefill_chunk_size(prefill_chunk_size), + m_cgraph(cgraph), + m_model_weights(model_weights), + m_model_params(model_params), + m_compute_params(compute_params) { + if (auto * env = getenv("GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS"); env && std::string(env) != "0") { +#ifdef _WIN32 + _putenv_s("GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS", ""); +#else + unsetenv("GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS"); +#endif + print_tensor_address_map(cgraph); + } + + validate_cgraph(); + + set_input_output(); + compute_model_inputs(); + compute_model_outputs(); + + for (int node_n = 0; node_n < cgraph->n_nodes; node_n++) { + m_node_info_list[node_n].node_op_case = compute_op_case(m_node_info_list[node_n].node); + m_node_info_list[node_n].node_op_type = compute_op_type(m_node_info_list[node_n].node); + } + + add_extra_inputs(); +} + +void GgmlOvDecoder::update_io(ggml_cgraph * cgraph) { + m_cgraph = cgraph; + m_model_inputs.clear(); + m_model_outputs.clear(); + m_node_info_list.clear(); + set_input_output(); + compute_model_inputs(); + compute_model_outputs(); +} + +GgmlOvDecoder::GgmlOvDecoder(ggml_cgraph * cgraph, std::map> & model_weights) { + m_cgraph = cgraph; + m_model_weights = model_weights; + m_naive = true; + set_input_output(); + compute_model_inputs(); + compute_model_outputs(); + for (int node_n = 0; node_n < cgraph->n_nodes; node_n++) { + m_node_info_list[node_n].node_op_case = compute_op_case(m_node_info_list[node_n].node); + m_node_info_list[node_n].node_op_type = compute_op_type(m_node_info_list[node_n].node); + } +} + +void GgmlOvDecoder::set_input_output() { + for (int node_n = 0; node_n < m_cgraph->n_nodes; node_n++) { + auto node = m_cgraph->nodes[node_n]; + + NodeInfo current_node_info; + auto node_name = std::string(node->name); + auto node_output_name = node_name; + auto * node_output = node; + if (node->op == GGML_OP_SET_ROWS) { + // SET_ROWS updates the tensor in place. For later ov op that uses the + // the view_src of SET_ROWS, we need to make sure they get the updated tensor + // by putting the view_src name in the tensor_map in + // /src/frontends/ggml/src/translate_session.cpp + node_output_name = std::string(node->view_src->name); + node_output = node->view_src; + } + + current_node_info.node = node; + current_node_info.node_name = node_name; + current_node_info.node_output = node_output; + current_node_info.node_output_name = node_output_name; + current_node_info.node_op_case = 0; + current_node_info.data_addr = node->data; + + for (int i = 0; i < GGML_MAX_SRC; i++) { + auto * src = node->src[i]; + if (src == nullptr) { + continue; + } + auto src_name = std::string(src->name); + if (src->flags & GGML_TENSOR_FLAG_INPUT) { + src_name = get_graph_input_ov_name(src, node); + } + current_node_info.node_inputs[src_name] = src; + current_node_info.node_inputs_names.push_back(src_name); + } + + m_node_info_list.push_back(current_node_info); + } +} + +int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const { + int op_case = 0; + switch (node->op) { + case GGML_OP_RESHAPE: { + auto * src = node->src[0]; + if (src->op == GGML_OP_RESHAPE && src->src[0]->ne[0] == node->ne[0] && src->src[0]->ne[1] == node->ne[1]) { + op_case = 4; + } else if (node->ne[0] * node->ne[1] == src->ne[0]) { + op_case = 1; + } else if (src->ne[0] * src->ne[1] == node->ne[0]) { + op_case = 2; + if (src->ne[2] * src->ne[3] == node->ne[1]) { + op_case = 5; + } + } else if (src->ne[0] * src->ne[1] == node->ne[1]) { + op_case = 3; + } else if (src->ne[1] * src->ne[2] == node->ne[1]) { + op_case = 6; + } + break; + } + case GGML_OP_CONT: { + if (node->src[0]->op == GGML_OP_PERMUTE) { + op_case = 1; + } else if (node->src[0]->op == GGML_OP_TRANSPOSE) { + op_case = 2; + } else if (node->src[0]->op == GGML_OP_VIEW) { + op_case = 3; + } + break; + } + case GGML_OP_PERMUTE: { + if (node->src[0]->op != GGML_OP_VIEW) { + op_case = 1; + } else if (node->src[0]->src[0]->op == GGML_OP_NONE) { + // kv cache tensor + std::string src_name(node->view_src->name); + int layer = extract_layer_from_name(src_name); + if (!is_swa_layer(layer)) { + op_case = 2; + } else { + op_case = 3; + } + } else { + // rope'ed query tensor + op_case = 4; + } + break; + } + case GGML_OP_MUL_MAT: { + if (node->src[0]->op == GGML_OP_CONT && node->src[0]->src[0]->op == GGML_OP_TRANSPOSE) { + op_case = 2; + } else if (node->src[0]->op == GGML_OP_VIEW && node->src[1]->op == GGML_OP_VIEW) { + op_case = 3; + } + break; + } + case GGML_OP_GET_ROWS: { + if (node->src[1]->op == GGML_OP_VIEW) { + op_case = 2; + } + break; + } + case GGML_OP_ROPE: { + if (node->src[0]->op == GGML_OP_VIEW) { + op_case = 2; + } + break; + } + case GGML_OP_VIEW: { + if (node->src[0]->op == GGML_OP_VIEW) { + auto * src = node->src[0]; + if (ggml_nelements(node) != ggml_nelements(src)) { + throw std::runtime_error("Unsupported VIEW case"); + } + op_case = 2; + } + { + auto * src = node->src[0]; + if ((ggml_nelements(node) != ggml_nelements(src)) && m_naive) { + // Compare each dimension of node and src, if only one dimension differs then op_case=3 + int diff_count = 0; + for (int i = 0; i < GGML_MAX_DIMS; i++) { + if (node->ne[i] != src->ne[i]) { + diff_count++; + } + } + if (diff_count == 1) { + op_case = 3; + } + } + } + break; + } + default: + break; + } + return op_case; +} + +int extract_layer_from_name(const std::string & name) { + size_t pos1 = name.find("_l"); + assert(pos1 != std::string::npos); + pos1 += 2; + size_t pos2 = name.find(' ', pos1); + if (pos2 == std::string::npos) { + pos2 = name.length(); + } + std::string layer_str = name.substr(pos1, pos2 - pos1); + int layer = std::stoi(layer_str); + return layer; +} + +std::pair GgmlOvDecoder::compute_llm_params(ggml_cgraph * cgraph, bool is_static) { + ModelParams model_params; + ComputeParams compute_params; + for (int i = 0; i < cgraph->n_nodes; i++) { + auto * node = cgraph->nodes[i]; + std::string name = std::string(node->name); + if (node->op == GGML_OP_FLASH_ATTN_EXT) { + model_params.n_heads = node->src[0]->ne[2]; + model_params.n_heads_kv = node->src[1]->ne[2]; + model_params.head_size = node->src[0]->ne[0]; + compute_params.input_len = node->src[0]->ne[1]; + + auto * cache_k_perm = node->src[1]; + if (cache_k_perm->op == GGML_OP_CPY) { + cache_k_perm = cache_k_perm->src[0]; + } + assert(cache_k_perm->op == GGML_OP_PERMUTE); + auto * cache_k_view = cache_k_perm->src[0]; + assert(cache_k_view->op == GGML_OP_VIEW); + + auto * cache_k = cache_k_view->src[0]; + int layer = extract_layer_from_name(cache_k->name); + auto * mask = node->src[3]; + std::string mask_name(mask->name); + + model_params.kv_buffer_ctx_id = ggml_backend_openvino_buffer_get_ctx_id(cache_k->buffer); + if (mask_name.find("swa") != std::string::npos) { + model_params.swa_layers.push_back(layer); + model_params.ctx_per_seq_swa = cache_k->ne[1]; + } else { + model_params.ctx_per_seq = cache_k->ne[1]; + model_params.n_seq = cache_k->ne[2]; + } + + compute_params.n_seq_active = mask->ne[3]; + auto seq_size = cache_k->ne[0] * cache_k->ne[1] * ggml_type_size(cache_k->type); + size_t offset; + memcpy(&offset, cache_k_view->op_params, sizeof(size_t)); + compute_params.seq_active_start = offset / seq_size; + compute_params.token_len_per_seq = node->ne[2]; + + if (mask_name.find("swa") != std::string::npos) { + compute_params.attention_size_swa = mask->ne[0]; + } else { + compute_params.attention_size = mask->ne[0]; + } + if (is_static) { + compute_params.attention_size = model_params.ctx_per_seq; + compute_params.attention_size_swa = model_params.ctx_per_seq_swa; + compute_params.token_len_per_seq = 1; + } + break; + } + if (node->op == GGML_OP_ROPE) { + memcpy(model_params.rope_params, node->op_params, sizeof(int32_t) * 15); + } + } + auto * output_tensor = cgraph->nodes[cgraph->n_nodes - 1]; + compute_params.output_len = output_tensor->ne[1]; + // for NPU, output_len is always 1 except for llama-perplexity + if (is_static && compute_params.output_len == 0) { + compute_params.output_len = 1; + } + model_params.ctx = model_params.ctx_per_seq * model_params.n_seq; + model_params.ctx_swa = model_params.ctx_per_seq_swa * model_params.n_seq; + return {model_params, compute_params}; +} + +void GgmlOvDecoder::validate_cgraph() const { + if (m_model_params.n_seq > 1 && m_is_static == true) { + throw std::runtime_error("n_seq > 1 is not supported on NPU. Try setting -np 1."); + } +} + +ov::PartialShape GgmlOvDecoder::get_graph_input_shape(const ggml_tensor * op, const ggml_tensor * input) const { + if (m_naive) { + return input!= nullptr ? ov::PartialShape{get_shape(input)} : ov::PartialShape{get_shape(op)}; + } + auto name = std::string(input->name); + ov::PartialShape input_shape; + + if (is_inp_tok(input, op) || is_inp_pos(input, op)) { + // tokens or positions + int len = m_is_static ? (m_is_prefill ? m_prefill_chunk_size : 1) : -1; + input_shape = ov::PartialShape{1, 1, 1, len}; + + } else if (is_output_idx(input, op)) { + // output index + input_shape = ov::PartialShape{1, 1, 1, m_is_static ? m_compute_params.output_len : -1}; + + } else if (is_inp_mask(input, op)) { + // mask + if (m_is_static) { + input_shape = ov::PartialShape{1, 1, m_is_prefill ? m_prefill_chunk_size : 1, m_model_params.ctx}; + } else if (m_is_stateful) { + input_shape = ov::PartialShape{1, 1, -1, -1}; + } else { + input_shape = ov::PartialShape{-1, 1, -1, -1}; + } + + } else if (is_kvcache(input, op)) { + // kvcache + input_shape = ov::PartialShape{get_shape(input)}; + if (!m_is_static) { + // do not fix ctx size to make llama-bench work across test params + input_shape[2] = -1; + } + if (is_stateful()) { + // Convert stateless KV cache layout [1, 1, seq, n_heads_kv * head_size] + // to stateful layout [1, seq, n_heads_kv, head_size]. + assert(input_shape.size() == 4 && input_shape[0] == 1 && input_shape[1] == 1 && + input_shape[2].is_dynamic() && + input_shape[3] == (m_model_params.n_heads_kv * m_model_params.head_size)); + input_shape = {input_shape[0], ov::Dimension::dynamic(), m_model_params.n_heads_kv, + m_model_params.head_size}; + } + + } else if (is_kv_idx(input, op)) { + // kv update index + int len = m_is_static ? (m_is_prefill ? m_prefill_chunk_size : 1) : -1; + input_shape = ov::PartialShape{1, 1, 1, len}; + + } else { + input_shape = ov::PartialShape{get_shape(input)}; + } + return input_shape; +} + +void GgmlOvDecoder::add_extra_inputs() { + // Extra inputs: + // 1. `attention_size`, used in FLASH_ATTN where the shape of the matmul's are 256 aligned, + // see llama_kv_cache_unified::get_n_kv and llama_kv_cache_unified::get_padding. + // 2. `n_seq_active` and `seq_active_start`, used in FLASH_ATTN_EXT to indicate the active sequences in the batch + + auto create_1d_input = [this](const std::string & name, int64_t value) { + if (m_is_static) { + auto constant = + std::make_shared(ov::element::i64, ov::Shape{1}, std::vector{value}); + constant->set_friendly_name(name); + m_model_extra_inputs[name] = constant; + } else { + auto param_node = std::make_shared(ov::element::i64, ov::Shape{1}); + param_node->set_friendly_name(name); + param_node->output(0).get_tensor().set_names({name}); + m_model_extra_inputs[name] = param_node; + + auto tensor = std::make_shared(ov::element::i64, ov::Shape{1}); + *tensor->data() = value; + m_model_extra_input_values[name] = tensor; + } + }; + + create_1d_input("attention_size", m_compute_params.attention_size); + if (m_compute_params.attention_size_swa != -1) { + create_1d_input("attention_size_swa", m_compute_params.attention_size_swa); + } + create_1d_input("n_seq_active", m_compute_params.n_seq_active); + create_1d_input("seq_active_start", m_compute_params.seq_active_start); + create_1d_input("seq_active_end", m_compute_params.seq_active_start + m_compute_params.n_seq_active); + create_1d_input("token_len_per_seq", m_compute_params.token_len_per_seq); + // create_1d_input("token_len", m_token_len_per_seq * m_n_seq_active); +} + +bool GgmlOvDecoder::node_is_used_as_src(const int node_idx) { + ggml_tensor * node = m_cgraph->nodes[node_idx]; + for (int i = node_idx; i < m_cgraph->n_nodes; i++) { + ggml_tensor * other_node = m_cgraph->nodes[i]; + for (int j = 0; j < GGML_MAX_SRC; j++) { + if (other_node->src[j] == node) { + return true; + } + } + } + return false; +} + +void GgmlOvDecoder::compute_model_inputs() { + m_model_inputs.clear(); + m_inputs.clear(); + for (int i = 0; i < m_cgraph->n_nodes; i++) { + ggml_tensor * node = m_cgraph->nodes[i]; + // the node op is NONE means this node maybe as input of later nodes, we should add it to model inputs for this node. + if (node->op == GGML_OP_NONE && node_is_used_as_src(i)) { + std::string node_name(node->name); + if (m_model_weights.find(node_name) == m_model_weights.end()) { + m_inputs[node_name] = node; + auto param_node = + std::make_shared(get_ov_type(node), get_graph_input_shape(node, nullptr)); + param_node->set_friendly_name(node_name); + param_node->output(0).get_tensor().set_names({node_name}); + m_model_inputs[node_name] = param_node; + } + continue; + } + for (int i = 0; i < GGML_MAX_SRC; i++) { + auto * src = node->src[i]; + if (src == nullptr) { + continue; + } + std::string src_name = std::string(src->name); + if (src->flags & GGML_TENSOR_FLAG_INPUT) { + src_name = get_graph_input_ov_name(src, node); + } + if (m_model_weights.find(src_name) != m_model_weights.end()) { + continue; + } + + bool is_intermediate_node = false; + for (const auto & node_info : m_node_info_list) { + if (node_info.node == src) { + is_intermediate_node = true; + break; + } + } + if (is_intermediate_node) { + continue; + } + if (m_model_inputs.find(src_name) != m_model_inputs.end()) { + continue; + } + + m_inputs[src_name] = src; + + ggml_backend_buffer * buffer = src->buffer; + // GGML_BACKEND_BUFFER_USAGE_ANY are kv caches + if (buffer->usage == GGML_BACKEND_BUFFER_USAGE_ANY) { + if (auto it = std::find(m_model_params.kv_names.begin(), m_model_params.kv_names.end(), src_name); + it == m_model_params.kv_names.end()) { + m_model_params.kv_names.push_back(src_name); + } + } + ov::PartialShape param_shape = get_graph_input_shape(node, src); + auto param_node = std::make_shared(get_ov_type(src), param_shape); + param_node->set_friendly_name(src_name); + param_node->output(0).get_tensor().set_names({src_name}); + m_model_inputs[src_name] = param_node; + } + } +} + +void GgmlOvDecoder::compute_model_outputs() { + m_model_outputs.clear(); + m_model_output_names.clear(); + for (int node_n = 0; node_n < m_cgraph->n_nodes; node_n++) { + auto * cur_node = m_cgraph->nodes[node_n]; + // if the node op is NONE means this node is not used at all, we can skip it directly without adding to model outputs. + if (cur_node->op == GGML_OP_NONE) { + continue; + } + auto cur_node_use_count = m_cgraph->use_counts[ggml_hash_find(&m_cgraph->visited_hash_set, cur_node)]; + if (cur_node_use_count == 0) { + // The output of SET_ROWS is the view_src tensor, which is updated in place. We should use the view_src name as the output name to make sure it can be correctly matched with the later ops that use the view_src. + if (cur_node != nullptr && cur_node->op == GGML_OP_SET_ROWS) { + cur_node = cur_node->view_src; + } + } else { + int input_use_count = 0; + for (int i = 0; i < m_cgraph->n_nodes; i++) { + ggml_tensor * node = m_cgraph->nodes[i]; + for (int j = 0; j < GGML_MAX_SRC; j++) { + if (node->src[j] != NULL && node->src[j] == cur_node) { + input_use_count++; + } + } + } + if (input_use_count == cur_node_use_count) { + cur_node = nullptr; + } + } + if (cur_node != nullptr) { + std::string node_output_name(cur_node->name); + m_model_outputs[node_output_name] = cur_node; + m_model_output_names.push_back(node_output_name); + } + } +} + +const ggml_tensor * GgmlOvDecoder::get_tensor_used_op(const ggml_tensor * tensor) const { + if (tensor == nullptr) { + return nullptr; + } + for (int i = 0; i < m_cgraph->n_nodes; i++) { + const auto * node = m_cgraph->nodes[i]; + for (int j = 0; j < GGML_MAX_SRC; j++) { + if (node->src[j] == tensor) { + return node; + } + } + } + return nullptr; +} + +const ggml_tensor * GgmlOvDecoder::get_tensor_from_name(const std::string & name) const { + for (int i = 0; i < m_cgraph->n_nodes; i++) { + const auto * node = m_cgraph->nodes[i]; + for (int j = 0; j < GGML_MAX_SRC; j++) { + const auto * src = node->src[j]; + if (src == nullptr) { + break; + } + if (std::string(src->name) == name) { + return src; + } + } + } + return nullptr; +} + +std::map GgmlOvDecoder::get_kv_param_res_names() const { + std::map kv_param_res_names; + for (const auto & name : m_model_params.kv_names) { + kv_param_res_names[name] = name; + } + return kv_param_res_names; +} + +std::map> GgmlOvDecoder::create_weight_nodes(ggml_cgraph * cgraph, bool naive) { + static std::mutex weights_mutex; + std::lock_guard lock(weights_mutex); + + std::map> model_weights; + auto * nodes = cgraph->nodes; + auto n_nodes = cgraph->n_nodes; + for (int node_i = 0; node_i < n_nodes; node_i++) { + auto * node = nodes[node_i]; + for (int i = 0; i < GGML_MAX_SRC; i++) { + auto * src = node->src[i]; + if (src == nullptr) { + continue; + } + + std::string src_name(src->name); + if (is_rope_freqs_weight(src, node)) { + src_name = "rope_freqs.weight"; + } + if (!src->view_src) { + ggml_backend_buffer * buffer = src->buffer; + if (buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS || ggml_is_quantized(src->type)) { + if (model_weights.find(src_name) == model_weights.end()) { + auto weight_node = create_weight_node(src, naive); + weight_node->set_friendly_name(src_name); + model_weights[src_name] = weight_node; + } + } + } + } + } + return model_weights; +} + +std::shared_ptr GgmlOvDecoder::create_weight_node(ggml_tensor * tensor, bool naive) { + const bool is_ov_buffer = ggml_backend_buffer_is_openvino(tensor->buffer); + + // Check if we have a pre-built constant from the OpenVINO backend buffer + // This is set during ggml_backend_openvino_buffer_set_tensor + if (tensor->extra) { + OPENVINO_ASSERT(is_ov_buffer, "Unsupported weight tensor: " + std::string(tensor->name) + + " Possibly this is a cpu backend repacked quantized weights"); + // Cast to our extra base type and check the type + auto * extra_base = static_cast(tensor->extra); + + if (extra_base->type == ggml_openvino_extra_base::Type::WEIGHT) { + // F16/F32/BF16 weight with shared-memory constant + auto * weight_extra = static_cast(tensor->extra); + if (weight_extra->weight_node) { + // GGML_LOG_DEBUG("%s: using pre-built weight node for %s\n", __func__, tensor->name); + return weight_extra->weight_node; + } + } else if (extra_base->type == ggml_openvino_extra_base::Type::QUANTIZED_WEIGHT) { + // Quantized weight with pre-extracted data + auto * quant_extra = static_cast(tensor->extra); + if (quant_extra->weight_node) { + // GGML_LOG_DEBUG("%s: using pre-extracted quantized weight node for %s\n", __func__, tensor->name); + return quant_extra->weight_node; + } + } + } + + // There are three cases where we need to create a new weight node: + // 1. weights are in openvino_host_buffer. Weight loading to host buffer will not trigger backend_buffer_set_tensor + // 2. weights are in cpu/cpu_mapped buffer. On token_embd.weight goes to case 1 or 2, depending on whether mmap or direct_io is used + // 3. test-backend-ops. buffers in test-backend-ops does not set USAGE_WEIGHT so backend_buffer_set_tensor will not create weight node + + // GGML_LOG_DEBUG("%s: creating new weight node for %s\n", __func__, tensor->name); + static const std::set weight_types = {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16, + GGML_TYPE_Q8_0, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, + GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, GGML_TYPE_Q6_K}; + if (weight_types.find(tensor->type) == weight_types.end()) { + throw std::runtime_error("Unexpected weight tensor type: " + std::string(tensor->name) + " with type " + + ggml_type_name(tensor->type)); + } + + OvWeight ov_weight; + if (ggml_is_quantized(tensor->type)) { + auto use_bias = naive; + if (is_ov_buffer) { + // For quantized weights, copy raw data to a temp buffer first because + // process_weight_tensor reads from data and writes extracted results + // (weights/scales/zp) to output_base_ptr — they would overlap if both + // point to tensor->data. + size_t raw_size = ggml_nbytes(tensor); + std::vector tmp(raw_size); + memcpy(tmp.data(), tensor->data, raw_size); + ov_weight = process_weight_tensor(tensor, tmp.data(), tensor->data, use_bias); + } else { + ov_weight = process_weight_tensor(tensor, tensor->data, nullptr, use_bias); + } + } else { + // For non-quantized weights (F16/F32/BF16), data is already in tensor->data. + // process_weight_tensor will create an ov::Tensor wrapping tensor->data directly. + ov_weight = process_weight_tensor(tensor, tensor->data, tensor->data); + } + + ov_weight.weight_node->set_friendly_name(tensor->name); + if (!is_ov_buffer) { + return ov_weight.weight_node; + } + + ggml_openvino_extra_base * extra; + if (ov_weight.is_quantized()) { + extra = new ggml_openvino_quantized_weight_extra(std::move(ov_weight.weights), std::move(ov_weight.scales), + std::move(ov_weight.zp), ov_weight.weight_node); + } else { + extra = new ggml_openvino_weight_extra(std::move(ov_weight.weights), ov_weight.weight_node); + } + ggml_openvino_buffer_register_extra(tensor, extra); + + return ov_weight.weight_node; +} + +void GgmlOvDecoder::dump_cgraph(const ggml_cgraph * cgraph, std::string & filename) { + std::ofstream file(filename); + if (!file.is_open()) { + std::cerr << "Failed to open file" << std::endl; + return; + } + + file << "=== GRAPH ===\n"; + + // clang-format off + file << "n_nodes = " << cgraph->n_nodes << "\n"; + file << " " << std::setw(3) << "nodes" + << std::setw(15) << "shape" + << std::setw(20) << "op" + << std::setw(20) << "name" + << std::setw(3) << " " + << std::setw(62) << "stride" + << std::setw(20) << "buffer_type" + << "\n"; + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_tensor * node = cgraph->nodes[i]; + + // Get buffer type name + const char * buf_name = "none"; + ggml_backend_buffer_t buf = node->view_src ? node->view_src->buffer : node->buffer; + if (buf) { + buf_name = ggml_backend_buffer_name(buf); + } + + file << " - " << std::setw(3) << i << ": [ " + << std::setw(5) << node->ne[0] << ", " + << std::setw(5) << node->ne[1] << ", " + << std::setw(5) << node->ne[2] << ", " + << std::setw(5) << node->ne[3] << "] " + << std::left << std::setw(20) << ggml_op_name(node->op) << std::right << " " + << std::left << std::setw(45) << node->name << std::right + << std::setw(2) << "[ " + << std::setw(0) << node->nb[0] << ", " + << std::setw(5) << node->nb[1] << ", " + << std::setw(5) << node->nb[2] << ", " + << std::setw(5) << node->nb[3] << "] " + << std::right << std::setw(15) << buf_name << std::right + << "\n"; + + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (auto* src = node->src[i]) { + // Get buffer type name for source + const char * src_buf_name = "none"; + ggml_backend_buffer_t src_buf = src->view_src ? src->view_src->buffer : src->buffer; + if (src_buf) { + src_buf_name = ggml_backend_buffer_name(src_buf); + } + + file << std::setw(10) << " [ " + << std::setw(5) << src->ne[0] << ", " + << std::setw(5) << src->ne[1] << ", " + << std::setw(5) << src->ne[2] << ", " + << std::setw(5) << src->ne[3] << "] " + << std::setw(12) + << i << ": " << std::left << std::setw(12) << ggml_op_name(src->op) << std::right; + file << std::left << std::setw(30) << src->name << std::right + << std::setw(16) << "[ " + << std::setw(0) << src->nb[0] << ", " + << std::setw(5) << src->nb[1] << ", " + << std::setw(5) << src->nb[2] << ", " + << std::setw(5) << src->nb[3] << "] " + << std::right << std::setw(15) << src_buf_name << std::right + << "\n"; + } + } + } + + file << "n_leafs = " << cgraph->n_leafs << "\n"; + for (int i = 0; i < cgraph->n_leafs; i++) { + ggml_tensor * node = cgraph->leafs[i]; + + // Get buffer type name for leaf + const char * leaf_buf_name = "none"; + ggml_backend_buffer_t leaf_buf = node->view_src ? node->view_src->buffer : node->buffer; + if (leaf_buf) { + leaf_buf_name = ggml_backend_buffer_name(leaf_buf); + } + + file << " - " << std::setw(3) << i << ": [ " + << std::setw(5) << node->ne[0] << ", " + << std::setw(5) << node->ne[1] << "] " + << std::setw(8) << ggml_op_name(node->op) << " " + << std::setw(16) << ggml_get_name(node) + << std::setw(20) << leaf_buf_name << "\n"; + } + // clang-format on + file << "========================================\n"; + + file.close(); +} + +void print_tensor_address_map(const ggml_cgraph * cgraph) { + std::map> address_map; + for (int node_n = 0; node_n < cgraph->n_nodes; node_n++) { + auto * node = cgraph->nodes[node_n]; + if (node->data) { + auto it = address_map.find(node->data); + if (it == address_map.end()) { + address_map[node->data] = std::vector(); + } + address_map[node->data].push_back(node->name); + } + } + for (const auto & pair : address_map) { + std::cout << "Address: " << pair.first << std::endl; + for (const auto & name : pair.second) { + std::cout << name << " ; "; + } + std::cout << std::endl << std::endl; + } +} + +ov::Shape GgmlOvDecoder::get_shape(const ggml_tensor * tensor) { + std::vector shape; + for (int i = GGML_MAX_DIMS - 1; i >= 0; --i) { + shape.push_back(static_cast(tensor->ne[i])); + } + return shape; +} + +std::vector GgmlOvDecoder::get_stride(const ggml_tensor * tensor) { + std::vector stride; + for (int i = GGML_MAX_DIMS - 1; i >= 0; --i) { + stride.push_back(static_cast(tensor->nb[i])); + } + return stride; +} + +ov::element::Type GgmlOvDecoder::get_ov_type(const ggml_tensor * tensor) { + switch (tensor->type) { + case GGML_TYPE_F64: + return ov::element::f64; + case GGML_TYPE_F32: + return ov::element::f32; + case GGML_TYPE_F16: + return ov::element::f16; + case GGML_TYPE_BF16: + return ov::element::bf16; + case GGML_TYPE_I8: + return ov::element::i8; + case GGML_TYPE_I16: + return ov::element::i16; + case GGML_TYPE_I32: + return ov::element::i32; + case GGML_TYPE_I64: + return ov::element::i64; + default: + return ov::element::dynamic; + } +} + +ov::PartialShape GgmlOvDecoder::get_input_shape(int node_idx, const std::string & name) const { + return ov::PartialShape(get_shape(m_node_info_list[node_idx].node_inputs.at(name))); +} + +std::vector GgmlOvDecoder::get_input_stride(int node_idx, const std::string & name) const { + return get_stride(m_node_info_list[node_idx].node_inputs.at(name)); +} + +ov::element::Type GgmlOvDecoder::get_input_type(int node_idx, const std::string & name) const { + return get_ov_type(m_node_info_list[node_idx].node_inputs.at(name)); +} + +size_t GgmlOvDecoder::get_input_size() const { + return m_model_inputs.size(); +} + +size_t GgmlOvDecoder::get_input_size(int node_idx) const { + return m_node_info_list[node_idx].node_inputs_names.size(); +} + +std::vector GgmlOvDecoder::get_input_names(int node_idx) const { + return m_node_info_list[node_idx].node_inputs_names; +} + +ov::PartialShape GgmlOvDecoder::get_output_shape(int node_idx) const { + auto * ggml_tensor = m_node_info_list[node_idx].node_output; + return ov::PartialShape(get_shape(ggml_tensor)); +} + +ov::element::Type GgmlOvDecoder::get_output_type(const int node_idx) const { + return get_ov_type(m_node_info_list[node_idx].node); +} + +std::vector GgmlOvDecoder::get_output_names(int node_idx) const { + return {m_node_info_list[node_idx].node_output_name}; +} + +const std::string & GgmlOvDecoder::get_op_name() const { + static const std::string unknown_name = "UNKNOWN_OP_NAME"; + return unknown_name; +} + +const std::string & GgmlOvDecoder::get_op_name(int node_idx) const { + return m_node_info_list[node_idx].node_name; +} + +int32_t * GgmlOvDecoder::get_input_op_params(int node_idx, const std::string & name) const { + return m_node_info_list[node_idx].node_inputs.at(name)->op_params; +} + +int32_t * GgmlOvDecoder::get_output_op_params(int node_idx) const { + return m_node_info_list[node_idx].node->op_params; +} + +void GgmlOvDecoder::visit_subgraph(std::function, int node_idx)> node_visitor) const { + for (int node_idx = 0; node_idx < m_cgraph->n_nodes; node_idx++) { + if (m_cgraph->nodes[node_idx]->op == GGML_OP_NONE) { + continue; + } + node_visitor(std::make_shared(*this), node_idx); + } +} + +std::string GgmlOvDecoder::compute_op_type(const ggml_tensor * node) { + static const std::map ops = { + {GGML_OP_NONE, "GGML_OP_NONE" }, + {GGML_OP_ACC, "GGML_OP_ACC" }, + {GGML_OP_ADD, "GGML_OP_ADD" }, + {GGML_OP_ADD1, "GGML_OP_ADD1" }, + {GGML_OP_CONT, "GGML_OP_CONT" }, + {GGML_OP_DIV, "GGML_OP_DIV" }, + {GGML_OP_DUP, "GGML_OP_DUP" }, + {GGML_OP_GET_ROWS, "GGML_OP_GET_ROWS" }, + {GGML_OP_MUL, "GGML_OP_MUL" }, + {GGML_OP_MUL_MAT, "GGML_OP_MUL_MAT" }, + {GGML_OP_PERMUTE, "GGML_OP_PERMUTE" }, + {GGML_OP_RESHAPE, "GGML_OP_RESHAPE" }, + {GGML_OP_RMS_NORM, "GGML_OP_RMS_NORM" }, + {GGML_OP_ROPE, "GGML_OP_ROPE" }, + {GGML_OP_SCALE, "GGML_OP_SCALE" }, + {GGML_OP_SOFT_MAX, "GGML_OP_SOFT_MAX" }, + {GGML_OP_SUB, "GGML_OP_SUB" }, + {GGML_OP_TRANSPOSE, "GGML_OP_TRANSPOSE" }, + {GGML_OP_VIEW, "GGML_OP_VIEW" }, + {GGML_OP_SET_ROWS, "GGML_OP_SET_ROWS" }, + {GGML_OP_CPY, "GGML_OP_CPY" }, + {GGML_OP_FLASH_ATTN_EXT, "GGML_OP_FLASH_ATTN_EXT"}, + }; + static const std::map unary_ops = { + {GGML_UNARY_OP_ABS, "GGML_UNARY_OP_ABS" }, + {GGML_UNARY_OP_SGN, "GGML_UNARY_OP_SGN" }, + {GGML_UNARY_OP_NEG, "GGML_UNARY_OP_NEG" }, + {GGML_UNARY_OP_STEP, "GGML_UNARY_OP_STEP" }, + {GGML_UNARY_OP_TANH, "GGML_UNARY_OP_TANH" }, + {GGML_UNARY_OP_ELU, "GGML_UNARY_OP_ELU" }, + {GGML_UNARY_OP_RELU, "GGML_UNARY_OP_RELU" }, + {GGML_UNARY_OP_SIGMOID, "GGML_UNARY_OP_SIGMOID" }, + {GGML_UNARY_OP_GELU, "GGML_UNARY_OP_GELU" }, + {GGML_UNARY_OP_GELU_QUICK, "GGML_UNARY_OP_GELU_QUICK" }, + {GGML_UNARY_OP_SILU, "GGML_UNARY_OP_SILU" }, + {GGML_UNARY_OP_HARDSWISH, "GGML_UNARY_OP_HARDSWISH" }, + {GGML_UNARY_OP_HARDSIGMOID, "GGML_UNARY_OP_HARDSIGMOID"}, + {GGML_UNARY_OP_EXP, "GGML_UNARY_OP_EXP" }, + {GGML_UNARY_OP_COUNT, "GGML_UNARY_OP_COUNT" } + }; + static const std::map glu_ops = { + {GGML_GLU_OP_SWIGLU, "GGML_GLU_OP_SWIGLU"}, + {GGML_GLU_OP_GEGLU, "GGML_GLU_OP_GEGLU" }, + {GGML_GLU_OP_REGLU, "GGML_GLU_OP_REGLU" } + }; + + switch (node->op) { + case GGML_OP_UNARY: + return unary_ops.at(ggml_get_unary_op(node)); + case GGML_OP_GLU: + return glu_ops.at(ggml_get_glu_op(node)); + default: + return ops.at(node->op); + } + static const std::string unknown_op = "UNKNOWN_GGML_OP"; + return unknown_op; +} + +const std::string & GgmlOvDecoder::get_op_type(int node_idx) const { + return m_node_info_list[node_idx].node_op_type; +} + +const std::string & GgmlOvDecoder::get_op_type() const { + static const std::string unknown_op = "UNKNOWN_GGML_OP"; + return unknown_op; +} diff --git a/ggml/src/ggml-openvino/ggml-decoder.h b/ggml/src/ggml-openvino/ggml-decoder.h new file mode 100644 index 0000000000..3ae25ddda3 --- /dev/null +++ b/ggml/src/ggml-openvino/ggml-decoder.h @@ -0,0 +1,294 @@ +#pragma once + +#include "ggml-quants.h" +#include "ggml.h" +#include "openvino/decoder.h" + +#include +#include +#include +#include +#include +#include +#include + +struct ModelParams { + int ctx = -1; + int ctx_swa = -1; + int ctx_per_seq = -1; + int ctx_per_seq_swa = -1; + int n_seq = 1; + int n_heads = -1; + int n_heads_kv = -1; + int head_size = -1; + int32_t rope_params[15]; + std::vector swa_layers; + + std::vector kv_names; + size_t kv_buffer_ctx_id = 0; + + bool same_rope_params(const ModelParams & other) const { + return memcmp(rope_params, other.rope_params, sizeof(int32_t) * 15) == 0; + } + + bool can_reuse_dynamically(const ModelParams & other) const { return same_rope_params(other); } + + bool can_reuse_statically(const ModelParams & other) const { return same_rope_params(other) && ctx == other.ctx; } + + bool kv_buffer_changed(const ModelParams & other) const { return kv_buffer_ctx_id != other.kv_buffer_ctx_id; } +}; + +struct ComputeParams { + int n_seq_active = 1; + int seq_active_start = 0; + int attention_size = -1; + int attention_size_swa = -1; + int input_len = -1; + int token_len_per_seq = -1; + int past_kv_len = -1; + int output_len = 1; +}; + +class GgmlOvDecoder : public ov::frontend::ggml::GgmlDecoder { +public: + struct NodeInfo { + ggml_tensor * node; + std::string node_name; + std::string node_op_type; + std::map node_inputs; + std::vector node_inputs_names; + ggml_tensor * node_output; + std::string node_output_name; + int node_op_case = 0; + void * data_addr; + }; + // Graph decoder + GgmlOvDecoder(ggml_cgraph * cgraph, + ModelParams & model_params, + ComputeParams & compute_params, + std::map> & model_weights, + bool is_static, + bool is_stateful = false, + bool is_prefill = false, + int prefill_chunk_size = 256); + + // Naive graph decoder + GgmlOvDecoder(ggml_cgraph * cgraph, std::map> & model_weights); + + virtual ov::Any get_attribute(const std::string & name) const override { + return nullptr; + GGML_UNUSED(name); + } + + virtual ov::PartialShape get_input_shape(int node_idx, const std::string & name) const override; + + virtual std::vector get_input_stride(int node_idx, const std::string & name) const override; + + virtual ov::element::Type get_input_type(int node_idx, const std::string & name) const override; + + virtual size_t get_input_size() const override; + + virtual size_t get_input_size(int node_idx) const override; + + virtual void get_input_node(size_t input_port_idx, + std::string & producer_name, + std::string & producer_output_port_name, + size_t & producer_output_port_index) const override { + GGML_UNUSED(input_port_idx); + GGML_UNUSED(producer_name); + GGML_UNUSED(producer_output_port_name); + GGML_UNUSED(producer_output_port_index); + } + + virtual std::vector get_input_names(int node_idx) const override; + + virtual ov::PartialShape get_output_shape(int node_idx) const override; + + virtual ov::element::Type get_output_type(int node_idx) const override; + + virtual int32_t * get_input_op_params(int node_idx, const std::string & name) const override; + + virtual int32_t * get_output_op_params(int node_idx) const override; + + virtual std::vector get_output_names(int node_idx) const override; + + virtual const std::string & get_op_type() const override; + + virtual const std::string & get_op_type(int node_idx) const override; + + virtual const std::string & get_op_name() const override; + + virtual const std::string & get_op_name(int node_idx) const override; + + virtual void visit_subgraph(std::function, int node_idx)> node_visitor) const override; + + ggml_tensor * get_input_ggml_tensor(const std::string & name) const { return m_inputs.at(name); } + + virtual int get_op_case(int node_idx) const override { return m_node_info_list[node_idx].node_op_case; } + + virtual const std::map> & get_model_inputs() const override { + return m_model_inputs; + } + + virtual const std::map> & get_model_extra_inputs() const override { + return m_model_extra_inputs; + } + + virtual const std::map> & get_model_extra_input_values() const { + return m_model_extra_input_values; + } + + virtual const std::map> & get_model_weights() const override { + return m_model_weights; + } + + virtual std::vector get_model_output_names() const override { + return m_model_output_names; + } + + const std::map & get_model_outputs() const { return m_model_outputs; } + + virtual int get_ctx_size() const { return m_model_params.ctx; } + + virtual int get_ctx_swa_size() const { return m_model_params.ctx_swa; } + + virtual int get_ctx_per_seq() const { return m_model_params.ctx_per_seq; } + + virtual int get_ctx_per_seq_swa() const { return m_model_params.ctx_per_seq_swa; } + + virtual int get_n_seq() const { return m_model_params.n_seq; } + + virtual int is_swa_layer(int layer) const override { + return std::find(m_model_params.swa_layers.begin(), m_model_params.swa_layers.end(), layer) != + m_model_params.swa_layers.end(); + } + + int get_past_kv_len() const { return m_compute_params.past_kv_len; } + + int get_input_len() const { return m_compute_params.input_len; } + + virtual int32_t * get_rope_params() const override { return const_cast(m_model_params.rope_params); } + + virtual std::map get_kv_param_res_names() const override; + + virtual bool is_static() const override { return m_is_static; } + + virtual bool is_stateful() const override { return m_is_stateful; } + + ov::PartialShape get_graph_input_shape(const ggml_tensor * op, const ggml_tensor * input) const; + + static void dump_cgraph(const ggml_cgraph * cgraph, std::string & filename); + + static std::shared_ptr create_weight_node(ggml_tensor * tensor, bool naive = false); + + static std::map> create_weight_nodes(ggml_cgraph * cgraph, + bool naive = false); + + const ggml_tensor * get_tensor_used_op(const ggml_tensor * tensor) const; + + const ggml_tensor * get_tensor_from_name(const std::string & name) const; + + void clear_model_weights() { m_model_weights.clear(); } + + static std::pair compute_llm_params(ggml_cgraph * cgraph, bool is_static); + + ModelParams get_model_params() const { return m_model_params; } + + ComputeParams get_compute_params() const { return m_compute_params; } + + void set_model_params(const ModelParams & model_params) { m_model_params = model_params; } + + void set_compute_params(const ComputeParams & compute_params) { m_compute_params = compute_params; } + + bool m_is_static = false; + bool m_is_stateful = false; + bool m_is_prefill = false; + bool m_naive = false; + int m_prefill_chunk_size = 0; + + static ov::Shape get_shape(const ggml_tensor * tensor); + static std::vector get_stride(const ggml_tensor * tensor); + static ov::element::Type get_ov_type(const ggml_tensor * tensor); + static std::string compute_op_type(const ggml_tensor * node); + void add_extra_inputs(); + + void update_io(ggml_cgraph * cgraph); + + inline static bool is_inp_tok(const ggml_tensor * tensor, const ggml_tensor * op) { + return op->op == GGML_OP_GET_ROWS && tensor == op->src[1] && op->src[0]->op == GGML_OP_NONE; + } + + inline static bool is_inp_pos(const ggml_tensor * tensor, const ggml_tensor * op) { + return op->op == GGML_OP_ROPE && tensor == op->src[1]; + } + + inline static bool is_inp_emb(const ggml_tensor * tensor, const ggml_tensor * op) { + return tensor->op == GGML_OP_GET_ROWS && op->op == GGML_OP_RMS_NORM; + } + + inline static bool is_inp_mask(const ggml_tensor * tensor, const ggml_tensor * op) { + return op->op == GGML_OP_CPY || (op->op == GGML_OP_FLASH_ATTN_EXT && tensor == op->src[3]); + } + + inline static bool is_rope_freqs_weight(const ggml_tensor * tensor, const ggml_tensor * op) { + return op->op == GGML_OP_ROPE && tensor == op->src[2]; + } + + inline static bool is_kvcache(const ggml_tensor * tensor, const ggml_tensor * op) { + return op->op == GGML_OP_SET_ROWS && op->src[2] == tensor; + } + + inline static bool is_kv_idx(const ggml_tensor * tensor, const ggml_tensor * op) { + return op->op == GGML_OP_SET_ROWS && op->src[1] == tensor; + } + + inline static bool is_output_idx(const ggml_tensor * tensor, const ggml_tensor * op) { + return op->op == GGML_OP_GET_ROWS && tensor == op->src[1] && op->src[0]->op != GGML_OP_NONE; + } + + static std::string get_graph_input_ov_name(const ggml_tensor * tensor, const ggml_tensor * op) { + if (is_inp_tok(tensor, op)) { + return "inp_tokens"; + } + if (is_inp_pos(tensor, op)) { + return "inp_pos"; + } + if (is_inp_emb(tensor, op)) { + return "embd"; + } + if (is_output_idx(tensor, op)) { + return "inp_out_ids"; + } + if (is_inp_mask(tensor, op)) { + return std::string(tensor->name).find("swa") == std::string::npos ? "self_kq_mask" : "self_kq_mask_swa"; + } + return tensor->name; + } + +private: + void set_input_output(); + int compute_op_case(const ggml_tensor * node) const; + bool node_is_used_as_src(const int node_idx); + void compute_model_inputs(); + void compute_model_outputs(); + + void validate_cgraph() const; + + ggml_cgraph * m_cgraph = nullptr; + std::map m_inputs; + + std::map> m_model_inputs; + std::map> m_model_extra_inputs; + std::map> m_model_extra_input_values; + std::map> m_model_weights; + std::map m_model_outputs; + std::vector m_model_output_names; + std::vector m_node_info_list; + + ModelParams m_model_params; + ComputeParams m_compute_params; +}; + +void print_tensor_address_map(const ggml_cgraph * cgraph); + +int extract_layer_from_name(const std::string & name); diff --git a/ggml/src/ggml-openvino/ggml-openvino-extra.cpp b/ggml/src/ggml-openvino/ggml-openvino-extra.cpp new file mode 100644 index 0000000000..cc3cb4583c --- /dev/null +++ b/ggml/src/ggml-openvino/ggml-openvino-extra.cpp @@ -0,0 +1,373 @@ +#include "ggml-openvino-extra.h" + +#include "ggml-impl.h" +#include "ggml.h" + +#include +#include +#include +#include + +ov::Core & ov_singleton_core() { + static ov::Core core; + return core; +} + +// ===================================================== +// Device Configuration Implementations +// ===================================================== + +void ggml_openvino_device_config::init() { + if (initialized) { + return; + } + device_name = getenv("GGML_OPENVINO_DEVICE") ? getenv("GGML_OPENVINO_DEVICE") : "CPU"; + auto available_devices = ov_singleton_core().get_available_devices(); + if (std::find(available_devices.begin(), available_devices.end(), device_name) == available_devices.end()) { + GGML_LOG_WARN("GGML OpenVINO Backend: device %s is not available, fallback to CPU\n", device_name.c_str()); + device_name = "CPU"; + } + is_npu = (device_name == "NPU"); + + auto * cache_dir = getenv("GGML_OPENVINO_CACHE_DIR"); + if (device_name == "NPU") { + compile_config = { + {"NPU_COMPILER_DYNAMIC_QUANTIZATION", "YES" }, + {"NPU_USE_NPUW", "YES" }, + {"NPUW_DEVICES", "NPU" }, + {"NPUW_FOLD", "YES" }, + {"NPUW_WEIGHTS_BANK", "shared"}, + {"NPUW_FUNCALL_FOR_ALL", "YES" }, + {"NPUW_FUNCALL_ASYNC", "YES" }, + {"NPUW_DQ", "YES" }, + {"NPUW_DQ_FULL", "NO" }, + }; + if (cache_dir) { + compile_config["NPUW_CACHE_DIR"] = cache_dir; + } + } else if (cache_dir) { + ov_singleton_core().set_property(ov::cache_dir(cache_dir)); + } + + // Initialize remote context with queue sharing for GPU + if (device_name == "GPU") { + // Create OpenCL context and queue + cl_int err; + cl_platform_id platform; + err = clGetPlatformIDs(1, &platform, nullptr); + if (err != CL_SUCCESS) { + GGML_LOG_ERROR("Failed to get OpenCL platform: %d\n", err); + return; + } + + cl_device_id cl_device; + err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 1, &cl_device, nullptr); + if (err != CL_SUCCESS) { + GGML_LOG_ERROR("Failed to get OpenCL device: %d\n", err); + return; + } + + cl_context cl_ctx = clCreateContext(nullptr, 1, &cl_device, nullptr, nullptr, &err); + if (err != CL_SUCCESS) { + GGML_LOG_ERROR("Failed to create OpenCL context: %d\n", err); + return; + } + + cl_queue = clCreateCommandQueueWithProperties(cl_ctx, cl_device, nullptr, &err); + if (err != CL_SUCCESS) { + GGML_LOG_ERROR("Failed to create OpenCL command queue: %d\n", err); + clReleaseContext(cl_ctx); + return; + } + + // Create OpenVINO remote context with queue sharing + remote_context = ov::intel_gpu::ocl::ClContext(ov_singleton_core(), cl_queue); + + // Release the context (queue keeps a reference) + clReleaseContext(cl_ctx); + } else if (device_name == "NPU") { + // remote tensor is not used for NPU yet + // remote_context = ov_singleton_core().get_default_context(device_name); + } + + initialized = true; +} + +ggml_openvino_device_config::~ggml_openvino_device_config() { + if (cl_queue != nullptr) { + clReleaseCommandQueue(cl_queue); + cl_queue = nullptr; + } +} + +// Get the global device config singleton +ggml_openvino_device_config & ggml_openvino_get_device_config() { + static ggml_openvino_device_config config; + return config; +} + +// Initialize device config (call during backend init) +void ggml_openvino_init_device_config() { + ggml_openvino_get_device_config().init(); +} + +// Get the device name +const std::string & ggml_openvino_get_device_name() { + return ggml_openvino_get_device_config().device_name; +} + +// Check if running on NPU +bool ggml_openvino_is_npu() { + return ggml_openvino_get_device_config().is_npu; +} + +// Get the remote context for the current device (returns empty optional for CPU) +std::optional ggml_openvino_get_remote_context() { + return ggml_openvino_get_device_config().remote_context; +} + +// Get the compile config for the current device +const ov::AnyMap & ggml_openvino_get_compile_config() { + return ggml_openvino_get_device_config().compile_config; +} + +// Get the OpenCL command queue for GPU operations +cl_command_queue ggml_openvino_get_cl_queue() { + return ggml_openvino_get_device_config().cl_queue; +} + +// Get the clEnqueueMemFillINTEL function pointer (lazy load) +clEnqueueMemFillINTEL_fn ggml_openvino_get_clEnqueueMemFillINTEL() { + static clEnqueueMemFillINTEL_fn fn = nullptr; + static bool loaded = false; + if (!loaded) { + loaded = true; + cl_platform_id platform; + if (clGetPlatformIDs(1, &platform, nullptr) == CL_SUCCESS) { + fn = (clEnqueueMemFillINTEL_fn) clGetExtensionFunctionAddressForPlatform(platform, "clEnqueueMemFillINTEL"); + } + } + return fn; +} + +// Get the clEnqueueMemcpyINTEL function pointer (lazy load) +clEnqueueMemcpyINTEL_fn ggml_openvino_get_clEnqueueMemcpyINTEL() { + static clEnqueueMemcpyINTEL_fn fn = nullptr; + static bool loaded = false; + if (!loaded) { + loaded = true; + cl_platform_id platform; + if (clGetPlatformIDs(1, &platform, nullptr) == CL_SUCCESS) { + fn = (clEnqueueMemcpyINTEL_fn) clGetExtensionFunctionAddressForPlatform(platform, "clEnqueueMemcpyINTEL"); + } + } + return fn; +} + +// Get requantization type for a tensor type (returns nullopt if no requant needed) +std::optional ggml_openvino_get_requant_type(const ggml_tensor * tensor, bool no_requant) { + if (no_requant) { + return std::nullopt; + } + if (strncmp(tensor->name, "token_embd.weight", 17) == 0) { + return ((ggml_openvino_is_npu() && tensor->type == GGML_TYPE_Q6_K) ? ExtraQuantType::F16 : ExtraQuantType::Q8_0_C); + } + if (strncmp(tensor->name, "output.weight", 13) == 0) { + return ExtraQuantType::Q8_0_C; + } + if (ggml_openvino_is_npu()) { + return ExtraQuantType::Q4_0_128; + } + switch (tensor->type) { + case GGML_TYPE_Q6_K: + case GGML_TYPE_Q5_K: + return ExtraQuantType::Q8_0_C; + default: + return std::nullopt; + } +} + +// ===================================================== +// Extracted Layout Calculation +// ===================================================== + +ggml_openvino_extracted_layout ggml_openvino_get_extracted_layout(const ggml_tensor * tensor, bool use_bias) { + ggml_openvino_extracted_layout layout = {}; + layout.is_symmetric = false; + + if (!ggml_is_quantized(tensor->type)) { + return layout; + } + + // Only handle 2D weight tensors + if (tensor->ne[2] != 1 || tensor->ne[3] != 1) { + return layout; + } + + int64_t n_elements = ggml_nelements(tensor); + const size_t alignment = 64; // Good for SIMD + + // Check if requantization is needed (NPU-specific) + auto requant_type = ggml_openvino_get_requant_type(tensor, use_bias); + if (requant_type.has_value()) { + layout.is_requant = true; + layout.requant_type = requant_type; + + // Special case: requant to F16 - just store F16 weights, no scales/zp + if (requant_type.value() == ExtraQuantType::F16) { + layout.weights_size = n_elements * sizeof(uint16_t); // F16 = 2 bytes + layout.total_size = layout.weights_size; + layout.weights_offset = 0; + // No scales/zp for F16 + return layout; + } + + // Requant to different quantized format (e.g., Q4_0_128) + switch (requant_type.value()) { + case ExtraQuantType::Q4_0_128: + layout.is_u4 = true; + layout.weights_per_block = 128; + layout.is_symmetric = true; + break; + case ExtraQuantType::Q4_0_C: + layout.is_u4 = true; + layout.weights_per_block = tensor->ne[0]; + layout.is_symmetric = true; + break; + case ExtraQuantType::Q8_0_32: + layout.is_u4 = false; + layout.weights_per_block = 32; + layout.is_symmetric = true; + break; + case ExtraQuantType::Q8_0_C: + layout.is_u4 = false; + layout.weights_per_block = tensor->ne[0]; + layout.is_symmetric = true; + break; + case ExtraQuantType::Q8_1_C: + layout.is_u4 = false; + layout.weights_per_block = tensor->ne[0]; + break; + default: + layout.weights_per_block = -1; + GGML_ABORT("Code of re-quantizing to channel-wise is not updated"); + break; + } + + if (layout.is_requant) { + // Calculate sizes for requantized format + layout.weights_size = layout.is_u4 ? (n_elements / 2) : n_elements; + int64_t n_blocks = n_elements / layout.weights_per_block; + layout.scales_size = n_blocks * sizeof(uint16_t); + // For symmetric quantization, we only need one zp value (not one per block) + // Zero points are stored in U4 or U8 format matching the weight type + size_t n_zp_elements = layout.is_symmetric ? 1 : n_blocks; + layout.zp_size = layout.is_u4 ? ((n_zp_elements + 1) / 2) : n_zp_elements; + + layout.weights_offset = 0; + layout.scales_offset = ((layout.weights_size + alignment - 1) / alignment) * alignment; + layout.zp_offset = layout.scales_offset + ((layout.scales_size + alignment - 1) / alignment) * alignment; + layout.total_size = layout.zp_offset + layout.zp_size; + layout.total_size = std::max(layout.total_size, ggml_nbytes(tensor)); + return layout; + } + } + + // Normal extraction (no requant) - determine format based on tensor type + layout.is_u4 = false; + layout.weights_per_block = 32; + layout.is_symmetric = false; + + switch (tensor->type) { + case GGML_TYPE_Q4_0: + layout.is_u4 = true; + layout.is_symmetric = true; + break; + + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_K: + layout.is_u4 = true; + break; + + case GGML_TYPE_Q8_0: + layout.is_symmetric = true; + break; + + case GGML_TYPE_Q6_K: + layout.weights_per_block = 16; + layout.is_symmetric = true; + break; + + case GGML_TYPE_Q5_K: + break; + + default: + // Unsupported quantization type + return layout; + } + + // Calculate sizes + // Weights: U4 = n_elements/2 bytes, U8 = n_elements bytes + layout.weights_size = layout.is_u4 ? (n_elements / 2) : n_elements; + + // Scales: F16 per block + int64_t n_blocks = n_elements / layout.weights_per_block; + layout.scales_size = n_blocks * sizeof(uint16_t); // F16 = 2 bytes + // Zero points: U4 or U8 matching weight type + // For symmetric quantization, we only need one zp value (not one per block) + size_t n_zp_elements = layout.is_symmetric ? 1 : n_blocks; + layout.zp_size = layout.is_u4 ? ((n_zp_elements + 1) / 2) : n_zp_elements; + + // Layout in buffer: [weights | scales | zp] with alignment + layout.weights_offset = 0; + layout.scales_offset = ((layout.weights_size + alignment - 1) / alignment) * alignment; + layout.zp_offset = layout.scales_offset + ((layout.scales_size + alignment - 1) / alignment) * alignment; + layout.total_size = layout.zp_offset + layout.zp_size; + layout.total_size = std::max(layout.total_size, ggml_nbytes(tensor)); + + return layout; +} + +ggml_openvino_tensor_extra * ggml_openvino_create_tensor_extra(const ggml_tensor * tensor, bool is_remote) { + ov::Shape shape; + for (int i = GGML_MAX_DIMS - 1; i >= 0; --i) { + shape.push_back(static_cast(tensor->ne[i])); + } + + ov::element::Type element_type; + switch (tensor->type) { + case GGML_TYPE_F32: + element_type = ov::element::f32; + break; + case GGML_TYPE_F16: + element_type = ov::element::f16; + break; + case GGML_TYPE_BF16: + element_type = ov::element::bf16; + break; + case GGML_TYPE_I32: + element_type = ov::element::i32; + break; + case GGML_TYPE_I64: + element_type = ov::element::i64; + break; + default: + // GGML_LOG_WARN("%s: unsupported tensor type for ov::Tensor: %s\n", __func__, ggml_type_name(tensor->type)); + return nullptr; + } + + const auto & device_name = ggml_openvino_get_device_name(); + auto remote_context = ggml_openvino_get_remote_context(); + + std::shared_ptr ov_tensor; + if (is_remote) { + GGML_ASSERT(device_name == "GPU"); + auto gpu_context = remote_context->as(); + auto usm_tensor = gpu_context.create_tensor(element_type, shape, tensor->data); + ov_tensor = std::make_shared(std::move(usm_tensor)); + } else { + ov_tensor = std::make_shared(element_type, shape, tensor->data); + } + + return new ggml_openvino_tensor_extra(ov_tensor); +} diff --git a/ggml/src/ggml-openvino/ggml-openvino-extra.h b/ggml/src/ggml-openvino/ggml-openvino-extra.h new file mode 100644 index 0000000000..cd0baf4a68 --- /dev/null +++ b/ggml/src/ggml-openvino/ggml-openvino-extra.h @@ -0,0 +1,182 @@ +#pragma once + +#include "ggml.h" +#include "openvino/runtime/core.hpp" + +#define CL_TARGET_OPENCL_VERSION 300 +#include + +#include +#include +#include +#include +#include +#include +#include + +// ExtraQuantType enum - defines requantization target formats +enum class ExtraQuantType { F16, Q4_0_C, Q8_1_C, Q4_0_128, Q8_0_C, Q8_0_32 }; + +ov::Core & ov_singleton_core(); + +// Get the remote context for the current device (returns empty optional for CPU) +std::optional ggml_openvino_get_remote_context(); + +// Get the compile config for the current device +const ov::AnyMap & ggml_openvino_get_compile_config(); + +// Get the OpenCL command queue for GPU operations (returns nullptr for CPU/NPU) +cl_command_queue ggml_openvino_get_cl_queue(); + +// Intel USM extension function type +typedef cl_int(CL_API_CALL * clEnqueueMemFillINTEL_fn)(cl_command_queue queue, + void * dst_ptr, + const void * pattern, + size_t pattern_size, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event); + +typedef cl_int(CL_API_CALL * clEnqueueMemcpyINTEL_fn)(cl_command_queue queue, + cl_bool blocking, + void * dst_ptr, + const void * src_ptr, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event); + +// Get the clEnqueueMemFillINTEL function pointer (returns nullptr if not available) +clEnqueueMemFillINTEL_fn ggml_openvino_get_clEnqueueMemFillINTEL(); + +// Get the clEnqueueMemcpyINTEL function pointer (returns nullptr if not available) +clEnqueueMemcpyINTEL_fn ggml_openvino_get_clEnqueueMemcpyINTEL(); + +// ===================================================== +// Global Device Configuration (singleton) +// ===================================================== +// Initialized once during backend init from GGML_OPENVINO_DEVICE env var + +struct ggml_openvino_device_config { + std::string device_name = "CPU"; + bool is_npu = false; + bool initialized = false; + std::optional remote_context; + ov::AnyMap compile_config; + cl_command_queue cl_queue = nullptr; + + void init(); + ~ggml_openvino_device_config(); +}; + +// Get the global device config singleton +ggml_openvino_device_config & ggml_openvino_get_device_config(); + +// Initialize device config (call during backend init) +void ggml_openvino_init_device_config(); + +// Get the device name +const std::string & ggml_openvino_get_device_name(); + +// Check if running on NPU +bool ggml_openvino_is_npu(); + +// Get requantization type for a tensor type (returns nullopt if no requant needed) +std::optional ggml_openvino_get_requant_type(const ggml_tensor * tensor, bool no_requant = false); + +// ===================================================== +// OpenVINO Tensor Extra Types +// ===================================================== +// These types are stored in tensor->extra by the OpenVINO backend buffer. +// They allow: +// 1. Pre-built ov::Constant nodes for weights (avoiding memcpy during graph construction) +// 2. ov::Tensor wrappers for KV cache / compute tensors (for direct use with infer_request) + +// Base class for OpenVINO tensor extra data +struct ggml_openvino_extra_base { + enum class Type { WEIGHT, QUANTIZED_WEIGHT, TENSOR }; + Type type; + virtual ~ggml_openvino_extra_base() = default; +protected: + explicit ggml_openvino_extra_base(Type t) : type(t) {} +}; + +// Extra data for F16/F32/BF16 weight tensors - stores the pre-built weight node +struct ggml_openvino_weight_extra : public ggml_openvino_extra_base { + ov::Tensor weights; // The underlying weight data tensor + std::shared_ptr weight_node; // Pre-built OpenVINO weight node + + ggml_openvino_weight_extra(ov::Tensor w, std::shared_ptr n) : + ggml_openvino_extra_base(Type::WEIGHT), + weights(std::move(w)), + weight_node(std::move(n)) {} +}; + +// Extra data for quantized weight tensors - stores extracted weights/scales/zp and weight node +struct ggml_openvino_quantized_weight_extra : public ggml_openvino_extra_base { + ov::Tensor weights; // U4 or U8 extracted weights + ov::Tensor scales; // F16 scales + ov::Tensor zp; // U4 or U8 zero points (same type as weights) + std::shared_ptr weight_node; // Pre-built OpenVINO weight subgraph + + ggml_openvino_quantized_weight_extra(ov::Tensor w, ov::Tensor s, ov::Tensor z, std::shared_ptr n) : + ggml_openvino_extra_base(Type::QUANTIZED_WEIGHT), + weights(std::move(w)), + scales(std::move(s)), + zp(std::move(z)), + weight_node(std::move(n)) {} +}; + +// Extra data for KV cache / compute tensors - stores ov::Tensor for infer_request +struct ggml_openvino_tensor_extra : public ggml_openvino_extra_base { + std::shared_ptr tensor; // For direct use with infer_request + + explicit ggml_openvino_tensor_extra(std::shared_ptr t) + : ggml_openvino_extra_base(Type::TENSOR), tensor(std::move(t)) {} +}; + +// ===================================================== +// Extracted Size Calculation for Quantized Tensors +// ===================================================== +// For quantized tensors, we need extra space to store extracted weights, scales, and zero points. +// Returns the total size needed in the buffer for extracted data. + +struct ggml_openvino_extracted_layout { + size_t total_size = 0; // Total bytes needed + size_t weights_offset = 0; // Offset to weights in buffer + size_t weights_size = 0; // Size of weights in bytes + size_t scales_offset = 0; // Offset to scales in buffer + size_t scales_size = 0; // Size of scales in bytes + size_t zp_offset = 0; // Offset to zero points in buffer + size_t zp_size = 0; // Size of zero points in bytes (U4 or U8) + bool is_u4; // true for U4 weights, false for U8 + int64_t weights_per_block; // weights per scale/zp block + bool is_symmetric; // true for symmetric quantization + + // Requantization info + bool is_requant = false; // true if this tensor needs requantization + std::optional requant_type; // target requant type if is_requant +}; + +// Calculate the buffer layout for extracted quantized data +ggml_openvino_extracted_layout ggml_openvino_get_extracted_layout(const ggml_tensor * tensor, bool use_bias = false); + +ggml_openvino_tensor_extra * ggml_openvino_create_tensor_extra(const ggml_tensor * tensor, bool is_remote); + +// Register an extra with the tensor's OpenVINO buffer context for proper lifetime management. +// This sets tensor->extra and tracks the extra in the buffer context for cleanup. +void ggml_openvino_buffer_register_extra(ggml_tensor * tensor, ggml_openvino_extra_base * extra); + +// ===================================================== +// OpenVINO Backend Context and Interface +// ===================================================== +struct ggml_backend_openvino_context { + int device = 0; + std::string name = "OpenVINO"; + std::string description = "OpenVINO Backend Context"; + + std::shared_ptr runtime_context = nullptr; + + ggml_backend_openvino_context() = default; +}; diff --git a/ggml/src/ggml-openvino/ggml-openvino.cpp b/ggml/src/ggml-openvino/ggml-openvino.cpp new file mode 100644 index 0000000000..0031cb7369 --- /dev/null +++ b/ggml/src/ggml-openvino/ggml-openvino.cpp @@ -0,0 +1,1110 @@ +#include "ggml-openvino.h" + +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" +#include "ggml-openvino-extra.h" +#include "ggml-openvino/utils.h" +#include "ggml-quants.h" +#include "ggml.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined(_WIN32) +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +#else +# include +#endif + +// ===================================================== +// OpenVINO Buffer Implementation using ov::Tensor +// ===================================================== +// +// Design: This implementation uses a hybrid approach: +// 1. For weight tensors: Store a pre-built ov::op::v0::Constant in tensor->extra +// - This avoids the memcpy during graph construction +// - For quantized weights, the constant is already converted to OpenVINO format +// 2. For KV cache / compute tensors: Store an ov::Tensor in tensor->extra +// - This can be directly passed to infer_request +// - Future: can be changed to ov::RemoteTensor for GPU/NPU +// +// This design is similar to: +// - CUDA split buffer: tensor->extra stores device pointers +// - CPU repack buffer: tensor->extra stores tensor_traits with repacked data +// ===================================================== + +// Buffer context that manages per-tensor allocations (no contiguous buffer for weights) +struct ggml_backend_openvino_buffer_context { + int device; + std::string name; + size_t id; + + // For non-weight buffers (KV cache, compute), we still use contiguous allocation + void * data; + size_t size; + bool is_remote; + + // Wrapping of the buffer + std::shared_ptr ov_buffer; + + // Track all extras for cleanup + std::map tensor_extras; + + // Used for re-allocation on device for kvcache + void * data_prev; + + ggml_backend_openvino_buffer_context(int device, size_t size, bool is_remote = false) : + device(device), + name(std::string(GGML_OPENVINO_NAME) + std::to_string(device)), + id([]() { + static std::atomic next_id{1}; + return next_id.fetch_add(1); + }()), + data(nullptr), + size(size), + is_remote(is_remote) { + if (size == 0) { + return; + } + + const auto & device_name = ggml_openvino_get_device_name(); + + if (is_remote) { + GGML_ASSERT(device_name == "GPU"); + auto remote_context = ggml_openvino_get_remote_context(); + auto gpu_context = remote_context->as(); + ov::intel_gpu::ocl::USMTensor usm_tensor = + gpu_context.create_usm_device_tensor(ov::element::u8, ov::Shape{size}); + data = usm_tensor.get(); + ov_buffer = std::make_shared(std::move(usm_tensor)); + } else { + data = ggml_aligned_malloc(size); + ov_buffer = std::make_shared(ov::element::u8, ov::Shape{size}, data); + } + + if (data == nullptr) { + GGML_LOG_ERROR("%s: failed to allocate %zu bytes\n", __func__, size); + return; + } + + if (reinterpret_cast(data) % TENSOR_ALIGNMENT != 0) { + GGML_LOG_ERROR("%s: %s buffer is not aligned to %d bytes\n", __func__, device_name.c_str(), + TENSOR_ALIGNMENT); + GGML_ABORT("fatal error"); + } + } + + ~ggml_backend_openvino_buffer_context() { + // Clean up all tensor extras + // GGML_LOG_DEBUG("Deleting OpenVINO buffer context #%zu for device %d, size %zu MB\n", id, device, + // size / 1024 / 1024); + for (auto & pair : tensor_extras) { + delete pair.second; + } + tensor_extras.clear(); + if (!is_remote && data != nullptr) { + ggml_aligned_free(data, size); + } + } +}; + +// Buffer type context (per-device) +struct ggml_backend_openvino_buffer_type_context { + int device; + std::string name; +}; + +// Buffer interface functions +static void ggml_backend_openvino_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + delete ctx; +} + +static void * ggml_backend_openvino_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + return ctx->data; +} + +static enum ggml_status ggml_backend_openvino_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + // GGML_LOG_DEBUG("%s: buffer usage=%d, tensor name=%s\n", __func__, buffer->usage, tensor->name); + ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + + // Put kvcache on device memory for GPU (NPU memory is too small even for kvcache) + if (strncmp(tensor->name, "cache_", 6) == 0 && !ctx->is_remote && ggml_openvino_get_device_name() == "GPU" && + !getenv("GGML_OPENVINO_STATEFUL_EXECUTION")) { + GGML_ASSERT(ctx->tensor_extras.empty()); + auto device = ctx->device; + auto size = ctx->size; + auto * data_prev = ctx->data; + delete ctx; + ctx = new ggml_backend_openvino_buffer_context(device, size, true); + buffer->context = ctx; + tensor->data = (char *) ctx->data + ((char *) tensor->data - (char *) data_prev); + } + + // Views share the extra from view_src + if (tensor->view_src != nullptr) { + GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); + if (tensor->view_src->extra != nullptr) { + tensor->extra = tensor->view_src->extra; + } + return GGML_STATUS_SUCCESS; + } + + ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + + if (tensor->data != nullptr && !ggml_is_quantized(tensor->type)) { + ggml_openvino_tensor_extra * extra = ggml_openvino_create_tensor_extra(tensor, ctx->is_remote); + if (extra != nullptr) { + auto it = ctx->tensor_extras.find(tensor); + if (it != ctx->tensor_extras.end()) { + delete it->second; + } + ctx->tensor_extras[tensor] = extra; + tensor->extra = extra; + } + } + + return GGML_STATUS_SUCCESS; +} + +static void ggml_backend_openvino_buffer_memset_tensor(ggml_backend_buffer_t buffer, + ggml_tensor * tensor, + uint8_t value, + size_t offset, + size_t size) { + // GGML_LOG_DEBUG("%s: buffer usage=%d, tensor name=%s\n", __func__, buffer->usage, tensor->name); + GGML_ASSERT(tensor != nullptr && tensor->data != nullptr); + ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + + if (ctx->is_remote) { + // For remote (device) buffers, use OpenCL USM memfill + cl_command_queue queue = ggml_openvino_get_cl_queue(); + auto mem_fill_fn = ggml_openvino_get_clEnqueueMemFillINTEL(); + if (queue != nullptr && mem_fill_fn != nullptr) { + uint8_t pattern = value; + cl_int err = mem_fill_fn(queue, (char *) tensor->data + offset, &pattern, sizeof(pattern), size, 0, nullptr, + nullptr); + if (err != CL_SUCCESS) { + GGML_LOG_ERROR("%s: clEnqueueMemFillINTEL failed with error %d\n", __func__, err); + } + clFinish(queue); + } else { + GGML_LOG_ERROR("%s: no OpenCL queue or clEnqueueMemFillINTEL not available for GPU buffer\n", __func__); + } + } else { + memset((char *) tensor->data + offset, value, size); + } +} + +static void ggml_backend_openvino_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + // GGML_LOG_DEBUG("%s: buffer usage=%d, tensor name=%s\n", __func__, buffer->usage, tensor->name); + GGML_ASSERT(tensor != nullptr && tensor->data != nullptr); + ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + + // Check if this is a weight buffer (usage is set BEFORE set_tensor is called, except in test-backend-ops) + bool is_weight_buffer = (buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS); + // Full tensor set: offset=0, full size, not a view + bool is_full_tensor_set = (offset == 0 && size == ggml_nbytes(tensor) && tensor->view_src == nullptr); + // 2D tensor (typical weight shape) + bool is_2d = (tensor->ne[2] == 1 && tensor->ne[3] == 1); + + if (is_weight_buffer && is_full_tensor_set && is_2d) { + try { + auto result = process_weight_tensor(tensor, data, tensor->data); + result.weight_node->set_friendly_name(tensor->name); + + // const auto & layout = result.layout; + ggml_openvino_extra_base * extra; + + // Quantized path with extracted weight/scale/zp tensors + if (result.is_quantized()) { + extra = new ggml_openvino_quantized_weight_extra(std::move(result.weights), std::move(result.scales), + std::move(result.zp), result.weight_node); + + // if (layout.is_requant) { + // GGML_LOG_DEBUG("%s: requantized %s to %s (u%d, block_size=%ld)\n", __func__, tensor->name, + // extra_quant_type_name(layout.requant_type.value()), layout.is_u4 ? 4 : 8, + // layout.weights_per_block); + // } else { + // int64_t n_blocks = ggml_nelements(tensor) / layout.weights_per_block; + // GGML_LOG_DEBUG("%s: extracted quantized weight node for %s (u%d, %zu weights, %ld blocks)\n", + // __func__, tensor->name, layout.is_u4 ? 4 : 8, layout.weights_size, n_blocks); + // } + } else { + // F16/F32/BF16 weight or F16-requant + extra = new ggml_openvino_weight_extra(std::move(result.weights), result.weight_node); + + // if (layout.total_size > 0) { + // GGML_LOG_DEBUG("%s: requantized %s to F16\n", __func__, tensor->name); + // } else { + // GGML_LOG_DEBUG("%s: created shared-memory weight node for %s\n", __func__, tensor->name); + // } + } + + ctx->tensor_extras[tensor] = extra; + tensor->extra = extra; + + } catch (const std::exception & e) { + GGML_LOG_ERROR("%s: failed to process weight tensor for %s: %s\n", __func__, tensor->name, e.what()); + memcpy((char *) tensor->data + offset, data, size); + } + } else { + // Non-weight tensor (KV cache, activations, etc.) - copy data. test-backend-ops also goes here + if (ctx->is_remote) { + cl_command_queue queue = ggml_openvino_get_cl_queue(); + auto mem_cpy_fn = ggml_openvino_get_clEnqueueMemcpyINTEL(); + if (queue != nullptr && mem_cpy_fn != nullptr) { + cl_int err = + mem_cpy_fn(queue, CL_TRUE, (char *) tensor->data + offset, data, size, 0, nullptr, nullptr); + if (err != CL_SUCCESS) { + GGML_LOG_ERROR("%s: clEnqueueMemcpyINTEL failed with error %d\n", __func__, err); + } + } else { + GGML_LOG_ERROR("%s: no OpenCL queue or clEnqueueMemcpyINTEL not available for GPU buffer\n", __func__); + } + } else { + memcpy((char *) tensor->data + offset, data, size); + } + + ggml_openvino_tensor_extra * extra = ggml_openvino_create_tensor_extra(tensor, ctx->is_remote); + if (extra == nullptr) { + // GGML_LOG_ERROR("%s: failed to create tensor extra for %s\n", __func__, tensor->name); + return; + } + + auto it = ctx->tensor_extras.find(tensor); + if (it != ctx->tensor_extras.end()) { + delete it->second; + } + ctx->tensor_extras[tensor] = extra; + tensor->extra = extra; + } +} + +static void ggml_backend_openvino_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + // GGML_LOG_DEBUG("%s: buffer usage=%d, tensor name=%s\n", __func__, buffer->usage, tensor->name); + GGML_ASSERT(tensor != nullptr && tensor->data != nullptr); + ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + + if (ctx->is_remote) { + // For remote (device) buffers, use OpenCL USM memcpy (device-to-host) + cl_command_queue queue = ggml_openvino_get_cl_queue(); + auto mem_cpy_fn = ggml_openvino_get_clEnqueueMemcpyINTEL(); + if (queue != nullptr && mem_cpy_fn != nullptr) { + cl_int err = + mem_cpy_fn(queue, CL_TRUE, data, (const char *) tensor->data + offset, size, 0, nullptr, nullptr); + if (err != CL_SUCCESS) { + GGML_LOG_ERROR("%s: clEnqueueMemcpyINTEL failed with error %d\n", __func__, err); + } + } else { + GGML_LOG_ERROR("%s: no OpenCL queue or clEnqueueMemcpyINTEL not available for GPU buffer\n", __func__); + } + } else { + memcpy(data, (const char *) tensor->data + offset, size); + } +} + +static bool ggml_backend_openvino_buffer_cpy_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor * src, + ggml_tensor * dst) { + // GGML_LOG_DEBUG("%s: src tensor name=%s, dst tensor name=%s\n", __func__, src->name, dst->name); + GGML_ASSERT(src != nullptr && dst != nullptr); + ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + + if (ctx->is_remote) { + // For remote (device) buffers, use OpenCL USM memcpy + cl_command_queue queue = ggml_openvino_get_cl_queue(); + auto mem_cpy_fn = ggml_openvino_get_clEnqueueMemcpyINTEL(); + if (queue == nullptr || mem_cpy_fn == nullptr) { + GGML_LOG_ERROR("%s: no OpenCL queue or clEnqueueMemcpyINTEL not available for GPU buffer\n", __func__); + return false; + } + // Can copy from host to device + if (ggml_backend_buffer_is_host(src->buffer)) { + cl_int err = mem_cpy_fn(queue, CL_TRUE, dst->data, src->data, ggml_nbytes(src), 0, nullptr, nullptr); + if (err != CL_SUCCESS) { + GGML_LOG_ERROR("%s: clEnqueueMemcpyINTEL (host-to-device) failed with error %d\n", __func__, err); + return false; + } + return true; + } + // Can also copy from device to device if both are OpenVINO remote buffers + if (ggml_backend_buffer_is_openvino(src->buffer)) { + ggml_backend_openvino_buffer_context * src_ctx = + (ggml_backend_openvino_buffer_context *) src->buffer->context; + if (src_ctx->is_remote) { + cl_int err = + mem_cpy_fn(queue, CL_TRUE, dst->data, src->data, ggml_nbytes(src), 0, nullptr, nullptr); + if (err != CL_SUCCESS) { + GGML_LOG_ERROR("%s: clEnqueueMemcpyINTEL (device-to-device) failed with error %d\n", __func__, + err); + return false; + } + return true; + } + } + return false; + } + + // Host buffer - can copy from any host buffer + if (ggml_backend_buffer_is_host(src->buffer)) { + memcpy(dst->data, src->data, ggml_nbytes(src)); + return true; + } + return false; +} + +static void ggml_backend_openvino_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + GGML_ASSERT(ctx->data != nullptr); + if (ctx->is_remote) { + cl_command_queue queue = ggml_openvino_get_cl_queue(); + auto mem_fill_fn = ggml_openvino_get_clEnqueueMemFillINTEL(); + if (queue != nullptr && mem_fill_fn != nullptr) { + uint8_t pattern = value; + cl_int err = mem_fill_fn(queue, ctx->data, &pattern, sizeof(pattern), ctx->size, 0, nullptr, nullptr); + if (err != CL_SUCCESS) { + GGML_LOG_WARN("%s: clEnqueueMemFillINTEL failed with error %d\n", __func__, err); + } + clFinish(queue); + } else { + GGML_LOG_WARN("%s: no OpenCL queue or clEnqueueMemFillINTEL not available for GPU buffer clear\n", + __func__); + } + } else { + memset(ctx->data, value, ctx->size); + } +} + +static const ggml_backend_buffer_i ggml_backend_openvino_buffer_interface = { + /* .free_buffer = */ ggml_backend_openvino_buffer_free_buffer, + /* .get_base = */ ggml_backend_openvino_buffer_get_base, + /* .init_tensor = */ ggml_backend_openvino_buffer_init_tensor, + /* .memset_tensor = */ ggml_backend_openvino_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_openvino_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_openvino_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_openvino_buffer_cpy_tensor, + /* .clear = */ ggml_backend_openvino_buffer_clear, + /* .reset = */ NULL, +}; + +// Buffer type interface functions +static const char * ggml_backend_openvino_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + ggml_backend_openvino_buffer_type_context * ctx = (ggml_backend_openvino_buffer_type_context *) buft->context; + return ctx->name.c_str(); +} + +static ggml_backend_buffer_t ggml_backend_openvino_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, + size_t size) { + ggml_backend_openvino_buffer_type_context * buft_ctx = (ggml_backend_openvino_buffer_type_context *) buft->context; + + // Create buffer context with contiguous memory allocation + ggml_backend_openvino_buffer_context * ctx = new ggml_backend_openvino_buffer_context(buft_ctx->device, size); + + if (ctx->data == nullptr && size > 0) { + GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size); + delete ctx; + return nullptr; + } + + return ggml_backend_buffer_init(buft, ggml_backend_openvino_buffer_interface, ctx, size); +} + +static size_t ggml_backend_openvino_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + GGML_UNUSED(buft); + return TENSOR_ALIGNMENT; +} + +static size_t ggml_backend_openvino_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + GGML_UNUSED(buft); + return SIZE_MAX; +} + +static size_t ggml_backend_openvino_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, + const ggml_tensor * tensor) { + GGML_UNUSED(buft); + + // For quantized 2D tensors (weights), we need extra space for extracted data + if (ggml_is_quantized(tensor->type) && tensor->ne[2] == 1 && tensor->ne[3] == 1) { + ggml_openvino_extracted_layout layout = ggml_openvino_get_extracted_layout(tensor); + if (layout.total_size > 0) { + // GGML_LOG_DEBUG("%s: tensor %s needs %zu bytes (original %zu, extracted: weights=%zu scales=%zu zp=%zu)\n", + // __func__, tensor->name, layout.total_size, ggml_nbytes(tensor), layout.weights_size, + // layout.scales_size, layout.zp_size); + return layout.total_size; + } + } + + return ggml_nbytes(tensor); +} + +static const ggml_backend_buffer_type_i ggml_backend_openvino_buffer_type_interface = { + /* .get_name = */ ggml_backend_openvino_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_openvino_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_openvino_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_openvino_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_openvino_buffer_type_get_alloc_size, + /* .is_host = */ nullptr, +}; + +// Get buffer type for a specific device +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_openvino_buffer_type(int device) { + GGML_ASSERT(device >= 0 && device < ggml_backend_openvino_get_device_count()); + + static std::mutex mutex; + std::lock_guard lock(mutex); + + static std::vector buffer_types; + static std::vector buffer_type_contexts; + + if (buffer_types.empty()) { + int device_count = ggml_backend_openvino_get_device_count(); + buffer_types.resize(device_count); + buffer_type_contexts.resize(device_count); + + for (int i = 0; i < device_count; i++) { + buffer_type_contexts[i].device = i; + buffer_type_contexts[i].name = std::string(GGML_OPENVINO_NAME) + std::to_string(i); + + buffer_types[i] = ggml_backend_buffer_type{ + /* .iface = */ ggml_backend_openvino_buffer_type_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_openvino_reg(), i), + /* .context = */ &buffer_type_contexts[i], + }; + } + } + + return &buffer_types[device]; +} + +// ===================================================== +// OpenVINO Host Buffer Implementation +// ===================================================== + +static const char * ggml_backend_openvino_host_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + ggml_backend_openvino_buffer_type_context * ctx = (ggml_backend_openvino_buffer_type_context *) buft->context; + static std::string name; + name = ctx->name + "_HOST"; + return name.c_str(); +} + +static bool ggml_backend_openvino_host_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + GGML_UNUSED(buft); + return true; +} + +static const ggml_backend_buffer_type_i ggml_backend_openvino_host_buffer_type_interface = { + /* .get_name = */ ggml_backend_openvino_host_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_openvino_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_openvino_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_openvino_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_openvino_buffer_type_get_alloc_size, + /* .is_host = */ ggml_backend_openvino_host_buffer_type_is_host, +}; + +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_openvino_host_buffer_type(int device) { + GGML_ASSERT(device >= 0 && device < ggml_backend_openvino_get_device_count()); + + static std::mutex mutex; + std::lock_guard lock(mutex); + + static std::vector buffer_types; + static std::vector buffer_type_contexts; + + if (buffer_types.empty()) { + int device_count = ggml_backend_openvino_get_device_count(); + buffer_types.resize(device_count); + buffer_type_contexts.resize(device_count); + + for (int i = 0; i < device_count; i++) { + buffer_type_contexts[i].device = i; + buffer_type_contexts[i].name = std::string(GGML_OPENVINO_NAME) + std::to_string(i); + + buffer_types[i] = ggml_backend_buffer_type{ + /* .iface = */ ggml_backend_openvino_host_buffer_type_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_openvino_reg(), i), + /* .context = */ &buffer_type_contexts[i], + }; + } + } + + return &buffer_types[device]; +} + +bool ggml_backend_buffer_is_openvino(ggml_backend_buffer_t buffer) { + return buffer->iface.free_buffer == ggml_backend_openvino_buffer_free_buffer; +} + +size_t ggml_backend_openvino_buffer_get_ctx_id(ggml_backend_buffer_t buffer) { + if (!ggml_backend_buffer_is_openvino(buffer)) { + return 0; + } + ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context; + return ctx->id; +} + +void ggml_openvino_buffer_register_extra(ggml_tensor * tensor, ggml_openvino_extra_base * extra) { + GGML_ASSERT(tensor != nullptr); + GGML_ASSERT(tensor->buffer != nullptr); + GGML_ASSERT(ggml_backend_buffer_is_openvino(tensor->buffer)); + + auto * ctx = static_cast(tensor->buffer->context); + + auto it = ctx->tensor_extras.find(tensor); + if (it != ctx->tensor_extras.end()) { + delete it->second; + } + + ctx->tensor_extras[tensor] = extra; + tensor->extra = extra; +} + +bool ggml_backend_buft_is_openvino(ggml_backend_buffer_type_t buft) { + return buft->iface.get_name == ggml_backend_openvino_buffer_type_get_name; +} + +bool ggml_backend_buft_is_openvino_host(ggml_backend_buffer_type_t buft) { + return buft->iface.get_name == ggml_backend_openvino_host_buffer_type_get_name; +} + +static void ggml_backend_openvino_free(ggml_backend_t backend) { + ggml_backend_openvino_context * ctx = (ggml_backend_openvino_context *) backend->context; + delete ctx; + delete backend; +} + +static const char * ggml_backend_openvino_get_name(ggml_backend_t backend) { + return GGML_OPENVINO_NAME; + GGML_UNUSED(backend); +} + +static enum ggml_status ggml_backend_openvino_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + return ov_graph_compute(cgraph, backend); + GGML_UNUSED(backend); +} + +static const ggml_backend_i ggml_backend_openvino_interface = { + /* .get_name = */ ggml_backend_openvino_get_name, + /* .free = */ ggml_backend_openvino_free, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_openvino_graph_compute, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .graph_optimize = */ NULL, +}; + +int ggml_backend_openvino_get_device_count() { + return 1; +} + +static ggml_guid_t ggml_backend_openvino_guid(void) { + static ggml_guid guid = {0x12, 0xa8, 0xae, 0xf4, 0xc0, 0x1e, 0x61, 0x97, + 0x8f, 0xeb, 0x33, 0x04, 0xa1, 0x33, 0x51, 0x2d}; + return &guid; +} + +static std::shared_ptr get_ov_runtime_context_ptr() { + static std::shared_ptr r_ctx = std::make_shared(); + return r_ctx; +} + +// backend API +GGML_BACKEND_API ggml_backend_t ggml_backend_openvino_init(int device) { + if (device < 0 || device >= ggml_backend_openvino_get_device_count()) { + GGML_LOG_ERROR("%s: invalid device %d\n", __func__, device); + return nullptr; + } + + ggml_backend_openvino_context * ctx = new ggml_backend_openvino_context; + if (ctx == nullptr) { + GGML_LOG_ERROR("%s: failed to allocate context\n", __func__); + return nullptr; + } + + ctx->runtime_context = get_ov_runtime_context_ptr(); + if (ctx->runtime_context == nullptr) { + GGML_LOG_ERROR("%s: failed to allocate runtime context\n", __func__); + delete ctx; + return nullptr; + } + + std::shared_ptr r_ctx = std::static_pointer_cast(ctx->runtime_context); + r_ctx->device = ggml_openvino_get_device_name(); + r_ctx->stateful = getenv("GGML_OPENVINO_STATEFUL_EXECUTION") && !ggml_openvino_is_npu(); + + ggml_backend_t openvino_backend = new ggml_backend{ + /* .guid = */ ggml_backend_openvino_guid(), + /* .interface = */ ggml_backend_openvino_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_openvino_reg(), device), + /* .context = */ ctx, + }; + + return openvino_backend; +} + +GGML_BACKEND_API bool ggml_backend_is_openvino(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_openvino_guid()); +} + +struct ggml_backend_openvino_device_context { + int device; + std::string name; + std::string description; +}; + +static const char * ggml_backend_openvino_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_openvino_device_context * ctx = (ggml_backend_openvino_device_context *) dev->context; + return ctx->name.c_str(); +} + +static const char * ggml_backend_openvino_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_openvino_device_context * ctx = (ggml_backend_openvino_device_context *) dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_openvino_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { +#ifdef _WIN32 + MEMORYSTATUSEX status; + status.dwLength = sizeof(status); + GlobalMemoryStatusEx(&status); + *total = status.ullTotalPhys; + *free = status.ullAvailPhys; +#else + long pages = sysconf(_SC_PHYS_PAGES); + long page_size = sysconf(_SC_PAGE_SIZE); + *total = pages * page_size; + + // "free" system memory is ill-defined, for practical purposes assume that all of it is free: + *free = *total; +#endif // _WIN32 + + GGML_UNUSED(dev); +} + +static enum ggml_backend_dev_type ggml_backend_openvino_device_get_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU; +} + +static void ggml_backend_openvino_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { + props->name = ggml_backend_openvino_device_get_name(dev); + props->description = ggml_backend_openvino_device_get_description(dev); + props->type = ggml_backend_openvino_device_get_type(dev); + ggml_backend_openvino_device_get_memory(dev, &props->memory_free, &props->memory_total); + + props->caps = { + /* .async = */ false, + /* .host_buffer = */ false, + /* .buffer_from_host_ptr = */ false, + /* .events = */ false, + }; +} + +static ggml_backend_t ggml_backend_openvino_device_init(ggml_backend_dev_t dev, const char * params) { + GGML_UNUSED(params); + ggml_backend_openvino_device_context * ctx = (ggml_backend_openvino_device_context *) dev->context; + return ggml_backend_openvino_init(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_openvino_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_openvino_device_context * ctx = (ggml_backend_openvino_device_context *) dev->context; + return ggml_backend_openvino_buffer_type(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_openvino_device_get_host_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_openvino_device_context * ctx = (ggml_backend_openvino_device_context *) dev->context; + return ggml_backend_openvino_host_buffer_type(ctx->device); +} + +static bool has_view_op_input(const ggml_tensor * op) { + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (op->src[i] == nullptr) { + break; + } + if (op->src[i]->op == GGML_OP_VIEW) { + return true; + } + } + return false; +} + +static bool is_supported_flash_attn_pattern(const ggml_tensor * op) { + // pattern of q,k,v should be q->op==PERMUTE, q->src[0]->op==VIEW, q->src[0]->src[0]->view_src==nullptr + for (int i = 0; i < 3; i++) { + const ggml_tensor * src = op->src[i]; + if (src->op != GGML_OP_PERMUTE || src->src[0] == nullptr || src->src[0]->op != GGML_OP_VIEW || + src->src[0]->src[0] == nullptr || src->src[0]->src[0]->view_src != nullptr) { + return false; + } + } + return true; +} + +static bool is_op_unsupported_case(const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_GET_ROWS: + case GGML_OP_SET_ROWS: { + if (op->ne[3] != 1) { + return true; + } + break; + } + case GGML_OP_ADD: + case GGML_OP_MUL: { + if (op->src[1]->op == GGML_OP_PERMUTE) { + return true; + } + for (int i = 0; i < 4; i++) { + if (op->src[0]->ne[i] != op->src[1]->ne[i] && (op->src[0]->ne[i] != 1 && op->src[1]->ne[i] != 1)) { + return true; + } + } + break; + } + case GGML_OP_SOFT_MAX: { + if (op->src[2] != nullptr) { + // GGML_LOG_WARN("OpenVINO backend does not support SOFT_MAX with sinks\n"); + return true; + } + float scale = 1.0f; + float max_bias = 0.0f; + const auto * op_params = op->op_params; + memcpy(&scale, (const float *) op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) op_params + 1, sizeof(float)); + if (max_bias > 0) { + // GGML_LOG_WARN("OpenVINO backend does not support SOFT_MAX with max_bias > 0\n"); + return true; + } + break; + } + case GGML_OP_FLASH_ATTN_EXT: { + if (op->src[4] != nullptr) { + // GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with sinks\n"); + return true; + } + if (!is_supported_flash_attn_pattern(op)) { + return true; + } + float scale = 1.0f; + float max_bias = 0.0f; + float logit_softcap = 0.0f; + const auto * op_params = op->op_params; + memcpy(&scale, (const float *) op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (const float *) op_params + 2, sizeof(float)); + if (max_bias > 0) { + // GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with max_bias > 0\n"); + return true; + } + if (logit_softcap != 0) { + // GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with logit_softcap != 0\n"); + return true; + } + break; + } + case GGML_OP_PERMUTE: { + if (op->type == GGML_TYPE_BF16) { + // err msg: [GPU] Could not find a suitable kernel for transpose + // GGML_LOG_WARN("OpenVINO backend does not support PERMUTE with BF16 type\n"); + return true; + } + break; + } + case GGML_OP_CPY: { + if (op->src[1] != op) { + // GGML_LOG_WARN("OpenVINO backend only supports CPY that is a cast\n"); + return true; + } + break; + } + case GGML_OP_MUL_MAT: { + if (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F16) { + // Has accuracy issue, try enabling this and see `test-backend-ops -o "MUL_MAT"` + // GGML_LOG_WARN("OpenVINO backend does not support MUL_MAT with two F16 tensors\n"); + return true; + } + if (op->src[0]->ne[3] != op->src[1]->ne[3] && op->src[0]->ne[3] != 1 && op->src[1]->ne[3] != 1) { + return true; + } + if (op->src[0]->op == GGML_OP_PERMUTE || op->src[1]->op == GGML_OP_PERMUTE) { + return true; + } + if (ggml_is_quantized(op->src[0]->type) && op->src[0]->ne[1] == 1) { + // MUL_MAT(type_a=q4_0,type_b=f32,m=1,n=2048,k=8192,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1) + // triggers a bug in ov matmul_shape_inference.hpp + return true; + } + if (op->src[0]->op == GGML_OP_VIEW && op->src[1]->op == GGML_OP_VIEW) { + return true; + } + break; + } + case GGML_OP_ROPE: { + const int32_t * op_params = op->op_params; + const int n_dims = op_params[1]; + const int mode = op_params[2]; + if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX) { + // GGML_LOG_WARN("OpenVINO backend does not support ROPE with mode %d\n", mode); + return true; + } + if (n_dims != 0.0f && n_dims != op->src[0]->ne[0]) { + // GGML_LOG_WARN("OpenVINO backend does not support ROPE with n_dims %d != src[0]->ne[0] %ld\n", n_dims, + // op->src[0]->ne[0]); + return true; + } + if (op->type != GGML_TYPE_F32) { + // GGML_LOG_WARN("OpenVINO backend does not support ROPE with type %s\n", ggml_type_name(op->type)); + return true; + } + float freq_scale; + float ext_factor; + memcpy(&freq_scale, op_params + 6, sizeof(float)); + memcpy(&ext_factor, op_params + 7, sizeof(float)); + if (ext_factor != 0.0f) { + // GGML_LOG_WARN("OpenVINO backend does not support ROPE with ext_factor %f != 0.0f\n", ext_factor); + return true; + } + if (op->src[0]->op == GGML_OP_VIEW) { + if (op->src[0]->view_src->ne[1] != op->src[0]->ne[2]) { + // GGML_LOG_WARN( + // "OpenVINO backend does not support ROPE with src[0]->view_src->ne[1] %ld != src[0]->ne[2] " + // "%ld\n", + // op->src[0]->view_src->ne[1], op->src[0]->ne[2]); + return true; + } + } + break; + } + default: + break; + } + if (op->op == GGML_OP_GET_ROWS) { + if (op->ne[0] == 256 && (op->src[0]->type == GGML_TYPE_Q4_K || op->src[0]->type == GGML_TYPE_Q5_K)) { + // ERR = 0.000000306 > 0.000000100 GET_ROWS(type=q4_K,n=256,m=5,r=4,be1=1,be2=1,v=0) + // ERR = 0.000000197 > 0.000000100 GET_ROWS(type=q5_K,n=256,m=5,r=4,be1=1,be2=1,v=0) + return true; + } + } + return false; +} + +static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + GGML_ASSERT(dev->reg != nullptr); + + static std::set supported_types{GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_I64, + GGML_TYPE_I32, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, GGML_TYPE_Q4_K, + GGML_TYPE_Q5_K, GGML_TYPE_Q8_0, GGML_TYPE_Q6_K}; + + static const std::set supported_ops{GGML_OP_NONE, GGML_OP_ADD, GGML_OP_MUL, GGML_OP_MUL_MAT, GGML_OP_VIEW, + /*GGML_OP_CONT,*/ GGML_OP_RESHAPE, GGML_OP_PERMUTE, GGML_OP_TRANSPOSE, + GGML_OP_GET_ROWS, GGML_OP_ROPE, GGML_OP_RMS_NORM, GGML_OP_SCALE, + // softmax is not updated due to replaced by flash_attn_ext + // GGML_OP_SOFT_MAX, + GGML_OP_SET_ROWS, GGML_OP_FLASH_ATTN_EXT, GGML_OP_CPY}; + static const std::set supported_unary_ops{ + GGML_UNARY_OP_SILU, + }; + static const std::set supported_glu_ops{ + GGML_GLU_OP_SWIGLU, + GGML_GLU_OP_GEGLU, + }; + + switch (op->op) { + case GGML_OP_UNARY: { + auto supported = supported_unary_ops.find(ggml_get_unary_op(op)) != supported_unary_ops.end(); + if (!supported) { + // GGML_LOG_WARN("OpenVINO backend does not support unary op %s\n", ggml_unary_op_name(ggml_get_unary_op(op))); + return false; + } + if (has_view_op_input(op)) { + // GGML_LOG_WARN("OpenVINO backend does not support unary op %s with view input\n", + // ggml_unary_op_name(ggml_get_unary_op(op))); + return false; + } + break; + } + case GGML_OP_GLU: { + auto supported = supported_glu_ops.find(ggml_get_glu_op(op)) != supported_glu_ops.end(); + if (!supported) { + // GGML_LOG_WARN("OpenVINO backend does not support GLU op %s\n", ggml_glu_op_name(ggml_get_glu_op(op))); + return false; + } + if (has_view_op_input(op)) { + // GGML_LOG_WARN("OpenVINO backend does not support unary op %s with view input\n", + // ggml_glu_op_name(ggml_get_glu_op(op))); + return false; + } + if (op->src[1] == nullptr && op->src[0]->ne[0] % 2 != 0) { + // triggers bug in ov gpu + return false; + } + break; + } + default: { + auto supported = supported_ops.find(op->op) != supported_ops.end(); + if (!supported) { + // GGML_LOG_WARN("OpenVINO backend does not support op %s\n", ggml_op_name(op->op)); + return false; + } + static std::set ops_not_support_view_input{ + GGML_OP_GET_ROWS, + GGML_OP_RMS_NORM, + }; + if (ops_not_support_view_input.find(op->op) != ops_not_support_view_input.end() && has_view_op_input(op)) { + // GGML_LOG_WARN("OpenVINO backend does not support op %s with view input\n", ggml_op_name(op->op)); + return false; + } + } + } + + if (supported_types.find(op->type) == supported_types.end()) { + // GGML_LOG_WARN("OpenVINO backend does not support tensor type %s\n", ggml_type_name(op->type)); + return false; + } + for (int i = 0; i < GGML_MAX_SRC; i++) { + auto * src = op->src[i]; + if (src == nullptr) { + break; + } + if (supported_types.find(src->type) == supported_types.end()) { + // GGML_LOG_WARN("OpenVINO backend does not support tensor type %s\n", ggml_type_name(src->type)); + return false; + } + if (ggml_is_quantized(src->type) && src->ne[2] != 1) { + // GGML_LOG_WARN("OpenVINO backend does not support 3D quantized tensors\n"); + return false; + } + } + + if (is_op_unsupported_case(op)) { + return false; + } + return true; +} + +static bool ggml_backend_openvino_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + return ggml_backend_buft_is_openvino(buft) || ggml_backend_buft_is_host(buft); + GGML_UNUSED(dev); +} + +static const struct ggml_backend_device_i ggml_backend_openvino_device_interface = { + /* .get_name = */ ggml_backend_openvino_device_get_name, + /* .get_description = */ ggml_backend_openvino_device_get_description, + /* .get_memory = */ ggml_backend_openvino_device_get_memory, + /* .get_type = */ ggml_backend_openvino_device_get_type, + /* .get_props = */ ggml_backend_openvino_device_get_props, + /* .init_backend = */ ggml_backend_openvino_device_init, + /* .get_buffer_type = */ ggml_backend_openvino_device_get_buffer_type, + /* .get_host_buffer_type = */ ggml_backend_openvino_device_get_host_buffer_type, + /* .buffer_from_host_ptr = */ NULL, + /* .supports_op = */ ggml_backend_openvino_device_supports_op, + /* .supports_buft = */ ggml_backend_openvino_device_supports_buft, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; + +struct ggml_backend_openvino_reg_context { + std::vector devices; +}; + +static const char * ggml_backend_openvino_reg_get_name(ggml_backend_reg_t reg) { + return GGML_OPENVINO_NAME; + GGML_UNUSED(reg); +} + +static size_t ggml_backend_openvino_reg_get_device_count(ggml_backend_reg_t reg) { + GGML_UNUSED(reg); + return (size_t) ggml_backend_openvino_get_device_count(); +} + +static ggml_backend_dev_t ggml_backend_openvino_reg_get_device(ggml_backend_reg_t reg, size_t index) { + ggml_backend_openvino_reg_context * ctx = (ggml_backend_openvino_reg_context *) reg->context; + GGML_ASSERT(index < ctx->devices.size()); + return ctx->devices[index]; +} + +static const struct ggml_backend_reg_i ggml_backend_openvino_reg_interface = { + /* .get_name = */ ggml_backend_openvino_reg_get_name, + /* .get_device_count = */ ggml_backend_openvino_reg_get_device_count, + /* .get_device = */ ggml_backend_openvino_reg_get_device, + /* .get_proc_address = */ NULL, +}; + +static void ggml_openvino_init() { + // Initialize device config singleton from env var + ggml_openvino_init_device_config(); + GGML_LOG_INFO("OpenVINO: using device %s\n", ggml_openvino_get_device_name().c_str()); +} + +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_openvino_reg(void) { + static ggml_backend_reg reg; + + static bool initialized = false; + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + ggml_openvino_init(); + + ggml_backend_openvino_reg_context * ctx = new ggml_backend_openvino_reg_context; + + for (int i = 0; i < ggml_backend_openvino_get_device_count(); i++) { + ggml_backend_openvino_device_context * dev_ctx = new ggml_backend_openvino_device_context; + dev_ctx->device = i; + dev_ctx->name = GGML_OPENVINO_NAME + std::to_string(i); + + dev_ctx->description = ov::get_openvino_version().description; + + ggml_backend_dev_t dev = + new ggml_backend_device{/* .interface = */ ggml_backend_openvino_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx}; + ctx->devices.push_back(dev); + } + + reg = ggml_backend_reg{/* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_openvino_reg_interface, + /* .context = */ ctx}; + } + + initialized = true; + } + + return ® +} diff --git a/ggml/src/ggml-openvino/ggml-quants.cpp b/ggml/src/ggml-openvino/ggml-quants.cpp new file mode 100644 index 0000000000..dbf38646dd --- /dev/null +++ b/ggml/src/ggml-openvino/ggml-quants.cpp @@ -0,0 +1,884 @@ +#include "ggml-quants.h" + +#include "ggml-common.h" +#include "ggml-impl.h" +#include "ggml.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +void unpack_32_4(const uint8_t * data, uint8_t * dst) { + std::fill_n(dst, 16, 0); + for (int j = 0; j < 16; ++j) { + uint8_t x = (data[j] & 0x0F); + uint8_t y = (data[j] >> 4); + if (j % 2 != 0) { + x <<= 4; + y <<= 4; + } + dst[j / 2] |= x; + dst[8 + j / 2] |= y; // Last 16 weights are in the higher bits + } +} + +// Extracts (weight, scales, zp) from Q4_0 tensors. +// Data layout is: |16 bit scale|32 x 4bit weights|. +void extract_q4_0_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr) { + const uint64_t bytes_per_block = 18; // 2 bytes scale, 32x0.5 byte weights + + auto * data = static_cast(tensor->data); + auto * weights = static_cast(weights_arr.data()); + auto * scales = scales_arr.data::value_type>(); + auto * zp = static_cast(zp_arr.data()); + + bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization + + // For Q4_0, zero point is always 8 + if (is_scalar_zp) { + zp[0] = 8 | (8 << 4); // Pack two 4-bit values + } + + ov::parallel_for(scales_arr.get_size(), [&](size_t i) { + scales[i] = ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block))); + // For asymmetric quantization, compute per-block zero points + if (!is_scalar_zp) { + // Pack two 4-bit zero points per byte + if (i % 2 == 0) { + zp[i / 2] = 8; // Lower nibble + } else { + zp[i / 2] |= (8 << 4); // Upper nibble + } + } + unpack_32_4(data + i * bytes_per_block + 2, weights + i * 16); + }); +} + +// Extracts (weight, scales, zp) from Q4_1 tensors. +// Data layout is: |16 bit scale|16 bit min|32 x 4bit weights|. +void extract_q4_1_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + bool use_bias) { + const uint64_t bytes_per_block = 20; // 2 bytes scale, 2 bytes min, 32x0.5 byte weights + + auto * data = static_cast(tensor->data); + auto * weights = static_cast(weights_arr.data()); + auto * scales = scales_arr.data::value_type>(); + + if (use_bias) { + // Store bias (min) directly as f16 instead of computing u4 zero points + auto * bias = zp_arr.data::value_type>(); + ov::parallel_for(scales_arr.get_size(), [&](size_t i) { + float scale = static_cast(ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block)))); + float min = static_cast(ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block + 2)))); + scales[i] = ov::float16(scale); + bias[i] = ov::float16(min); // bias = min, dequant: w*s + bias + unpack_32_4(data + i * bytes_per_block + 4, weights + i * 16); + }); + } else { + auto * zp = static_cast(zp_arr.data()); + ov::parallel_for(scales_arr.get_size(), [&](size_t i) { + float scale = static_cast(ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block)))); + float min = static_cast(ov::float16::from_bits(*((uint16_t *) (data + i * bytes_per_block + 2)))); + scales[i] = ov::float16(scale); + // zp = -min / scale (bias = min, so zp = -bias/scale) + uint8_t zp_val = (scale != 0.0f) ? (uint8_t) std::round(-min / scale) : 0; + // Pack two 4-bit zero points per byte + if (i % 2 == 0) { + zp[i / 2] = zp_val & 0x0F; // Lower nibble + } else { + zp[i / 2] |= (zp_val << 4); // Upper nibble + } + unpack_32_4(data + i * bytes_per_block + 4, weights + i * 16); + }); + } +} + +// Extracts (weight, scales, zp) from Q8_0 tensors. +// Data layout is: |16 bit scale|32 x 8bit weights|. +void extract_q8_0_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr) { + const uint64_t weights_per_block = 32; + const uint64_t bytes_per_block = 34; // 2 bytes scale, 32x1 byte weights + + auto * data = static_cast(tensor->data); + auto * weights = static_cast(weights_arr.data()); + auto * scales = scales_arr.data::value_type>(); + auto * zp = static_cast(zp_arr.data()); + + bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization + + // For Q8_0, zero point is always 128 + if (is_scalar_zp) { + zp[0] = 128; + } + + ov::parallel_for(scales_arr.get_size(), [&](size_t i) { + uint8_t * block_data = data + i * bytes_per_block; + scales[i] = ov::float16::from_bits(*(uint16_t *) block_data); + // For asymmetric quantization, store per-block zero points + if (!is_scalar_zp) { + zp[i] = 128; + } + for (size_t j = 0; j < weights_per_block; ++j) { + uint8_t x = block_data[j + 2]; // j+2 to skip the scale bytes. + // Original data is in int8_t, so we add a bias of -128 and invert the first bit. + x ^= 1 << 7; + weights[i * weights_per_block + j] = x; + } + }); +} + +void unpack_256_4(const uint8_t * data, uint8_t * dst) { + // Initialize the output array with zeros + std::fill_n(dst, 128, 0); + + for (size_t i = 0; i < 4; ++i) { + for (int j = 0; j < 32; ++j) { + uint8_t x = (data[i * 32 + j] & 0x0F); + uint8_t y = (data[i * 32 + j] >> 4); + if (j % 2 != 0) { + x <<= 4; + y <<= 4; + } + dst[i * 32 + j / 2] |= x; + dst[i * 32 + 16 + j / 2] |= y; // Last 16 weights are in the higher bits + } + } +} + +void extract_q4_k_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + bool use_bias) { + const uint64_t bytes_per_block = 2 + 2 + 12 + 128; + const uint64_t n_super_block = tensor->nb[3] / bytes_per_block; + + auto * data = static_cast(tensor->data); + auto * weights = static_cast(weights_arr.data()); + auto * scales = scales_arr.data::value_type>(); + + // For bias path, zp_arr holds f16 bias values; for zp path, it holds packed u4 zero points + auto * zp_u4 = use_bias ? nullptr : static_cast(zp_arr.data()); + auto * bias_f16 = use_bias ? zp_arr.data::value_type>() : nullptr; + + ov::parallel_for(n_super_block, [&](size_t i) { + uint8_t * block_data = data + i * bytes_per_block; + + // Extract scale factors and offsets + float scale_scales = static_cast(ov::float16::from_bits(*((uint16_t *) block_data))); + float scale_mins = static_cast(ov::float16::from_bits(*((uint16_t *) block_data + 1))); + + // Extract qs1 and qs2 + uint8_t * qs1 = block_data + 4; + + // Calculate scales + float scale_vals[8]; + scale_vals[0] = scale_scales * static_cast((*(qs1) & 0b111111)); + scale_vals[1] = scale_scales * static_cast((*(qs1 + 1) & 0b111111)); + scale_vals[2] = scale_scales * static_cast((*(qs1 + 2) & 0b111111)); + scale_vals[3] = scale_scales * static_cast((*(qs1 + 3) & 0b111111)); + scale_vals[4] = scale_scales * static_cast((*(qs1 + 8) & 0b00001111) | ((*(qs1) >> 6) << 4)); + scale_vals[5] = scale_scales * static_cast((*(qs1 + 9) & 0b00001111) | ((*(qs1 + 1) >> 6) << 4)); + scale_vals[6] = scale_scales * static_cast((*(qs1 + 10) & 0b00001111) | ((*(qs1 + 2) >> 6) << 4)); + scale_vals[7] = scale_scales * static_cast((*(qs1 + 11) & 0b00001111) | ((*(qs1 + 3) >> 6) << 4)); + + // Calculate min values (bias = -min) + float min_vals[8]; + min_vals[0] = scale_mins * static_cast((*(qs1 + 4) & 0b111111)); + min_vals[1] = scale_mins * static_cast((*(qs1 + 5) & 0b111111)); + min_vals[2] = scale_mins * static_cast((*(qs1 + 6) & 0b111111)); + min_vals[3] = scale_mins * static_cast((*(qs1 + 7) & 0b111111)); + min_vals[4] = scale_mins * static_cast((*(qs1 + 8) >> 4) | ((*(qs1 + 4) >> 6) << 4)); + min_vals[5] = scale_mins * static_cast((*(qs1 + 9) >> 4) | ((*(qs1 + 5) >> 6) << 4)); + min_vals[6] = scale_mins * static_cast((*(qs1 + 10) >> 4) | ((*(qs1 + 6) >> 6) << 4)); + min_vals[7] = scale_mins * static_cast((*(qs1 + 11) >> 4) | ((*(qs1 + 7) >> 6) << 4)); + + // Store scales and compute zero points or bias + for (int j = 0; j < 8; j++) { + scales[i * 8 + j] = ov::float16(scale_vals[j]); + if (use_bias) { + // Store bias = -min directly as f16, dequant: w*s + bias + bias_f16[i * 8 + j] = ov::float16(-min_vals[j]); + } else { + // zp = min / scale (since bias = -min and zp = -bias/scale) + uint8_t zp_val = (scale_vals[j] != 0.0f) ? (uint8_t) std::round(min_vals[j] / scale_vals[j]) : 0; + // Pack two 4-bit zero points per byte + size_t idx = i * 8 + j; + if (idx % 2 == 0) { + zp_u4[idx / 2] = zp_val & 0x0F; + } else { + zp_u4[idx / 2] |= (zp_val << 4); + } + } + } + unpack_256_4(block_data + 16, weights + i * 128); + }); +} + +void extract_q6_k_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr) { + const uint64_t bytes_per_block = 128 + 64 + 16 + 2; + const uint64_t n_super_block = tensor->nb[3] / bytes_per_block; + + auto * data = static_cast(tensor->data); + auto * weights = static_cast(weights_arr.data()); + auto * scales = scales_arr.data::value_type>(); + auto * zp = static_cast(zp_arr.data()); + + bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization + + // For Q6_K, zero point is always 32 + if (is_scalar_zp) { + zp[0] = 32; + } + + ov::parallel_for(n_super_block, [&](size_t i) { + uint8_t * block_data = data + i * bytes_per_block; + + float scale_factor = + static_cast(ov::float16::from_bits(*((uint16_t *) block_data + 104))); // (128+64+16)/2 + + for (size_t j = 0; j < 16; j++) { + scales[j + i * 16] = + ov::float16(scale_factor * static_cast(*((int8_t *) (block_data + 128 + 64 + j)))); + // For asymmetric quantization, store per-block zero points + if (!is_scalar_zp) { + zp[j + i * 16] = 32; + } + } + + uint8_t * ql = block_data; + uint8_t * qh = block_data + 128; + + for (int64_t j = 0; j < 32; ++j) { + weights[i * 256 + j] = (ql[j] & 0xF) | (((qh[j] >> 0) & 3) << 4); + weights[i * 256 + j + 32] = (ql[32 + j] & 0xF) | (((qh[j] >> 2) & 3) << 4); + weights[i * 256 + j + 64] = (ql[j] >> 4) | (((qh[j] >> 4) & 3) << 4); + weights[i * 256 + j + 96] = (ql[32 + j] >> 4) | (((qh[j] >> 6) & 3) << 4); + weights[i * 256 + j + 128] = (ql[64 + j] & 0xF) | (((qh[32 + j] >> 0) & 3) << 4); + weights[i * 256 + j + 160] = (ql[96 + j] & 0xF) | (((qh[32 + j] >> 2) & 3) << 4); + weights[i * 256 + j + 192] = (ql[64 + j] >> 4) | (((qh[32 + j] >> 4) & 3) << 4); + weights[i * 256 + j + 224] = (ql[96 + j] >> 4) | (((qh[32 + j] >> 6) & 3) << 4); + } + }); +} + +static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t * d, uint8_t * m) { + if (j < 4) { + *d = q[j] & 63; + *m = q[j + 4] & 63; + } else { + *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4); + *m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4); + } +} + +void extract_q5_k_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + bool use_bias) { + const uint64_t bytes_per_block = 4 + 12 + 32 + 128; + const uint64_t n_super_block = tensor->nb[3] / bytes_per_block; + + auto * data = static_cast(tensor->data); + auto * weights = static_cast(weights_arr.data()); + auto * scales = scales_arr.data::value_type>(); + + // For bias path, zp_arr holds f16 bias values; for zp path, it holds u8 zero points + auto * zp_u8 = use_bias ? nullptr : static_cast(zp_arr.data()); + auto * bias_f16 = use_bias ? zp_arr.data::value_type>() : nullptr; + + ov::parallel_for(n_super_block, [&](size_t i) { + uint8_t * block_data = data + i * bytes_per_block; + + const float d = static_cast(ov::float16::from_bits(*((uint16_t *) block_data))); + const float min_factor = static_cast(ov::float16::from_bits(*((uint16_t *) block_data + 1))); + + const uint8_t * scales_data = block_data + 4; // 12 bytes of scales + const uint8_t * qh = block_data + 4 + 12; // 32 bytes of high bits + const uint8_t * ql = block_data + 4 + 12 + 32; // 128 bytes of low bits + + int is = 0; + uint8_t u1 = 1; + uint8_t u2 = 2; + + // Process 2 blocks in one iteration + for (int j = 0; j < 256; j += 64) { // 256 = QK_K, so 4 iterations of 64 + uint8_t sc; + uint8_t m; + + // Get scale and min for first 32 elements + get_scale_min_k4(is + 0, scales_data, &sc, &m); + const float d1 = d * sc; + const float m1 = min_factor * m; + + // Get scale and min for second 32 elements + get_scale_min_k4(is + 1, scales_data, &sc, &m); + const float d2 = d * sc; + const float m2 = min_factor * m; + + scales[i * 8 + is] = ov::float16(d1); + scales[i * 8 + is + 1] = ov::float16(d2); + if (use_bias) { + // Store bias = -min directly as f16, dequant: w*s + bias + bias_f16[i * 8 + is] = ov::float16(-m1); + bias_f16[i * 8 + is + 1] = ov::float16(-m2); + } else { + // zp = min / scale (since bias = -min and zp = -bias/scale) + zp_u8[i * 8 + is] = (d1 != 0.0f) ? (uint8_t) std::round(m1 / d1) : 0; + zp_u8[i * 8 + is + 1] = (d2 != 0.0f) ? (uint8_t) std::round(m2 / d2) : 0; + } + + // Extract weights for first 32 elements (matching deq formula exactly) + for (int l = 0; l < 32; ++l) { + weights[i * 256 + j + l] = (ql[l] & 0xF) + ((qh[l] & u1) ? 16 : 0); + } + + // Extract weights for second 32 elements + for (int l = 0; l < 32; ++l) { + weights[i * 256 + j + l + 32] = (ql[l] >> 4) + ((qh[l] & u2) ? 16 : 0); + } + + ql += 32; + is += 2; + u1 <<= 2; + u2 <<= 2; + } + }); +} + +// TODO Reorder for make_intX_weights + +ov::Output make_int8_weights(ov::Tensor & weight, + ov::Tensor & scales, + ov::Tensor & zp, + size_t group_size, + bool use_bias) { + ov::Shape orig_shape = weight.get_shape(); + + // Expand dimensions for scales and zp/bias + auto scale_shape = scales.get_shape(); + auto zp_shape = zp.get_shape(); + bool is_scalar_zp = zp_shape.empty(); // Symmetric quantization + + ov::Shape packed_shape = {orig_shape[0], orig_shape[1] / group_size, group_size}; + + if (packed_shape[1] == 1) { + // Requantized channel-wise case + packed_shape.erase(packed_shape.begin() + 1); + } else { + scale_shape.push_back(1); + scales.set_shape(scale_shape); + // For symmetric quantization, zp remains scalar (don't resize) + if (!is_scalar_zp) { + zp_shape.push_back(1); + zp.set_shape(zp_shape); + } + } + + // Create graph nodes + auto weights_node = std::make_shared(ov::element::u8, packed_shape, + static_cast(weight.data()), nullptr); + weights_node->get_rt_info()["__gguf_tensor_holder"] = weight; + auto scales_f16 = std::make_shared(scales); + auto weights_f16 = std::make_shared(weights_node, ov::element::f16); + + ov::Output result; + if (use_bias && !is_scalar_zp) { + // Bias path: w * s + b (zp tensor holds f16 bias values) + auto bias_f16 = std::make_shared(zp); + auto w_s = std::make_shared(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY); + result = std::make_shared(w_s, bias_f16, ov::op::AutoBroadcastType::NUMPY); + } else { + // Zero point path: (w - zp) * s + auto zero_point = std::make_shared(zp); + float zp_value; + if (ov::op::util::get_single_value(zero_point, zp_value)) { + zero_point = ov::op::v0::Constant::create(zero_point->get_element_type(), {}, {zp_value}); + } + auto zero_point_f16 = std::make_shared(zero_point, ov::element::f16); + auto w_zp = + std::make_shared(weights_f16, zero_point_f16, ov::op::AutoBroadcastType::NUMPY); + result = std::make_shared(w_zp, scales_f16, ov::op::AutoBroadcastType::NUMPY); + } + + if (packed_shape.size() != 2) { + // If not requantized channel-wise case, reshape back to original shape + auto final_shape = + std::make_shared(ov::element::i64, ov::Shape{orig_shape.size()}, orig_shape); + result = std::make_shared(result, final_shape, false); + } + + return std::make_shared(result, ov::element::f32); +} + +ov::Output make_int4_weights(ov::Tensor & weight, + ov::Tensor & scales, + ov::Tensor & zp, + size_t group_size, + bool use_bias) { + ov::Shape orig_weight_shape = weight.get_shape(); + + // Expand dimensions for scales and zp/bias + ov::Shape scale_shape = scales.get_shape(); + auto zp_shape = zp.get_shape(); + bool is_scalar_zp = zp_shape.empty(); // Symmetric quantization + + // Create INT4 weight tensor + ov::Shape packed_shape = {orig_weight_shape[0], orig_weight_shape[1] / group_size, group_size}; + + if (packed_shape[1] == 1) { + // Requantized channel-wise case + packed_shape.erase(packed_shape.begin() + 1); + } else { + scale_shape.push_back(1); + scales.set_shape(scale_shape); + // For symmetric quantization, zp remains scalar (don't resize) + if (!is_scalar_zp) { + zp_shape.push_back(1); + zp.set_shape(zp_shape); + } + } + + auto weights_node = std::make_shared(ov::element::u4, packed_shape, + static_cast(weight.data()), nullptr); + weights_node->get_rt_info()["__gguf_tensor_holder"] = weight; + auto weights_f16 = std::make_shared(weights_node, ov::element::f16); + auto scales_f16 = std::make_shared(scales); + + ov::Output result; + if (use_bias && !is_scalar_zp) { + // Bias path: w * s + b (zp tensor holds f16 bias values) + auto bias_f16 = std::make_shared(zp); + auto w_s = std::make_shared(weights_f16, scales_f16, ov::op::AutoBroadcastType::NUMPY); + result = std::make_shared(w_s, bias_f16, ov::op::AutoBroadcastType::NUMPY); + } else { + // Zero point path: (w - zp) * s + auto zero_points_node = std::make_shared(zp); + float zp_value; + if (ov::op::util::get_single_value(zero_points_node, zp_value)) { + zero_points_node = ov::op::v0::Constant::create(zero_points_node->get_element_type(), {}, {zp_value}); + } + auto zero_points_f16 = std::make_shared(zero_points_node, ov::element::f16); + auto w_zp = + std::make_shared(weights_f16, zero_points_f16, ov::op::AutoBroadcastType::NUMPY); + result = std::make_shared(w_zp, scales_f16, ov::op::AutoBroadcastType::NUMPY); + } + + if (packed_shape.size() != 2) { + // If not requantized channel-wise case, reshape back to original shape + auto final_shape = std::make_shared(ov::element::i64, ov::Shape{orig_weight_shape.size()}, + orig_weight_shape); + result = std::make_shared(result, final_shape, false); + } + + return std::make_shared(result, ov::element::f32); +} + +// Extract quantized weights from tensor and create weight subgraph +std::shared_ptr extract_quantized_weights(const ggml_tensor * tensor, + const void * data, + ov::Tensor & weights, + ov::Tensor & scales, + ov::Tensor & zp, + bool use_bias) { + // Create a temporary tensor for extraction functions that read from tensor->data + ggml_tensor temp_tensor = *tensor; + temp_tensor.data = const_cast(data); + + // Determine block size based on tensor type + int64_t weights_per_block; + bool is_u4; + switch (tensor->type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_K: + is_u4 = true; + weights_per_block = 32; + break; + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q5_K: + is_u4 = false; + weights_per_block = 32; + break; + case GGML_TYPE_Q6_K: + is_u4 = false; + weights_per_block = 16; + break; + default: + throw std::runtime_error("Unsupported quantized type for extraction: " + + std::string(ggml_type_name(tensor->type))); + } + + // Extract quantized data + switch (tensor->type) { + case GGML_TYPE_Q4_0: + extract_q4_0_data(&temp_tensor, weights, scales, zp); + break; + case GGML_TYPE_Q4_1: + extract_q4_1_data(&temp_tensor, weights, scales, zp, use_bias); + break; + case GGML_TYPE_Q4_K: + extract_q4_k_data(&temp_tensor, weights, scales, zp, use_bias); + break; + case GGML_TYPE_Q8_0: + extract_q8_0_data(&temp_tensor, weights, scales, zp); + break; + case GGML_TYPE_Q6_K: + extract_q6_k_data(&temp_tensor, weights, scales, zp); + break; + case GGML_TYPE_Q5_K: + extract_q5_k_data(&temp_tensor, weights, scales, zp, use_bias); + break; + default: + throw std::runtime_error("Unsupported quantized type: " + std::string(ggml_type_name(tensor->type))); + } + + // Create the OpenVINO weight subgraph + ov::Output weight_node; + if (is_u4) { + weight_node = make_int4_weights(weights, scales, zp, weights_per_block, use_bias); + } else { + weight_node = make_int8_weights(weights, scales, zp, weights_per_block, use_bias); + } + + auto result = weight_node.get_node_shared_ptr(); + result->set_friendly_name(tensor->name); + return result; +} + +// Requantize weights to target format, writing to provided buffers +std::shared_ptr requantize_to_buffers(const ggml_tensor * tensor, + const void * data, + ExtraQuantType requant_type, + int64_t block_size, + ov::Tensor & weights, + ov::Tensor & scales, + ov::Tensor & zp) { + int64_t n_elements = ggml_nelements(tensor); + + // First dequantize to F32 + std::vector weights_f32(n_elements); + ggml_get_type_traits(tensor->type)->to_float(data, weights_f32.data(), n_elements); + + // Handle F16 case - just convert and create constant + if (requant_type == ExtraQuantType::F16) { + ggml_get_type_traits(GGML_TYPE_F16)->from_float_ref(weights_f32.data(), weights.data(), n_elements); + auto result = std::make_shared(weights); + result->set_friendly_name(tensor->name); + return result; + } + + // Requantize to target quantized format + bool is_u4 = (requant_type == ExtraQuantType::Q4_0_C || requant_type == ExtraQuantType::Q4_0_128); + + if (is_u4) { + quantize_q4_0(weights_f32.data(), weights, scales, zp, n_elements, block_size); + } else if (requant_type == ExtraQuantType::Q8_1_C) { + quantize_q8_1(weights_f32.data(), weights, scales, zp, n_elements, block_size); + } else { + quantize_q8_0(weights_f32.data(), weights, scales, zp, n_elements, block_size); + } + + // Create the OpenVINO weight subgraph + ov::Output weight_node; + if (is_u4) { + weight_node = make_int4_weights(weights, scales, zp, block_size); + } else { + weight_node = make_int8_weights(weights, scales, zp, block_size); + } + + auto result = weight_node.get_node_shared_ptr(); + result->set_friendly_name(tensor->name); + return result; +} + +OvWeight process_weight_tensor(const ggml_tensor * tensor, const void * data, void * output_base_ptr, bool use_bias) { + GGML_ASSERT(tensor != nullptr); + GGML_ASSERT(data != nullptr); + + OvWeight result; + + // Get 2D shape for weights [rows, cols] + ov::Shape node_shape = {static_cast(tensor->ne[1]), static_cast(tensor->ne[0])}; + + // Handle F16/F32/BF16 weights + if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) { + ov::element::Type element_type; + switch (tensor->type) { + case GGML_TYPE_F32: + element_type = ov::element::f32; + break; + case GGML_TYPE_F16: + element_type = ov::element::f16; + break; + case GGML_TYPE_BF16: + element_type = ov::element::bf16; + break; + default: + OPENVINO_THROW("Unexpected tensor type in F16/F32/BF16 path"); + } + + if (output_base_ptr && output_base_ptr != data) { + // Using external buffer - copy data and create shared-memory constant + size_t tensor_bytes = ggml_nbytes(tensor); + memcpy(output_base_ptr, data, tensor_bytes); + result.weights = ov::Tensor(element_type, node_shape, output_base_ptr); + } else { + result.weights = ov::Tensor(element_type, node_shape, data); + } + result.weight_node = std::make_shared(result.weights); + return result; + } + + // Handle quantized weights + if (!ggml_is_quantized(tensor->type)) { + OPENVINO_THROW("Unsupported weight tensor type: ", ggml_type_name(tensor->type)); + } + + result.layout = ggml_openvino_get_extracted_layout(tensor, use_bias); + const auto & layout = result.layout; + if (layout.total_size == 0) { + OPENVINO_THROW("Unsupported quantized type: ", ggml_type_name(tensor->type)); + } + + if (use_bias) { + OPENVINO_ASSERT(!layout.is_requant, + "use_bias is only used for test-backend-ops, which should not have requantization"); + // bias node will be created on the fly and not use backend buffer + output_base_ptr = nullptr; + } + + // F16 requant path - no separate scales/zp needed in result + if (layout.is_requant && layout.requant_type.has_value() && layout.requant_type.value() == ExtraQuantType::F16) { + if (output_base_ptr) { + result.weights = ov::Tensor(ov::element::f16, node_shape, + static_cast(output_base_ptr) + layout.weights_offset); + } else { + result.weights = ov::Tensor(ov::element::f16, node_shape); + } + ov::Tensor dummy_scales, dummy_zp; // Not used for F16 + result.weight_node = + requantize_to_buffers(tensor, data, ExtraQuantType::F16, 0, result.weights, dummy_scales, dummy_zp); + return result; + } + + // Quantized path (normal extraction or quantized requant) + // Create weight/scale/zp tensors - shared between both paths + ov::element::Type weight_type = layout.is_u4 ? ov::element::u4 : ov::element::u8; + ov::Shape scale_shape = {node_shape[0], node_shape[1] / layout.weights_per_block}; + ov::Shape zp_shape = layout.is_symmetric ? ov::Shape{} : scale_shape; + + if (output_base_ptr) { + uint8_t * buf_base = static_cast(output_base_ptr); + result.weights = ov::Tensor(weight_type, node_shape, buf_base + layout.weights_offset); + result.scales = ov::Tensor(ov::element::f16, scale_shape, buf_base + layout.scales_offset); + result.zp = ov::Tensor(weight_type, zp_shape, buf_base + layout.zp_offset); + } else { + result.weights = ov::Tensor(weight_type, node_shape); + result.scales = ov::Tensor(ov::element::f16, scale_shape); + if (use_bias && !layout.is_symmetric) { + // bias only has effect for asymmetric quant + result.zp = ov::Tensor(ov::element::f16, zp_shape); + } else { + result.zp = ov::Tensor(weight_type, zp_shape); + } + } + + if (layout.is_requant && layout.requant_type.has_value()) { + result.weight_node = requantize_to_buffers(tensor, data, layout.requant_type.value(), layout.weights_per_block, + result.weights, result.scales, result.zp); + } else { + result.weight_node = + extract_quantized_weights(tensor, data, result.weights, result.scales, result.zp, use_bias); + } + + return result; +} + +void quantize_q4_0(const float * x, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + int64_t k, + int64_t qk) { + assert(k % qk == 0); + const int nb = k / qk; + + auto * weights = static_cast(weights_arr.data()); + auto * scales = scales_arr.data::value_type>(); + auto * zp = static_cast(zp_arr.data()); + bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization + + // For Q4_0, zero point is always 8 + if (is_scalar_zp) { + zp[0] = 8 | (8 << 4); // Pack two 4-bit values + } + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + float max = 0.0f; + + for (int j = 0; j < qk; j++) { + const float v = x[i * qk + j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + max = v; + } + } + + const float d = max / -8; + + if (d == 0) { + scales[i] = ov::float16(1.0f); + // zp is already set to 8 for symmetric, or set per-block for asymmetric + if (!is_scalar_zp) { + if (i % 2 == 0) { + zp[i / 2] = 8; + } else { + zp[i / 2] |= (8 << 4); + } + } + memset(weights + i * qk / 2, 8 | (8 << 4), qk / 2); + continue; + } + + const float id = 1.0f / d; + scales[i] = ov::float16(d); + // For asymmetric quantization, store per-block zero points + if (!is_scalar_zp) { + if (i % 2 == 0) { + zp[i / 2] = 8; + } else { + zp[i / 2] |= (8 << 4); + } + } + + for (int j = 0; j < qk / 2; ++j) { + const float x0 = x[i * qk + 2 * j] * id; + const float x1 = x[i * qk + 2 * j + 1] * id; + const uint8_t xi0 = MIN(15, (int8_t) (x0 + 8.5f)); + const uint8_t xi1 = MIN(15, (int8_t) (x1 + 8.5f)); + weights[i * qk / 2 + j] = xi0 | (xi1 << 4); + } + } +} + +void quantize_q8_0(const float * x, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + int64_t k, + int64_t qk) { + assert(k % qk == 0); + const int nb = k / qk; + + auto * weights = static_cast(weights_arr.data()); + auto * scales = scales_arr.data::value_type>(); + auto * zp = static_cast(zp_arr.data()); + bool is_scalar_zp = (zp_arr.get_size() == 1); // Symmetric quantization + + // For Q8_0, zero point is always 128 + if (is_scalar_zp) { + zp[0] = 128; + } + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + + for (int j = 0; j < qk; j++) { + const float v = x[i * qk + j]; + if (amax < fabsf(v)) { + amax = fabsf(v); + } + } + + const float d = amax / 127.0f; + const float id = d ? 1.0f / d : 0.0f; + scales[i] = ov::float16(d); + // For asymmetric quantization, store per-block zero points + if (!is_scalar_zp) { + zp[i] = 128; + } + + for (int j = 0; j < qk; ++j) { + const float x0 = x[i * qk + j] * id; + const int8_t xi0 = roundf(x0); + weights[i * qk + j] = (uint8_t) (xi0 + 128); + } + } +} + +void quantize_q8_1(const float * x, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + int64_t k, + int64_t qk) { + assert(k % qk == 0); + const int nb = k / qk; + + auto * weights = static_cast(weights_arr.data()); + auto * scales = scales_arr.data::value_type>(); + auto * zp = static_cast(zp_arr.data()); + for (int i = 0; i < nb; i++) { + float min = std::numeric_limits::max(); + float max = std::numeric_limits::lowest(); + + for (int j = 0; j < qk; j++) { + const float v = x[i * qk + j]; + if (v < min) { + min = v; + } + if (v > max) { + max = v; + } + } + + const float d = (max - min) / ((1 << 8) - 1); + const float id = d ? 1.0f / d : 0.0f; + scales[i] = ov::float16(d); + // zp = -min / scale (Q8_1 is asymmetric) + zp[i] = (d != 0.0f) ? (uint8_t) std::round(-min / d) : 0; + + for (int j = 0; j < qk; ++j) { + const float x0 = (x[i * qk + j] - min) * id; + const uint8_t xi0 = roundf(x0); + weights[i * qk + j] = xi0; + } + } +} diff --git a/ggml/src/ggml-openvino/ggml-quants.h b/ggml/src/ggml-openvino/ggml-quants.h new file mode 100644 index 0000000000..e4a02297ca --- /dev/null +++ b/ggml/src/ggml-openvino/ggml-quants.h @@ -0,0 +1,153 @@ +#pragma once +#include "ggml-openvino-extra.h" // For ExtraQuantType +#include "ggml.h" + +#include +#include +#include + +void unpack_32_4(const uint8_t* data, uint8_t* dst); + +void extract_q4_0_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr); + +void extract_q4_1_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + bool use_bias = false); + +void extract_q8_0_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr); + +void unpack_256_4(const uint8_t* data, uint8_t* dst); + +void extract_q4_k_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + bool use_bias = false); + +void extract_q5_k_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + bool use_bias = false); + +void extract_q6_k_data(const ggml_tensor * tensor, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr); + +static constexpr size_t GGML_QUANTIZATION_GROUP_SIZE = 32; + +ov::Output make_int8_weights(ov::Tensor & weight, + ov::Tensor & scales, + ov::Tensor & zp, + size_t group_size = GGML_QUANTIZATION_GROUP_SIZE, + bool use_bias = false); + +ov::Output make_int4_weights(ov::Tensor & weight, + ov::Tensor & scales, + ov::Tensor & zp, + size_t group_size = GGML_QUANTIZATION_GROUP_SIZE, + bool use_bias = false); + +// Extract quantized weights from tensor and create weight subgraph +// If weights/scales/zp are provided (non-empty), uses them as output buffers +// Otherwise allocates new ov::Tensors internally +// Returns the weight node (make_int4_weights or make_int8_weights result) +std::shared_ptr extract_quantized_weights( + const ggml_tensor * tensor, + const void * data, // Source data pointer (may differ from tensor->data) + ov::Tensor & weights, + ov::Tensor & scales, + ov::Tensor & zp, + bool use_bias = false); // Use fp bias instead of quantized zero_point (for test-backend-ops) + +// Requantize weights from tensor to target format, writing to provided buffers +// For F16 target, only weights buffer is used (scales/zp ignored) +// Returns the weight node +std::shared_ptr requantize_to_buffers(const ggml_tensor * tensor, + const void * data, // Source data pointer + ExtraQuantType requant_type, + int64_t block_size, + ov::Tensor & weights, + ov::Tensor & scales, + ov::Tensor & zp); + +inline const char * extra_quant_type_name(ExtraQuantType t) { + switch (t) { + case ExtraQuantType::F16: + return "F16"; + case ExtraQuantType::Q4_0_C: + return "Q4_0_C"; + case ExtraQuantType::Q4_0_128: + return "Q4_0_128"; + case ExtraQuantType::Q8_0_C: + return "Q8_0_C"; + case ExtraQuantType::Q8_0_32: + return "Q8_0_32"; + case ExtraQuantType::Q8_1_C: + return "Q8_1_C"; + default: + return "unknown"; + } +} + +// Result from process_weight_tensor containing the weight node and tensors. +// For quantized weights, also contains the extracted layout and scale/zp tensors. +struct OvWeight { + std::shared_ptr weight_node; + ggml_openvino_extracted_layout layout; // Only meaningful for quantized (layout.total_size > 0) + ov::Tensor weights; + ov::Tensor scales; + ov::Tensor zp; + + bool is_quantized() const { return layout.scales_size > 0; } +}; + +// Process weight tensor and create an OpenVINO weight node +// Handles F16/F32/BF16 and quantized weights, with optional requantization +// If output_base_ptr is nullptr, allocates internal buffers (for decoder use) +// If output_base_ptr is provided, uses pre-allocated buffers at specified offsets (for backend buffer use) +// Returns OvWeight with the weight node and optional quantized tensors +OvWeight process_weight_tensor( + const ggml_tensor * tensor, + const void * data, // Source data pointer (may differ from tensor->data) + void * output_base_ptr = nullptr, // Base pointer for output buffers (or nullptr for internal allocation) + bool use_bias = false); // Use fp bias instead of quantized zero_point, only used in test-backend-ops + +void quantize_q4_0(const float * x, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + int64_t k, + int64_t qk); +void quantize_q8_1(const float * x, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + int64_t k, + int64_t qk); +void quantize_q8_0(const float * x, + ov::Tensor & weights_arr, + ov::Tensor & scales_arr, + ov::Tensor & zp_arr, + int64_t k, + int64_t qk); + +namespace ov { +namespace op { +namespace util { +// From /src/common/transformations/include/transformations/utils/utils.hpp +bool get_single_value(const std::shared_ptr& const_node, + float& value, + bool check_value_range = true); +} // namespace util +} // namespace op +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/decoder.h b/ggml/src/ggml-openvino/openvino/decoder.h new file mode 100644 index 0000000000..3b8da2be5d --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/decoder.h @@ -0,0 +1,74 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { + +class GgmlDecoder : public DecoderBase { +public: + virtual ov::Any get_attribute(const std::string& name) const = 0; + + virtual PartialShape get_input_shape(int node_idx, const std::string& name) const = 0; + + virtual std::vector get_input_stride(int node_idx, const std::string& name) const = 0; + + virtual element::Type get_input_type(int node_idx, const std::string& name) const = 0; + + virtual size_t get_input_size() const = 0; + + virtual size_t get_input_size(int node_idx) const = 0; + + virtual void get_input_node(size_t input_port_idx, + std::string& producer_name, + std::string& producer_output_port_name, + size_t& producer_output_port_index) const = 0; + + virtual std::vector get_input_names(int node_idx) const = 0; + + virtual PartialShape get_output_shape(int node_idx) const = 0; + + virtual element::Type get_output_type(const int node_idx) const = 0; + + virtual int32_t* get_input_op_params(int node_idx, const std::string& name) const = 0; + + virtual int32_t * get_output_op_params(int node_idx) const = 0; + + virtual std::vector get_output_names(int node_idx) const = 0; + + virtual const std::string& get_op_type() const = 0; + + virtual const std::string& get_op_type(int node_idx) const = 0; + + virtual const std::string& get_op_name() const = 0; + + virtual const std::string& get_op_name(int node_idx) const = 0; + + virtual void visit_subgraph(std::function, int node_idx)> node_visitor) const = 0; + + virtual int get_op_case(int node_idx) const = 0; + + virtual const std::map>& get_model_inputs() const = 0; + virtual const std::map>& get_model_extra_inputs() const = 0; + virtual const std::map>& get_model_weights() const = 0; + virtual std::vector get_model_output_names() const = 0; + + virtual int32_t* get_rope_params() const = 0; + + virtual std::map get_kv_param_res_names() const = 0; + + virtual bool is_static() const = 0; + + virtual bool is_stateful() const = 0; + + virtual int is_swa_layer(int layer) const = 0; +}; + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/frontend.cpp b/ggml/src/ggml-openvino/openvino/frontend.cpp new file mode 100644 index 0000000000..c2ba14e66e --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/frontend.cpp @@ -0,0 +1,27 @@ +#include "frontend.h" + +#include "input_model.h" +#include "op_table.h" +#include "translate_session.h" + +namespace ov { +namespace frontend { +namespace ggml { + +FrontEnd::FrontEnd() {} + +std::shared_ptr FrontEnd::convert(const InputModel::Ptr & model, bool naive) { + auto ggml_model = std::dynamic_pointer_cast(model); + FRONT_END_GENERAL_CHECK(ggml_model, "Invalid input model"); + std::shared_ptr converted_model; + const auto & supported_ops = get_supported_ops(); + { + TranslateSession translate_session(model, supported_ops, naive); + converted_model = translate_session.get_converted_model(); + } + return converted_model; +} + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/frontend.h b/ggml/src/ggml-openvino/openvino/frontend.h new file mode 100644 index 0000000000..f1c6f0c3e3 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/frontend.h @@ -0,0 +1,23 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include + +namespace ov { +namespace frontend { +namespace ggml { + +class FrontEnd { +public: + using Ptr = std::shared_ptr; + FrontEnd(); + + static std::shared_ptr convert(const InputModel::Ptr& model, bool naive = false); +}; + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/input_model.cpp b/ggml/src/ggml-openvino/openvino/input_model.cpp new file mode 100644 index 0000000000..39b004c931 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/input_model.cpp @@ -0,0 +1,17 @@ +#include "input_model.h" + +#include "decoder.h" + +namespace ov { +namespace frontend { +namespace ggml { + +InputModel::InputModel(const std::shared_ptr & gdecoder) : m_decoder(gdecoder) {} + +const std::shared_ptr & InputModel::get_model_decoder() const { + return m_decoder; +} + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/input_model.h b/ggml/src/ggml-openvino/openvino/input_model.h new file mode 100644 index 0000000000..ce8434426c --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/input_model.h @@ -0,0 +1,29 @@ +#pragma once + +#include + +#include "decoder.h" + +namespace ov { +namespace frontend { +namespace ggml { + +class FrontEnd; +class GgmlDecoder; +using ov::frontend::ggml::GgmlDecoder; + +class InputModel : public ov::frontend::InputModel { + friend class ::ov::frontend::ggml::FrontEnd; + +public: + explicit InputModel(const std::shared_ptr& gdecoder); + + const std::shared_ptr& get_model_decoder() const; + +private: + std::shared_ptr m_decoder; +}; + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/node_context.h b/ggml/src/ggml-openvino/openvino/node_context.h new file mode 100644 index 0000000000..aa484128a9 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/node_context.h @@ -0,0 +1,112 @@ +#pragma once + +#include +#include +#include + +#include "decoder.h" + +namespace ov { +namespace frontend { +namespace ggml { + +class TranslateSession; + +typedef std::map> TensorMap; + +class NodeContext : public frontend::NodeContext { +public: + NodeContext(const std::shared_ptr& decoder, + std::shared_ptr& tensor_map, + int node_idx, + TranslateSession* translate_session = nullptr) + : ov::frontend::NodeContext(decoder->get_op_type(node_idx)), + m_decoder(decoder), + m_tensor_map(tensor_map), + m_node_idx(node_idx), + m_translate_session(translate_session) { + m_input_names = decoder->get_input_names(m_node_idx); + m_output_names = decoder->get_output_names(m_node_idx); + } + + TranslateSession* get_translate_session() const { + return m_translate_session; + } + + const std::vector& get_input_names() const { return m_input_names; } + + size_t get_input_size() const override { + return m_decoder->get_input_size(m_node_idx); + } + + ov::element::Type get_input_type(size_t index) const { + return m_decoder->get_input_type(m_node_idx, m_input_names[index]); + } + + PartialShape get_input_shape(size_t input_index) const { + return m_decoder->get_input_shape(m_node_idx, m_input_names[input_index]); + } + + std::vector get_input_stride(size_t index) const { + return m_decoder->get_input_stride(m_node_idx, m_input_names[index]); + } + + std::string get_output_name() const { return m_output_names[0]; } + + PartialShape get_output_shape() const { return m_decoder->get_output_shape(m_node_idx); } + + int32_t* get_input_op_params(size_t index) const { + return m_decoder->get_input_op_params(m_node_idx, m_input_names[index]); + } + + int32_t * get_output_op_params() const { return m_decoder->get_output_op_params(m_node_idx); } + + ov::element::Type get_output_type() const { + return m_decoder->get_output_type(m_node_idx); + } + + Output get_input(int idx) const override { + return m_tensor_map->at(m_input_names[idx]); + } + + Output get_input(const std::string& name) const override { + if (m_tensor_map->find(name) == m_tensor_map->end()) { + throw std::runtime_error("'" + name + "' not found in tensor map."); + } + return m_tensor_map->at(name); + } + + bool has_input(const std::string& name) const { + return m_tensor_map->find(name) != m_tensor_map->end(); + } + + const std::string& get_name() const override { + return m_decoder->get_op_name(m_node_idx); + } + + ov::Any get_attribute_as_any(const std::string& name) const override { + return m_decoder->get_attribute(name); + } + + int get_op_case() const { + return m_decoder->get_op_case(m_node_idx); + } + + bool is_static() const { return m_decoder->is_static(); } + + bool is_stateful() const { return m_decoder->is_stateful(); } + +private: + std::shared_ptr m_decoder; + std::shared_ptr& m_tensor_map; + int m_node_idx; + TranslateSession* m_translate_session; + std::vector m_input_names; + std::vector m_output_names; +}; + +using CreatorFunction = std::function; + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/cont.cpp b/ggml/src/ggml-openvino/openvino/op/cont.cpp new file mode 100644 index 0000000000..6160dd7444 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/cont.cpp @@ -0,0 +1,48 @@ + +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_cont(const NodeContext & context) { + num_inputs_check(context, 1, 1); + + int op_case = context.get_op_case(); + FRONT_END_CHECK_IMPLEMENTED(op_case == 1 || op_case == 2 || op_case == 3, "Unsupported CONT case"); + + auto src_shape = context.get_input_shape(0).to_shape(); + auto dst_shape = context.get_output_shape().to_shape(); + ov::Output res; + + if (op_case == 1) { + // The input comes from a PERMUTE + throw std::runtime_error("Code of this case might be outdated"); + dst_shape[1] = -1; + res = std::make_shared( + context.get_input(0), ov::op::v0::Constant::create(ov::element::i64, {dst_shape.size()}, dst_shape), false); + } else if (op_case == 2) { + // The input comes from a TRANSPOSE + return {context.get_input(0)}; + } else { + // The input comes from a VIEW + res = process_view_input(context, 0); + } + + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/cpy.cpp b/ggml/src/ggml-openvino/openvino/op/cpy.cpp new file mode 100644 index 0000000000..831117208b --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/cpy.cpp @@ -0,0 +1,21 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_cpy(const NodeContext & context) { + auto res = std::make_shared(context.get_input(0), context.get_output_type()); + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/flash_attn_ext.cpp b/ggml/src/ggml-openvino/openvino/op/flash_attn_ext.cpp new file mode 100644 index 0000000000..42602a730a --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/flash_attn_ext.cpp @@ -0,0 +1,90 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_flash_attn_ext(const NodeContext & context) { + num_inputs_check(context, 4, 4); + auto q_f32 = context.get_input(0); + auto k = context.get_input(1); + auto v = context.get_input(2); + auto mask = context.get_input(3); + + float * params = reinterpret_cast(context.get_output_op_params()); + float scale = params[0]; + // float max_bias = params[1]; + // float logit_softcap = params[2]; + + auto q = std::make_shared(q_f32, ov::element::f16); + auto scale_node = std::make_shared(ov::element::f16, ov::Shape{}, std::vector{scale}); + + ov::Output mask_sliced, res; + std::string mask_name = "KQ_mask_sliced"; + if (context.get_input_names()[3].find("swa") != std::string::npos) { + mask_name = "KQ_mask_swa_sliced"; + } + if (context.has_input(mask_name)) { + mask_sliced = context.get_input(mask_name); + } else { + auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + auto two = ov::op::v0::Constant::create(ov::element::i64, {1}, {2}); + auto token_len = get_dimensions(q, {2}); + mask_sliced = std::make_shared(mask, zero, token_len, one, two); + } + + if (mask_sliced.get_element_type() != ov::element::f16) { + mask_sliced = std::make_shared(mask_sliced, ov::element::f16); + } + + auto tile_kv = [&](int64_t num_heads, int64_t num_heads_kv, int64_t head_size, ov::Output kv) { + int64_t factor = num_heads / num_heads_kv; + if (factor > 1 && num_heads_kv > 1) { + ov::Output kv_broadcast_shape, kv_unsqueezed, new_kv_shape; + auto unsqueeze_axes = ov::op::v0::Constant::create(ov::element::i64, Shape{}, {2}); + kv_unsqueezed = std::make_shared(kv, unsqueeze_axes); + + kv_broadcast_shape = ov::op::v0::Constant::create( + ov::element::i64, {5}, {(int64_t) 1, (int64_t) 1, factor, (int64_t) 1, (int64_t) 1}); + new_kv_shape = + ov::op::v0::Constant::create(ov::element::i64, {4}, {(int64_t) 0, num_heads, (int64_t) -1, head_size}); + + kv = std::make_shared(kv_unsqueezed, kv_broadcast_shape, + ov::op::BroadcastType::BIDIRECTIONAL); + kv = std::make_shared(kv, new_kv_shape, true); + } + return kv; + }; + + auto q_shape = context.get_input_shape(0).to_shape(); + auto k_shape = context.get_input_shape(1).to_shape(); + k = tile_kv(q_shape[1], k_shape[1], q_shape[3], k); + v = tile_kv(q_shape[1], k_shape[1], q_shape[3], v); + + auto sdpa = std::make_shared(q, k, v, mask_sliced, scale_node, false); + res = std::make_shared(sdpa, + ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 2, 1, 3})); + res = std::make_shared(res, ov::element::f32); + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/get_rows.cpp b/ggml/src/ggml-openvino/openvino/op/get_rows.cpp new file mode 100644 index 0000000000..49f51b7ca3 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/get_rows.cpp @@ -0,0 +1,69 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_get_rows(const NodeContext & context) { + num_inputs_check(context, 2, 2); + + int op_case = context.get_op_case(); + + Output res; + auto data = context.get_input(0); + auto indices = context.get_input(1); + + if (op_case == 2) { + // The input comes from a VIEW + indices = process_view_input(context, 1); + } + + // data[1,b,x,y] ind[1,1,b,x'] test-backend-ops case + // data[x,y] ind[1,1,1,x'] normal case + indices = + std::make_shared(indices, ov::op::v0::Constant::create(ov::element::i64, {2}, {0, 1})); + if (data.get_partial_shape().rank() == 4) { + if (!(data.get_partial_shape()[1].is_dynamic()) && data.get_partial_shape()[1].get_length() == 1) { + // Work-around for a bug in ov cpu plugin for test-backend-ops + data = std::make_shared(data, + ov::op::v0::Constant::create(ov::element::i64, {2}, {0, 1})); + auto axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {0}); + res = std::make_shared(data, indices, axis); + } else { + auto axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {1}); + data = + std::make_shared(data, ov::op::v0::Constant::create(ov::element::i64, {1}, {0})); + res = std::make_shared(data, indices, axis, 1); + } + } else if (context.is_stateful() && data.get_partial_shape().rank() == 3) { + auto axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {1}); + res = std::make_shared(data, indices, axis, 1); + } else { + auto axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {0}); + res = std::make_shared(data, indices, axis); + } + + if (res.get_element_type() != context.get_output_type()) { + res = std::make_shared(res, context.get_output_type()); + } + if (!(context.is_stateful())) { + res = std::make_shared(res, ov::op::v0::Constant::create(ov::element::i64, {1}, {0})); + } + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/glu_geglu.cpp b/ggml/src/ggml-openvino/openvino/op/glu_geglu.cpp new file mode 100644 index 0000000000..d9fa4c2436 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/glu_geglu.cpp @@ -0,0 +1,61 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_glu_geglu(const NodeContext & context) { + num_inputs_check(context, 1, 2); + + ov::Output src0; + ov::Output src1; + if (context.get_input_size() == 2) { + src0 = context.get_input(0); + src1 = context.get_input(1); + } else { + // GGML splits along ne[0] (OV last axis) using floor division: nc = ne[0] / 2. + // Both halves are nc elements; if the dimension is odd, the last element is dropped. + // Use Slice instead of Split to handle odd dimensions correctly. + auto combined = context.get_input(0); + auto combined_shape = combined.get_partial_shape(); + int64_t last_dim_val = combined_shape[combined_shape.rank().get_length() - 1].get_length(); + int64_t nc = last_dim_val / 2; + + auto axis = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1}); + auto step = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + auto start0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + auto stop0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc}); + auto start1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc}); + auto stop1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {2 * nc}); + + src0 = std::make_shared(combined, start0, stop0, step, axis); + src1 = std::make_shared(combined, start1, stop1, step, axis); + } + + int32_t * params = context.get_output_op_params(); + const int32_t swapped = params[1]; + if (swapped) { + std::swap(src0, src1); + } + + auto gelu = std::make_shared(src0); + auto res = std::make_shared(gelu, src1); + + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp b/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp new file mode 100644 index 0000000000..00ed7951a0 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp @@ -0,0 +1,62 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_glu_swiglu(const NodeContext & context) { + num_inputs_check(context, 1, 2); + + ov::Output src0; + ov::Output src1; + if (context.get_input_size() == 2) { + src0 = context.get_input(0); + src1 = context.get_input(1); + } else { + // GGML splits along ne[0] (OV last axis) using floor division: nc = ne[0] / 2. + // Both halves are nc elements; if the dimension is odd, the last element is dropped. + // Use Slice instead of Split to handle odd dimensions correctly. + auto combined = context.get_input(0); + auto combined_shape = combined.get_partial_shape(); + int64_t last_dim_val = combined_shape[combined_shape.rank().get_length() - 1].get_length(); + int64_t nc = last_dim_val / 2; + + auto axis = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1}); + auto step = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + auto start0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + auto stop0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc}); + auto start1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc}); + auto stop1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {2 * nc}); + + src0 = std::make_shared(combined, start0, stop0, step, axis); + src1 = std::make_shared(combined, start1, stop1, step, axis); + } + + int32_t * params = context.get_output_op_params(); + const int32_t swapped = params[1]; + if (swapped) { + std::swap(src0, src1); + } + + auto sigmoid = std::make_shared(src0); + auto silu = std::make_shared(src0, sigmoid); + auto res = std::make_shared(silu, src1); + + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/mulmat.cpp b/ggml/src/ggml-openvino/openvino/op/mulmat.cpp new file mode 100644 index 0000000000..38edec85dd --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/mulmat.cpp @@ -0,0 +1,90 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_mulmat(const NodeContext & context) { + num_inputs_check(context, 2, 2); + + int op_case = context.get_op_case(); + + ov::Output res; + ov::Output B = context.get_input(0); + ov::Output A = context.get_input(1); + + bool transpose_b = true; + if (op_case == 2) { + B = B.get_node_shared_ptr()->input_value(0); + transpose_b = false; + } else if (op_case == 3) { + B = process_view_input(context, 0); + A = process_view_input(context, 1); + } + if (A.get_element_type() != B.get_element_type()) { + B = std::make_shared(context.get_input(0), context.get_input_type(1)); + } + + auto B_shape = context.get_input_shape(0).to_shape(); + auto A_shape = context.get_input_shape(1).to_shape(); + int64_t A_batch = A_shape[1]; + int64_t B_batch = B_shape[1]; + + auto A_batch_larger = A_batch > B_batch; + auto batch_large = A_batch_larger ? A_batch : B_batch; + auto batch_small = A_batch_larger ? B_batch : A_batch; + + Output Z = A_batch_larger ? B : A; + int64_t factor = batch_large / batch_small; + if (factor > 1 && batch_small > 1) { + auto batch_large_node = ov::op::v0::Constant::create(ov::element::i64, {1}, std::vector{batch_large}); + auto batch_small_node = ov::op::v0::Constant::create(ov::element::i64, {1}, std::vector{batch_small}); + auto factor_node = ov::op::v0::Constant::create(ov::element::i64, {1}, std::vector{factor}); + + auto unsqueeze_axes = ov::op::v0::Constant::create(ov::element::i64, Shape{}, {2}); + auto Z_unsqueezed = std::make_shared(Z, unsqueeze_axes); + + auto broadcast_shape = ov::op::v0::Constant::create( + ov::element::i64, {5}, {(int64_t) 1, (int64_t) 1, factor, (int64_t) 1, (int64_t) 1}); + auto new_Z_shape = ov::op::v0::Constant::create(ov::element::i64, {4}, + {(int64_t) 0, batch_large, (int64_t) -1, (int64_t) A_shape[3]}); + + auto Z_broadcasted = std::make_shared(Z_unsqueezed, broadcast_shape, + ov::op::BroadcastType::BIDIRECTIONAL); + Z = std::make_shared(Z_broadcasted, new_Z_shape, true); + } + if (A_batch_larger) { + B = Z; + } else { + A = Z; + } + + res = std::make_shared(A, B, false, transpose_b); + + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/permute.cpp b/ggml/src/ggml-openvino/openvino/op/permute.cpp new file mode 100644 index 0000000000..4c800f9ee4 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/permute.cpp @@ -0,0 +1,102 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_permute(const NodeContext & context) { + num_inputs_check(context, 1, 1); + + int op_case = context.get_op_case(); + FRONT_END_CHECK_IMPLEMENTED(op_case == 1 || op_case == 2 || op_case == 3 || op_case == 4, + "Unsupported PERMUTE case"); + + ov::Output res; + auto src = context.get_input(0); + auto perm = ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 2, 1, 3}); + + if (op_case == 1 || context.is_stateful()) { + res = std::make_shared(src, perm); + } else if (op_case == 4) { + auto output_shape = context.get_output_shape().to_shape(); + auto n_heads = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[1]}); + auto head_size = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[3]}); + auto n_seq_active = context.has_input("n_seq_active") ? + context.get_input("n_seq_active") : + ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[0]}); + auto neg_one = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1}); + + auto new_shape = + std::make_shared(ov::OutputVector{n_seq_active, neg_one, n_heads, head_size}, 0); + + // // Alternative + // auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + // auto new_shape = std::make_shared(ov::OutputVector{n_seq_active, neg_one, zero, zero}, 0); + + auto reshaped = std::make_shared(src, new_shape, true); + res = std::make_shared(reshaped, perm); + } else { + auto cache_shape = src.get_partial_shape(); + auto output_shape = context.get_output_shape().to_shape(); + int64_t head_size = output_shape[3]; + int64_t n_heads = output_shape[1]; + int64_t ctx_per_seq = cache_shape[2].is_static() ? cache_shape[2].get_length() : -1; + int64_t n_seq = cache_shape[1].get_length(); + + Output attention_size; + if (!context.has_input("attention_size")) { + attention_size = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[2]}); + } else if (op_case == 2) { + attention_size = context.get_input("attention_size"); + } else { + attention_size = context.get_input("attention_size_swa"); + } + + Output seq_active_start; + Output seq_active_end; + if (context.has_input("seq_active_start")) { + seq_active_start = context.get_input("seq_active_start"); + seq_active_end = context.get_input("seq_active_end"); + } else { + int64_t n_seq_active = output_shape[0]; + size_t offset = *((size_t *) context.get_input_op_params(0)); + int64_t seq_active_start_val = offset / context.get_input_stride(0)[0]; + int64_t seq_active_end_val = seq_active_start_val + n_seq_active; + seq_active_start = ov::op::v0::Constant::create(ov::element::i64, {1}, {seq_active_start_val}); + seq_active_end = ov::op::v0::Constant::create(ov::element::i64, {1}, {seq_active_end_val}); + } + + // 1. reshape to [n_seq, ctx_per_seq, n_heads, head_size] + // 2. slice out the active sequences + // 3. slice out the attention part in each sequence + // 4. permute + auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + + auto src_reshaped = std::make_shared( + src, ov::op::v0::Constant::create(ov::element::i64, {4}, {n_seq, ctx_per_seq, n_heads, head_size}), false); + auto slice1 = std::make_shared(src_reshaped, seq_active_start, seq_active_end, one, zero); + auto slice2 = std::make_shared(slice1, zero, attention_size, one, one); + res = std::make_shared(slice2, perm); + } + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/reshape.cpp b/ggml/src/ggml-openvino/openvino/op/reshape.cpp new file mode 100644 index 0000000000..efd9a5a860 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/reshape.cpp @@ -0,0 +1,83 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_reshape(const NodeContext & context) { + num_inputs_check(context, 1, 1); + if (context.get_input_shape(0) == context.get_output_shape()) { + return {context.get_input(0)}; + } + + int op_case = context.get_op_case(); + FRONT_END_CHECK_IMPLEMENTED( + op_case == 1 || op_case == 2 || op_case == 3 || op_case == 4 || op_case == 5 || op_case == 6, + "Unsupported RESHAPE case"); + + auto output_shape = context.get_output_shape().to_shape(); + std::shared_ptr new_shape_node; + if (op_case == 1) { + if (context.is_stateful()) { + new_shape_node = ov::op::v0::Constant::create( + ov::element::i64, {3}, + std::vector{-1, (int64_t) output_shape[2], (int64_t) output_shape[3]}); + } else { + new_shape_node = ov::op::v0::Constant::create( + ov::element::i64, {4}, + std::vector{(int64_t) output_shape[0], -1, (int64_t) output_shape[2], (int64_t) output_shape[3]}); + } + } else if (op_case == 2) { + new_shape_node = ov::op::v0::Constant::create( + ov::element::i64, {4}, + std::vector{(int64_t) output_shape[0], (int64_t) output_shape[1], -1, (int64_t) output_shape[3]}); + + } else if (op_case == 3) { + throw std::runtime_error("might be outdated RESHAPE case"); + new_shape_node = ov::op::v0::Constant::create( + ov::element::i64, {4}, std::vector{(int64_t) output_shape[0], (int64_t) output_shape[1], -1, 1}); + + } else if (op_case == 4) { + return {context.get_input(0).get_node_shared_ptr()->input_value(0)}; + + } else if (op_case == 5) { + if (context.is_stateful()) { + std::vector shape_vec = {1, -1, (int64_t) context.get_output_shape().to_shape()[3]}; + new_shape_node = ov::op::v0::Constant::create(ov::element::i64, {3}, shape_vec); + } else { + std::vector shape_vec = {1, 1, -1, (int64_t) context.get_output_shape().to_shape()[3]}; + new_shape_node = ov::op::v0::Constant::create(ov::element::i64, {4}, shape_vec); + } + + // // Alternative + // auto token_len = context.get_input("token_len"); + // auto emb_size = + // ov::op::v0::Constant::create(ov::element::i64, {1}, {(int64_t) context.get_output_shape().to_shape()[3]}); + // auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + // new_shape_node = std::make_shared(ov::OutputVector{one, one, token_len, emb_size}, 0); + + } else if (op_case == 6) { + new_shape_node = ov::op::v0::Constant::create(ov::element::i64, {4}, context.get_output_shape().to_shape()); + } + auto res = std::make_shared(context.get_input(0), new_shape_node, false); + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/rms_norm.cpp b/ggml/src/ggml-openvino/openvino/op/rms_norm.cpp new file mode 100644 index 0000000000..72cf92283e --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/rms_norm.cpp @@ -0,0 +1,46 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_rms_norm(const NodeContext & context) { + num_inputs_check(context, 1, 1); + + auto input_node = context.get_input(0); + auto square = std::make_shared( + input_node, ov::op::v0::Constant::create(ov::element::f32, ov::Shape{1}, {2.0f})); + + auto mean = std::make_shared( + square, ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {-1}), true); + + float eps; + memcpy(&eps, context.get_output_op_params(), sizeof(float)); + + auto rms = std::make_shared( + std::make_shared(mean, ov::op::v0::Constant::create(ov::element::f32, ov::Shape{1}, {eps}))); + + auto reciprocal = + std::make_shared(ov::op::v0::Constant::create(ov::element::f32, ov::Shape{1}, {1.0f}), rms); + + auto res = std::make_shared(input_node, reciprocal); + + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/rope.cpp b/ggml/src/ggml-openvino/openvino/op/rope.cpp new file mode 100644 index 0000000000..26dc2d24f8 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/rope.cpp @@ -0,0 +1,123 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_rope(const NodeContext & context) { + num_inputs_check(context, 2, 3); + + int op_case = context.get_op_case(); + + ov::Output res; + + auto data_node = context.get_input(0).get_node_shared_ptr(); + auto output_shape = context.get_output_shape().to_shape(); + int32_t * op_params = context.get_output_op_params(); + + Output cos_theta_node; + Output sin_theta_node; + if (context.has_input("rope_cos")) { + cos_theta_node = context.get_input("rope_cos"); + sin_theta_node = context.get_input("rope_sin"); + } else { + auto inp_pos = context.get_input(1).get_node_shared_ptr(); + std::shared_ptr rope_freqs_weight; + if (context.get_input_size() == 3) { + rope_freqs_weight = context.get_input(2).get_node_shared_ptr(); + } + auto sin_cos = make_sin_cos(op_params, inp_pos, rope_freqs_weight); + sin_theta_node = sin_cos.first; + cos_theta_node = sin_cos.second; + } + + if (op_case == 2) { + // The input comes from a VIEW + int slice_len = output_shape[2] * output_shape[3]; + data_node = process_view_input(context, 0, slice_len).get_node_shared_ptr(); + if (context.is_stateful()) { + auto data_shape = ov::op::v0::Constant::create( + ov::element::i64, {3}, std::vector{-1, (int64_t) output_shape[2], (int64_t) output_shape[3]}); + data_node = std::make_shared(data_node, data_shape, false); + } else { + auto data_shape = ov::op::v0::Constant::create( + ov::element::i64, {4}, std::vector{1, -1, (int64_t) output_shape[2], (int64_t) output_shape[3]}); + data_node = std::make_shared(data_node, data_shape, false); + } + } + + const int mode = op_params[2]; + constexpr int ROPE_TYPE_NORMAL = 0; + constexpr int ROPE_TYPE_NEOX = 2; + + if (mode == ROPE_TYPE_NORMAL) { + auto neg_one = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1}); + auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + auto two = ov::op::v0::Constant::create(ov::element::i64, {1}, {2}); + auto end = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[3]}); + Output even_slice; + Output odd_slice; + int32_t unsqueeze_dim = context.is_stateful() ? 3 : 4; + even_slice = std::make_shared(data_node, zero, end, two, neg_one); + odd_slice = std::make_shared(data_node, one, end, two, neg_one); + + Output first_half = + std::make_shared(std::make_shared(even_slice, cos_theta_node), + std::make_shared(odd_slice, sin_theta_node)); + Output second_half = + std::make_shared(std::make_shared(even_slice, sin_theta_node), + std::make_shared(odd_slice, cos_theta_node)); + + first_half = std::make_shared(first_half, + ov::op::v0::Constant::create(ov::element::i64, {1}, {unsqueeze_dim})); + second_half = std::make_shared(second_half, + ov::op::v0::Constant::create(ov::element::i64, {1}, {unsqueeze_dim})); + auto stack = std::make_shared(OutputVector{first_half, second_half}, unsqueeze_dim); + + auto data_shape = ov::op::v0::Constant::create( + ov::element::i64, {4}, std::vector{1, -1, (int64_t) output_shape[2], (int64_t) output_shape[3]}); + res = std::make_shared(stack, data_shape, false); + } else if (mode == ROPE_TYPE_NEOX) { + auto data_split = std::make_shared( + data_node, ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {-1}), 2); + Output slice_data_node_0 = data_split->outputs()[0]; + Output slice_data_node_1 = data_split->outputs()[1]; + + auto first_half_node = std::make_shared( + std::make_shared(slice_data_node_0, cos_theta_node), + std::make_shared(slice_data_node_1, sin_theta_node)); + + auto second_half_node = std::make_shared( + std::make_shared(slice_data_node_0, sin_theta_node), + std::make_shared(slice_data_node_1, cos_theta_node)); + + res = std::make_shared(ov::OutputVector{first_half_node, second_half_node}, -1); + } + + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/scale.cpp b/ggml/src/ggml-openvino/openvino/op/scale.cpp new file mode 100644 index 0000000000..0f3d800c19 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/scale.cpp @@ -0,0 +1,41 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_scale(const NodeContext & context) { + num_inputs_check(context, 1, 1); + + float scale; + float bias; + memcpy(&scale, (float *) context.get_output_op_params() + 0, sizeof(float)); + memcpy(&bias, (float *) context.get_output_op_params() + 1, sizeof(float)); + + auto scale_node = std::make_shared(ov::element::f32, ov::Shape{}, std::vector{scale}); + auto scaled = std::make_shared(context.get_input(0), scale_node); + + std::shared_ptr res; + if (bias != 0.0f) { + auto bias_node = + std::make_shared(ov::element::f32, ov::Shape{}, std::vector{bias}); + res = std::make_shared(scaled, bias_node); + } else { + res = scaled; + } + + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/set_rows.cpp b/ggml/src/ggml-openvino/openvino/op/set_rows.cpp new file mode 100644 index 0000000000..136e4265b4 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/set_rows.cpp @@ -0,0 +1,76 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_set_rows(const NodeContext & context) { + num_inputs_check(context, 3, 3); + + auto data = context.get_input(0); + auto indices = context.get_input(1); + auto dst = context.get_input(2); + + data = std::make_shared(data, context.get_output_type()); + + auto dst_shape = context.get_output_shape().to_shape(); + + auto ind_squeezed = + std::make_shared(indices, ov::op::v0::Constant::create(ov::element::i64, {3}, {0, 1, 2})); + auto data_reshaped = std::make_shared( + data, + ov::op::v0::Constant::create(ov::element::i64, {4}, + {(int64_t) 1, (int64_t) 1, (int64_t) -1, (int64_t) dst_shape[3]}), + false); + auto axes = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {2}); + + Output res; + if (context.is_stateful()) { + int concat_axis = 1; + int64_t dim2 = dst.get_partial_shape()[2].get_length(); + int64_t dim3 = dst.get_partial_shape()[3].get_length(); + data = std::make_shared( + data, ov::op::v0::Constant::create(ov::element::i64, {4}, {(int64_t) 1, (int64_t) -1, dim2, dim3}), false); + res = std::make_shared(OutputVector{dst, data}, concat_axis); + } else { + res = std::make_shared(dst, ind_squeezed, data_reshaped, axes); + } + + if (auto dst_reshape = std::dynamic_pointer_cast(dst.get_node_shared_ptr())) { + // Fix the case of multiple sequences, reshape back to original shape [1, n_seq, ctx_per_seq, emb] + // ctx_per_seq is not fixed due to llama-bench compatibility + auto dst_shape_partial = dst_reshape->get_input_partial_shape(0); + std::vector dst_shape = {dst_shape_partial[0].get_length(), dst_shape_partial[1].get_length(), + dst_shape_partial[2].is_static() ? dst_shape_partial[2].get_length() : -1, + dst_shape_partial[3].get_length()}; + res = std::make_shared(res, ov::op::v0::Constant::create(ov::element::i64, {4}, dst_shape), + false); + } + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/softmax.cpp b/ggml/src/ggml-openvino/openvino/op/softmax.cpp new file mode 100644 index 0000000000..9f6330862b --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/softmax.cpp @@ -0,0 +1,89 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_soft_max(const NodeContext & context) { + // TODO code is outdated + num_inputs_check(context, 1, 2); + + auto input_node = context.get_input(0).get_node_shared_ptr(); + ov::Output res; + + float scale = 1.0f; + float max_bias = 0.0f; + auto * op_params = context.get_output_op_params(); + memcpy(&scale, (float *) op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) op_params + 1, sizeof(float)); + auto src0_shape = context.get_input_shape(0).get_shape(); + const uint32_t h = src0_shape[2]; + const uint32_t n_head = src0_shape[0]; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + const float slope = + (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2 * (h - n_head_log2) + 1) : 1.0f; + + auto scale_node = std::make_shared(ov::element::f32, ov::Shape{}, std::vector{scale}); + auto scaled_input = std::make_shared(input_node, scale_node); + + if (context.get_input_size() < 2) { + res = std::make_shared(scaled_input, 2); + return rename_outputs_with_suffix({res}, context.get_name()); + } + + ov::Output mask_node_sliced; + if (context.has_input("KQ_mask_sliced")) { + mask_node_sliced = context.get_input("KQ_mask_sliced"); + } else { + auto token_len = get_dimensions(input_node, {1}); + auto mask_node = context.get_input(1); + auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + mask_node_sliced = std::make_shared(mask_node, zero, token_len, one, one); + } + + if (mask_node_sliced.get_element_type() != context.get_output_type()) { + mask_node_sliced = std::make_shared(mask_node_sliced, context.get_output_type()); + } + + Output slope_mask; + if (slope != 1.0f) { + auto slope_node = + std::make_shared(ov::element::f32, ov::Shape{}, std::vector{slope}); + slope_mask = std::make_shared(mask_node_sliced, slope_node); + throw std::runtime_error("Slope != 1.0f in softmax has not been tested, verify it before use."); + } + slope_mask = mask_node_sliced; + + auto input_slope_mask_node = std::make_shared(scaled_input, slope_mask); + + res = std::make_shared(input_slope_mask_node, 2); + + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/transpose.cpp b/ggml/src/ggml-openvino/openvino/op/transpose.cpp new file mode 100644 index 0000000000..8e62e83c0d --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/transpose.cpp @@ -0,0 +1,23 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_transpose(const NodeContext & context) { + num_inputs_check(context, 1, 1); + + auto res = std::make_shared( + context.get_input(0), ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 1, 3, 2})); + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/unary_silu.cpp b/ggml/src/ggml-openvino/openvino/op/unary_silu.cpp new file mode 100644 index 0000000000..037e0b94df --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/unary_silu.cpp @@ -0,0 +1,27 @@ +#include "../node_context.h" +#include "../op_table.h" +#include "../utils.h" + +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_unary_silu(const NodeContext & context) { + num_inputs_check(context, 1, 1); + + auto input = context.get_input(0); + auto sigmoid = std::make_shared(input); + auto res = std::make_shared(input, sigmoid); + + return rename_outputs_with_suffix({res}, context.get_name()); +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op/view.cpp b/ggml/src/ggml-openvino/openvino/op/view.cpp new file mode 100644 index 0000000000..8528d25233 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op/view.cpp @@ -0,0 +1,53 @@ +#include "../op_table.h" +#include "../utils.h" +#include +namespace ov { +namespace frontend { +namespace ggml { +namespace op { + +OutputVector translate_view(const NodeContext & context) { + num_inputs_check(context, 1, 1); + + if (context.get_op_case() == 2) { + auto dst_shape = context.get_output_shape().to_shape(); + return rename_outputs_with_suffix({process_view_input(context, 0, dst_shape[2] * dst_shape[3])}, + context.get_name()); + } + // op_case 3 + if (context.get_op_case() == 3) { + auto input = context.get_input(0); + auto input_ov_shape = input.get_partial_shape(); + + auto input_llama_shape = context.get_input_shape(0).to_shape(); + + // if the input ov shape size is different from the input llama shape size, it means the input is already reshaped and we need to reshape it back to the original shape before slicing + if (input_ov_shape.size() != input_llama_shape.size()) { + input = std::make_shared(input, ov::op::v0::Constant::create(ov::element::i64, {input_llama_shape.size()}, input_llama_shape), false); + } + + auto dst_shape = context.get_output_shape().to_shape(); + + // find the index of dst_shape that is different from input shape, and use that index to slice the input + int slice_dim = -1; + for (size_t i = 0; i < dst_shape.size(); ++i) { + if (dst_shape[i] != input_llama_shape[i]) { + slice_dim = i; + break; + } + } + + auto begin = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + auto end = ov::op::v0::Constant::create(ov::element::i64, {1}, {dst_shape[slice_dim]}); + auto stride = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + auto axes = ov::op::v0::Constant::create(ov::element::i64, {1}, {slice_dim}); + auto sliced = std::make_shared(input, begin, end, stride, axes); + return {sliced}; + } + return {context.get_input(0)}; +} + +} // namespace op +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op_table.cpp b/ggml/src/ggml-openvino/openvino/op_table.cpp new file mode 100644 index 0000000000..beadafe810 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op_table.cpp @@ -0,0 +1,46 @@ +#include "op_table.h" + +#include "utils.h" + +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { + +std::unordered_map get_supported_ops() { + using namespace ov::op; + return { + {"GGML_OP_ADD", op::translate_1to1_match_2_inputs }, + {"GGML_OP_ADD1", op::translate_1to1_match_2_inputs }, + {"GGML_OP_CONT", op::translate_cont }, + {"GGML_OP_DIV", op::translate_1to1_match_2_inputs }, + {"GGML_OP_GET_ROWS", op::translate_get_rows }, + {"GGML_OP_MUL", op::translate_1to1_match_2_inputs}, + {"GGML_OP_MUL_MAT", op::translate_mulmat }, + {"GGML_OP_PERMUTE", op::translate_permute }, + {"GGML_OP_RESHAPE", op::translate_reshape }, + {"GGML_OP_RMS_NORM", op::translate_rms_norm }, + {"GGML_OP_ROPE", op::translate_rope }, + {"GGML_OP_SCALE", op::translate_scale }, + {"GGML_OP_SOFT_MAX", op::translate_soft_max }, + {"GGML_OP_SUB", op::translate_1to1_match_2_inputs}, + {"GGML_OP_TRANSPOSE", op::translate_transpose }, + {"GGML_UNARY_OP_SILU", op::translate_unary_silu }, + {"GGML_OP_VIEW", op::translate_view }, + {"GGML_GLU_OP_SWIGLU", op::translate_glu_swiglu }, + {"GGML_GLU_OP_GEGLU", op::translate_glu_geglu }, + {"GGML_OP_SET_ROWS", op::translate_set_rows }, + {"GGML_OP_CPY", op::translate_cpy }, + {"GGML_OP_FLASH_ATTN_EXT", op::translate_flash_attn_ext }, + }; +} + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/op_table.h b/ggml/src/ggml-openvino/openvino/op_table.h new file mode 100644 index 0000000000..37f763117a --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/op_table.h @@ -0,0 +1,39 @@ +#pragma once + +#include "node_context.h" + +namespace ov { +namespace frontend { +namespace ggml { + +namespace op { + +#define GGML_OP_CONVERTER(op) OutputVector op(const NodeContext& context) + +GGML_OP_CONVERTER(translate_add); +GGML_OP_CONVERTER(translate_cont); +GGML_OP_CONVERTER(translate_get_rows); +GGML_OP_CONVERTER(translate_mul); +GGML_OP_CONVERTER(translate_mulmat); +GGML_OP_CONVERTER(translate_permute); +GGML_OP_CONVERTER(translate_reshape); +GGML_OP_CONVERTER(translate_rms_norm); +GGML_OP_CONVERTER(translate_rope); +GGML_OP_CONVERTER(translate_scale); +GGML_OP_CONVERTER(translate_unary_silu); +GGML_OP_CONVERTER(translate_soft_max); +GGML_OP_CONVERTER(translate_transpose); +GGML_OP_CONVERTER(translate_view); +GGML_OP_CONVERTER(translate_glu_swiglu); +GGML_OP_CONVERTER(translate_glu_geglu); +GGML_OP_CONVERTER(translate_set_rows); +GGML_OP_CONVERTER(translate_cpy); +GGML_OP_CONVERTER(translate_flash_attn_ext); + +} // namespace op + +std::unordered_map get_supported_ops(); + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.cpp b/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.cpp new file mode 100644 index 0000000000..ed2a3ab6d1 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.cpp @@ -0,0 +1,123 @@ +#include "eliminate_zp.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace pass { + +EliminateZeroPoints::EliminateZeroPoints() { + // Find pattern: + // (Multiply Any(scale) + // (Subtract (Convert Constant(data))) + // (Convert Constant(zero_point))) + // where zero_point is a scalar + // If data is u4 and zp value is 8 (q4_0), Replace the Subtract with an i4 Constant whose value is data - zp_val + // If data is u8 and zp value is 128 (q8_0) or 32 (q6_k), Replace the Subtract with an i8 Constant + + auto m_data_constant = ov::pass::pattern::wrap_type(); + auto m_data_convert = ov::pass::pattern::wrap_type({m_data_constant}); + + auto m_zp_constant = ov::pass::pattern::wrap_type(); + auto m_zp_convert = ov::pass::pattern::wrap_type({m_zp_constant}); + + auto m_subtract = ov::pass::pattern::wrap_type({m_data_convert, m_zp_convert}); + auto m_scale = ov::pass::pattern::any_input(); + auto m_multiply = ov::pass::pattern::wrap_type({m_scale, m_subtract}); + + const auto callback = [=](ov::pass::pattern::Matcher & m) { + const auto & pattern_map = m.get_pattern_value_map(); + + auto multiply_node = + std::dynamic_pointer_cast(pattern_map.at(m_multiply).get_node_shared_ptr()); + auto subtract_node = + std::dynamic_pointer_cast(pattern_map.at(m_subtract).get_node_shared_ptr()); + auto data_constant = + std::dynamic_pointer_cast(pattern_map.at(m_data_constant).get_node_shared_ptr()); + auto zp_constant = + std::dynamic_pointer_cast(pattern_map.at(m_zp_constant).get_node_shared_ptr()); + + if (!multiply_node || !subtract_node || !data_constant || !zp_constant) { + return false; + } + + if (ov::shape_size(zp_constant->get_shape()) != 1) { + return false; + } + + auto data_type = data_constant->get_element_type(); + auto zp_data = zp_constant->cast_vector(); + + if (zp_data.empty()) { + return false; + } + + int zp_value = zp_data[0]; + + bool should_eliminate = false; + ov::element::Type target_type; + + if (data_type == ov::element::u4 && zp_value == 8) { + should_eliminate = true; + target_type = ov::element::i4; + } else if (data_type == ov::element::u8 && (zp_value == 128 || zp_value == 32)) { + should_eliminate = true; + target_type = ov::element::i8; + } + + if (!should_eliminate) { + return false; + } + + auto data_shape = data_constant->get_shape(); + size_t total_elements = ov::shape_size(data_shape); + + std::shared_ptr new_constant; + + // TODO improve performance + if (data_type == ov::element::u4) { + auto data_values = data_constant->cast_vector(); + std::vector adjusted_values(total_elements); + + ov::parallel_for(total_elements, [&](size_t i) { + adjusted_values[i] = static_cast(static_cast(data_values[i]) - 8); + }); + + new_constant = std::make_shared(target_type, data_shape, adjusted_values); + } else if (data_type == ov::element::u8) { + auto data_values = data_constant->cast_vector(); + std::vector adjusted_values(total_elements); + + ov::parallel_for(total_elements, [&, zp_value](size_t i) { + adjusted_values[i] = static_cast(static_cast(data_values[i]) - zp_value); + }); + + new_constant = std::make_shared(target_type, data_shape, adjusted_values); + } + + auto new_convert = + std::make_shared(new_constant, subtract_node->get_output_element_type(0)); + ov::replace_node(subtract_node, new_convert); + + return true; + }; + + register_matcher( + std::make_shared(m_multiply, "ov::frontend::ggml::pass::EliminateZeroPoints"), + callback); +} + +} // namespace pass +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.h b/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.h new file mode 100644 index 0000000000..edd3cd718d --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/pass/eliminate_zp.h @@ -0,0 +1,17 @@ +#include "openvino/pass/matcher_pass.hpp" + +namespace ov { +namespace frontend { +namespace ggml { +namespace pass { + +class EliminateZeroPoints : public ov::pass::MatcherPass { +public: + OPENVINO_MATCHER_PASS_RTTI("ov::frontend::ggml::pass::EliminateZeroPoints") + EliminateZeroPoints(); +}; + +} // namespace pass +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/pass/fuse_to_sdpa.cpp b/ggml/src/ggml-openvino/openvino/pass/fuse_to_sdpa.cpp new file mode 100644 index 0000000000..0671542ee3 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/pass/fuse_to_sdpa.cpp @@ -0,0 +1,60 @@ +#include "fuse_to_sdpa.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { +namespace pass { + +FuseToSDPA::FuseToSDPA() { + // Not maintained since FLASH_ATTN_EXT has replaced this pattern + const auto m_k = ov::pass::pattern::any_input(); + const auto m_q = ov::pass::pattern::any_input(); + const auto m_qk = ov::pass::pattern::wrap_type({m_q, m_k}); + const auto m_qk_f32 = ov::pass::pattern::wrap_type({m_qk}); + const auto m_scale = ov::pass::pattern::any_input(); + const auto m_scaled_qk = ov::pass::pattern::wrap_type({m_qk_f32, m_scale}); + const auto m_mask = ov::pass::pattern::any_input(); + const auto m_masked_qk = ov::pass::pattern::wrap_type({m_scaled_qk, m_mask}); + const auto m_softmax_qk = ov::pass::pattern::wrap_type({m_masked_qk}); + const auto m_softmax_qk_f16 = ov::pass::pattern::wrap_type({m_softmax_qk}); + const auto m_v = ov::pass::pattern::any_input(); + const auto m_qkv = ov::pass::pattern::wrap_type({m_softmax_qk_f16, m_v}); + + const auto callback = [=](ov::pass::pattern::Matcher & m) { + auto & pattern_to_output = m.get_pattern_value_map(); + auto k = pattern_to_output[m_k]; + auto q = pattern_to_output[m_q]; + auto v = pattern_to_output[m_v]; + auto mask = pattern_to_output[m_mask]; + auto scale = pattern_to_output[m_scale]; + + auto mask_f16 = register_new_node(mask, ov::element::f16); + auto scale_f16 = register_new_node(scale, ov::element::f16); + auto sdpa = std::make_shared(q, k, v, mask_f16, scale_f16, false); + + ov::replace_node(m.get_match_root(), sdpa); + ov::copy_runtime_info(m.get_matched_nodes(), sdpa); + + return true; + }; + register_matcher(std::make_shared(m_qkv, "ov::frontend::ggml::pass::FuseToSDPA"), + callback); +} + +} // namespace pass +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/pass/fuse_to_sdpa.h b/ggml/src/ggml-openvino/openvino/pass/fuse_to_sdpa.h new file mode 100644 index 0000000000..8b5164d232 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/pass/fuse_to_sdpa.h @@ -0,0 +1,17 @@ +#include "openvino/pass/matcher_pass.hpp" + +namespace ov { +namespace frontend { +namespace ggml { +namespace pass { + +class FuseToSDPA : public ov::pass::MatcherPass { +public: + OPENVINO_MATCHER_PASS_RTTI("ov::frontend::ggml::pass::FuseToSDPA") + FuseToSDPA(); +}; + +} // namespace pass +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/pass/mark_decompression_convert_constant_folding.h b/ggml/src/ggml-openvino/openvino/pass/mark_decompression_convert_constant_folding.h new file mode 100644 index 0000000000..b95385611e --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/pass/mark_decompression_convert_constant_folding.h @@ -0,0 +1,29 @@ +#pragma once + +#include "mark_decompression_convert_constant_folding.h" +#include "openvino/pass/matcher_pass.hpp" +#include "openvino/core/visibility.hpp" + +#ifdef OPENVINO_STATIC_LIBRARY +# define TRANSFORMATIONS_API +#else +# ifdef IMPLEMENT_OPENVINO_API +# define TRANSFORMATIONS_API OPENVINO_CORE_EXPORTS +# else +# define TRANSFORMATIONS_API OPENVINO_CORE_IMPORTS +# endif // IMPLEMENT_OPENVINO_API +#endif // OPENVINO_STATIC_LIBRARY + +namespace ov { +namespace pass { + +class TRANSFORMATIONS_API MarkCompressedFloatConstants; + +} // namespace pass +} // namespace ov + +class ov::pass::MarkCompressedFloatConstants : public MatcherPass { +public: + OPENVINO_MATCHER_PASS_RTTI("MarkCompressedFloatConstants") + MarkCompressedFloatConstants(); +}; diff --git a/ggml/src/ggml-openvino/openvino/pass/squeeze_matmul.cpp b/ggml/src/ggml-openvino/openvino/pass/squeeze_matmul.cpp new file mode 100644 index 0000000000..20a3a37493 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/pass/squeeze_matmul.cpp @@ -0,0 +1,58 @@ +#include "squeeze_matmul.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace opp = ov::pass::pattern; + +namespace ov { +namespace frontend { +namespace ggml { +namespace pass { + +// For quantized models, NPUW expects the activation to be 3d in DQ(DynamicQuantization) opt, e.g. DQMatMulGQ2i +SqueezeMatmul::SqueezeMatmul() { + auto m_act = opp::any_input(); + auto m_wei = opp::any_input(); + auto m_matmul = opp::wrap_type({m_act, m_wei}); + + const auto callback = [=](ov::pass::pattern::Matcher & m) { + const auto & pattern_map = m.get_pattern_value_map(); + auto matmul_node = + std::dynamic_pointer_cast(pattern_map.at(m_matmul).get_node_shared_ptr()); + auto act = pattern_map.at(m_act); + auto wei = pattern_map.at(m_wei); + auto act_shape = act.get_partial_shape(); + auto wei_shape = wei.get_partial_shape(); + if (act_shape.rank().is_dynamic() || wei_shape.rank().is_dynamic()) { + return false; + } + if (act_shape.rank().get_length() == 4 && wei_shape.rank().get_length() == 2) { + auto axis = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {0}); + auto squeezed_act = std::make_shared(act, axis); + auto new_matmul = std::make_shared(squeezed_act, wei, matmul_node->get_transpose_a(), + matmul_node->get_transpose_b()); + auto unsqueezed_output = std::make_shared(new_matmul, axis); + unsqueezed_output->set_friendly_name(matmul_node->get_friendly_name()); + ov::copy_runtime_info(matmul_node, {squeezed_act, new_matmul, unsqueezed_output}); + ov::replace_node(matmul_node, unsqueezed_output); + return true; + } + return false; + }; + + register_matcher(std::make_shared(m_matmul, "ov::frontend::ggml::pass::SqueezeMatmul"), + callback); +} + +} // namespace pass +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/pass/squeeze_matmul.h b/ggml/src/ggml-openvino/openvino/pass/squeeze_matmul.h new file mode 100644 index 0000000000..f8fbc69d54 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/pass/squeeze_matmul.h @@ -0,0 +1,17 @@ +#include "openvino/pass/matcher_pass.hpp" + +namespace ov { +namespace frontend { +namespace ggml { +namespace pass { + +class SqueezeMatmul : public ov::pass::MatcherPass { +public: + OPENVINO_MATCHER_PASS_RTTI("ov::frontend::ggml::pass::SqueezeMatmul") + SqueezeMatmul(); +}; + +} // namespace pass +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/translate_session.cpp b/ggml/src/ggml-openvino/openvino/translate_session.cpp new file mode 100644 index 0000000000..23a1dea249 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/translate_session.cpp @@ -0,0 +1,293 @@ +#include "translate_session.h" + +#include "ggml-openvino/openvino/node_context.h" +#include "ggml-openvino/openvino/utils.h" +#include "input_model.h" +#include "pass/eliminate_zp.h" +#include "pass/mark_decompression_convert_constant_folding.h" +#include "pass/squeeze_matmul.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { + +using namespace ov::op; + +namespace { + +ov::pass::MakeStateful::ParamResPairs get_kv_param_res_pairs( + const std::shared_ptr & model, + const std::map & kv_param_res_names) { + ov::pass::MakeStateful::ParamResPairs pairs; + const auto & params = model->get_parameters(); + const auto & results = model->get_results(); + + for (const auto & param_res : kv_param_res_names) { + const auto & param_name = param_res.first; + const auto & res_name = param_res.second; + + auto param_it = std::find_if(params.begin(), params.end(), [&](const std::shared_ptr & node) { + return node->get_friendly_name() == param_name; + }); + + OPENVINO_ASSERT(param_it != params.end(), "The tensor name ", param_name, + " is not associated with any of " + "Parameters in the network."); + + auto res_it = std::find_if(results.begin(), results.end(), [&](const std::shared_ptr & node) { + return node->get_friendly_name() == res_name; + }); + + OPENVINO_ASSERT(res_it != results.end(), "The tensor name ", res_name, + " is not associated with any of " + "Results in the network."); + + std::shared_ptr param = *param_it; + std::shared_ptr res = *res_it; + pairs.emplace_back(param, res); + } + return pairs; +} + +void add_sliced_mask(TensorMap & tensor_map, GgmlDecoder & ggml_model_decoder) { + + auto create_sliced_mask = [&](const std::string & mask_name, const std::string & sliced_name, bool is_static) { + if ((tensor_map.find(mask_name) != tensor_map.end()) && + (tensor_map.find("token_len_per_seq") != tensor_map.end())) { + auto token_len_per_seq = tensor_map.at("token_len_per_seq").get_node_shared_ptr(); + auto mask = tensor_map.at(mask_name).get_node_shared_ptr(); + std::shared_ptr mask_sliced; + if (is_static) { + mask_sliced = mask; + } else if (ggml_model_decoder.is_stateful()) { + auto zero_2d = ov::op::v0::Constant::create(ov::element::i64, {2}, {0,0}); + auto one_2d = ov::op::v0::Constant::create(ov::element::i64, {2}, {1,1}); + auto zero_1d = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + auto three_1d = ov::op::v0::Constant::create(ov::element::i64, {1}, {3}); + auto neg_one_1d = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1}); + auto axes = ov::op::v0::Constant::create(ov::element::i64, {2}, {-2,-1}); + auto inp_pos = tensor_map.at("inp_pos").get_node_shared_ptr(); + auto gather_inp_pos = std::make_shared(inp_pos, neg_one_1d, three_1d); + auto reshaped_inp_pos = std::make_shared(gather_inp_pos, ov::op::v0::Constant::create(ov::element::i64, {1}, {1}), false); + auto inp_pos_incremented = std::make_shared(reshaped_inp_pos, ov::op::v0::Constant::create(ov::element::i32, ov::Shape{1}, {1})); + auto stop = std::make_shared(ov::OutputVector{token_len_per_seq, std::make_shared(inp_pos_incremented, token_len_per_seq)}, 0); + mask_sliced = + std::make_shared(mask, zero_2d, stop, one_2d, axes); + mask_sliced = std::make_shared(mask_sliced, ov::element::f16); + mask_sliced->set_friendly_name(sliced_name); + } else { + auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0}); + auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + auto two = ov::op::v0::Constant::create(ov::element::i64, {1}, {2}); + mask_sliced = std::make_shared(mask, zero, token_len_per_seq, one, two); + mask_sliced = std::make_shared(mask_sliced, ov::element::f16); + mask_sliced->set_friendly_name(sliced_name); + } + tensor_map.insert({sliced_name, mask_sliced->output(0)}); + } + }; + + create_sliced_mask("self_kq_mask", "KQ_mask_sliced", ggml_model_decoder.is_static()); + create_sliced_mask("self_kq_mask_swa", "KQ_mask_swa_sliced", ggml_model_decoder.is_static()); +} + +void add_rope_sin_cos(TensorMap & tensor_map, GgmlDecoder & ggml_model_decoder) { + int32_t * rope_params = ggml_model_decoder.get_rope_params(); + if (tensor_map.find("inp_pos") == tensor_map.end() || rope_params == nullptr) { + return; + } + auto inp_pos = tensor_map.at("inp_pos").get_node_shared_ptr(); + std::shared_ptr rope_freqs_weight; + if (tensor_map.find("rope_freqs.weight") != tensor_map.end()) { + rope_freqs_weight = tensor_map.at("rope_freqs.weight").get_node_shared_ptr(); + } + + auto sin_cos = make_sin_cos(rope_params, inp_pos, rope_freqs_weight); + auto sin_theta = sin_cos.first; + auto cos_theta = sin_cos.second; + + cos_theta.get_node_shared_ptr()->set_friendly_name("rope_cos"); + sin_theta.get_node_shared_ptr()->set_friendly_name("rope_sin"); + tensor_map.insert({"rope_cos", cos_theta}); + tensor_map.insert({"rope_sin", sin_theta}); +} + +// Create common patterns +void preprocess(TensorMap & tensor_map, GgmlDecoder & ggml_model_decoder) { + add_sliced_mask(tensor_map, ggml_model_decoder); + add_rope_sin_cos(tensor_map, ggml_model_decoder); +} + +} // namespace + +TranslateSession::TranslateSession(const frontend::InputModel::Ptr & input_model, + const std::unordered_map & translator_map, + bool naive) : + m_input_model(input_model), + m_translator_map(translator_map), + m_ov_model(nullptr), + m_naive(naive) {} + +std::shared_ptr TranslateSession::get_converted_model() { + if (m_ov_model) { + return m_ov_model; + } + m_ov_model = translate_graph(m_input_model); + return m_ov_model; +} + +std::shared_ptr TranslateSession::translate_graph(const frontend::InputModel::Ptr & input_model) { + ov::ParameterVector params; + ov::ResultVector results; + auto tensor_map = std::make_shared(); + std::shared_ptr resulting_model; + + const auto & ggml_model = std::dynamic_pointer_cast(input_model); + std::shared_ptr ggml_model_decoder = ggml_model->get_model_decoder(); + + for (const auto & it : ggml_model_decoder->get_model_inputs()) { + params.push_back(std::dynamic_pointer_cast(it.second)); + (*tensor_map)[it.first] = it.second; + } + + for (const auto & it : ggml_model_decoder->get_model_extra_inputs()) { + if (std::dynamic_pointer_cast(it.second)) { + params.push_back(std::dynamic_pointer_cast(it.second)); + } + (*tensor_map)[it.first] = it.second; + } + + for (const auto & it : ggml_model_decoder->get_model_weights()) { + (*tensor_map)[it.first] = it.second; + } + + auto node_visitor = [&](std::shared_ptr decoder, int node_idx) { + auto operation_type = decoder->get_op_type(node_idx); + if (operation_type == "GGML_OP_NONE") { + return; + } + + ov::OutputVector converted_outputs; + auto it = m_translator_map.find(operation_type); + FRONT_END_OP_CONVERSION_CHECK(it != m_translator_map.end(), "Translation for operation type ", operation_type, + " is not implemented."); + NodeContext node_context(decoder, tensor_map, node_idx, this); + converted_outputs = it->second(node_context); + + const auto & node_output_names = decoder->get_output_names(node_idx); + FRONT_END_OP_CONVERSION_CHECK(node_output_names.size() == converted_outputs.size(), "Number of ", + operation_type, " outputs greater than number of converted outputs, which are ", + node_output_names.size(), " and ", converted_outputs.size(), " respectively."); + + for (size_t i = 0; i < node_output_names.size(); ++i) { + auto output_name = node_output_names[i]; + if (i < converted_outputs.size() && converted_outputs[i].get_node_shared_ptr() != nullptr) { + (*tensor_map)[output_name] = converted_outputs[i]; + } + } + }; + + if (!m_naive) { + preprocess(*tensor_map, *ggml_model_decoder); + } + ggml_model_decoder->visit_subgraph(node_visitor); + + for (const auto & name : ggml_model_decoder->get_model_output_names()) { + FRONT_END_GENERAL_CHECK(tensor_map->find(name) != tensor_map->end(), + "Output name not found in tensor map: ", name); + auto result = std::make_shared(tensor_map->at(name)); + result->set_friendly_name(name); + results.push_back(result); + } + + ov::ParameterVector used_params; + for (const auto & param : params) { + if (!param->output(0).get_target_inputs().empty()) { + used_params.push_back(param); + } + } + // if (auto diff = params.size() - used_params.size()) { + // GGML_LOG_INFO("%zu parameters are not used in the model.", diff); + // } + resulting_model = std::make_shared(results, used_params); + + apply_transformations(resulting_model); + return resulting_model; +} + +std::shared_ptr TranslateSession::apply_transformations(std::shared_ptr model) { + auto ggml_model_decoder = std::dynamic_pointer_cast(m_input_model)->get_model_decoder(); + { + ov::pass::Manager manager; + manager.set_per_pass_validation(true); + manager.register_pass(); + + if (ggml_model_decoder->is_stateful()) { + const auto kv_param_res_names = ggml_model_decoder->get_kv_param_res_names(); + const auto kv_param_res_pairs = get_kv_param_res_pairs(model, kv_param_res_names); + manager.register_pass(kv_param_res_pairs); + } + + if (ggml_model_decoder->is_static()) { + manager.register_pass(); + manager.register_pass(); + } + manager.run_passes(model); + if (ggml_model_decoder->is_stateful()) { + auto output_names = ggml_model_decoder->get_model_output_names(); + std::map model_output_indexes; + for (size_t i=0; iget_output_size(); i++) { + auto output_friendly_name = model->output(i).get_node_shared_ptr()->get_friendly_name(); + auto output_id = model_output_indexes[output_friendly_name]; + auto model_output_shape = model->output(i).get_partial_shape(); + auto decoder_output_shape = ggml_model_decoder->get_output_shape(output_id); + if (model_output_shape.rank().is_static() && decoder_output_shape.rank().is_static() + && model_output_shape.rank().get_length() + 1 == decoder_output_shape.rank().get_length() + && decoder_output_shape[0].is_static() && decoder_output_shape[0].get_length() == 1) { + ppp.output(i).postprocess().custom([](const ov::Output& node) { + auto axes = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{1}, {0}); + return std::make_shared(node, axes); + }); + } + } + model = ppp.build(); + } + } + return model; +} + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/translate_session.h b/ggml/src/ggml-openvino/openvino/translate_session.h new file mode 100644 index 0000000000..56a14ae7c0 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/translate_session.h @@ -0,0 +1,28 @@ +#pragma once + +#include "input_model.h" +#include "node_context.h" + +namespace ov { +namespace frontend { +namespace ggml { + +class TranslateSession { +public: + TranslateSession(const frontend::InputModel::Ptr& input_model, + const std::unordered_map& translator_map, bool naive = false); + + std::shared_ptr get_converted_model(); + std::shared_ptr translate_graph(const frontend::InputModel::Ptr& input_model); + +private: + std::shared_ptr apply_transformations(std::shared_ptr model); + const frontend::InputModel::Ptr m_input_model; + const std::unordered_map& m_translator_map; + std::shared_ptr m_ov_model; + bool m_naive; +}; + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/utils.cpp b/ggml/src/ggml-openvino/openvino/utils.cpp new file mode 100644 index 0000000000..65356a51b5 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/utils.cpp @@ -0,0 +1,226 @@ +#include "utils.h" + +#include "ggml-impl.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace frontend { +namespace ggml { + +std::string getCurrentTime() { + std::time_t now = std::time(nullptr); + char buf[100]; + std::strftime(buf, sizeof(buf), "%Y-%m-%d %H:%M:%S", std::localtime(&now)); + return buf; +} + +void num_inputs_check(const NodeContext & context, size_t min_inputs, size_t max_inputs) { + auto input_size = context.get_input_size(); + FRONT_END_OP_CONVERSION_CHECK(input_size >= min_inputs, "Got less inputs than expected"); + FRONT_END_OP_CONVERSION_CHECK(input_size <= max_inputs, "Got more inputs than expected"); +} + +int non_cont_dim(std::vector ne, std::vector nb) { + int dim = nb.size() - 1; + size_t bytes = nb[dim]; + for (int i = dim; i > 0; i--) { + bytes *= ne[i]; + if (bytes != nb[i - 1]) { + return i; + } + } + return 0; +} + +std::shared_ptr get_dimensions(const std::shared_ptr & shape, + const std::vector & dims) { + using namespace ov::op; + const auto zero = v0::Constant::create(ov::element::i32, ov::Shape{}, {0}); + const auto dims_const = v0::Constant::create(ov::element::i32, ov::Shape{dims.size()}, dims); + return std::make_shared(shape, dims_const, zero); +} + +std::shared_ptr get_dimensions(const std::shared_ptr & node, const std::vector & dims) { + return get_dimensions(std::make_shared(node), dims); +} + +OutputVector rename_outputs_with_suffix(const OutputVector & outputs, const std::string & suffix) { + for (const auto & output : outputs) { + auto node = output.get_node_shared_ptr(); + std::string name = node->get_friendly_name(); + name += "_"; + name += suffix; + node->set_friendly_name(name); + // std::cout << name << " " << output.get_partial_shape() << std::endl; + } + return outputs; +} + +namespace { +ov::Output rope_yarn_ramp_mix(int n_dims, const float corr_dims[2], float ext_factor) { + int half_n_dims = n_dims / 2; + std::vector dim_ids_vec(half_n_dims); + std::iota(dim_ids_vec.begin(), dim_ids_vec.end(), 0); + auto dim_ids = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1, (size_t) half_n_dims}, dim_ids_vec); + auto corr_low = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1, 1}, {corr_dims[0]}); + auto corr_high = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1, 1}, {corr_dims[1]}); + auto denom = std::make_shared( + std::make_shared(corr_high, corr_low), + ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1, 1}, {0.001f})); + auto ramp_y = + std::make_shared(std::make_shared(dim_ids, corr_low), denom); + auto ramp_clamped = std::make_shared(ramp_y, 0.0f, 1.0f); + auto ext_factor_node = ov::op::v0::Constant::create(ov::element::f32, Shape{}, {ext_factor}); + auto ramp_mix = std::make_shared(ramp_clamped, ext_factor_node); + return ramp_mix; +} + +float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) { +#ifndef M_PI +# define M_PI 3.14159265358979323846 +#endif + return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float) M_PI)) / (2 * logf(base)); +} + +void ggml_rope_yarn_corr_dims(int n_dims, + int n_ctx_orig, + float freq_base, + float beta_fast, + float beta_slow, + float dims[2]) { + float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base)); + float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base)); + dims[0] = std::max(0.0f, start); + dims[1] = std::min(static_cast(n_dims - 1), end); +} +} // namespace + +std::pair, ov::Output> make_sin_cos(int32_t * rope_params, + std::shared_ptr inp_pos, + std::shared_ptr rope_freqs_weight, + bool stateful) { + if (stateful) { + inp_pos = std::make_shared(inp_pos, ov::op::v0::Constant::create(ov::element::i64, {1}, {0})); + inp_pos = std::make_shared(inp_pos, ov::element::f32); + auto pos_perm = + std::make_shared(ov::element::i64, ov::Shape{3}, std::vector{2, 1, 0}); + inp_pos = std::make_shared(inp_pos, pos_perm); + } else { + inp_pos = std::make_shared(inp_pos, ov::element::f32); + auto pos_perm = + std::make_shared(ov::element::i64, ov::Shape{4}, std::vector{0, 3, 1, 2}); + inp_pos = std::make_shared(inp_pos, pos_perm); + } + + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; + const int n_dims = rope_params[1]; + const int n_ctx_orig = rope_params[4]; + memcpy(&freq_base, rope_params + 5, sizeof(float)); + memcpy(&freq_scale, rope_params + 6, sizeof(float)); + memcpy(&ext_factor, rope_params + 7, sizeof(float)); + memcpy(&attn_factor, rope_params + 8, sizeof(float)); + memcpy(&beta_fast, rope_params + 9, sizeof(float)); + memcpy(&beta_slow, rope_params + 10, sizeof(float)); + + const float theta_scale = powf(freq_base, -2.0f / n_dims); + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + std::vector factor(n_dims / 2); + factor[0] = 1.0f; + for (size_t i = 1; i < factor.size(); i++) { + factor[i] = theta_scale * factor[i - 1]; + } + + Output freq_factors; + if (stateful) { + freq_factors = + std::make_shared(ov::element::f32, ov::Shape{1, 1, factor.size()}, factor); + } else { + freq_factors = + std::make_shared(ov::element::f32, ov::Shape{1, 1, 1, factor.size()}, factor); + } + if (rope_freqs_weight) { + freq_factors = std::make_shared(freq_factors, rope_freqs_weight); + } + + auto theta_extrap = std::make_shared(freq_factors, inp_pos); + auto theta_interp = std::make_shared( + theta_extrap, ov::op::v0::Constant::create(ov::element::f32, {1}, {freq_scale})); + + Output theta; + float mscale = attn_factor; + if (ext_factor == 0.0f) { + theta = theta_interp; + } else { + auto ramp_mix = rope_yarn_ramp_mix(n_dims, corr_dims, ext_factor); + Output one; + if (stateful) { + one = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1}, {1.0f}); + } else { + one = ov::op::v0::Constant::create(ov::element::f32, Shape{1, 1, 1, 1}, {1.0f}); + } + auto one_minus_ramp = std::make_shared(one, ramp_mix); + + theta = std::make_shared(std::make_shared(theta_interp, one_minus_ramp), + std::make_shared(theta_extrap, ramp_mix)); + mscale *= (1.0f + 0.1f * std::log(1.0f / freq_scale)); + } + + Output cos_theta = std::make_shared(theta); + Output sin_theta = std::make_shared(theta); + + auto mscale_node = ov::op::v0::Constant::create(ov::element::f32, Shape{}, {mscale}); + + cos_theta = std::make_shared(cos_theta, mscale_node); + sin_theta = std::make_shared(sin_theta, mscale_node); + return std::make_pair(sin_theta, cos_theta); +} + +ov::Output process_view_input(const NodeContext & context, int input_index, int slice_len) { + // Only works for VIEW operations that slice at the lowest dimension + // If the VIEW also reshape the result, `slice_len` should be provided + auto input = context.get_input(input_index); + auto * op_params = (size_t *) context.get_input_op_params(input_index); + auto src1_stride = context.get_input_stride(input_index); + + int64_t split_addr = op_params[0] / src1_stride[3]; + if (slice_len == 0) { + slice_len = context.get_input_shape(input_index)[3].get_length(); + } + int64_t slice_end = split_addr + slice_len; + + auto begin = ov::op::v0::Constant::create(ov::element::i64, {1}, {split_addr}); + auto end = ov::op::v0::Constant::create(ov::element::i64, {1}, {slice_end}); + auto stride = ov::op::v0::Constant::create(ov::element::i64, {1}, {1}); + auto axes = ov::op::v0::Constant::create(ov::element::i64, {1}, {context.is_stateful() ? 2 : 3}); + auto sliced = std::make_shared(input, begin, end, stride, axes); + return sliced; +} + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/openvino/utils.h b/ggml/src/ggml-openvino/openvino/utils.h new file mode 100644 index 0000000000..88dcad4c90 --- /dev/null +++ b/ggml/src/ggml-openvino/openvino/utils.h @@ -0,0 +1,85 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include "node_context.h" + +namespace ov { +namespace frontend { +namespace ggml { + +std::string getCurrentTime(); + +void dump_ov_model(std::shared_ptr model); + +void num_inputs_check(const NodeContext& context, size_t min_inputs, size_t max_inputs); + +int non_cont_dim(std::vector ne, std::vector nb); + +template +std::vector argsort_descend(const std::vector& v) { + std::vector idx(v.size()); + std::iota(idx.begin(), idx.end(), 0); + std::sort(idx.begin(), idx.end(), [&v](int i1, int i2) { + return v[i1] > v[i2]; + }); + return idx; +} + +template +std::vector sorted_descend(std::vector v) { + std::sort(v.begin(), v.end(), [](T a, T b) { + return a > b; + }); + return v; +} + +template +bool is_permuted(const std::vector& strides) { + for (size_t i = 0; i < strides.size() - 1; ++i) { + if (strides[i] < strides[i + 1]) { + return true; + } + } + return false; +} + +template +std::vector permute(const std::vector& x, const std::vector& perm) { + std::vector result; + result.reserve(perm.size()); + for (int i : perm) { + result.push_back(x[i]); + } + return result; +} + +std::shared_ptr get_dimensions(const std::shared_ptr& shape, + const std::vector& dims); +std::shared_ptr get_dimensions(const std::shared_ptr& node, const std::vector& dims); + +OutputVector rename_outputs_with_suffix(const OutputVector& outputs, const std::string& suffix); + +std::pair, ov::Output> make_sin_cos(int32_t* rope_params, + std::shared_ptr inp_pos, + std::shared_ptr rope_freqs_weight = nullptr, + bool stateful = false); + +ov::Output process_view_input(const NodeContext& context, int input_index, int slice_len = 0); + +namespace op { +template +OutputVector translate_1to1_match_2_inputs(const NodeContext& context) { + num_inputs_check(context, 2, 2); + auto res = std::make_shared(context.get_input(0), context.get_input(1)); + return rename_outputs_with_suffix({res}, context.get_name()); +} +} // namespace op + +} // namespace ggml +} // namespace frontend +} // namespace ov diff --git a/ggml/src/ggml-openvino/utils.cpp b/ggml/src/ggml-openvino/utils.cpp new file mode 100644 index 0000000000..1b553a0de0 --- /dev/null +++ b/ggml/src/ggml-openvino/utils.cpp @@ -0,0 +1,823 @@ +#include "utils.h" + +#include "ggml-impl.h" +#include "ggml-openvino-extra.h" +#include "ggml-openvino/ggml-decoder.h" +#include "ggml.h" +#include "openvino/frontend.h" +#include "openvino/input_model.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// Suppress deprecation warning for ov::Tensor::data() +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wdeprecated-declarations" + +enum ggml_status ov_graph_compute(ggml_cgraph * cgraph, ggml_backend_t backend) { + ggml_backend_openvino_context * ctx = (ggml_backend_openvino_context *) backend->context; + try { + if (getenv("GGML_OPENVINO_DUMP_CGRAPH")) { + std::string filename = "cgraph_ov.txt"; + GgmlOvDecoder::dump_cgraph(cgraph, filename); + } + + const auto is_static = ggml_openvino_is_npu(); + + GGML_ASSERT(ctx->runtime_context != nullptr); + std::shared_ptr r_ctx = std::static_pointer_cast(ctx->runtime_context); + + return is_static ? ov_graph_compute_static(cgraph, r_ctx) : ov_graph_compute_dynamic(cgraph, r_ctx); + } catch (const ov::Exception & e) { + GGML_LOG_ERROR("GGML OpenVINO backend ov::Exception: %s\n", e.what()); + return GGML_STATUS_FAILED; + } catch (const std::exception & e) { + GGML_LOG_ERROR("GGML OpenVINO backend std::exception: %s\n", e.what()); + return GGML_STATUS_FAILED; + } catch (...) { + GGML_LOG_ERROR("GGML OpenVINO backend unknown exception\n"); + return GGML_STATUS_FAILED; + } +} + +ov::Tensor create_ov_output_tensor(std::shared_ptr ggml_decoder, + std::shared_ptr infer_request, + int output_index, + const ggml_tensor * ggml_tensor) { + auto output_type = ggml_decoder->get_ov_type(ggml_tensor); + ov::Shape output_shape; + if (ggml_decoder->is_static()) { + output_shape = infer_request->get_output_tensor(output_index).get_shape(); + } else { + output_shape = ggml_decoder->get_shape(ggml_tensor); + } + + ov::Tensor output_tensor(output_type, output_shape, ggml_tensor->data); + return output_tensor; +} + +enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr r_ctx) { + auto & core = ov_singleton_core(); + const auto & config = ggml_openvino_get_compile_config(); + auto device = r_ctx->device; + bool stateful = r_ctx->stateful; + static auto is_static = false; + + if (is_naive(cgraph)) { + return naive_compute(cgraph, core, device, config); + } + + auto start_time = ggml_time_us(); + + std::shared_ptr ggml_decoder; + std::shared_ptr infer_request; + ModelParams m_params; + ComputeParams c_params; + std::tie(m_params, c_params) = GgmlOvDecoder::compute_llm_params(cgraph, is_static); + + graph_key key(cgraph); + bool cache_hit; + + int64_t decoder_end_time; + int64_t conversion_end_time; + int64_t compile_end_time; + int64_t infer_end_time; + + { + std::lock_guard lock(r_ctx->ov_compute_mutex); + + auto it = r_ctx->decoder_cache.find(key); + + cache_hit = it != r_ctx->decoder_cache.end(); + ModelParams old_m_params; + if (cache_hit) { + ggml_decoder = it->second; + old_m_params = ggml_decoder->get_model_params(); + cache_hit = old_m_params.can_reuse_dynamically(m_params); + } + + if (cache_hit) { + std::map> model_weights; + ggml_decoder->set_compute_params(c_params); + ggml_decoder->set_model_params(m_params); + if (old_m_params.kv_buffer_changed(m_params)) { + ggml_decoder->update_io(cgraph); + } + ggml_decoder->add_extra_inputs(); + infer_request = r_ctx->infer_request_cache.at(key); + + if (stateful) { + const auto * inp_pos = get_inp_pos_tensor(cgraph); + int32_t * pos_data = (int32_t *) inp_pos->data; + auto pos_shape = ggml_decoder->get_shape(inp_pos); + if (pos_data[0] == 0) { + infer_request->reset_state(); + r_ctx->stateful_kv_size = pos_shape[3]; + } else if (r_ctx->stateful_kv_size == static_cast(pos_data[0])) { + r_ctx->stateful_kv_size += pos_shape[3]; + } else { + auto states = infer_request->query_state(); + for (auto state : states) { + auto state_tensor = state.get_state(); + auto state_tensor_shape = state_tensor.get_shape(); + if (static_cast(pos_data[0]) > r_ctx->stateful_kv_size) { + std::string state_name; + try { + state_name = r_ctx->kv_state_input_name_map.at(state.get_name()); + } catch (...) { + GGML_LOG_ERROR("GGML OpenVINO backend stateful inference failed: no input found for the state\n"); + return GGML_STATUS_FAILED; + } + auto kv_tensor = get_ov_input_tensor(ggml_decoder, state_name); + kv_tensor.set_shape({state_tensor_shape[0], kv_tensor.get_shape()[2], + state_tensor_shape[2], state_tensor_shape[3]}); + state_tensor = kv_tensor; + state_tensor_shape = state_tensor.get_shape(); + } + ov::Coordinate begin = {0, 0, 0, 0}; + ov::Coordinate end = {state_tensor_shape[0], static_cast(pos_data[0]), + state_tensor_shape[2], state_tensor_shape[3]}; + ov::Tensor new_state_tensor(state_tensor, begin, end); + state.set_state(new_state_tensor); + } + r_ctx->stateful_kv_size = pos_data[0] + 1; + } + } + + decoder_end_time = ggml_time_us(); + conversion_end_time = decoder_end_time; + compile_end_time = decoder_end_time; + } else { + r_ctx->infer_request_cache.erase(key); + + std::shared_ptr model; + auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph); + + ggml_decoder = std::make_shared(cgraph, m_params, c_params, model_weights, is_static, stateful); + decoder_end_time = ggml_time_us(); + + auto input_model = std::make_shared(ggml_decoder); + model = ov::frontend::ggml::FrontEnd::convert(input_model); + ggml_decoder->clear_model_weights(); + conversion_end_time = ggml_time_us(); + + if (getenv("GGML_OPENVINO_DUMP_IR")) { + char timestamped_filename[64]; + auto timestamp = (long long) ggml_time_us(); + snprintf(timestamped_filename, sizeof(timestamped_filename), "model_%lld.xml", timestamp); + ov::serialize(model, timestamped_filename); + } + + ov::CompiledModel compiled_model; + auto remote_context = ggml_openvino_get_remote_context(); + if (remote_context.has_value()) { + compiled_model = core.compile_model(model, remote_context.value(), config); + } else { + compiled_model = core.compile_model(model, device, config); + } + compile_end_time = ggml_time_us(); + infer_request = std::make_shared(compiled_model.create_infer_request()); + r_ctx->infer_request_cache[key] = infer_request; + r_ctx->decoder_cache[key] = ggml_decoder; + + std::vector ov_input_names; + std::vector ov_output_names; + for (const auto & ov_param : model->get_parameters()) { + ov_input_names.push_back(ov_param->get_friendly_name()); + } + for (const auto & ov_output : model->get_results()) { + ov_output_names.push_back(ov_output->get_friendly_name()); + } + r_ctx->ov_input_names_cache[key] = std::move(ov_input_names); + r_ctx->ov_output_names_cache[key] = std::move(ov_output_names); + + if (stateful) { + const auto * inp_pos = get_inp_pos_tensor(cgraph); + auto pos_shape = ggml_decoder->get_shape(inp_pos); + r_ctx->stateful_kv_size = pos_shape[3]; + const auto kv_param_res_names = ggml_decoder->get_kv_param_res_names(); + for (const auto& pair : kv_param_res_names) { + r_ctx->kv_state_input_name_map[pair.first+pair.second] = pair.first; + } + } + } + + auto ov_input_names = r_ctx->ov_input_names_cache[key]; + auto ov_output_names = r_ctx->ov_output_names_cache[key]; + + for (size_t i = 0; i < ov_input_names.size(); i++) { + auto param_name = ov_input_names[i]; + auto input_tensor = get_ov_input_tensor(ggml_decoder, param_name); + infer_request->set_input_tensor(i, input_tensor); + + if (getenv("GGML_OPENVINO_DEBUG_INPUT")) { + print_input_tensor_info(param_name, input_tensor); + } + } + + for (size_t i = 0; i < ov_output_names.size(); i++) { + auto * ggml_tensor = ggml_decoder->get_model_outputs().at(ov_output_names[i]); + auto output_tensor = create_ov_output_tensor(ggml_decoder, infer_request, i, ggml_tensor); + infer_request->set_output_tensor(i, output_tensor); + } + + infer_request->infer(); + infer_end_time = ggml_time_us(); + + if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) { + for (size_t i = 0; i < ov_output_names.size(); i++) { + const auto output_tensor = infer_request->get_output_tensor(i); + print_output_tensor_info(ov_output_names[i], output_tensor, output_tensor.data()); + } + } + + if (getenv("GGML_OPENVINO_PROFILING")) { + GGML_LOG_INFO("\nGGML OpenVINO Backend: \n"); + GGML_LOG_INFO(" - Graph decoder time: %ld ms \n", (decoder_end_time - start_time) / 1000); + if (!cache_hit) { + GGML_LOG_INFO(" - Graph conversion time: %ld ms \n", (conversion_end_time - decoder_end_time) / 1000); + GGML_LOG_INFO(" - Graph compile time: %ld ms \n", (compile_end_time - conversion_end_time) / 1000); + } + GGML_LOG_INFO(" - Graph inference time: %ld ms \n", (infer_end_time - compile_end_time) / 1000); + } + } + + return GGML_STATUS_SUCCESS; +} + +enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr r_ctx) { + auto & core = ov_singleton_core(); + + auto get_prefill_chunk_size = [] { + const char * chunk_size_str = getenv("GGML_OPENVINO_PREFILL_CHUNK_SIZE"); + if (chunk_size_str && atoi(chunk_size_str) > 0) { + return atoi(chunk_size_str); + } + return 256; + }; + + static std::string device = "NPU"; + static auto is_static = true; + static auto stateful = false; + static auto prefill_chunk_size = get_prefill_chunk_size(); + const auto & config = ggml_openvino_get_compile_config(); + + if (is_naive(cgraph)) { + return naive_compute(cgraph, core, device, config); + } + + auto start_time = ggml_time_us(); + + std::shared_ptr ggml_decoder; + std::shared_ptr infer_request; + ModelParams m_params; + ComputeParams c_params; + std::tie(m_params, c_params) = GgmlOvDecoder::compute_llm_params(cgraph, is_static); + + const auto * inp_pos = get_inp_pos_tensor(cgraph); + const auto is_prefill = get_is_prefill(inp_pos); + graph_key key(cgraph); + bool cache_hit; + + int64_t decoder_end_time; + int64_t conversion_end_time; + int64_t compile_end_time; + int64_t infer_end_time; + + auto it = r_ctx->decoder_cache.find(key); + + cache_hit = it != r_ctx->decoder_cache.end(); + ModelParams old_m_params; + if (cache_hit) { + ggml_decoder = it->second; + old_m_params = ggml_decoder->get_model_params(); + cache_hit = old_m_params.can_reuse_statically(m_params); + } + + if (cache_hit) { + std::map> model_weights; + ggml_decoder->m_is_prefill = is_prefill; + ggml_decoder->set_model_params(m_params); + ggml_decoder->set_compute_params(c_params); + if (old_m_params.kv_buffer_changed(m_params)) { + ggml_decoder->update_io(cgraph); + } + ggml_decoder->add_extra_inputs(); + infer_request = is_prefill ? r_ctx->infer_request_cache_prefill.at(key) : r_ctx->infer_request_cache.at(key); + + decoder_end_time = ggml_time_us(); + conversion_end_time = decoder_end_time; + compile_end_time = decoder_end_time; + } else { + r_ctx->infer_request_cache.erase(key); + r_ctx->infer_request_cache_prefill.erase(key); + + std::shared_ptr model; + auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph); + + auto ggml_decoder_prefill = std::make_shared(cgraph, m_params, c_params, model_weights, + is_static, stateful, true, prefill_chunk_size); + auto ggml_decoder_decode = std::make_shared(cgraph, m_params, c_params, model_weights, is_static, + stateful, false, prefill_chunk_size); + decoder_end_time = ggml_time_us(); + + auto input_model_prefill = std::make_shared(ggml_decoder_prefill); + auto input_model_decode = std::make_shared(ggml_decoder_decode); + + auto model_prefill = ov::frontend::ggml::FrontEnd::convert(input_model_prefill); + ggml_decoder_prefill->clear_model_weights(); + auto model_decode = ov::frontend::ggml::FrontEnd::convert(input_model_decode); + ggml_decoder_decode->clear_model_weights(); + conversion_end_time = ggml_time_us(); + + if (getenv("GGML_OPENVINO_DUMP_IR")) { + char timestamped_filename[64]; + auto timestamp = (long long) ggml_time_us(); + snprintf(timestamped_filename, sizeof(timestamped_filename), "model_prefill_%lld.xml", timestamp); + ov::serialize(model_prefill, timestamped_filename); + snprintf(timestamped_filename, sizeof(timestamped_filename), "model_decode_%lld.xml", timestamp); + ov::serialize(model_decode, timestamped_filename); + } + + ov::CompiledModel compiled_model_prefill; + ov::CompiledModel compiled_model_decode; + auto remote_context = ggml_openvino_get_remote_context(); + if (remote_context.has_value()) { + compiled_model_prefill = core.compile_model(model_prefill, remote_context.value(), config); + compiled_model_decode = core.compile_model(model_decode, remote_context.value(), config); + } else { + compiled_model_prefill = core.compile_model(model_prefill, device, config); + compiled_model_decode = core.compile_model(model_decode, device, config); + } + + r_ctx->infer_request_cache_prefill[key] = + std::make_shared(compiled_model_prefill.create_infer_request()); + r_ctx->infer_request_cache[key] = + std::make_shared(compiled_model_decode.create_infer_request()); + compile_end_time = ggml_time_us(); + + model = is_prefill ? model_prefill : model_decode; + ggml_decoder = is_prefill ? ggml_decoder_prefill : ggml_decoder_decode; + infer_request = is_prefill ? r_ctx->infer_request_cache_prefill[key] : r_ctx->infer_request_cache[key]; + r_ctx->decoder_cache[key] = ggml_decoder; + + std::vector ov_input_names; + std::vector ov_output_names; + for (const auto & ov_param : model->get_parameters()) { + ov_input_names.push_back(ov_param->get_friendly_name()); + } + for (const auto & ov_output : model->get_results()) { + ov_output_names.push_back(ov_output->get_friendly_name()); + } + r_ctx->ov_input_names_cache[key] = std::move(ov_input_names); + r_ctx->ov_output_names_cache[key] = std::move(ov_output_names); + } + + auto ov_input_names = r_ctx->ov_input_names_cache[key]; + auto ov_output_names = r_ctx->ov_output_names_cache[key]; + + if (is_prefill) { + auto inp_len = inp_pos->ne[0]; + for (int chunk_index = 0; chunk_index * prefill_chunk_size < inp_len; chunk_index++) { + for (size_t i = 0; i < ov_input_names.size(); i++) { + auto param_name = ov_input_names[i]; + auto input_tensor = get_ov_input_tensor_static_prefill(ggml_decoder, param_name, chunk_index); + infer_request->set_input_tensor(i, input_tensor); + + if (getenv("GGML_OPENVINO_DEBUG_INPUT")) { + const auto input_tensor = infer_request->get_input_tensor(i); + print_input_tensor_info(param_name, input_tensor); + } + } + + for (size_t i = 0; i < ov_output_names.size(); i++) { + auto * ggml_tensor = ggml_decoder->get_model_outputs().at(ov_output_names[i]); + auto output_tensor = create_ov_output_tensor(ggml_decoder, infer_request, i, ggml_tensor); + infer_request->set_output_tensor(i, output_tensor); + } + + infer_request->infer(); + + if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) { + for (size_t i = 0; i < ov_output_names.size(); i++) { + const auto output_tensor = infer_request->get_output_tensor(i); + print_output_tensor_info(ov_output_names[i], output_tensor, output_tensor.data()); + } + } + } + infer_end_time = ggml_time_us(); + } else { + for (size_t i = 0; i < ov_input_names.size(); i++) { + auto param_name = ov_input_names[i]; + auto input_tensor = get_ov_input_tensor_static_decode(ggml_decoder, param_name); + infer_request->set_input_tensor(i, input_tensor); + + if (getenv("GGML_OPENVINO_DEBUG_INPUT")) { + const auto input_tensor = infer_request->get_input_tensor(i); + print_input_tensor_info(param_name, input_tensor); + } + } + + for (size_t i = 0; i < ov_output_names.size(); i++) { + auto * ggml_tensor = ggml_decoder->get_model_outputs().at(ov_output_names[i]); + auto output_tensor = create_ov_output_tensor(ggml_decoder, infer_request, i, ggml_tensor); + infer_request->set_output_tensor(i, output_tensor); + } + + infer_request->infer(); + infer_end_time = ggml_time_us(); + + if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) { + for (size_t i = 0; i < ov_output_names.size(); i++) { + const auto output_tensor = infer_request->get_output_tensor(i); + print_output_tensor_info(ov_output_names[i], output_tensor, output_tensor.data()); + } + } + } + + if (getenv("GGML_OPENVINO_PROFILING")) { + GGML_LOG_INFO("\nGGML OpenVINO Backend: \n"); + GGML_LOG_INFO(" - Graph decoder time: %ld ms \n", (decoder_end_time - start_time) / 1000); + if (!cache_hit) { + GGML_LOG_INFO(" - Graph conversion time: %ld ms \n", (conversion_end_time - decoder_end_time) / 1000); + GGML_LOG_INFO(" - Graph compile time: %ld ms \n", (compile_end_time - conversion_end_time) / 1000); + } + GGML_LOG_INFO(" - Graph inference time: %ld ms \n", (infer_end_time - compile_end_time) / 1000); + } + + return GGML_STATUS_SUCCESS; +} + +bool is_naive(ggml_cgraph * cgraph) { + constexpr int naive_graph_size_threshold = 20; + int count = 0; + for (int i = 0; i < cgraph->n_nodes; i++) { + if (cgraph->nodes[i]->op != GGML_OP_NONE) { + count++; + } + } + return count < naive_graph_size_threshold; +} + +enum ggml_status naive_compute(ggml_cgraph * cgraph, + ov::Core & core, + const std::string & device, + const ov::AnyMap & config) { + if (cgraph->n_nodes == 1 && (cgraph->nodes[0]->op == GGML_OP_NONE || cgraph->nodes[0]->op == GGML_OP_VIEW)) { + return GGML_STATUS_SUCCESS; + } + + bool naive = true; + auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph, naive); + auto decoder = std::make_shared(cgraph, model_weights); + auto input_model = std::make_shared(decoder); + auto model = ov::frontend::ggml::FrontEnd::convert(input_model, naive); + if (getenv("GGML_OPENVINO_DUMP_IR")) { + ov::serialize(model, "IR_naive.xml"); + } + + std::shared_ptr infer_request; + auto remote_context = ggml_openvino_get_remote_context(); + if (cgraph->nodes[0]->op == GGML_OP_MUL_MAT) { + // TODO ACCURACY hint triggers a bug in GPU plugin/driver on Lunar Lake. Remove once CVS-182166 is resolved + core.set_property(device, ov::hint::execution_mode(ov::hint::ExecutionMode::PERFORMANCE)); + } else { + core.set_property(device, ov::hint::execution_mode(ov::hint::ExecutionMode::ACCURACY)); + } + if (remote_context.has_value()) { + infer_request = std::make_shared( + core.compile_model(model, remote_context.value(), config).create_infer_request()); + } else { + infer_request = + std::make_shared(core.compile_model(model, device, config).create_infer_request()); + } + + auto ov_params = model->get_parameters(); + for (size_t i = 0; i < ov_params.size(); i++) { + auto param_name = ov_params[i]->get_friendly_name(); + auto input_tensor = get_ov_input_tensor(decoder, param_name); + infer_request->set_input_tensor(i, input_tensor); + } + + auto ov_results = model->get_results(); + for (size_t i = 0; i < ov_results.size(); i++) { + auto * ggml_tensor = decoder->get_model_outputs().at(ov_results[i]->get_friendly_name()); + auto output_tensor = create_ov_output_tensor(decoder, infer_request, i, ggml_tensor); + infer_request->set_output_tensor(i, output_tensor); + } + + infer_request->infer(); + return GGML_STATUS_SUCCESS; +} + +namespace { +ov::Tensor convert_ggml_input_to_ov(std::shared_ptr ggml_decoder, const std::string & name) { + const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(name); + + if (ggml_tensor->extra != nullptr) { + // GGML_LOG_DEBUG("Using ggml_tensor->extra as ov::Tensor for input: %s\n", name.c_str()); + auto * extra_base = static_cast(ggml_tensor->extra); + if (extra_base->type != ggml_openvino_extra_base::Type::TENSOR) { + throw std::runtime_error("ggml tensor extra is not of type TENSOR for input: " + name); + } + auto * tensor_extra = static_cast(extra_base); + return *tensor_extra->tensor; + } + + // GGML_LOG_DEBUG("Converting ggml tensor to ov::Tensor for input: %s\n", name.c_str()); + auto * input_data = ggml_tensor->data; + ov::Shape input_shape; + if (ggml_tensor->op == GGML_OP_VIEW) { + // This case is added to make test-backend-ops work + input_shape = ggml_decoder->get_shape(ggml_tensor->view_src); + } else { + input_shape = ggml_decoder->get_shape(ggml_tensor); + } + auto input_tensor = ov::Tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape, input_data); + return input_tensor; +} +} // namespace + +ov::Tensor get_ov_input_tensor(std::shared_ptr ggml_decoder, const std::string & param_name) { + ov::Tensor input_tensor; + if (ggml_decoder->get_model_extra_inputs().find(param_name) != ggml_decoder->get_model_extra_inputs().end()) { + input_tensor = *ggml_decoder->get_model_extra_input_values().at(param_name); + } else { + input_tensor = convert_ggml_input_to_ov(ggml_decoder, param_name); + } + return input_tensor; +} + +ov::Tensor get_ov_input_tensor_static_decode(std::shared_ptr ggml_decoder, + const std::string & param_name) { + // NPU decoding stage + const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(param_name); + const auto * op = ggml_decoder->get_tensor_used_op(ggml_tensor); + + if (GgmlOvDecoder::is_inp_tok(ggml_tensor, op) || GgmlOvDecoder::is_inp_pos(ggml_tensor, op) || + GgmlOvDecoder::is_kv_idx(ggml_tensor, op)) { + assert(ggml_tensor->ne[0] == 1); + ov::Shape input_shape = {1, 1, 1, 1}; + ov::Tensor input_tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape); + if (ggml_tensor->type == GGML_TYPE_I32) { + *input_tensor.data() = *((int32_t *) ggml_tensor->data); + } else if (ggml_tensor->type == GGML_TYPE_I64) { + *input_tensor.data() = *((int64_t *) ggml_tensor->data); + } else { + throw std::runtime_error("Unexpected tensor type for " + param_name); + } + return input_tensor; + } + + if (GgmlOvDecoder::is_output_idx(ggml_tensor, op)) { + ov::Shape input_shape = {1, 1, 1, 1}; + ov::Tensor input_tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape); + int32_t inp_out_id = *((int32_t *) ggml_tensor->data); + assert(ggml_tensor->ne[0] == 1); + assert(inp_out_id == 0); + *input_tensor.data() = inp_out_id; + return input_tensor; + } + + if (GgmlOvDecoder::is_inp_mask(ggml_tensor, op)) { + size_t context_size = ggml_decoder->get_ctx_size(); + std::vector padded_data = pad_input(ggml_tensor, 1, context_size, -INFINITY); + ov::Tensor input_tensor(ov::element::f32, ov::Shape{1, 1, 1, context_size}); + auto * data_ptr = input_tensor.data(); + std::copy(padded_data.begin(), padded_data.begin() + context_size, data_ptr); + return input_tensor; + } + + return get_ov_input_tensor(ggml_decoder, param_name); +} + +ov::Tensor get_ov_input_tensor_static_prefill(std::shared_ptr ggml_decoder, + const std::string & param_name, + int chunk_index) { + // NPU prompt processing stage + const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(param_name); + const auto * op = ggml_decoder->get_tensor_used_op(ggml_tensor); + + const size_t input_len = ggml_decoder->get_input_len(); + const size_t chunk_size = ggml_decoder->m_prefill_chunk_size; + const size_t chunk_valid_size = std::min(chunk_size, input_len - chunk_index * chunk_size); + const size_t chunk_pad_size = chunk_size - chunk_valid_size; + + if (GgmlOvDecoder::is_inp_tok(ggml_tensor, op) || GgmlOvDecoder::is_inp_pos(ggml_tensor, op) || + GgmlOvDecoder::is_kv_idx(ggml_tensor, op)) { + ov::Shape input_shape = {1, 1, 1, chunk_size}; + ov::Tensor input_tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape); + // copy the chunk_index-th chunk from ggml_tensor + size_t element_size = ggml_type_size(ggml_tensor->type); + void * input_data = (char *) ggml_tensor->data + chunk_index * chunk_size * element_size; + std::memcpy(input_tensor.data(), input_data, chunk_valid_size * element_size); + // pad the rest with last_value + 1, so that kv's of padded positions are inserted + // to the next row after the valids row in the kvcache + if (chunk_pad_size > 0) { + if (ggml_tensor->type == GGML_TYPE_I32) { + int32_t last_value = + *((int32_t *) ggml_tensor->data + (chunk_index * chunk_size + chunk_valid_size - 1)); + int32_t * output_data = input_tensor.data(); + std::fill(output_data + chunk_valid_size, output_data + chunk_size, last_value + 1); + } else if (ggml_tensor->type == GGML_TYPE_I64) { + int64_t last_value = + *((int64_t *) ggml_tensor->data + (chunk_index * chunk_size + chunk_valid_size - 1)); + int64_t * output_data = input_tensor.data(); + std::fill(output_data + chunk_valid_size, output_data + chunk_size, last_value + 1); + } else { + throw std::runtime_error("Unexpected tensor type for " + param_name); + } + } + return input_tensor; + } + + if (GgmlOvDecoder::is_output_idx(ggml_tensor, op)) { + size_t output_len = ggml_decoder->get_compute_params().output_len; + ov::Shape input_shape = {1, 1, 1, output_len}; + ov::Tensor input_tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape); + if (ggml_tensor->ne[0] == 0) { + *input_tensor.data() = 0; + } else { + auto * data_addr = input_tensor.data(); + for (size_t i = 0; i < output_len; i++) { + data_addr[i] = ((int32_t *) ggml_tensor->data)[i] % chunk_size; + } + } + return input_tensor; + } + + if (GgmlOvDecoder::is_inp_mask(ggml_tensor, op)) { + size_t cols = ggml_tensor->ne[0]; + size_t rows = ggml_tensor->ne[1]; + float * ggml_data = (float *) ggml_tensor->data + chunk_index * chunk_size * cols; + size_t chunk_valid_rows = std::min(chunk_size, rows - chunk_index * chunk_size); + size_t context_size = ggml_decoder->get_ctx_size(); + std::vector padded_data = + pad_input(ggml_data, chunk_valid_rows, cols, chunk_size, context_size, -INFINITY); + set_zero_diagonal(padded_data, chunk_size, context_size); + ov::Tensor input_tensor(ov::element::f32, ov::Shape{1, 1, chunk_size, context_size}); + auto * data_ptr = input_tensor.data(); + std::copy(padded_data.begin(), padded_data.begin() + chunk_size * context_size, data_ptr); + return input_tensor; + } + + return get_ov_input_tensor(ggml_decoder, param_name); +} + +size_t checksum(const void * data, size_t size) { + const uint8_t * bytes = static_cast(data); + size_t sum = 0; + for (size_t i = 0; i < size; ++i) { + sum += (uint8_t) i; + sum += bytes[i]; + } + return sum; +} + +void print_input_tensor_info(const std::string & name, const ov::Tensor & tensor) { + std::cout << "Input name: " << name << ", Input shape: " << tensor.get_shape() << ", Address: " << tensor.data() + << std::endl; + switch (tensor.get_element_type()) { + case ov::element::f32: { + if (name.find("self_kq_mask") == std::string::npos) { + std::cout << *(tensor.data()) << std::endl; + } else { + size_t rows = tensor.get_shape()[2]; + size_t cols = tensor.get_shape()[3]; + auto * data = tensor.data(); + for (size_t i = 0; i < rows; ++i) { + for (size_t j = 0; j < cols; ++j) { + float val = data[i * cols + j]; + if (std::isinf(val) && val < 0) { + std::cout << std::setw(5) << "-inf"; + } else { + std::cout << std::setw(5) << val; + } + } + std::cout << std::endl; + } + } + + break; + } + case ov::element::f16: + std::cout << *(tensor.data()) << std::endl; + break; + case ov::element::i32: + for (size_t i = 0; i < tensor.get_size(); ++i) { + std::cout << tensor.data()[i] << " "; + } + std::cout << std::endl; + break; + case ov::element::i64: + for (size_t i = 0; i < tensor.get_size(); ++i) { + std::cout << tensor.data()[i] << " "; + } + std::cout << std::endl; + break; + default: + break; + } +} + +void print_output_tensor_info(const std::string & name, const ov::Tensor & tensor, const void * output_dst) { + std::cout << "Output name: " << name << ", Output shape: " << tensor.get_shape() << ", Address: " << output_dst + << std::endl; + + auto print_float_stats = [](const std::string & type_name, size_t size, auto get_value) { + if (size == 0) { + return; + } + + float first = get_value(0); + float min = first; + float max = first; + double sum = first; + + for (size_t i = 1; i < size; ++i) { + float v = get_value(i); + if (v < min) { + min = v; + } + if (v > max) { + max = v; + } + sum += v; + } + double mean = sum / size; + + std::cout << std::right << std::setw(6) << type_name << std::right << std::setw(12) << "First" << std::setw(12) + << "Min" << std::setw(12) << "Max" << std::setw(12) << "Mean" << std::endl; + std::cout << std::right << std::setw(6) << "" << std::right << std::setw(12) << first << std::setw(12) << min + << std::setw(12) << max << std::setw(12) << mean << std::endl; + }; + + switch (tensor.get_element_type()) { + case ov::element::f32: { + const float * data = tensor.data(); + size_t size = tensor.get_size(); + print_float_stats("[f32]", size, [data](size_t i) { return data[i]; }); + break; + } + case ov::element::f16: { + const ov::float16 * data = tensor.data(); + size_t size = tensor.get_size(); + print_float_stats("[f16]", size, [data](size_t i) { return static_cast(data[i]); }); + break; + } + default: + break; + } +} + +void set_zero_diagonal(std::vector & matrix, size_t rows, size_t cols) { + for (size_t i = 0; i < rows; ++i) { + size_t diag_col = std::min(i, cols - 1); + matrix[i * cols + diag_col] = 0.0f; + } +} + +const ggml_tensor * get_inp_pos_tensor(ggml_cgraph * cgraph) { + for (int i = 0; i < cgraph->n_nodes; ++i) { + auto * op = cgraph->nodes[i]; + for (int j = 0; j < GGML_MAX_SRC; ++j) { + auto * src = op->src[j]; + if (src == nullptr) { + break; + } + if (GgmlOvDecoder::is_inp_pos(src, op)) { + return src; + } + } + } + GGML_LOG_ERROR("get_inp_pos_tensor: inp_pos not found in cgraph"); + throw std::runtime_error("get_inp_pos_tensor: inp_pos not found in cgraph"); +} + +bool get_is_prefill(const ggml_tensor * inp_pos) { + return inp_pos->ne[0] > 1; +} + +#pragma GCC diagnostic pop diff --git a/ggml/src/ggml-openvino/utils.h b/ggml/src/ggml-openvino/utils.h new file mode 100644 index 0000000000..656573d138 --- /dev/null +++ b/ggml/src/ggml-openvino/utils.h @@ -0,0 +1,123 @@ +#include "ggml-backend-impl.h" +#include "ggml-decoder.h" +#include "ggml-impl.h" + +#include +#include +#include +#include +#include +#include +#include +#include + +struct graph_key { + int n_nodes; + std::string first_node_name; + std::string last_node_name; + + graph_key(const ggml_cgraph * cgraph) : n_nodes(cgraph->n_nodes) { + if (n_nodes > 0) { + first_node_name = cgraph->nodes[0]->name; + last_node_name = cgraph->nodes[n_nodes - 1]->name; + } + } + + bool operator==(const graph_key & other) const { + return n_nodes == other.n_nodes && first_node_name == other.first_node_name && + last_node_name == other.last_node_name; + } +}; + +struct graph_key_hash { + size_t operator()(const graph_key & key) const { + size_t h = std::hash{}(key.n_nodes); + if (key.n_nodes > 0) { + h ^= std::hash{}(key.first_node_name) + 0x9e3779b9 + (h << 6) + (h >> 2); + h ^= std::hash{}(key.last_node_name) + 0x9e3779b9 + (h << 6) + (h >> 2); + } + return h; + } +}; + +struct ov_runtime_context { + std::mutex ov_compute_mutex; + std::string device; + bool stateful; + std::unordered_map, graph_key_hash> decoder_cache; + std::unordered_map, graph_key_hash> infer_request_cache; + std::unordered_map, graph_key_hash> infer_request_cache_prefill; + std::unordered_map, graph_key_hash> ov_input_names_cache; + std::unordered_map, graph_key_hash> ov_output_names_cache; + //TODO: Stateful is only supported for single request at a time. + // Simultanous stateful inference request support to be added. + size_t stateful_kv_size; + std::map kv_state_input_name_map; + + ov_runtime_context() : + device("CPU"), + stateful(false), + stateful_kv_size(0) {} +}; + +enum ggml_status ov_graph_compute(struct ggml_cgraph * cgraph, ggml_backend_t backend); + +enum ggml_status ov_graph_compute_dynamic(struct ggml_cgraph * cgraph, std::shared_ptr r_ctx); +enum ggml_status ov_graph_compute_static(struct ggml_cgraph * cgraph, std::shared_ptr r_ctx); + +size_t checksum(const void * data, size_t size); + +void print_input_tensor_info(const std::string & name, const ov::Tensor & tensor); + +void print_output_tensor_info(const std::string & name, const ov::Tensor & tensor, const void * output_dst); + +template +std::vector pad_input(const T * data, + size_t rows, + size_t cols, + size_t padded_rows, + size_t padded_cols, + T pad_value) { + std::vector padded(padded_rows * padded_cols, pad_value); + + for (size_t i = 0; i < std::min(rows, padded_rows); ++i) { + for (size_t j = 0; j < std::min(cols, padded_cols); ++j) { + padded[i * padded_cols + j] = data[i * cols + j]; + } + } + + return padded; +} + +template +std::vector pad_input(const ggml_tensor * tensor, size_t padded_rows, size_t padded_cols, T pad_value) { + return pad_input(reinterpret_cast(tensor->data), + static_cast(tensor->ne[1]), // rows + static_cast(tensor->ne[0]), // cols + padded_rows, padded_cols, pad_value); +} + +void set_zero_diagonal(std::vector & matrix, size_t rows, size_t cols); + +const ggml_tensor * get_inp_pos_tensor(struct ggml_cgraph * cgraph); + +bool get_is_prefill(const ggml_tensor * inp_pos); + +ov::Tensor get_ov_input_tensor(std::shared_ptr ggml_decoder, const std::string & param_name); +ov::Tensor get_ov_input_tensor_static_decode(std::shared_ptr ggml_decoder, + const std::string & param_name); +ov::Tensor get_ov_input_tensor_static_prefill(std::shared_ptr ggml_decoder, + const std::string & param_name, + int chunk_index); + +ov::Tensor create_ov_output_tensor(std::shared_ptr ggml_decoder, + std::shared_ptr infer_request, + int output_index, + const ggml_tensor * ggml_tensor); + +bool is_naive(struct ggml_cgraph * cgraph); + +enum ggml_status naive_compute(struct ggml_cgraph * cgraph, + ov::Core & core, + const std::string & device, + const ov::AnyMap & config); diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c index de5cbd75e8..48695a61ea 100644 --- a/ggml/src/ggml-quants.c +++ b/ggml/src/ggml-quants.c @@ -304,6 +304,41 @@ void quantize_row_mxfp4_ref(const float * GGML_RESTRICT x, block_mxfp4 * GGML_RE } } +void quantize_row_nvfp4_ref(const float * GGML_RESTRICT x, block_nvfp4 * GGML_RESTRICT y, int64_t k) { + static const int qk = QK_NVFP4; + static const int qk_sub = QK_NVFP4_SUB; + static const int n_sub = QK_NVFP4 / QK_NVFP4_SUB; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + for (int s = 0; s < n_sub; s++) { + const float * xb = x + i*qk + s*qk_sub; + + float amax = 0.0f; + for (int j = 0; j < qk_sub; j++) { + if (amax < fabsf(xb[j])) { + amax = fabsf(xb[j]); + } + } + + // UE4M3 scale: amax / 6.0 maps the max E2M1 value (6.0) to amax + const uint8_t ue = ggml_fp32_to_ue4m3(amax / 6.0f); + y[i].d[s] = ue; + const float d = ggml_ue4m3_to_fp32(ue); + + for (int j = 0; j < qk_sub/2; ++j) { + const uint8_t x0 = best_index_mxfp4(xb[0 + j], d); + const uint8_t x1 = best_index_mxfp4(xb[qk_sub/2 + j], d); + + y[i].qs[s*(qk_sub/2) + j] = x0 | (x1 << 4); + } + } + } +} + void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) { static const int qk = QK4_0; @@ -434,6 +469,31 @@ void dequantize_row_mxfp4(const block_mxfp4 * GGML_RESTRICT x, float * GGML_REST } } +void dequantize_row_nvfp4(const block_nvfp4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) { + static const int qk = QK_NVFP4; + static const int qk_sub = QK_NVFP4_SUB; + static const int n_sub = QK_NVFP4 / QK_NVFP4_SUB; + + assert(k % qk == 0); + + const int nb = k / qk; + + for (int i = 0; i < nb; i++) { + for (int s = 0; s < n_sub; s++) { + const float d = ggml_ue4m3_to_fp32(x[i].d[s]); + float * yb = y + i*qk + s*qk_sub; + + for (int j = 0; j < qk_sub/2; ++j) { + const int8_t v0 = kvalues_mxfp4[x[i].qs[s*(qk_sub/2) + j] & 0x0F]; + const int8_t v1 = kvalues_mxfp4[x[i].qs[s*(qk_sub/2) + j] >> 4]; + + yb[j + 0 ] = v0*d; + yb[j + qk_sub/2] = v1*d; + } + } + } +} + // // 2-6 bit quantization in super-blocks // @@ -2098,6 +2158,12 @@ size_t quantize_mxfp4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, return nrow * ggml_row_size(GGML_TYPE_MXFP4, n_per_row); } +size_t quantize_nvfp4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { + GGML_UNUSED(quant_weights); + quantize_row_nvfp4_ref(src, dst, (int64_t)nrow*n_per_row); + return nrow * ggml_row_size(GGML_TYPE_NVFP4, n_per_row); +} + // ====================== Ternary (de)-quantization (BitNet b1.58 and TriLMs) void quantize_row_tq1_0_ref(const float * GGML_RESTRICT x, block_tq1_0 * GGML_RESTRICT y, int64_t k) { @@ -3104,6 +3170,11 @@ static void quantize_row_iq2_xxs_impl(const float * GGML_RESTRICT x, void * GGML } float scale = make_qp_quants(32, kMaxQ+1, xval, (uint8_t*)L, weight); float eff_max = scale*kMaxQ; + if (eff_max <= 0) { + scales[ib] = 0; + memset(L, 0, 32); + continue; + } float best = 0; for (int is = -6; is <= 6; ++is) { float id = (2*kMaxQ-1+is*0.1f)/eff_max; @@ -3273,9 +3344,9 @@ static void quantize_row_iq2_xs_impl(const float * GGML_RESTRICT x, void * GGML_ } float max = xval[0]; for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); + memset(L, 0, 16); if (max < GROUP_MAX_EPS) { scales[ib] = 0; - memset(L, 0, 16); continue; } float best = 0; @@ -3714,9 +3785,9 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * GGML_RESTRICT } float max = xval[0]; for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); + memset(L, 0, 32); if (max < GROUP_MAX_EPS_IQ3_XXS) { scales[ib] = 0; - memset(L, 0, 32); continue; } float best = 0; @@ -3922,6 +3993,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * GGML_RESTRICT } float max = xval[0]; for (int i = 1; i < block_size; ++i) max = MAX(max, xval[i]); + memset(L, 0, block_size); if (!max) { scales[ib] = 0; continue; @@ -4245,6 +4317,7 @@ static void quantize_row_iq1_s_impl(const float * GGML_RESTRICT x, void * GGML_R for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i])); if (max < GROUP_MAX_EPS_IQ1_S) { scales[ib] = 0; + shifts[ib] = 1; memset(L, 1, block_size); continue; } @@ -4285,7 +4358,12 @@ static void quantize_row_iq1_s_impl(const float * GGML_RESTRICT x, void * GGML_R } } } - GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_shift != 0); + if (besti1 < 0 || besti2 < 0 || best_shift == 0) { + scales[ib] = 0; + shifts[ib] = 1; + memset(L, 1, block_size); + continue; + } for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0; for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1; for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2; @@ -4429,6 +4507,7 @@ static void quantize_row_iq1_m_impl(const float * GGML_RESTRICT x, void * GGML_R for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i])); if (max < GROUP_MAX_EPS_IQ1_M) { scales[ib] = 0; + shifts[ib] = 0; memset(L, 1, block_size); continue; } @@ -4527,7 +4606,12 @@ static void quantize_row_iq1_m_impl(const float * GGML_RESTRICT x, void * GGML_R } } } - GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_k >= 0); + if (besti1 < 0 || besti2 < 0 || best_k < 0) { + scales[ib] = 0; + shifts[ib] = 0; + memset(L, 1, block_size); + continue; + } for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0; for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1; for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2; @@ -4683,7 +4767,7 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block sumqx += w*q*xb[j]; sumq2 += w*q*q; } - d = sumqx/sumq2; + d = sumq2 > 0 ? sumqx/sumq2 : 0.f; float best = d*sumqx; for (int itry = -ntry; itry <= ntry; ++itry) { id = (itry + values[0])/max; @@ -4874,6 +4958,7 @@ static void quantize_row_iq2_s_impl(const float * GGML_RESTRICT x, void * GGML_R } float max = xval[0]; for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); + memset(L, 0, 16); if (max < GROUP_MAX_EPS_IQ2_S) { scales[ib] = 0; continue; @@ -5225,6 +5310,12 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte { VALIDATE_ROW_DATA_E_E8M0_IMPL(block_mxfp4, data, nb); } break; + case GGML_TYPE_NVFP4: + { + // UE4M3 scales are uint8_t — all byte values are valid + GGML_UNUSED(data); + GGML_UNUSED(nb); + } break; case GGML_TYPE_Q2_K: { VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin); diff --git a/ggml/src/ggml-quants.h b/ggml/src/ggml-quants.h index 3b688f31c2..00604f75c0 100644 --- a/ggml/src/ggml-quants.h +++ b/ggml/src/ggml-quants.h @@ -22,6 +22,7 @@ GGML_API void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_API void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k); GGML_API void quantize_row_mxfp4_ref(const float * GGML_RESTRICT x, block_mxfp4 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_nvfp4_ref(const float * GGML_RESTRICT x, block_nvfp4 * GGML_RESTRICT y, int64_t k); GGML_API void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k); GGML_API void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k); @@ -48,6 +49,7 @@ GGML_API void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GG //GGML_API void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); GGML_API void dequantize_row_mxfp4(const block_mxfp4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_nvfp4(const block_nvfp4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); GGML_API void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); GGML_API void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); @@ -95,6 +97,7 @@ GGML_API size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTR GGML_API size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); GGML_API size_t quantize_mxfp4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_nvfp4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); GGML_API void iq2xs_init_impl(enum ggml_type type); GGML_API void iq2xs_free_impl(enum ggml_type type); diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt index eefdd9725c..7b07b22787 100644 --- a/ggml/src/ggml-sycl/CMakeLists.txt +++ b/ggml/src/ggml-sycl/CMakeLists.txt @@ -25,6 +25,11 @@ ggml_add_backend_library(ggml-sycl file(GLOB GGML_HEADERS_SYCL "*.hpp") file(GLOB GGML_SOURCES_SYCL "*.cpp") +file(GLOB SRCS "template-instances/fattn-tile*.cpp") +list(APPEND GGML_SOURCES_SYCL ${SRCS}) +file(GLOB SRCS "template-instances/fattn-vec*.cpp") +list(APPEND GGML_SOURCES_SYCL ${SRCS}) + target_sources(ggml-sycl PRIVATE ${GGML_HEADERS_SYCL} ${GGML_SOURCES_SYCL}) if (WIN32) @@ -145,6 +150,7 @@ else() endif() if (GGML_SYCL_GRAPH) + message(STATUS "find GGML_SYCL_GRAPH") target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_GRAPH) endif() diff --git a/ggml/src/ggml-sycl/add-id.cpp b/ggml/src/ggml-sycl/add-id.cpp index 00c073cf93..8929017a99 100644 --- a/ggml/src/ggml-sycl/add-id.cpp +++ b/ggml/src/ggml-sycl/add-id.cpp @@ -55,7 +55,11 @@ void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { const int32_t* src2_d = (const int32_t*)src2->data; float* dst_d = (float*)dst->data; - int threads = std::min((int)ne00, 768); // cols + const unsigned int max_work_group_size = ggml_sycl_info().max_work_group_sizes[ctx.device]; + assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0); + + int threads = std::min((unsigned int)ne00, max_work_group_size); // cols + ctx.stream()->parallel_for( sycl::nd_range<3>( sycl::range<3>(1, ne02, ne01) * sycl::range<3>(1, 1, threads), diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp index 75657f3fca..b30b7f2beb 100644 --- a/ggml/src/ggml-sycl/backend.hpp +++ b/ggml/src/ggml-sycl/backend.hpp @@ -23,6 +23,7 @@ #include "dequantize.hpp" #include "dmmv.hpp" #include "element_wise.hpp" +#include "fattn.hpp" #include "gla.hpp" #include "im2col.hpp" #include "mmq.hpp" diff --git a/ggml/src/ggml-sycl/binbcast.cpp b/ggml/src/ggml-sycl/binbcast.cpp index 0a3883ae1e..92dd18889f 100644 --- a/ggml/src/ggml-sycl/binbcast.cpp +++ b/ggml/src/ggml-sycl/binbcast.cpp @@ -11,8 +11,8 @@ static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst, int ne0, int ne1, int ne2, int ne3, int ne10, int ne11, int ne12, int ne13, /*int s0, */ int s1, int s2, int s3, - /*int s00,*/ int s01, int s02, int s03, - /*int s10,*/ int s11, int s12, int s13, + int s00, int s01, int s02, int s03, + int s10, int s11, int s12, int s13, const sycl::nd_item<3> &item_ct1) { const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) + item_ct1.get_local_id(2); @@ -44,7 +44,7 @@ static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst, for (int i0 = i0s; i0 < ne0; i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) { const int i10 = i0 % ne10; - dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0*s00] : 0.0f, (float)src1_row[i10*s10]); } } @@ -53,8 +53,8 @@ static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t int ne0, int ne1, int ne2, int ne3, int ne10, int ne11, int ne12, int ne13, /*int s0, */ int s1, int s2, int s3, - /*int s00,*/ int s01, int s02, int s03, - /*int s10,*/ int s11, int s12, int s13, + int s00, int s01, int s02, int s03, + int s10, int s11, int s12, int s13, const sycl::nd_item<3> &item_ct1) { const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + @@ -82,7 +82,7 @@ static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t dst_t * dst_row = dst + i_dst; const int i10 = i0 % ne10; - dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0*s00] : 0.0f, (float)src1_row[i10*s10]); } @@ -95,7 +95,8 @@ struct bin_bcast_sycl { const int64_t ne3, const size_t nb00, const size_t nb01, const size_t nb02, const size_t nb03, const size_t nb10, const size_t nb11, const size_t nb12, const size_t nb13, const size_t nb0, const size_t nb1, const size_t nb2, const size_t nb3, const bool src0_is_contiguous, - const bool src1_is_contiguous, const bool dst_is_contiguous, queue_ptr stream) { + const bool src1_is_contiguous, const bool src0_is_permuted, const bool src1_is_permuted, + queue_ptr stream) { int nr0 = ne10 / ne0; int nr1 = ne11/ne1; int nr2 = ne12/ne2; @@ -123,7 +124,7 @@ struct bin_bcast_sycl { cnb[3] *= cne[3]; }; - if (src0_is_contiguous && src1_is_contiguous && dst_is_contiguous) { + if (src0_is_contiguous && src1_is_contiguous && !src0_is_permuted && !src1_is_permuted) { for (int i = 0; i < 4; i++) { if (nr[i] != 1) { break; @@ -164,7 +165,7 @@ struct bin_bcast_sycl { size_t nb12 = cnb1[2]; size_t nb13 = cnb1[3]; - size_t s0 = nb0 / sizeof(dst_t); + // size_t s0 = nb0 / sizeof(dst_t); size_t s1 = nb1 / sizeof(dst_t); size_t s2 = nb2 / sizeof(dst_t); size_t s3 = nb3 / sizeof(dst_t); @@ -196,9 +197,6 @@ struct bin_bcast_sycl { GGML_ASSERT(nb12 % sizeof(src1_t) == 0); GGML_ASSERT(nb13 % sizeof(src1_t) == 0); - GGML_ASSERT(s0 == 1); - GGML_ASSERT(s10 == 1); - const int block_size = 128; int64_t hne0 = std::max(ne0/2LL, 1LL); @@ -232,8 +230,8 @@ struct bin_bcast_sycl { [=](sycl::nd_item<3> item_ct1) { k_bin_bcast_unravel( src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3, - ne10, ne11, ne12, ne13, s1, s2, s3, s01, s02, - s03, s11, s12, s13, item_ct1); + ne10, ne11, ne12, ne13, s1, s2, s3, s00, s01, s02, + s03, s10, s11, s12, s13, item_ct1); }); } } else { @@ -251,7 +249,7 @@ struct bin_bcast_sycl { [=](sycl::nd_item<3> item_ct1) { k_bin_bcast(src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3, ne10, ne11, ne12, ne13, - s1, s2, s3, s01, s02, s03, s11, s12, s13, + s1, s2, s3, s00, s01, s02, s03, s10, s11, s12, s13, item_ct1); }); } @@ -268,24 +266,27 @@ inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_t if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { op()((const float *) src0->data, (const float *) src1->data, (float *) dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1, nb2, nb3, - ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst), main_stream); + ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1), main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { op()((const sycl::half *) src0->data, (const sycl::half *) src1->data, (sycl::half *) dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, - nb0, nb1, nb2, nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst), + nb0, nb1, nb2, nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1), main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { op()((const sycl::half *) src0->data, (const float *) src1->data, (sycl::half *) dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1, - nb2, nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst), main_stream); + nb2, nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1), + main_stream); } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) { op()((const int32_t *) src0->data, (const int32_t *) src1->data, (int32_t *) dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1, nb2, - nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst), main_stream); + nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1), + main_stream); } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) { op()((const int16_t *) src0->data, (const int16_t *) src1->data, (int16_t *) dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1, nb2, - nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst), main_stream); + nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1), + main_stream); } else { fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type)); diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index 519638fd41..fcb0db99c6 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -19,10 +19,13 @@ #include #include "dpct/helper.hpp" +#include "ggml.h" +#include "ggml-impl.h" #include "ggml-sycl.h" #include "presets.hpp" #include "sycl_hw.hpp" +namespace syclexp = sycl::ext::oneapi::experimental; #if GGML_SYCL_DNNL #include "dnnl.hpp" @@ -31,6 +34,9 @@ #define GGML_COMMON_DECL_SYCL #define GGML_COMMON_IMPL_SYCL +#define SYCL_FLASH_ATTN //remove it to disable FLASH_ATTENTION in building. +#define SYCL_FAST_FP16 //don't change. remove it will break fattn-tile.hpp building + /* suppress warning spam */ #pragma clang diagnostic push #pragma clang diagnostic ignored "-Wnested-anon-types" @@ -45,6 +51,8 @@ void ggml_sycl_host_free(void* ptr); extern int g_ggml_sycl_debug; extern int g_ggml_sycl_disable_optimize; extern int g_ggml_sycl_prioritize_dmmv; +extern int g_ggml_sycl_enable_flash_attention; + #if defined(__clang__) && __has_builtin(__builtin_expect) // Hint the optimizer to pipeline the more likely following instruction in branches @@ -76,10 +84,10 @@ extern int g_ggml_sycl_prioritize_dmmv; #define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP -#define VER_4VEC 610 // todo for hardward optimize. -#define VER_GEN9 700 // todo for hardward optimize. -#define VER_GEN12 1000000 // todo for hardward optimize. -#define VER_GEN13 (VER_GEN12 + 1030) // todo for hardward optimize. +#define VER_4VEC 610 // todo for hardware optimize. +#define VER_GEN9 700 // todo for hardware optimize. +#define VER_GEN12 1000000 // todo for hardware optimize. +#define VER_GEN13 (VER_GEN12 + 1030) // todo for hardware optimize. #define GGML_SYCL_MAX_NODES 8192 // TODO: adapt to hardwares @@ -170,6 +178,10 @@ static size_t g_scratch_offset = 0; int get_current_device_id(); +inline int ggml_sycl_get_device() { + return get_current_device_id(); +} + inline dpct::err0 ggml_sycl_set_device(const int device) try { int current_device_id; SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id())); @@ -194,11 +206,14 @@ struct optimize_feature { }; struct sycl_device_info { - int cc; // compute capability + int cc; // compute capability int nsm; // number of streaming multiprocessors (CUDA) maps to the maximum // number of compute units on a SYCL device. // size_t smpb; // max. shared memory per block size_t smpbo; // max. shared memory per block (with opt-in) + int warp_size; // WARP_SIZE(16)|WARP_32_SIZE(32)|WARP_16_SIZE(16). For Intel GPU, 16 is better in most cases. Some OP support 32 only. + int max_wg_per_cu; // max work groups per compute unit - refer to + // cudaOccupancyMaxActiveBlocksPerMultiprocessor bool vmm; // virtual memory support size_t total_vram; //sycl_hw_info hw_info; \\ device id and aarch, currently not used @@ -435,13 +450,15 @@ warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3>& item_ct1) { return a; } -template +/* use WARP_SIZE or WARP_32_SIZE*/ +template static __dpct_inline__ int warp_reduce_sum(int x) { return sycl::reduce_over_group( sycl::ext::oneapi::this_work_item::get_sub_group(), x, sycl::plus<>()); } -template +/* use WARP_SIZE or WARP_32_SIZE*/ +template static __dpct_inline__ float warp_reduce_sum(float x) { #pragma unroll for (int offset = width / 2; offset > 0; offset >>= 1) { @@ -451,7 +468,19 @@ static __dpct_inline__ float warp_reduce_sum(float x) { return x; } -template +/* use WARP_SIZE or WARP_32_SIZE*/ +template +static __dpct_inline__ float warp_reduce_sum(float x, const sycl::nd_item<3>& item_ct1) { +#pragma unroll + for (int offset = width / 2; offset > 0; offset >>= 1) { + x += dpct::permute_sub_group_by_xor( + item_ct1.get_sub_group(), x, offset); + } + return x; +} + +/* use WARP_SIZE or WARP_32_SIZE*/ +template static __dpct_inline__ sycl::float2 warp_reduce_sum(sycl::float2 a) { #pragma unroll for (int offset = width / 2; offset > 0; offset >>= 1) { @@ -465,7 +494,8 @@ static __dpct_inline__ sycl::float2 warp_reduce_sum(sycl::float2 a) { return a; } -template +/* use WARP_SIZE or WARP_32_SIZE*/ +template static __dpct_inline__ sycl::half2 warp_reduce_sum(sycl::half2 a) { #pragma unroll for (int offset = width / 2; offset > 0; offset >>= 1) { @@ -481,7 +511,52 @@ static constexpr int ggml_sycl_get_physical_warp_size() { return WARP_SIZE; } -template +/* use WARP_SIZE or WARP_32_SIZE*/ +template +static __dpct_inline__ int warp_reduce_all(int x) { + if (width == ggml_sycl_get_physical_warp_size()) { + return sycl::all_of_group( + sycl::ext::oneapi::this_work_item::get_sub_group(), + (~0xffffffff & + (0x1 << sycl::ext::oneapi::this_work_item::get_sub_group() + .get_local_linear_id())) || + x); + } else { +#pragma unroll + for (int offset = width / 2; offset > 0; offset >>= 1) { + x = dpct::permute_sub_group_by_xor( + sycl::ext::oneapi::this_work_item::get_sub_group(), x, + offset, width) && + x; + } + return x; + } +} + +/* use WARP_SIZE or WARP_32_SIZE*/ +template +static __dpct_inline__ int warp_reduce_any(int x) { + if (width == ggml_sycl_get_physical_warp_size()) { + return sycl::any_of_group( + sycl::ext::oneapi::this_work_item::get_sub_group(), + (0xffffffff & + (0x1 << sycl::ext::oneapi::this_work_item::get_sub_group() + .get_local_linear_id())) && + x); + } else { +#pragma unroll + for (int offset = width / 2; offset > 0; offset >>= 1) { + x = dpct::permute_sub_group_by_xor( + sycl::ext::oneapi::this_work_item::get_sub_group(), x, + offset, width) || + x; + } + return x; + } +} + +/* use WARP_SIZE or WARP_32_SIZE*/ +template static __dpct_inline__ float warp_reduce_max(float x) { #pragma unroll for (int offset = width / 2; offset > 0; offset >>= 1) { @@ -629,6 +704,42 @@ static const sycl::uint3 init_fastdiv_values(uint32_t d) { return sycl::uint3(mp, L, d); } +// Maximum number of bytes that can be copied in a single instruction. +// Set by test result. +static constexpr int ggml_sycl_get_max_cpy_bytes() { + return 16; +} + +// Aligned memory transfers of 8/16 bytes can be faster than 2 transfers with 4 bytes. +template +static __dpct_inline__ void ggml_sycl_memcpy_1(void * dst, const void * src) { + if constexpr (alignment != 0) { + static_assert(nbytes % alignment == 0, "bad alignment"); + } + constexpr int nb_per_cpy = alignment == 0 ? nbytes : alignment; + +#pragma unroll + for (int i = 0; i < nbytes/nb_per_cpy; ++i) { + if constexpr (nb_per_cpy == 1) { + ((char *) dst)[i] = ((const char *) src)[i]; + } else if constexpr (nb_per_cpy == 2) { + ((short *) dst)[i] = ((const short *) src)[i]; + } else if constexpr (nb_per_cpy == 4) { + ((int *) dst)[i] = ((const int *) src)[i]; + } else if constexpr (nb_per_cpy == 8) { + ((sycl::int2 *) dst)[i] = ((const sycl::int2 *) src)[i]; + } else if constexpr (nb_per_cpy == 16) { + ((sycl::int4 *) dst)[i] = ((const sycl::int4 *) src)[i]; + } else { + static_assert(nbytes == 0 && nbytes == -1, "bad nbytes"); + } + } +} +template +sycl::half2 __dpct_inline__ make_half2( T x, T y) { + sycl::half2 res(static_cast(x),static_cast(y)); + return res; +} static __dpct_inline__ uint32_t fastdiv(uint32_t n, const sycl::uint3 fastdiv_values) { const uint32_t hi = sycl::mul_hi(n, fastdiv_values.x()); @@ -636,6 +747,17 @@ static __dpct_inline__ uint32_t fastdiv(uint32_t n, const sycl::uint3 fastdiv_va } +template +sycl::float2 __dpct_inline__ make_float2( T x, T y) { + sycl::float2 res(static_cast(x),static_cast(y)); + return res; +} + +sycl::float2 __dpct_inline__ __half22float2(sycl::half2 &H) { + sycl::float2 float2_value(static_cast(H.x()), static_cast(H.y())); + return float2_value; +} + static __dpct_inline__ sycl::uint2 fast_div_modulo(uint32_t n, const sycl::uint3 fastdiv_values) { const uint32_t div_val = fastdiv(n, fastdiv_values); const uint32_t mod_val = n - div_val * fastdiv_values.z(); @@ -659,5 +781,188 @@ static __dpct_inline__ float ggml_sycl_e8m0_to_fp32(uint8_t x) { return result; } +sycl::float2 __dpct_inline__ __half22float2(const sycl::half2 &H) { + sycl::float2 float2_value(static_cast(H.x()), static_cast(H.y())); + return float2_value; +} + +float __dpct_inline__ __half2float(sycl::half H) { + return static_cast(H); +} + +static __dpct_inline__ void ggml_sycl_mad(float & acc, const float v, const float u) { + acc += v*u; +} + +static __dpct_inline__ void ggml_sycl_mad(float & acc, const sycl::float2 v, const sycl::float2 u) { + acc += v.x() * u.x(); + acc += v.y() * u.y(); +} + +static __dpct_inline__ void ggml_sycl_mad(float & acc, const sycl::half2 v, const sycl::half2 u) { +#ifdef GGML_SYCL_F16 + const sycl::float2 tmp = (v * u).template convert(); + acc += tmp.x() + tmp.y(); +#else + const sycl::float2 tmpv = __half22float2(v); + const sycl::float2 tmpu = __half22float2(u); + acc += tmpv.x() * tmpu.x(); + acc += tmpv.y() * tmpu.y(); +#endif // GGML_SYCL_F16 +} + +static __dpct_inline__ void ggml_sycl_mad(sycl::half2 & acc, const sycl::half2 v, const sycl::half2 u) { +#ifdef GGML_SYCL_F16 + acc += v*u; +#else + const sycl::float2 tmpv = __half22float2(v); + const sycl::float2 tmpu = __half22float2(u); + sycl::float2 tmpacc = __half22float2(acc); + // tmpacc.x += tmpv.x() * tmpu.x(); + // tmpacc.y += tmpv.y() * tmpu.y(); + sycl::float2 tmp1(tmpacc.x() + tmpv.x() * tmpu.x(), tmpacc.y() + tmpv.y() * tmpu.y()); + acc = make_half2(tmp1.x(), tmp1.y()); +#endif // GGML_SYCL_F16 +} + +template +struct ggml_sycl_unroll { + template + void operator()(const Func & f, Args... args) const { + f(n - 1, args...); + ggml_sycl_unroll{}(f, args...); + } +}; + +template <> +struct ggml_sycl_unroll<1> { + template + void operator()(const Func & f, Args... args) const { + f(0, args...); + } +}; + +static __dpct_inline__ sycl::half2 ggml_sycl_hmax2(const sycl::half2 a, const sycl::half2 b) { + sycl::half2 ret; + reinterpret_cast(ret.x()) = + sycl::vec(sycl::fmax(a[0], b[0])).convert()[0]; + reinterpret_cast(ret.y()) = + sycl::vec(sycl::fmax(a[1], b[1])).convert()[0]; + return ret; +} + +static __dpct_inline__ sycl::half ggml_sycl_hmax(const sycl::half a, const sycl::half b) { + return sycl::vec( + sycl::fmax(sycl::vec(a).convert()[0], + sycl::vec(b).convert()[0])) + .convert()[0]; +} + +static __dpct_inline__ uint32_t __hgt2_mask(const sycl::half2 a, const sycl::half2 b) { + const uint32_t mask_low = 0x0000FFFF * (float(a[0]) > float(b[0])); + const uint32_t mask_high = 0xFFFF0000 * (float(a[1]) > float(b[1])); + return mask_low | mask_high; +} + +static __dpct_inline__ uint32_t fastmodulo(uint32_t n, const sycl::uint3 fastdiv_values) { + // expects fastdiv_values to contain in (see init_fastdiv_values) + return n - fastdiv(n, fastdiv_values) * fastdiv_values.z(); +} + +static bool fast_fp16_available(const int cc) { + GGML_UNUSED(cc); + return true; //Intel GPUs always support FP16. +} + +enum class block_reduce_method { + MAX, + SUM, +}; + +template +struct block_reduce_policy; + +template +inline constexpr bool is_any = (std::is_same_v || ...); + +template +inline constexpr bool ggml_sycl_dependent_false_v = false; + +#define WARP_32_SIZE 32 + +template struct block_reduce_policy { + static T reduce(T val) { + if constexpr (is_any) { + return warp_reduce_sum(val); + } else { + static_assert(ggml_sycl_dependent_false_v, "Unsupported type for block reduce sum"); + } + } + + static T sentinel() { + if constexpr (std::is_same_v) { + return 0.0f; + } else if constexpr (std::is_same_v) { + return sycl::float2(0.0f, 0.0f); + } else if constexpr (std::is_same_v) { + return sycl::half2(0.0f, 0.0f); + } else if constexpr (std::is_same_v) { + return 0; + } else { + static_assert(ggml_sycl_dependent_false_v, "Unsupported type for block reduce sum"); + } + } +}; + +template struct block_reduce_policy { + static T reduce(T val) { + if constexpr (is_any) { + return warp_reduce_max(val); + } else { + static_assert(ggml_sycl_dependent_false_v, "Unsupported type for block reduce max"); + } + } + + static T sentinel() { + if constexpr (std::is_same_v) { + return -INFINITY; + } else if constexpr (std::is_same_v) { + return sycl::half2(-INFINITY, -INFINITY); + } else { + static_assert(ggml_sycl_dependent_false_v, "Unsupported type for block reduce max"); + } + } +}; + + +template +static T block_reduce(T val, T * shared_vals, int block_size_template) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + val = block_reduce_policy::reduce(val); + const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template; + const int nthreads = item_ct1.get_local_range(2); + const int nwarps = nthreads / WARP_SIZE; + + if (block_size > warp_size) { + assert((block_size <= 1024) && (block_size % warp_size) == 0); + const int warp_id = item_ct1.get_local_id(2) / warp_size; + const int lane_id = item_ct1.get_local_id(2) % warp_size; + if (lane_id == 0) { + shared_vals[warp_id] = val; + } + item_ct1.barrier(sycl::access::fence_space::local_space); + + size_t nreduce = nwarps / WARP_SIZE; + float tmp = 0.f; + if (lane_id < (static_cast(block_size) / warp_size)) { + for (size_t i = 0; i < nreduce; i += 1) + { + tmp += shared_vals[lane_id + i * WARP_SIZE]; + } + } + return block_reduce_policy::reduce(tmp); + } + return val; +} #endif // GGML_SYCL_COMMON_HPP diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index 8bdae36458..d17aca2cac 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -482,6 +482,63 @@ static void dequantize_row_mxfp4_sycl(const void * vx, dst_t * y, const int64_t }); } +template +static void dequantize_block_nc(const void * __restrict__ vx, dst_t * __restrict__ y, + const int64_t ne00, const int64_t ne01, const int64_t ne02, + const int64_t s01, const int64_t s02, const int64_t s03) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int64_t i00 = 2 * (int64_t(item_ct1.get_local_range(2)) * item_ct1.get_group(2) + item_ct1.get_local_id(2)); + + if (i00 >= ne00) { + return; + } + + const int64_t i01 = item_ct1.get_group(1); + const int64_t i02 = item_ct1.get_group(0) % ne02; + const int64_t i03 = item_ct1.get_group(0) / ne02; + + const int64_t ibx0 = i03*s03 + i02*s02 + i01*s01; + + const int64_t ib = ibx0 + i00/qk; // block index + const int64_t iqs = (i00%qk)/qr; // quant index + const int64_t iybs = i00 - i00%qk; // y block start index + const int64_t y_offset = qr == 1 ? 1 : qk/2; + + // dequantize + #ifdef GGML_SYCL_F16 + sycl::half2 v; + #else + sycl::float2 v; + #endif + + dequantize_kernel(vx, ib, iqs, v); + + const int64_t iy0 = ((i03*ne02 + i02)*ne01 + i01)*ne00 + iybs + iqs; + y[iy0 + 0] = ggml_sycl_cast(v.x()); + y[iy0 + y_offset] = ggml_sycl_cast(v.y()); +} + + +template +static void dequantize_block_nc_sycl(const void * vx, + dst_t * y, + const int64_t ne00, + const int64_t ne01, + const int64_t ne02, + const int64_t ne03, + const int64_t s01, + const int64_t s02, + const int64_t s03, + dpct::queue_ptr stream) { + const dpct::dim3 num_blocks((ne00 + 2 * SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2 * SYCL_DEQUANTIZE_BLOCK_SIZE), ne01, + ne02 * ne03); + stream->parallel_for(sycl::nd_range<3>(num_blocks * sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + dequantize_block_nc(vx, y, ne00, ne01, ne02, s01, s02, s03); + }); +} template static void convert_unary_nc(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t s01, const int64_t s02, const int64_t s03, @@ -662,7 +719,8 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) { } } -to_fp16_nc_sycl_t get_to_fp16_nc_sycl(ggml_type type) { + +to_fp16_nc_sycl_t ggml_get_to_fp16_nc_sycl(ggml_type type) { switch (type) { case GGML_TYPE_F32: return convert_unary_nc_sycl; @@ -670,6 +728,16 @@ to_fp16_nc_sycl_t get_to_fp16_nc_sycl(ggml_type type) { case GGML_TYPE_BF16: return convert_unary_nc_sycl; #endif + case GGML_TYPE_Q4_0: + return dequantize_block_nc_sycl; + case GGML_TYPE_Q4_1: + return dequantize_block_nc_sycl; + case GGML_TYPE_Q5_0: + return dequantize_block_nc_sycl; + case GGML_TYPE_Q5_1: + return dequantize_block_nc_sycl; + case GGML_TYPE_Q8_0: + return dequantize_block_nc_sycl; default: return nullptr; } diff --git a/ggml/src/ggml-sycl/convert.hpp b/ggml/src/ggml-sycl/convert.hpp index f8cb573e36..6e621f2154 100644 --- a/ggml/src/ggml-sycl/convert.hpp +++ b/ggml/src/ggml-sycl/convert.hpp @@ -29,6 +29,27 @@ using to_t_nc_sycl_t = void (*)(const void * x, T * y, int64_t ne00, int64_t ne0 int64_t s01, int64_t s02, int64_t s03, dpct::queue_ptr queue); typedef to_t_nc_sycl_t to_fp16_nc_sycl_t; -to_fp16_nc_sycl_t get_to_fp16_nc_sycl(ggml_type type); +to_fp16_nc_sycl_t ggml_get_to_fp16_nc_sycl(ggml_type type); + +template + inline dst_t ggml_sycl_cast(src_t x) { + if constexpr (std::is_same_v) { + return x; + } else if constexpr (std::is_same_v) { + return sycl::ext::oneapi::bfloat16(float(x)); + } else if constexpr (std::is_same_v) { + return static_cast(x); + } else if constexpr (std::is_same_v && std::is_same_v) { + return x.template convert(); + } else if constexpr (std::is_same_v && + std::is_same_v>) { + return {x.x, x.y}; + } else if constexpr(std::is_same_v) { + return int32_t(x); + } else { + return float(x); + } +} + #endif // GGML_SYCL_CONVERT_HPP diff --git a/ggml/src/ggml-sycl/count-equal.cpp b/ggml/src/ggml-sycl/count-equal.cpp index b0a8b4820d..4580354cd9 100644 --- a/ggml/src/ggml-sycl/count-equal.cpp +++ b/ggml/src/ggml-sycl/count-equal.cpp @@ -18,7 +18,7 @@ static void count_equal(const T *__restrict__ x, const T *__restrict__ y, nequal += xi == yi; } - nequal = warp_reduce_sum(nequal); + nequal = warp_reduce_sum(nequal); if (item_ct1.get_local_id(2) != 0) { return; diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index ece66a7ac1..791d3cac52 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -2997,6 +2997,778 @@ namespace dpct return 0; } + template + class args_selector; + + /// args_selector is a helper class for extracting arguments from an + /// array of pointers to arguments or buffer of arguments to pass to a + /// kernel function. + /// + /// \param R(Ts...) The type of the kernel + /// \param n_nondefault_params The number of nondefault parameters of the + /// kernel (excluding parameters that like sycl::nd_item, etc.) \param + /// n_default_params The number of default parameters of the kernel + /// + /// Example usage: + /// With the following kernel: + /// void foo(sycl::float2 *x, int n, sycl::nd_item<3> item_ct1, float + /// f=.1) {} + /// and with the declaration: + /// args_selector<2, 1, decltype(foo)> selector(kernelParams, extra); + /// we have: + /// selector.get<0>() returns a reference to sycl::float*, + /// selector.get<1>() returns a reference to int, + /// selector.get<2>() returns a reference to float + template + class args_selector { + private: + void **kernel_params; + char *args_buffer; + + template static constexpr int account_for_default_params() { + constexpr int n_total_params = sizeof...(Ts); + if constexpr (i >= n_nondefault_params) { + return n_total_params - n_default_params + + (i - n_nondefault_params); + } else { + return i; + } + } + + public: + /// Get the type of the ith argument of R(Ts...) + /// \param [in] i Index of parameter to get + /// \returns Type of ith parameter + template + using arg_type = std::tuple_element_t(), + std::tuple>; + static constexpr int params_num = sizeof...(Ts); + + private: + template static constexpr int get_offset() { + if constexpr (i == 0) { + // we can assume args_buffer is properly aligned to the + // first argument + return 0; + } else { + constexpr int prev_off = get_offset(); + constexpr int prev_past_end = + prev_off + sizeof(arg_type); + using T = arg_type; + // is the past-the-end of the i-1st element properly aligned + // with the ith element's alignment? + if constexpr (prev_past_end % alignof(T) == 0) { + return prev_past_end; + } + // otherwise bump prev_past_end to match alignment + else { + return prev_past_end + + (alignof(T) - (prev_past_end % alignof(T))); + } + } + } + + static char *get_args_buffer(void **extra) { + if (!extra) + return nullptr; + for (; (std::size_t)*extra != 0; ++extra) { + if ((std::size_t)*extra == 1) { + return static_cast(*(extra + 1)); + } + } + return nullptr; + } + + public: + /// If kernel_params is nonnull, then args_selector will + /// extract arguments from kernel_params. Otherwise, it + /// will extract them from extra. + /// \param [in] kernel_params Array of pointers to arguments + /// a or null pointer. + /// \param [in] extra Array containing pointer to argument buffer. + args_selector(void **kernel_params, void **extra) + : kernel_params(kernel_params), + args_buffer(get_args_buffer(extra)) {} + + /// Get a reference to the ith argument extracted from kernel_params + /// or extra. + /// \param [in] i Index of argument to get + /// \returns Reference to the ith argument + template arg_type &get() { + if (kernel_params) { + return *static_cast *>(kernel_params[i]); + } else { + return *reinterpret_cast *>(args_buffer + + get_offset()); + } + } + }; // COPY from DPCT head file + // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/util.hpp + + /// Utility class for launching SYCL kernels through kernel + /// function wrapper. + /// For example: + /// A SYCL kernel function: + /// void kernel_func(int *ptr, sycl::nd_item<3> item); + /// Kernel function wrapper: + /// void kernel_func_wrapper(int *ptr) { + /// sycl::queue queue = *dpct::kernel_launcher::_que; + /// unsigned int localMemSize = dpct::kernel_launcher::_local_mem_size; + /// sycl::nd_range<3> nr = dpct::kernel_launcher::_nr; + /// queue.parallel_for( + /// nr, + /// [=](sycl::nd_item<3> item_ct1) { + /// kernel_func(ptr, item_ct1); + /// }); + /// } + /// Then launch the kernel through wrapper like: + /// typedef void(*fpt)(int *); + /// fpt fp = kernel_func_wrapper; + /// dpct::kernel_launcher::launch(fp, dpct::dim3(1), dpct::dim3(1), 0, 0, + /// device_ptr); + /// If the origin function type is erased, then need to register it first: + /// void *fp = (void *)wrapper_register(&kernel_func_wrapper).get(); + /// dpct::kernel_launcher::launch(fp, dpct::dim3(1), dpct::dim3(1), args, + /// 0, 0); + class kernel_launcher { + template + static void launch_helper(FuncT &&func, ArgSelector &selector, + std::index_sequence) { + func(selector.template get()...); + } + static void set_execution_config(dim3 group_range, dim3 local_range, + unsigned int local_mem_size, + queue_ptr que) { + if (que) { + _que = que; + } else { + _que = &get_default_queue(); + } + _nr = sycl::nd_range<3>( + static_cast>(group_range * local_range), + static_cast>(local_range)); + _local_mem_size = local_mem_size; + + + }; + static inline std::mutex kernel_function_ptr_map_mutex; + + public: + /// Variables for storing execution configuration. + static inline thread_local sycl::queue *_que = nullptr; + static inline thread_local sycl::nd_range<3> _nr = sycl::nd_range<3>(); + static inline thread_local unsigned int _local_mem_size = 0; + /// Map for retrieving launchable functor from a raw pointer. + static inline std::map< + const void *, + std::function> + kernel_function_ptr_map = {}; + + /// Registers a kernel function pointer with a corresponding launchable + /// functor. + /// \param [in] func Pointer to the kernel function. + /// \param [in] launcher Functor to handle kernel invocation. + static void register_kernel_ptr( + const void *func, + std::function + launcher) { + std::lock_guard lock(kernel_function_ptr_map_mutex); + kernel_function_ptr_map[func] = std::move(launcher); + } + /// Launches a kernel function with arguments provided directly through + /// kernel function wrapper. + /// \tparam FuncT Type of the kernel function wrapper. + /// \tparam ArgsT Types of kernel arguments. + /// \param [in] func Pointer to the kernel function wrapper. + /// \param [in] group_range SYCL group range. + /// \param [in] local_range SYCL local range. + /// \param [in] local_mem_size The size of local memory required by the + /// kernel function. \param [in] que SYCL queue used to execute kernel. + /// \param [in] args Kernel arguments. + template + static std::enable_if_t, void> + launch(FuncT *func, dim3 group_range, dim3 local_range, + unsigned int local_mem_size, queue_ptr que, ArgsT... args) { + set_execution_config(group_range, local_range, local_mem_size, que); + func(args...); + } + /// Launches a kernel function through registered kernel function + /// wrapper. \param [in] func Pointer to the registered kernel function + /// wrapper. \param [in] group_range SYCL group range. \param [in] + /// local_range SYCL local range. \param [in] args Array of pointers to + /// kernel arguments. \param [in] local_mem_size The size of local + /// memory required by the kernel function. \param [in] que SYCL queue + /// used to execute kernel. + static void launch(const void *func, dim3 group_range, dim3 local_range, + void **args, unsigned int local_mem_size, + queue_ptr que) { + std::lock_guard lock(kernel_function_ptr_map_mutex); + auto Iter = kernel_function_ptr_map.find(func); + if (Iter == kernel_function_ptr_map.end()) { + throw std::runtime_error("dpct::launch() : no registered " + "kernel function wrapper found."); + } + (Iter->second)(group_range, local_range, args, local_mem_size, que); + } + /// Launches a kernel function with packed arguments through kernel + /// function wrapper. + /// \tparam FuncT Type of the kernel function wrapper. + /// \param [in] func Pointer to the kernel function wrapper. + /// \param [in] group_range SYCL group range. + /// \param [in] local_range SYCL local range. + /// \param [in] args Array of pointers to kernel arguments. + /// \param [in] local_mem_size The size of local memory required by the + /// kernel function. \param [in] que SYCL queue used to execute kernel. + template + static std::enable_if_t, void> + launch(FuncT *func, dim3 group_range, dim3 local_range, void **args, + unsigned int local_mem_size, queue_ptr que) { + constexpr size_t p_num = args_selector<0, 0, FuncT>::params_num; + set_execution_config(group_range, local_range, local_mem_size, que); + args_selector selector(args, nullptr); + launch_helper(func, selector, std::make_index_sequence{}); + } + }; // COPY from DPCT head file + // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/kernel.hpp + + // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/util.hpp + template + T select_from_sub_group( + sycl::sub_group g, + T x, + int remote_local_id, + int logical_sub_group_size = 32) { + unsigned int start_index = g.get_local_linear_id() / + logical_sub_group_size * + logical_sub_group_size; + return sycl::select_from_group( + g, x, start_index + remote_local_id % logical_sub_group_size); + } + + // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/math.hpp + template + void ldmatrix(uintptr_t addr, T* m, bool trans = false, unsigned mat = 0) { + auto sg = sycl::ext::oneapi::this_work_item::get_sub_group(); + int lane = sg.get_local_linear_id(); + + int lane_group8_row = lane / 8; + int lane_group8_col = lane % 8; + + if (!trans) { + // calculate the source lane + int src_lane = 2 * lane_group8_row; + if (lane_group8_col >= 4) + src_lane += 1; + + // Broadcast the address from the source lane + auto recv_addr_uintp = + dpct::select_from_sub_group(sg, addr, mat * 8 + src_lane); + + // Cast the received address from uintptr_t to the type of 'm' + auto recv_addr = reinterpret_cast(recv_addr_uintp); + + // Non-transposed load + *m = recv_addr[lane_group8_col % 4]; + } else { + // calculate the source lane + int src_lane = (lane % 4) * 2; + + // Broadcast the address from the source lane + auto recv_addr_uintp_1 = + dpct::select_from_sub_group(sg, addr, mat * 8 + src_lane); + auto recv_addr_uintp_2 = + dpct::select_from_sub_group(sg, addr, mat * 8 + src_lane + 1); + + // Cast the received address from uintptr_t to 'half *' + auto recv_addr_1 = reinterpret_cast(recv_addr_uintp_1); + auto recv_addr_2 = reinterpret_cast(recv_addr_uintp_2); + + // Transposed load + int index = lane / 4; + sycl::half val0 = recv_addr_1[index]; + sycl::half val1 = recv_addr_2[index]; + + // Combine the two 16-bits into one 32-bit value + sycl::half2 val = sycl::half2(val0, val1); + *m = *reinterpret_cast(&val); + } + } + + template + void ldmatrix(uintptr_t addr, T* m1, T* m2, bool trans = false) { + // Load 1st matrix + ldmatrix(addr, m1, trans, 0); + // Load 2nd matrix + ldmatrix(addr, m2, trans, 1); + } + + template + void ldmatrix( + uintptr_t addr, T* m1, T* m2, T* m3, T* m4, bool trans = false) { + // Load 1st matrix + ldmatrix(addr, m1, trans, 0); + // Load 2nd matrix + ldmatrix(addr, m2, trans, 1); + // Load 3rd matrix + ldmatrix(addr, m3, trans, 2); + // Load 4th matrix + ldmatrix(addr, m4, trans, 3); + } + + // /opt/intel/oneapi/dpcpp-ct/latest/include/dpct/math.hpp + + /// A helper struct that defines the pack type for the input matrix + /// fragments + /// of mma() function based on the type of input matrix fragments. + /// The MMAType struct is specialized for different types of input matrices. + /// Currently, the specialization for f16, bf16 and s8 types is defined + /// below. \tparam [in] T The type of the input matrix fragments + template + struct MMAType { + using PackType = uint32_t; + }; + + /// Each work item of a sub-group (limited to size 32) calling this function + /// calculates a subset fragment for the output matrix D using MAD operation + /// on A, B & C matrix fragments (D = A * B + C). Current supported shapes & + /// types: + /// - m8n8k4 (f32.f16.f16.f32) + /// - m8n8k16 (s32.s8.s8.s32) + /// - m16n8k8 (f32.f16.f16.f32 & f32.bf16.bf16.f32) + /// - m16n8k16 (f32.f16.f16.f32 & s32.s8.s8.s32) + /// - m16n8k32 (s32.s8.s8.s32) + /// Here, m, n & k define the shapes of A, B & C matrices respectively + /// (A = [m x k], B = [k x n], C = [m x n]). + /// \tparam [in] M The rows of A, C & D matrices + /// \tparam [in] N The columns of B, C, D matrices + /// \tparam [in] K The columns & rows of A & B matrices respectively + /// \tparam [in] ABType The type of the input matrix (A & B) fragment + /// \tparam [in] CDType The type of the output matrix (C & D) fragment + /// \param [out] d_mat_frag The fragment of the output matrix D to store the + /// result of A * B + C + /// \param [in] a_mat_frag The fragment of the input matrix A to be + /// multiplied with B matrix fragment \param [in] b_mat_frag The fragment of + /// the input matrix B to be multiplied with A matrix fragment \param [in] + /// c_mat_frag The fragment of the input matrix C to be added with the + /// result of A * B fragments + template + void mma( + volatile void** d_mat_frag, + void* a_mat_frag, + void* b_mat_frag, + void* c_mat_frag) { + auto d = reinterpret_cast(d_mat_frag); + auto a = + reinterpret_cast::PackType*>(a_mat_frag); + auto b = + reinterpret_cast::PackType*>(b_mat_frag); + auto c = reinterpret_cast(c_mat_frag); + + auto sg = sycl::ext::oneapi::this_work_item::get_sub_group(); + int lane = sg.get_local_linear_id(); + + static_assert( + (M == 8 && N == 8 && K == 4) || (M == 8 && N == 8 && K == 16) || + (M == 16 && N == 8 && K == 8) || (M == 16 && N == 8 && K == 16) || + (M == 16 && N == 8 && K == 32), + "Unsupported MMA shape!"); + + short row_load_offset = 4 * (lane >> 2); + short col_load_offset = 8 * (lane % 4); + + if constexpr (M == 8 && N == 8 && K == 4) { + if constexpr (std::is_floating_point_v) { + col_load_offset = row_load_offset % 16; + + // Init D matrix with fragments of C matrix + *d[0] = c[0]; + *d[1] = c[1]; + *d[2] = c[2]; + *d[3] = c[3]; + *d[4] = c[4]; + *d[5] = c[5]; + *d[6] = c[6]; + *d[7] = c[7]; + + // Calculate the row and col offset indices to iterate through the row + // & col fragments of A & B matrices + int r_ind = (lane % 2) ? 1 : 0; + int c_ind = ((lane % 4) / 2) ? 2 : 0; + + // Each sub-group is responsible for computing a fragment size of 8*8 + // elements of matrix D for each of 4 MMA computations. + // Each work item computes 8 elements of matrix D by gathering + // their corresponding col & row matrix fragments of length k (4) + // from A & B matrices respectively using below mapping logic: + // row0 = (i % 4) if (lane < 16) else (i % 4) + 4 + // col0 = (lane % 4) + // As each row & col fragment of A & B matrices is distributed across + // 4 work items, each iteration of below loop loads a partial fragment + // of matrix A (row) and matrix B (col) using the row & col offsets. + typename MMAType::PackType recv_a[2], recv_b[2]; + + for (int i = 0; i < 4; i++) { + // Load partial fragment from col0 of matrix A ({a0, a1}) + recv_a[0] = + dpct::select_from_sub_group(sg, a[0], row_load_offset + i); + // Load partial fragment from col0 of matrix A ({a2, a3}) + recv_a[1] = + dpct::select_from_sub_group(sg, a[1], row_load_offset + i); + + // Load partial fragment from row0 of matrix B ({b0, b1}) + recv_b[0] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i); + // Load partial fragment from row0 of matrix B ({b2, b3}) + recv_b[1] = + dpct::select_from_sub_group(sg, b[1], col_load_offset + i); + + auto ra = reinterpret_cast(recv_a); + auto rb = reinterpret_cast(recv_b); + + // Each work item calculates a partial product of A & B matrix + // fragments and adds it to the corresponding D matrix fragment (for + // even work item indices) d0 += col0{ a0 } * row0{ b0 } d1 += col0{ + // a0 } * row0{ b1 } d2 += col1{ a2 } * row0{ b0 } d3 += col1{ a2 } + // * row0{ b1 } (for odd work item indices) d0 += col0{ a1 } * row0{ + // b2 } d1 += col0{ a1 } * row0{ b3 } d2 += col1{ a3 } * row0{ b2 } + // d3 += col1{ a3 } * row0{ b3 } + *d[0] += + static_cast(ra[r_ind]) * static_cast(rb[c_ind]); + *d[1] += static_cast(ra[r_ind]) * + static_cast(rb[c_ind + 1]); + *d[2] += static_cast(ra[r_ind + 2]) * + static_cast(rb[c_ind]); + *d[3] += static_cast(ra[r_ind + 2]) * + static_cast(rb[c_ind + 1]); + + // Load partial fragment from row1 of matrix B ({b0, b1}) + recv_b[0] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 16); + // Load partial fragment from row1 of matrix B ({b2, b3}) + recv_b[1] = + dpct::select_from_sub_group(sg, b[1], col_load_offset + i + 16); + + // (for even work item indices) + // d0 += col0{ a0 } * row1{ b0 } + // d1 += col0{ a0 } * row1{ b1 } + // d2 += col1{ a2 } * row1{ b0 } + // d3 += col1{ a2 } * row1{ b1 } + // (for odd work item indices) + // d0 += col0{ a1 } * row1{ b2 } + // d1 += col0{ a1 } * row1{ b3 } + // d2 += col1{ a3 } * row1{ b2 } + // d3 += col1{ a3 } * row1{ b3 } + *d[4] += + static_cast(ra[r_ind]) * static_cast(rb[c_ind]); + *d[5] += static_cast(ra[r_ind]) * + static_cast(rb[c_ind + 1]); + *d[6] += static_cast(ra[r_ind + 2]) * + static_cast(rb[c_ind]); + *d[7] += static_cast(ra[r_ind + 2]) * + static_cast(rb[c_ind + 1]); + } + } + } else if constexpr (M == 8 && N == 8 && K == 16) { + if constexpr (std::is_integral_v) { + // Init D matrix with fragments of C matrix + *d[0] = c[0]; + *d[1] = c[1]; + + // Each sub-group is responsible for computing a fragment size of 16*8 + // elements of matrix D. + // Each work item computes 2 elements of matrix D by gathering + // their corresponding row & col matrix fragments of length k (16) + // from A & B matrices respectively using below mapping logic: + // row0 = ((lane % 4) * 4) + i + // col0 = (lane >> 2) + // As each row & col fragment of A & B matrices is distributed across + // 4 work items, each iteration of below loop loads a partial fragment + // of matrix A (row) and matrix B (col) using the row & col offsets. + for (int i = 0; i < 4; i++) { + typename MMAType::PackType recv_a, recv_b[2]; + + // Load partial fragment from row0 of matrix A ({a0, a1, a2, a3}) + recv_a = dpct::select_from_sub_group(sg, a[0], row_load_offset + i); + // Load partial fragment from col0 of matrix B ({b0, b1, b2, b3}) + recv_b[0] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i); + // Load partial fragment from col1 of matrix B ({b0, b1, b2, b3}) + recv_b[1] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 4); + + auto a = reinterpret_cast(&recv_a); + auto b = reinterpret_cast(recv_b); + + // Each work item calculates a partial product of A & B matrix + // fragments and adds it to the corresponding D matrix fragment d0 + // += row0{ a0, a1, a2, a3 } * col0{ b0, b1, b2, b3 } d1 += row0{ + // a0, a1, a2, a3 } * col1{ b0, b1, b2, b3 } d2 += row0{ a0, a1, a2, + // a3 } * col0{ b0, b1, b2, b3 } d3 += row0{ a0, a1, a2, a3 } * + // col1{ b0, b1, b2, b3 } + for (int j = 0; j < 4; j++) { + *d[0] += a[j] * b[j]; + *d[1] += a[j] * b[j + 4]; + } + } + } + } else if constexpr (M == 16 && N == 8 && K == 8) { + if constexpr (std::is_floating_point_v) { + // Init D matrix fragment with C matrix fragment + *d[0] = c[0]; + *d[1] = c[1]; + *d[2] = c[2]; + *d[3] = c[3]; + + // Each sub-group is responsible for computing a fragment size of 16*8 + // elements of matrix D. + // Each work item computes 4 elements of matrix D by gathering + // their corresponding row & col matrix fragments of length k (8) + // from A & B matrices respectively using below mapping logic: + // row0 = (lane >> 2) & row1 = (lane >> 2) + 8 + // col0 = (lane % 4) * 2 + (i & 0x1) + // As each row & col fragment of A & B matrices is distributed across + // 4 work items, each iteration of below loop loads a partial fragment + // of matrix A (row) and matrix B (col) using the row & col offsets. + for (int i = 0; i < 4; i++) { + typename MMAType::PackType recv_a[2], recv_b[2]; + + // Load partial fragment from row0 of matrix A ({a0, a1}) + recv_a[0] = + dpct::select_from_sub_group(sg, a[0], row_load_offset + i); + // Load partial fragment from row1 of matrix A ({a2, a3}) + recv_a[1] = + dpct::select_from_sub_group(sg, a[1], row_load_offset + i); + // Load partial fragment from col0 of matrix B ({b0, b1}) + recv_b[0] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i); + // Load partial fragment from col1 of matrix B ({b0, b1}) + recv_b[1] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 4); + + auto ra = reinterpret_cast(recv_a); + auto rb = reinterpret_cast(recv_b); + + // Each work item calculates a partial product of A & B matrix + // fragments and adds it to the corresponding D matrix fragment d0 + // += row0{ a0, a1 } * col0{ b0, b1 } d1 += row0{ a0, a1 } * col1{ + // b0, b1 } d2 += row1{ a2, a3 } * col0{ b0, b1 } d3 += row1{ a2, a3 + // } * col1{ b0, b1 } + for (int j = 0; j < 2; j++) { + *d[0] += static_cast(ra[j]) * static_cast(rb[j]); + *d[1] += + static_cast(ra[j]) * static_cast(rb[j + 2]); + *d[2] += + static_cast(ra[j + 2]) * static_cast(rb[j]); + *d[3] += + static_cast(ra[j + 2]) * static_cast(rb[j + 2]); + } + } + } + } else if constexpr (M == 16 && N == 8 && K == 16) { + if constexpr (std::is_floating_point_v) { + // Init D matrix fragment with C matrix fragment + *d[0] = c[0]; + *d[1] = c[1]; + *d[2] = c[2]; + *d[3] = c[3]; + + // Each sub-group is responsible for computing a fragment size of 16*8 + // elements of matrix D. + // Each work item computes 4 elements of matrix D by gathering + // their corresponding row & col matrix fragments of length k (8) + // from A & B matrices respectively using below mapping logic: + // row0 = (lane >> 2) & row1 = (lane >> 2) + 8 + // col0 = (lane % 4) * 2 & col1 = (lane % 4) * 2 + 1 + // As each row & col fragment of A & B matrices is distributed across + // 4 work items, each iteration of below loop loads a partial fragment + // of matrix A (row) and matrix B (col) using the row & col offsets. + for (int i = 0; i < 4; i++) { + typename MMAType::PackType recv_a[4], recv_b[4]; + + // Load partial fragment from row0 of matrix A ({a0, a1}) + recv_a[0] = + dpct::select_from_sub_group(sg, a[0], row_load_offset + i); + // Load partial fragment from row0 of matrix A ({a2, a3}) + recv_a[1] = + dpct::select_from_sub_group(sg, a[2], row_load_offset + i); + // Load partial fragment from row1 of matrix A ({a0, a1}) + recv_a[2] = + dpct::select_from_sub_group(sg, a[1], row_load_offset + i); + // Load partial fragment from row1 of matrix A ({a2, a3}) + recv_a[3] = + dpct::select_from_sub_group(sg, a[3], row_load_offset + i); + + // Load partial fragment from col0 of matrix B ({b0, b1}) + recv_b[0] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i); + // Load partial fragment from col0 of matrix B ({b2, b3}) + recv_b[1] = + dpct::select_from_sub_group(sg, b[1], col_load_offset + i); + // Load partial fragment from col1 of matrix B ({b0, b1}) + recv_b[2] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + 4 + i); + // Load partial fragment from col1 of matrix B ({b2, b3}) + recv_b[3] = + dpct::select_from_sub_group(sg, b[1], col_load_offset + 4 + i); + + auto ra = reinterpret_cast(recv_a); + auto rb = reinterpret_cast(recv_b); + + // Each work item calculates a partial product of A & B matrix + // fragments and adds it to the corresponding D matrix fragment d0 + // += row0{ a0, a1, a2, a3 } * col0{ b0, b1, b2, b3 } d1 += row0{ + // a0, a1, a2, a3 } * col1{ b0, b1, b2, b3 } d2 += row1{ a0, a1, a2, + // a3 } * col0{ b0, b1, b2, b3 } d3 += row1{ a0, a1, a2, a3 } * + // col1{ b0, b1, b2, b3 } + for (int j = 0; j < 4; j++) { + *d[0] += static_cast(ra[j]) * static_cast(rb[j]); + *d[1] += + static_cast(ra[j]) * static_cast(rb[j + 4]); + *d[2] += + static_cast(ra[j + 4]) * static_cast(rb[j]); + *d[3] += static_cast(ra[j + 4]) * + static_cast(rb[j + 4]); + } + } + } else if constexpr (std::is_integral_v) { + // Init D matrix with fragments of C matrix + *d[0] = c[0]; + *d[1] = c[1]; + *d[2] = c[2]; + *d[3] = c[3]; + + // Each sub-group is responsible for computing a fragment size of 16*8 + // elements of matrix D. + // Each work item computes 4 elements of matrix D by gathering + // their corresponding row & col matrix fragments of length k (8) + // from A & B matrices respectively using below mapping logic: + // row0 = (lane >> 2) & row1 = (lane >> 2) + 8 + // col0 = (lane % 4) * 2 & col1 = (lane % 4) * 2 + 1 + // As each row & col fragment of A & B matrices is distributed across + // 4 work items, each iteration of below loop loads a partial fragment + // of matrix A (row) and matrix B (col) using the row & col offsets. + for (int i = 0; i < 4; i++) { + typename MMAType::PackType recv_a[2], recv_b[2]; + + // Load partial fragment from row0 of matrix A ({a0, a1, a2, a3}) + recv_a[0] = + dpct::select_from_sub_group(sg, a[0], row_load_offset + i); + // Load partial fragment from row1 of matrix A ({a4, a5, a6, a7}) + recv_a[1] = + dpct::select_from_sub_group(sg, a[1], row_load_offset + i); + // Load partial fragment from col0 of matrix B ({b0, b1, b2, b3}) + recv_b[0] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i); + // Load partial fragment from col1 of matrix B ({b4, b5, b6, b7}) + recv_b[1] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 4); + + auto ra = reinterpret_cast(recv_a); + auto rb = reinterpret_cast(recv_b); + + // Each work item calculates a partial product of A & B matrix + // fragments and adds it to the corresponding D matrix fragment d0 + // += row0{ a0, a1, a2, a3 } * col0{ b0, b1, b2, b3 } d1 += row0{ + // a0, a1, a2, a3 } * col1{ b4, b5, b6, b7 } d2 += row1{ a4, a5, a6, + // a7 } * col0{ b0, b1, b2, b3 } d3 += row1{ a4, a5, a6, a7 } * + // col1{ b4, b5, b6, b7 } + for (int i = 0; i < 4; i++) { + *d[0] += ra[i] * rb[i]; + *d[1] += ra[i] * rb[i + 4]; + *d[2] += ra[i + 4] * rb[i]; + *d[3] += ra[i + 4] * rb[i + 4]; + } + } + } + } else if constexpr (M == 16 && N == 8 && K == 32) { + if constexpr (std::is_integral_v) { + // Init D matrix with fragments of C matrix + *d[0] = c[0]; + *d[1] = c[1]; + *d[2] = c[2]; + *d[3] = c[3]; + + // Each sub-group is responsible for computing a fragment size of 16*8 + // elements of matrix D. + // Each work item computes 4 elements of matrix D by gathering + // their corresponding row & col matrix fragments of length k (32) + // from A & B matrices respectively using below mapping logic: + // row0 = (lane >> 2) & row1 = (lane >> 2) + 8 + // col0 = ((lane % 4) * 4) + (i & 0x3) & col1 = ((lane % 4) * 4) + (i + // & 0x3) As each row & col fragment of A & B matrices is distributed + // across 4 work items, each iteration of below loop loads a partial + // fragment of matrix A (row) and matrix B (col) using the row & col + // offsets. + for (int i = 0; i < 4; i++) { + typename MMAType::PackType recv_a[2], recv_b[2]; + + // Load partial fragment from row0 of matrix A ({a0, a1, a2, a3}) + recv_a[0] = + dpct::select_from_sub_group(sg, a[0], row_load_offset + i); + // Load partial fragment from row1 of matrix A ({a4, a5, a6, a7}) + recv_a[1] = + dpct::select_from_sub_group(sg, a[1], row_load_offset + i); + // Load partial fragment from col0 of matrix B ({b0, b1, b2, b3}) + recv_b[0] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i); + // Load partial fragment from col1 of matrix B ({b0, b1, b2, b3}) + recv_b[1] = + dpct::select_from_sub_group(sg, b[0], col_load_offset + i + 4); + + auto a = reinterpret_cast(recv_a); + auto b = reinterpret_cast(recv_b); + + // Each work item calculates a partial product of A & B matrix + // fragments and adds it to the corresponding D matrix fragment d0 + // += row0{ a0, a1, a2, a3 } * col0{ b0, b1, b2, b3 } d1 += row0{ + // a0, a1, a2, a3 } * col1{ b0, b1, b2, b3 } d2 += row1{ a4, a5, a6, + // a7 } * col0{ b0, b1, b2, b3 } d3 += row1{ a4, a5, a6, a7 } * + // col1{ b0, b1, b2, b3 } + for (int j = 0; j < 4; j++) { + *d[0] += a[j] * b[j]; + *d[1] += a[j] * b[j + 4]; + *d[2] += a[j + 4] * b[j]; + *d[3] += a[j + 4] * b[j + 4]; + } + } + + for (int i = 0; i < 4; i++) { + typename MMAType::PackType recv_a[2], recv_b[2]; + + // Load partial fragment from row0 of matrix A ({a8, a9, a10, a11}) + recv_a[0] = + dpct::select_from_sub_group(sg, a[2], row_load_offset + i); + // Load partial fragment from row1 of matrix A ({a12, a13, a14, + // a15}) + recv_a[1] = + dpct::select_from_sub_group(sg, a[3], row_load_offset + i); + // Load partial fragment from col0 of matrix B ({b4, b5, b6, b7}) + recv_b[0] = + dpct::select_from_sub_group(sg, b[1], col_load_offset + i); + // Load partial fragment from col1 of matrix B ({b4, b5, b6, b7}) + recv_b[1] = + dpct::select_from_sub_group(sg, b[1], col_load_offset + i + 4); + + auto a = reinterpret_cast(recv_a); + auto b = reinterpret_cast(recv_b); + + // Each work item calculates a partial product of A & B matrix + // fragments and adds it to the corresponding D matrix fragment d0 + // += row0{ a8, a9, a10, a11 } * col0{ b4, b5, b6, b7 } d1 += row0{ + // a8, a9, a10, a11 } * col1{ b4, b5, b6, b7 } d2 += row1{ a12, a13, + // a14, a15 } * col0{ b4, b5, b6, b7 } d3 += row1{ a12, a13, a14, + // a15 } * col1{ b4, b5, b6, b7 } + for (int j = 0; j < 4; j++) { + *d[0] += a[j] * b[j]; + *d[1] += a[j] * b[j + 4]; + *d[2] += a[j + 4] * b[j]; + *d[3] += a[j + 4] * b[j + 4]; + } + } + } + } + } } // COPY from DPCT head files #endif // GGML_SYCL_DPCT_HELPER_HPP diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index 00d54b83f8..acd51bf45b 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -9,23 +9,32 @@ #define SYCL_LOCAL_ID_CALC(ITEM, IDX) \ (ITEM.get_local_range(IDX) * ITEM.get_group(IDX) + ITEM.get_local_id(IDX)) +static void acc_f32(const float * x, const float * y, float * dst, const int64_t ne, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13, + const int64_t s11, const int64_t s12, const int64_t s13, const int64_t offset) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int64_t i = SYCL_LOCAL_ID_CALC(item_ct1, 2); -static void acc_f32(const float * x, const float * y, float * dst, const int ne, - const int ne10, const int ne11, const int ne12, - const int nb1, const int nb2, int offset, const sycl::nd_item<1> &item_ct1) { - const int i = SYCL_LOCAL_ID_CALC(item_ct1, 0); if (i >= ne) { return; } - int src1_idx = i - offset; - int oz = src1_idx / nb2; - int oy = (src1_idx - (oz * nb2)) / nb1; - int ox = src1_idx % nb1; - if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) { - dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11]; - } else { - dst[i] = x[i]; + + int64_t src1_idx = i - offset; + + int64_t tmp = src1_idx; + const int64_t i13 = tmp / s13; + tmp -= i13 * s13; + const int64_t i12 = tmp / s12; + tmp -= i12 * s12; + const int64_t i11 = tmp / s11; + tmp -= i11 * s11; + const int64_t i10 = tmp; + + float val = x[i]; + if (src1_idx >= 0 && i10 < ne10 && i11 < ne11 && i12 < ne12 && i13 < ne13) { + val += y[((i13*ne12 + i12) * ne11 + i11) * ne10 + i10]; } + dst[i] = val; } /* Unary OP funcs */ @@ -364,18 +373,15 @@ static void gated_op_fused_geglu_quick(const T * x, const T * g, T * dst, const namespace ggml_sycl_detail { static void acc_f32_sycl(const float *x, const float *y, float *dst, - const int n_elements, const int ne10, const int ne11, - const int ne12, const int nb1, const int nb2, - const int offset, queue_ptr stream) { - int num_blocks = ceil_div(n_elements, SYCL_ACC_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<1>(sycl::range<1>(num_blocks) * - sycl::range<1>(SYCL_ACC_BLOCK_SIZE), - sycl::range<1>(SYCL_ACC_BLOCK_SIZE)), - [=](sycl::nd_item<1> item_ct1) { - acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset, - item_ct1); - }); + const int64_t n_elements, const int64_t ne10, const int64_t ne11, + const int64_t ne12, const int64_t ne13, const int64_t s1, const int64_t s2, const int64_t s3, + const int64_t offset, queue_ptr stream) { + const int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE; + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, ne13, s1, s2, s3, offset); + }); } template @@ -402,25 +408,19 @@ static void upscale_sycl(const T *x, T *dst, const int nb00, const int nb01, template static inline void dispatch_ggml_sycl_op_unary(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) { -#if defined (GGML_SYCL_F16) GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); -#else - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); -#endif GGML_ASSERT(dst->src[0]->type == dst->type); + dpct::queue_ptr main_stream = ctx.stream(); SYCL_CHECK(ggml_sycl_set_device(ctx.device)); switch (dst->type) { -#if defined (GGML_SYCL_F16) case GGML_TYPE_F16: { auto data_pts = cast_data(dst); kernel_invoker(data_pts.src, data_pts.dst, (int)ggml_nelements(dst->src[0]), main_stream, std::forward(args)...); break; } -#endif case GGML_TYPE_F32: { auto data_pts = cast_data(dst); @@ -434,14 +434,10 @@ static inline void dispatch_ggml_sycl_op_unary(ggml_backend_sycl_context & ctx, template static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) { -#if defined (GGML_SYCL_F16) GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); -#else - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); -#endif GGML_ASSERT(dst->src[0]->type == dst->type); + dpct::queue_ptr main_stream = ctx.stream(); SYCL_CHECK(ggml_sycl_set_device(ctx.device)); const ggml_tensor * src0 = dst->src[0]; @@ -463,7 +459,6 @@ static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & c GGML_ASSERT(src0->type == src1->type); } switch (dst->type) { -#if defined (GGML_SYCL_F16) case GGML_TYPE_F16: { sycl::half * src0_p = (sycl::half *) src0_d; @@ -484,7 +479,6 @@ static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & c std::forward(args)...); break; } -#endif case GGML_TYPE_F32: { float * src0_p = (float *) src0_d; @@ -513,13 +507,9 @@ static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & c template static inline void dispatch_ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) { -#if defined (GGML_SYCL_F16) GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); -#else - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); -#endif + GGML_ASSERT(dst->src[0]->type == dst->type); dpct::queue_ptr main_stream = ctx.stream(); @@ -530,7 +520,6 @@ static inline void dispatch_ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx const float sf2 = (float) dst->ne[2] / dst->src[0]->ne[2]; const float sf3 = (float) dst->ne[3] / dst->src[0]->ne[3]; switch (dst->type) { -#if defined (GGML_SYCL_F16) case GGML_TYPE_F16: { auto data_pts = cast_data(dst); @@ -539,7 +528,6 @@ static inline void dispatch_ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx main_stream, std::forward(args)...); break; } -#endif case GGML_TYPE_F32: { auto data_pts = cast_data(dst); @@ -868,22 +856,31 @@ static inline void ggml_sycl_op_trunc(ggml_backend_sycl_context & ctx, ggml_tens } static inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, ggml_tensor *dst) { - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); - GGML_ASSERT(dst->src[1]->type == GGML_TYPE_F32); + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + const float * src0_d = (const float *) src0->data; + const float * src1_d = (const float *) src1->data; + float * dst_d = (float *) dst->data; + + dpct::queue_ptr stream = ctx.stream(); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported - dpct::queue_ptr main_stream = ctx.stream(); - SYCL_CHECK(ggml_sycl_set_device(ctx.device)); - const float * src0_dd = static_cast(dst->src[0]->data); - const float * src1_dd = static_cast(dst->src[1]->data); - float * dst_dd = static_cast(dst->data); - int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 - int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 - // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused - int offset = dst->op_params[3] / 4; // offset in bytes + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(dst->nb[0] == ggml_element_size(dst)); + GGML_ASSERT(ggml_is_contiguously_allocated(dst)); - ggml_sycl_detail::acc_f32_sycl(src0_dd, src1_dd, dst_dd, (int)ggml_nelements(dst), (int)dst->src[1]->ne[0], (int)dst->src[1]->ne[1], (int)dst->src[1]->ne[2], nb1, nb2, offset, main_stream); + const int64_t s1 = dst->op_params[0] / sizeof(float); + const int64_t s2 = dst->op_params[1] / sizeof(float); + const int64_t s3 = dst->op_params[2] / sizeof(float); + const int64_t offset = dst->op_params[3] / sizeof(float); + + ggml_sycl_detail::acc_f32_sycl(src0_d, src1_d, dst_d, ggml_nelements(dst), + src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + s1, s2, s3, offset, stream); } static inline void ggml_sycl_op_geglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { diff --git a/ggml/src/ggml-sycl/fattn-common.hpp b/ggml/src/ggml-sycl/fattn-common.hpp new file mode 100644 index 0000000000..ed00d03c3b --- /dev/null +++ b/ggml/src/ggml-sycl/fattn-common.hpp @@ -0,0 +1,1179 @@ +#pragma once + +#include +#include "dpct/helper.hpp" +#include "common.hpp" +#include "convert.hpp" +#include "vecdotq.hpp" + +#include "ggml.h" + +#include +#include +#include + + +#define FATTN_KQ_STRIDE 256 +#define HALF_MAX_HALF sycl::half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. +#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. +#define FATTN_KQ_MAX_OFFSET (3.0f*0.6931f) + +typedef void (*fattn_kernel_t)( + const char* Q, + const char* K, + const char* V, + const char* mask, + const char* sinks, + const int* KV_max, + float* dst, + sycl::float2* dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const float logit_softcap, + const int32_t ne00, + const sycl::uint3 ne01, + const int32_t ne02, + const int32_t ne03, + const int32_t nb01, + const int32_t nb02, + const int32_t nb03, + const int32_t ne10, + const int32_t ne11, + const int32_t ne12, + const int32_t ne13, + const int32_t nb11, + const int32_t nb12, + const int64_t nb13, + const int32_t nb21, + const int32_t nb22, + const int64_t nb23, + const int32_t ne31, + const int32_t ne32, + const int32_t ne33, + const int32_t nb31, + const int32_t nb32, + const int64_t nb33); + +typedef float (*vec_dot_KQ_t)( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds); + +template +static __dpct_inline__ float vec_dot_fattn_vec_KQ_f16(const char * __restrict__ K_c, + const void * __restrict__ Q_v, + const int * __restrict__ Q_q8, + const void * __restrict__ Q_ds_v) { + const sycl::half2 * K_h2 = (const sycl::half2 *) K_c; + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + + constexpr int cpy_nb = ggml_sycl_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + float sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += nthreads*cpy_ne) { + sycl::half2 tmp[cpy_ne]; + ggml_sycl_memcpy_1( + tmp, + K_h2 + k_KQ_0 + (sycl::ext::oneapi::this_work_item::get_nd_item<3>().get_local_id(2) % nthreads) * cpy_ne); +#pragma unroll + for (int k_KQ_1 = 0; k_KQ_1 < cpy_ne; ++k_KQ_1) { +#ifdef GGML_SYCL_F16 + ggml_sycl_mad(sum, tmp[k_KQ_1] , ((const sycl::half2 *) Q_v)[k_KQ_0/nthreads + k_KQ_1]); +#else + ggml_sycl_mad(sum, __half22float2(tmp[k_KQ_1]), ((const sycl::float2 *) Q_v)[k_KQ_0/nthreads + k_KQ_1]); +#endif // GGML_SYCL_F16 + } + } + + return sum; +} + +template +static __dpct_inline__ float vec_dot_fattn_vec_KQ_q4_0(const char * __restrict__ K_c, + const void * __restrict__ Q_v, + const int * __restrict__ Q_q8, + const void * __restrict__ Q_ds_v) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + + const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c; + GGML_UNUSED(Q_v); + + float sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += nthreads) { + const int k_KQ = + k_KQ_0 + (nthreads == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads); + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI4_0; + const int shift = k_KQ & (QI8_1/2); + + int v; + ggml_sycl_memcpy_1(&v, K_q4_0[ib].qs + sizeof(int)*iqs4); + v = (v >> shift) & 0x0F0F0F0F; + const int u = Q_q8[k_KQ_0/nthreads]; + + const int sumi = ggml_sycl_dp4a(v, u, 0); + + const sycl::float2 Q_ds = ((const sycl::float2 *) Q_ds_v)[k_KQ_0 / nthreads]; + sum += __half2float(K_q4_0[ib].d) * (sumi*Q_ds.x() - (8/QI8_1)*Q_ds.y()); + } + + return sum; +} + +template +static __dpct_inline__ float vec_dot_fattn_vec_KQ_q4_1(const char * __restrict__ K_c, + const void * __restrict__ Q_v, + const int * __restrict__ Q_q8, + const void * __restrict__ Q_ds_v) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c; + GGML_UNUSED(Q_v); + + float sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += nthreads) { + const int k_KQ = + k_KQ_0 + (nthreads == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads); + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI4_1; + const int shift = k_KQ & (QI8_1/2); + + int v; + ggml_sycl_memcpy_1(&v, K_q4_1[ib].qs + sizeof(int)*iqs4); + v = (v >> shift) & 0x0F0F0F0F; + const int u = Q_q8[k_KQ_0/nthreads]; + + const int sumi = ggml_sycl_dp4a(v, u, 0); + + const sycl::float2 K_dm = (K_q4_1[ib].dm).template convert(); + const sycl::float2 Q_ds = ((const sycl::float2 *) Q_ds_v)[k_KQ_0 / nthreads]; + + sum += K_dm.x()*Q_ds.x()*sumi + K_dm.y()*Q_ds.y()/QI8_1; + } + + return sum; +} + +template +static __dpct_inline__ float vec_dot_fattn_vec_KQ_q5_0(const char * __restrict__ K_c, + const void * __restrict__ Q_v, + const int * __restrict__ Q_q8, + const void * __restrict__ Q_ds_v) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c; + GGML_UNUSED(Q_v); + + float sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += nthreads) { + const int k_KQ = + k_KQ_0 + (nthreads == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads); + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI5_0; + const int iqs8 = k_KQ % QI8_1; + const int shift = k_KQ & (QI8_1/2); + + int v; + ggml_sycl_memcpy_1(&v, K_q5_0[ib].qs + sizeof(int)*iqs4); + v = (v >> shift) & 0x0F0F0F0F; + + { + int vh; + ggml_sycl_memcpy_1(&vh, K_q5_0[ib].qh); + vh >>= iqs8 * QI5_0; + + v |= (vh << 4) & 0x00000010; // 0 -> 4 + v |= (vh << 11) & 0x00001000; // 1 -> 12 + v |= (vh << 18) & 0x00100000; // 2 -> 20 + v |= (vh << 25) & 0x10000000; // 3 -> 28 + } + + const int u = Q_q8[k_KQ_0/nthreads]; + + const int sumi = ggml_sycl_dp4a(v, u, 0); + + const sycl::float2 Q_ds = ((const sycl::float2 *) Q_ds_v)[k_KQ_0 / nthreads]; + + sum += __half2float(K_q5_0[ib].d) * (sumi*Q_ds.x() - (16/QI8_1)*Q_ds.y()); + } + + return sum; +} + +template +static __dpct_inline__ float vec_dot_fattn_vec_KQ_q5_1(const char * __restrict__ K_c, + const void * __restrict__ Q_v, + const int * __restrict__ Q_q8, + const void * __restrict__ Q_ds_v) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c; + GGML_UNUSED(Q_v); + + float sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += nthreads) { + const int k_KQ = + k_KQ_0 + (nthreads == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads); + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI5_1; + const int iqs8 = k_KQ % QI8_1; + const int shift = k_KQ & (QI8_1/2); + + int v; + ggml_sycl_memcpy_1(&v, K_q5_1[ib].qs + sizeof(int)*iqs4); + v = (v >> shift) & 0x0F0F0F0F; + + { + int vh; + ggml_sycl_memcpy_1(&vh, K_q5_1[ib].qh); + vh >>= iqs8 * QI5_0; + + v |= (vh << 4) & 0x00000010; // 0 -> 4 + v |= (vh << 11) & 0x00001000; // 1 -> 12 + v |= (vh << 18) & 0x00100000; // 2 -> 20 + v |= (vh << 25) & 0x10000000; // 3 -> 28 + } + + const int u = Q_q8[k_KQ_0/nthreads]; + + const int sumi = ggml_sycl_dp4a(v, u, 0); + + const sycl::float2 K_dm = (K_q5_1[ib].dm).template convert(); + const sycl::float2 Q_ds = ((const sycl::float2 *) Q_ds_v)[k_KQ_0 / nthreads]; + + sum += K_dm.x()*Q_ds.x()*sumi + K_dm.y()*Q_ds.y()/QI8_1; + } + + return sum; +} + +template +static __dpct_inline__ float vec_dot_fattn_vec_KQ_q8_0(const char * __restrict__ K_c, + const void * __restrict__ Q_v, + const int * __restrict__ Q_q8, + const void * __restrict__ Q_ds_v) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c; + GGML_UNUSED(Q_v); + + float sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < int(D/sizeof(int)); k_KQ_0 += nthreads) { + const int k_KQ = + k_KQ_0 + (nthreads == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads); + + const int ib = k_KQ / QI8_0; + const int iqs = k_KQ % QI8_0; + + int v; + ggml_sycl_memcpy_1(&v, K_q8_0[ib].qs + 4*iqs); + + const sycl::float2 * Q_ds = (const sycl::float2 *) Q_ds_v; + const float Q_d = Q_ds[k_KQ_0 / nthreads].x(); + + sum += vec_dot_q8_0_q8_1_impl(&v, &Q_q8[k_KQ_0/nthreads], K_q8_0[ib].d, Q_d); + } + + return sum; +} + +template +static __dpct_inline__ void quantize_q8_1_to_shared(const float * __restrict__ x, + const float scale, + int * __restrict__ yq32, + void * __restrict__ yds) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + + float vals[sizeof(int)] = { 0.0f }; +#pragma unroll + for (int l = 0; l < int(sizeof(int)); ++l) { + vals[l] = + (ni == warp_size || item_ct1.get_local_id(2) < ni) ? scale * x[4 * item_ct1.get_local_id(2) + l] : 0.0f; + } + + float amax = sycl::fabs(vals[0]); + float sum = vals[0]; +#pragma unroll + for (int l = 1; l < int(sizeof(int)); ++l) { + amax = sycl::fmax(amax, sycl::fabs(vals[l])); + sum += vals[l]; + } +#pragma unroll + for (int mask = QI8_1/2; mask > 0; mask >>= 1) { + amax = sycl::fmax( + amax, dpct::permute_sub_group_by_xor(sycl::ext::oneapi::this_work_item::get_sub_group(), amax, mask)); + sum += dpct::permute_sub_group_by_xor(sycl::ext::oneapi::this_work_item::get_sub_group(), sum, mask); + } + + const float d = amax / 127; + int q32 = 0; + int8_t * q8 = (int8_t *) &q32; + + if (d != 0.0f) { +#pragma unroll + for (int l = 0; l < int(sizeof(int)); ++l) { + q8[l] = sycl::round(vals[l] / d); + } + } + + yq32[item_ct1.get_local_id(2)] = q32; + if (item_ct1.get_local_id(2) % QI8_1 == 0 && (ni == warp_size || item_ct1.get_local_id(2) < ni)) { + if (std::is_same::value) { + ((sycl::half2 *) yds)[item_ct1.get_local_id(2)/QI8_1] = make_half2(d, sum); + } else { + ((sycl::float2 *) yds)[item_ct1.get_local_id(2)/QI8_1] = make_float2(d, sum); + } + } +} + +typedef void (*dequantize_V_t)(const void *, void *, const int64_t); + +template +static __dpct_inline__ void dequantize_V_f16(const void * __restrict__ vx, void * __restrict__ dst, const int64_t i0) { + if constexpr (std::is_same_v) { + ggml_sycl_memcpy_1(dst, (const sycl::half *) vx + i0); + } else if constexpr (std::is_same_v) { + static_assert(ne % 2 == 0, "bad ne"); + sycl::half2 tmp[ne / 2]; + ggml_sycl_memcpy_1(tmp, (const sycl::half *) vx + i0); + sycl::float2 * dst_f2 = (sycl::float2 *) dst; +#pragma unroll + for (int l = 0; l < ne/2; ++l) { + dst_f2[l] = tmp[l].template convert(); + } + } else { + static_assert(std::is_same_v, "unsupported type"); + } +} + +template +static __dpct_inline__ void dequantize_V_q4_0(const void * __restrict__ vx, void * __restrict__ dst, const int64_t i0) { + const block_q4_0 * x = (const block_q4_0 *) vx; + + const int64_t ib = i0 / QK4_0; + const int iqs = i0 % (QK4_0/2); + const int shift = (i0 % QK4_0) / (QK4_0/2); + + int q; + static_assert(ne == 2 || ne == 4, "bad ne"); + ggml_sycl_memcpy_1(&q, x[ib].qs + iqs); + q >>= 4*shift; + q &= 0x0F0F0F0F; + q = dpct::vectorized_binary(q, 0x08080808, dpct::sub_sat()); + + const int8_t * q8 = (const int8_t *) &q; + +#ifdef GGML_SYCL_F16 + if constexpr (std::is_same_v) { + const sycl::half2 d = sycl::half2(x[ib].d); + +#pragma unroll + for (int l0 = 0; l0 < ne; l0 += 2) { + ((sycl::half2 *) dst)[l0 / 2] = d * sycl::half2(q8[l0 + 0], q8[l0 + 1]); + } + } else +#endif // GGML_SYCL_F16 + if constexpr (std::is_same_v) { + const float d = x[ib].d; + +#pragma unroll + for (int l = 0; l < ne; ++l) { + ((float *) dst)[l] = d * q8[l]; + } + } else { + static_assert(std::is_same_v, "bad type"); + } +} + +template +static __dpct_inline__ void dequantize_V_q4_1(const void * __restrict__ vx, void * __restrict__ dst, const int64_t i0) { + const block_q4_1 * x = (const block_q4_1 *) vx; + + const int64_t ib = i0 / QK4_1; + const int iqs = i0 % (QK4_1/2); + const int shift = (i0 % QK4_1) / (QK4_1/2); + + int q; + static_assert(ne == 2 || ne == 4, "bad ne"); + ggml_sycl_memcpy_1(&q, x[ib].qs + iqs); + q >>= 4*shift; + q &= 0x0F0F0F0F; + + const int8_t * q8 = (const int8_t *) &q; + +#ifdef GGML_SYCL_F16 + if constexpr (std::is_same_v) { + const sycl::half2 dm = x[ib].dm; + const sycl::half2 d = sycl::half2(dm[0]); + const sycl::half2 m = sycl::half2(dm[1]); + +#pragma unroll + for (int l0 = 0; l0 < ne; l0 += 2) { + ((sycl::half2 *) dst)[l0 / 2] = d * sycl::half2(q8[l0 + 0], q8[l0 + 1]) + m; + } + } else +#endif // GGML_SYCL_F16 + if constexpr (std::is_same_v) { + const sycl::float2 dm = (x[ib].dm).template convert(); + +#pragma unroll + for (int l = 0; l < ne; ++l) { + ((float *) dst)[l] = dm.x() * q8[l] + dm.y(); + } + } else { + static_assert(std::is_same_v, "bad type"); + } +} + +template +static __dpct_inline__ void dequantize_V_q5_0(const void * __restrict__ vx, void * __restrict__ dst, const int64_t i0) { + const block_q5_0 * x = (const block_q5_0 *) vx; + + const int64_t ib = i0 / QK5_0; + const int idq = i0 % QK5_0; + const int iqs = i0 % (QK5_0/2); + const int shift = (i0 % QK5_0) / (QK5_0/2); + + int q; + static_assert(ne == 2 || ne == 4, "bad ne"); + ggml_sycl_memcpy_1(&q, x[ib].qs + iqs); + q >>= 4*shift; + q &= 0x0F0F0F0F; + + { + int qh; + ggml_sycl_memcpy_1(&qh, x[ib].qh); +#pragma unroll + for (int l = 0; l < ne; ++l) { + q |= ((qh >> (idq + l)) & 0x00000001) << (8*l + 4); + } + } + + q = dpct::vectorized_binary(q, 0x10101010, dpct::sub_sat()); + + const int8_t * q8 = (const int8_t *) &q; + +#ifdef GGML_SYCL_F16 + if constexpr (std::is_same_v) { + const sycl::half2 d = sycl::half2(x[ib].d); + +#pragma unroll + for (int l0 = 0; l0 < ne; l0 += 2) { + ((sycl::half2 *) dst)[l0 / 2] = d * sycl::half2(q8[l0 + 0], q8[l0 + 1]); + } + } else +#endif // GGML_SYCL_F16 + if constexpr (std::is_same_v) { + const float d = x[ib].d; + +#pragma unroll + for (int l = 0; l < ne; ++l) { + ((float *) dst)[l] = d * q8[l]; + } + } else { + static_assert(std::is_same_v, "bad type"); + } +} + +template +static __dpct_inline__ void dequantize_V_q5_1(const void * __restrict__ vx, void * __restrict__ dst, const int64_t i0) { + const block_q5_1 * x = (const block_q5_1 *) vx; + + const int64_t ib = i0 / QK5_1; + const int idq = i0 % QK5_1; + const int iqs = i0 % (QK5_1/2); + const int shift = (i0 % QK5_1) / (QK5_1/2); + + int q; + static_assert(ne == 2 || ne == 4, "bad ne"); + ggml_sycl_memcpy_1(&q, x[ib].qs + iqs); + q >>= 4*shift; + q &= 0x0F0F0F0F; + + { + int qh; + ggml_sycl_memcpy_1(&qh, x[ib].qh); +#pragma unroll + for (int l = 0; l < ne; ++l) { + q |= ((qh >> (idq + l)) & 0x00000001) << (8*l + 4); + } + } + + const int8_t * q8 = (const int8_t *) &q; + +#ifdef GGML_SYCL_F16 + if constexpr (std::is_same_v) { + const sycl::half2 dm = x[ib].dm; + const sycl::half2 d = sycl::half2(dm[0]); + const sycl::half2 m = sycl::half2(dm[1]); + +#pragma unroll + for (int l0 = 0; l0 < ne; l0 += 2) { + ((sycl::half2 *) dst)[l0 / 2] = d * sycl::half2(q8[l0 + 0], q8[l0 + 1]) + m; + } + } else +#endif // GGML_SYCL_F16 + if constexpr (std::is_same_v) { + const sycl::float2 dm = (x[ib].dm).template convert(); + +#pragma unroll + for (int l = 0; l < ne; ++l) { + ((float *) dst)[l] = dm.x() * q8[l] + dm.y(); + } + } else { + static_assert(std::is_same_v, "bad type"); + } +} + +template +static __dpct_inline__ void dequantize_V_q8_0(const void * __restrict__ vx, void * __restrict__ dst, const int64_t i0) { + const block_q8_0 * x = (const block_q8_0 *) vx; + + const int64_t ib = i0 / QK8_0; + const int iqs = i0 % QK8_0; + + static_assert(ne % 2 == 0, "bad ne"); + int8_t qs[ne]; + ggml_sycl_memcpy_1(qs, x[ib].qs + iqs); + +#ifdef GGML_SYCL_F16 + if constexpr (std::is_same::value) { + const sycl::half2 d = sycl::half2(x[ib].d); + +#pragma unroll + for (int l0 = 0; l0 < ne; l0 += 2) { + ((sycl::half2 *) dst)[l0 / 2] = d * make_half2(qs[l0 + 0], qs[l0 + 1]); + } + } else +#endif // GGML_SYCL_F16 + if constexpr (std::is_same::value) { + const float d = x[ib].d; + +#pragma unroll + for (int l = 0; l < ne; ++l) { + ((float *) dst)[l] = d * qs[l]; + } + } else { + static_assert(std::is_same_v, "unsupported type"); + } +} + +template +constexpr vec_dot_KQ_t get_vec_dot_KQ() { + if constexpr (type_K == GGML_TYPE_F16) { + return vec_dot_fattn_vec_KQ_f16; + } else if constexpr (type_K == GGML_TYPE_Q4_0) { + return vec_dot_fattn_vec_KQ_q4_0; + } else if constexpr (type_K == GGML_TYPE_Q4_1) { + return vec_dot_fattn_vec_KQ_q4_1; + } else if constexpr (type_K == GGML_TYPE_Q5_0) { + return vec_dot_fattn_vec_KQ_q5_0; + } else if constexpr (type_K == GGML_TYPE_Q5_1) { + return vec_dot_fattn_vec_KQ_q5_1; + } else if constexpr (type_K == GGML_TYPE_Q8_0) { + return vec_dot_fattn_vec_KQ_q8_0; + } else { + static_assert(type_K == -1, "bad type"); + return nullptr; + } +} + +template +constexpr dequantize_V_t get_dequantize_V() { + if constexpr (type_V == GGML_TYPE_F16) { + return dequantize_V_f16; + } else if constexpr (type_V == GGML_TYPE_Q4_0) { + return dequantize_V_q4_0; + } else if constexpr (type_V == GGML_TYPE_Q4_1) { + return dequantize_V_q4_1; + } else if constexpr (type_V == GGML_TYPE_Q5_0) { + return dequantize_V_q5_0; + } else if constexpr (type_V == GGML_TYPE_Q5_1) { + return dequantize_V_q5_1; + } else if constexpr (type_V == GGML_TYPE_Q8_0) { + return dequantize_V_q8_0; + } else { + static_assert(type_V == -1, "bad type"); + return nullptr; + } +} + +template +static void flash_attn_mask_to_KV_max(const sycl::half2 * __restrict__ mask, + int * __restrict__ KV_max, + const int ne30, + const int s31, + const int s33, + int * buf_iw) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int ne31 = item_ct1.get_group_range(2); + const int tid = item_ct1.get_local_id(2); + const int sequence = item_ct1.get_group(1); + const int jt = item_ct1.get_group(2); + + mask += sequence*s33 + jt*ncols1*s31; + + if (tid < warp_size) { + buf_iw[tid] = 1; + } + item_ct1.barrier(sycl::access::fence_space::local_space); + + int KV_max_sj = (ne30 - 1) * FATTN_KQ_STRIDE; + for (; KV_max_sj >= 0; KV_max_sj -= FATTN_KQ_STRIDE) { + int all_inf = 1; + +#pragma unroll + for (int j = 0; j < ncols1; ++j) { + const sycl::float2 tmp = + mask[j * s31 + KV_max_sj / 2 + tid].template convert(); + all_inf = all_inf && int(sycl::isinf((float) (tmp.x()))) && int(sycl::isinf((float) (tmp.y()))); + } + + all_inf = warp_reduce_all(all_inf); + if (tid % warp_size == 0) { + buf_iw[tid / warp_size] = all_inf; + } + item_ct1.barrier(sycl::access::fence_space::local_space); + all_inf = buf_iw[tid % warp_size]; + item_ct1.barrier(sycl::access::fence_space::local_space); + all_inf = warp_reduce_all(all_inf); + + if (!all_inf) { + break; + } + } + + // If the break in the loop was not triggered, KV_max_sj is now -FATTN_KQ_STRIDE. + // If the break was triggered it's the lower edge of the tile with the first non-masked values. + // In either case, walk back the decrementation by FATTN_KQ_STRIDE. + KV_max_sj += FATTN_KQ_STRIDE; + + if (item_ct1.get_local_id(2) != 0) { + return; + } + + KV_max[sequence*ne31 + jt] = KV_max_sj; +} + +template // D == head size + +static void flash_attn_stream_k_fixup(float * __restrict__ dst, + const sycl::float2 * __restrict__ dst_fixup, + const int ne01, + const int ne02, + const int ne03, + const int ne11, + const int ne12, + const int nbatch_fa) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + constexpr int ncols = ncols1 * ncols2; + + const int bidx0 = item_ct1.get_group(2); + const int j = item_ct1.get_group(1); + const int c = item_ct1.get_group(0); + const int jc = j*ncols2 + c; + const int tid = item_ct1.get_local_id(2); + + const float * dst_fixup_data = ((const float *) dst_fixup) + item_ct1.get_group_range(2) * (2 * 2 * ncols); + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + + const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; + const int iter_j = (ne01 + (ncols1 - 1)) / ncols1; + const int iter_z_gqa = (gqa_ratio + (ncols2 - 1)) / ncols2; + + const int kbc0 = int64_t(bidx0 + 0) * (iter_k * iter_j * iter_z_gqa * ne12 * ne03) / item_ct1.get_group_range(2); + const int kbc0_stop = + int64_t(bidx0 + 1) * (iter_k * iter_j * iter_z_gqa * ne12 * ne03) / item_ct1.get_group_range(2); + + const bool did_not_have_any_data = kbc0 == kbc0_stop; + const bool wrote_beginning_of_tile = kbc0 % iter_k == 0; + const bool did_not_write_last = kbc0/iter_k == kbc0_stop/iter_k && kbc0_stop % iter_k != 0; + if (did_not_have_any_data || wrote_beginning_of_tile || did_not_write_last) { + return; + } + + // z_KV == K/V head index, zt_gqa = Q head start index per K/V head, jt = token position start index + const int sequence = kbc0 /(iter_k*iter_j*iter_z_gqa*ne12); + const int z_KV = (kbc0 - iter_k*iter_j*iter_z_gqa*ne12 * sequence)/(iter_k*iter_j*iter_z_gqa); + const int zt_gqa = (kbc0 - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV)/(iter_k*iter_j); + const int jt = (kbc0 - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV - iter_k*iter_j * zt_gqa) / iter_k; + + const int zt_Q = z_KV*gqa_ratio + zt_gqa*ncols2; // Global Q head start index. + + if (jt*ncols1 + j >= ne01 || zt_gqa*ncols2 + c >= gqa_ratio) { + return; + } + + dst += sequence*ne02*ne01*D + jt*ne02*(ncols1*D) + zt_Q*D + (j*ne02 + c)*D + tid; + + // Load the partial result that needs a fixup: + float dst_val = 0.0f; + float max_val = 0.0f; + float rowsum = 0.0f; + { + dst_val = *dst; + + const sycl::float2 tmp = dst_fixup[bidx0 * ncols + jc]; + max_val = tmp.x(); + rowsum = tmp.y(); + } + + // Iterate over previous blocks and compute the combined results. + // All SYCL blocks that get here must have a previous block that needs a fixup. + int bidx = bidx0 - 1; + int kbc_stop = kbc0; + while(true) { + const int kbc = int64_t(bidx) * (iter_k * iter_j * iter_z_gqa * ne12 * ne03) / item_ct1.get_group_range(2); + if (kbc == kbc_stop) { // Did not have any data. + bidx--; + kbc_stop = kbc; + continue; + } + + const float dst_add = dst_fixup_data[bidx*ncols*D + jc*D + tid]; + + const sycl::float2 tmp = dst_fixup[(item_ct1.get_group_range(2) + bidx) * ncols + jc]; + + // Scale the current and new value accumulators depending on the max. values. + const float max_val_new = sycl::fmax(max_val, tmp.x()); + + const float diff_val = max_val - max_val_new; + const float diff_add = tmp.x() - max_val_new; + + const float scale_val = diff_val >= SOFTMAX_FTZ_THRESHOLD ? sycl::native::exp(diff_val) : 0.0f; + const float scale_add = diff_add >= SOFTMAX_FTZ_THRESHOLD ? sycl::native::exp(diff_add) : 0.0f; + + dst_val = scale_val*dst_val + scale_add*dst_add; + rowsum = scale_val * rowsum + scale_add * tmp.y(); + + max_val = max_val_new; + + // If this block started in a previous tile we are done and don't need to combine additional partial results. + if (kbc % iter_k == 0 || kbc/iter_k < kbc0/iter_k) { + break; + } + bidx--; + kbc_stop = kbc; + } + + // Write back final result: + *dst = dst_val / rowsum; +} + +template // D == head size + +static void flash_attn_combine_results(const float * __restrict__ VKQ_parts, + const sycl::float2 * __restrict__ VKQ_meta, + float * __restrict__ dst, + const int parallel_blocks, + uint8_t * dpct_local) { + // Dimension 0: threadIdx.x + // Dimension 1: blockIdx.x + // Dimension 2: blockIdx.y + // Dimension 3: blockIdx.z + // Memory layout is permuted with [0, 2, 1, 3] + + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int ne01 = item_ct1.get_group_range(2); + const int ne02 = item_ct1.get_group_range(1); + + const int col = item_ct1.get_group(2); + const int head = item_ct1.get_group(1); + const int sequence = item_ct1.get_group(0); + + const int j_dst_unrolled = (sequence*ne01 + col)*ne02 + head; + + VKQ_parts += j_dst_unrolled * parallel_blocks*D; + VKQ_meta += j_dst_unrolled * parallel_blocks; + dst += j_dst_unrolled * D; + + const int tid = item_ct1.get_local_id(2); + __builtin_assume(tid < D); + + auto meta = (sycl::float2 *) dpct_local; + for (int i = tid; i < 2*parallel_blocks; i += D) { + ((float *) meta)[i] = ((const float *)VKQ_meta) [i]; + } + + item_ct1.barrier(sycl::access::fence_space::local_space); + + float kqmax = meta[0].x(); + for (int l = 1; l < parallel_blocks; ++l) { + kqmax = sycl::max(kqmax, meta[l].x()); + } + + float VKQ_numerator = 0.0f; + float VKQ_denominator = 0.0f; + for (int l = 0; l < parallel_blocks; ++l) { + const float KQ_max_scale = sycl::native::exp(meta[l].x() - kqmax); + + VKQ_numerator += KQ_max_scale * VKQ_parts[l*D + tid]; + VKQ_denominator += KQ_max_scale * meta[l].y(); + } + + dst[tid] = VKQ_numerator / VKQ_denominator; +} + +template +static void lauch_kernel( + dpct::dim3 group_range, + dpct::dim3 local_range, + queue_ptr q, + unsigned int local_mem_size, + const char* __restrict__ Q, + const char* __restrict__ K, + const char* __restrict__ V, + const char* __restrict__ mask, + const char* __restrict__ sinks, + const int* __restrict__ KV_max, + float* __restrict__ dst, + sycl::float2* __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const float logit_softcap, + const int32_t ne00, + const sycl::uint3 ne01, + const int32_t ne02, + const int32_t ne03, + const int32_t nb01, + const int32_t nb02, + const int32_t nb03, + const int32_t ne10, + const int32_t ne11, + const int32_t ne12, + const int32_t ne13, + const int32_t nb11, + const int32_t nb12, + const int64_t nb13, + const int32_t nb21, + const int32_t nb22, + const int64_t nb23, + const int32_t ne31, + const int32_t ne32, + const int32_t ne33, + const int32_t nb31, + const int32_t nb32, + const int64_t nb33) { + GGML_UNUSED(local_mem_size); + q->submit([&](sycl::handler &cgh) { + cgh.parallel_for( + sycl::nd_range<3>( + static_cast>(group_range * local_range), + static_cast>(local_range)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(warp_size)]] { + GGML_UNUSED(item_ct1); + fattn_kernel(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, + max_bias, m0, m1, n_head_log2, logit_softcap, ne00, + ne01, ne02, ne03, nb01, nb02, nb03, ne10, ne11, + ne12, ne13, nb11, nb12, nb13, nb21, nb22, nb23, + ne31, ne32, ne33, nb31, nb32, nb33); + }); + }); +} + +template +void launch_fattn( + ggml_backend_sycl_context & ctx, ggml_tensor * dst, const int nwarps, const size_t nbytes_shared, + const int nbatch_fa, const bool need_f16_K, const bool need_f16_V, const bool stream_k) { + + constexpr int ncols = ncols1 * ncols2; + + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const bool V_is_K_view = V->view_src && (V->view_src == K || (V->view_src == K->view_src && V->view_offs == K->view_offs)); + + const ggml_tensor * mask = dst->src[3]; + const ggml_tensor * sinks = dst->src[4]; + + ggml_tensor * KQV = dst; + + GGML_ASSERT(Q->type == GGML_TYPE_F32); + GGML_ASSERT(KQV->type == GGML_TYPE_F32); + + GGML_ASSERT(Q->nb[0] == ggml_element_size(Q)); + GGML_ASSERT(K->nb[0] == ggml_element_size(K)); + GGML_ASSERT(V->nb[0] == ggml_element_size(V)); + + GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); + + ggml_sycl_pool & pool = ctx.pool(); + dpct::queue_ptr main_stream = ctx.stream(); + const int id = ggml_sycl_get_device(); + const int nsm = ggml_sycl_info().devices[id].nsm; + + ggml_sycl_pool_alloc K_f16(pool); + ggml_sycl_pool_alloc V_f16(pool); + ggml_sycl_pool_alloc KV_max(pool); + ggml_sycl_pool_alloc dst_tmp(pool); + ggml_sycl_pool_alloc dst_tmp_meta(pool); + + const char * K_data = (const char *) K->data; + size_t nb11 = K->nb[1]; + size_t nb12 = K->nb[2]; + size_t nb13 = K->nb[3]; + + const char * V_data = (const char *) V->data; + size_t nb21 = V->nb[1]; + size_t nb22 = V->nb[2]; + size_t nb23 = V->nb[3]; + + if (need_f16_K && K->type != GGML_TYPE_F16) { + const size_t bs = ggml_blck_size(K->type); + const size_t ts = ggml_type_size(K->type); + + K_f16.alloc(ggml_nelements(K)); + if (ggml_is_contiguously_allocated(K)) { + to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(K->type, dst); + to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream); + + nb11 = nb11 * bs * sizeof(sycl::half) / ts; + nb12 = nb12 * bs * sizeof(sycl::half) / ts; + nb13 = nb13 * bs * sizeof(sycl::half) / ts; + } else { + GGML_ASSERT(K->nb[0] == ts); + to_fp16_nc_sycl_t to_fp16 = ggml_get_to_fp16_nc_sycl(K->type); + const int64_t s01 = nb11 / ts; + const int64_t s02 = nb12 / ts; + const int64_t s03 = nb13 / ts; + to_fp16(K_data, K_f16.ptr, K->ne[0], K->ne[1], K->ne[2], K->ne[3], s01, s02, s03, main_stream); + + nb11 = K->ne[0] * sizeof(sycl::half); + nb12 = K->ne[1] * nb11; + nb13 = K->ne[2] * nb12; + } + K_data = (char *) K_f16.ptr; + } + + if (need_f16_V && V->type != GGML_TYPE_F16) { + if (V_is_K_view) { + V_data = K_data; + nb21 = nb11; + nb22 = nb12; + nb23 = nb13; + } else { + const size_t bs = ggml_blck_size(V->type); + const size_t ts = ggml_type_size(V->type); + + V_f16.alloc(ggml_nelements(V)); + if (ggml_is_contiguously_allocated(V)) { + to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(V->type, dst); + to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream); + V_data = (char *) V_f16.ptr; + + nb21 = nb21 * bs * sizeof(sycl::half) / ts; + nb22 = nb22 * bs * sizeof(sycl::half) / ts; + nb23 = nb23 * bs * sizeof(sycl::half) / ts; + } else { + GGML_ASSERT(V->nb[0] == ts); + to_fp16_nc_sycl_t to_fp16 = ggml_get_to_fp16_nc_sycl(V->type); + const int64_t s01 = nb21 / ts; + const int64_t s02 = nb22 / ts; + const int64_t s03 = nb23 / ts; + to_fp16(V_data, V_f16.ptr, V->ne[0], V->ne[1], V->ne[2], V->ne[3], s01, s02, s03, main_stream); + + nb21 = V->ne[0] * sizeof(sycl::half); + nb22 = V->ne[1] * nb21; + nb23 = V->ne[2] * nb22; + } + V_data = (char *) V_f16.ptr; + } + } + + const int ntiles_x = ((Q->ne[1] + ncols1 - 1) / ncols1); + const int gqa_ratio = Q->ne[2] / K->ne[2]; + const int ntiles_z_gqa = ((gqa_ratio + ncols2 - 1) / ncols2); + const int ntiles_total = ntiles_x * ntiles_z_gqa * K->ne[2] * Q->ne[3]; + + // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped. + // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or + // multiple sequences of possibly different lengths. + if (mask && K->ne[1] % FATTN_KQ_STRIDE == 0 && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) { + const int s31 = mask->nb[1] / sizeof(sycl::half2); + const int s33 = mask->nb[3] / sizeof(sycl::half2); + + const dpct::dim3 blocks_num_KV_max(ntiles_x, Q->ne[3], 1); + const dpct::dim3 block_dim_KV_max(FATTN_KQ_STRIDE / 2, 1, 1); + + const int ne_KV_max = blocks_num_KV_max.x*blocks_num_KV_max.y; + const int iter_k = K->ne[1] / FATTN_KQ_STRIDE; + + KV_max.alloc(ne_KV_max); + { + dpct::has_capability_or_fail(main_stream->get_device(), { sycl::aspect::fp16 }); + + main_stream->submit([&](sycl::handler & cgh) { + sycl::local_accessor buf_iw_acc_ct1(sycl::range<1>(warp_size), cgh); + + auto mask_data_ct0 = (const sycl::half2 *) mask->data; + auto KV_max_ptr_ct1 = KV_max.ptr; + + cgh.parallel_for(sycl::nd_range<3>(blocks_num_KV_max * block_dim_KV_max, block_dim_KV_max), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + flash_attn_mask_to_KV_max( + mask_data_ct0, KV_max_ptr_ct1, iter_k, s31, s33, + buf_iw_acc_ct1.get_multi_ptr().get()); + }); + }); + } + SYCL_CHECK(0); + } + + const dpct::dim3 block_dim(warp_size, nwarps, 1); + + // Max. number of active blocks limited by occupancy. + int max_blocks_per_sm = ggml_sycl_info().devices[id].max_wg_per_cu; + int parallel_blocks = max_blocks_per_sm; + dpct::dim3 blocks_num; + if (stream_k) { + // For short contexts it can be faster to have the SMs work on whole tiles because this lets us skip the fixup. + const int max_blocks = max_blocks_per_sm*nsm; + const int nblocks_stream_k = max_blocks; + const bool use_stream_k = true; + + blocks_num.x = use_stream_k ? nblocks_stream_k : ntiles_total; + blocks_num.y = 1; + blocks_num.z = 1; + + if (ntiles_total % blocks_num.x != 0) { // Fixup is only needed if the SMs work on fractional tiles. + dst_tmp_meta.alloc((size_t(blocks_num.x) * ncols * (2 + DV/2))); + } + } else { + const int ntiles_KQ = (K->ne[1] + nbatch_fa - 1) / nbatch_fa; // Max. number of parallel blocks limited by tensor size. + + // parallel_blocks must not be larger than what the tensor size allows: + parallel_blocks = std::min(parallel_blocks, ntiles_KQ); + // todo fix the hard code change + // parallel_blocks = ntiles_KQ; + + // If ntiles_total % blocks_per_wave != 0 then some efficiency is lost due to tail effects. + // Test whether parallel_blocks can be set to a higher value for better efficiency. + const int blocks_per_wave = nsm * max_blocks_per_sm; + int nwaves_best = 0; + int efficiency_percent_best = 0; + for (int parallel_blocks_test = parallel_blocks; parallel_blocks_test <= ntiles_KQ; ++parallel_blocks_test) { + const int nblocks_total = ntiles_total * parallel_blocks_test; + const int nwaves = (nblocks_total + blocks_per_wave - 1) / blocks_per_wave; + const int efficiency_percent = 100 * nblocks_total / (nwaves*blocks_per_wave); + + // Stop trying configurations with more waves if we already have good efficiency to avoid excessive overhead. + if (efficiency_percent_best >= 95 && nwaves > nwaves_best) { + break; + } + + if (efficiency_percent > efficiency_percent_best) { + nwaves_best = nwaves; + efficiency_percent_best = efficiency_percent; + parallel_blocks = parallel_blocks_test; + } + } + + blocks_num.x = ntiles_x; + blocks_num.y = parallel_blocks; + blocks_num.z = ntiles_z_gqa*K->ne[2]*Q->ne[3]; + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + } + + float scale = 1.0f; + float max_bias = 0.0f; + float logit_softcap = 0.0f; + + memcpy(&scale, (const float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); + + if (logit_softcap != 0.0f) { + scale /= logit_softcap; + } + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << uint32_t(floorf(log2f(float(n_head)))); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + // TODO other tensor dimensions after removal of WMMA kernel: + const sycl::uint3 ne01 = init_fastdiv_values(Q->ne[1]); + + GGML_ASSERT(block_dim.x % warp_size == 0); + + lauch_kernel( + blocks_num, block_dim, main_stream, (unsigned int) nbytes_shared, (const char *) Q->data, K_data, V_data, + mask ? ((const char *) mask->data) : nullptr, sinks ? ((const char *) sinks->data) : nullptr, KV_max.ptr, + !stream_k && parallel_blocks > 1 ? dst_tmp.ptr : (float *) KQV->data, (sycl::float2 *)dst_tmp_meta.ptr, scale, max_bias, m0, m1, + n_head_log2, logit_softcap, Q->ne[0], ne01, Q->ne[2], Q->ne[3], Q->nb[1], Q->nb[2], Q->nb[3], K->ne[0], + K->ne[1], K->ne[2], K->ne[3], nb11, nb12, nb13, nb21, nb22, nb23, mask ? mask->ne[1] : 0, + mask ? mask->ne[2] : 0, mask ? mask->ne[3] : 0, mask ? mask->nb[1] : 0, mask ? mask->nb[2] : 0, + mask ? mask->nb[3] : 0); + SYCL_CHECK(0); + + if (stream_k) { + if (ntiles_total % blocks_num.x != 0) { // Fixup is only needed if the SMs work on fractional tiles. + const dpct::dim3 block_dim_combine(DV, 1, 1); + const dpct::dim3 blocks_num_combine = { blocks_num.x, ncols1, ncols2 }; + + main_stream->submit([&](sycl::handler & cgh) { + auto KQV_data_ct0 = (float *) KQV->data; + auto dst_tmp_meta_ptr_ct1 = dst_tmp_meta.ptr; + auto Q_ne_ct2 = Q->ne[1]; + auto Q_ne_ct3 = Q->ne[2]; + auto Q_ne_ct4 = Q->ne[3]; + auto K_ne_ct5 = K->ne[1]; + auto K_ne_ct6 = K->ne[2]; + + cgh.parallel_for(sycl::nd_range<3>(blocks_num_combine * block_dim_combine, block_dim_combine), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + flash_attn_stream_k_fixup(KQV_data_ct0, dst_tmp_meta_ptr_ct1, + Q_ne_ct2, Q_ne_ct3, Q_ne_ct4, + K_ne_ct5, K_ne_ct6, nbatch_fa); + }); + }); + } + } else if (parallel_blocks > 1) { + const dpct::dim3 block_dim_combine(DV, 1, 1); + const dpct::dim3 blocks_num_combine(Q->ne[1], Q->ne[2], Q->ne[3]); + const size_t nbytes_shared_combine = parallel_blocks * sizeof(sycl::float2); + main_stream->submit([&](sycl::handler & cgh) { + sycl::local_accessor dpct_local_acc_ct1(sycl::range<1>(nbytes_shared_combine), cgh); + + auto dst_tmp_ptr_ct0 = dst_tmp.ptr; + auto dst_tmp_meta_ptr_ct1 = dst_tmp_meta.ptr; + auto KQV_data_ct2 = (float *) KQV->data; + + cgh.parallel_for(sycl::nd_range<3>(blocks_num_combine * block_dim_combine, block_dim_combine), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + flash_attn_combine_results( + dst_tmp_ptr_ct0, dst_tmp_meta_ptr_ct1, KQV_data_ct2, parallel_blocks, + dpct_local_acc_ct1.get_multi_ptr().get()); + }); + }); + } + SYCL_CHECK(0); +} diff --git a/ggml/src/ggml-sycl/fattn-tile.cpp b/ggml/src/ggml-sycl/fattn-tile.cpp new file mode 100644 index 0000000000..9d4f019cf5 --- /dev/null +++ b/ggml/src/ggml-sycl/fattn-tile.cpp @@ -0,0 +1,55 @@ +#include +#include +#include "dpct/helper.hpp" +#include "common.hpp" +#include "fattn-common.hpp" +#include "fattn-tile.hpp" +#include +#include +namespace syclex = sycl::ext::oneapi::experimental; + +void ggml_sycl_flash_attn_ext_tile(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + switch (K->ne[0]) { + case 40: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_sycl_flash_attn_ext_tile_case< 40, 40>(ctx, dst); + } break; + case 64: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_sycl_flash_attn_ext_tile_case< 64, 64>(ctx, dst); + } break; + case 72: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_sycl_flash_attn_ext_tile_case< 72, 72>(ctx, dst); + } break; + case 80: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_sycl_flash_attn_ext_tile_case< 80, 80>(ctx, dst); + } break; + case 96: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_sycl_flash_attn_ext_tile_case< 96, 96>(ctx, dst); + } break; + case 112: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_sycl_flash_attn_ext_tile_case<112, 112>(ctx, dst); + } break; + case 128: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_sycl_flash_attn_ext_tile_case<128, 128>(ctx, dst); + } break; + case 256: { + GGML_ASSERT(V->ne[0] == K->ne[0]); + ggml_sycl_flash_attn_ext_tile_case<256, 256>(ctx, dst); + } break; + case 576: { + GGML_ASSERT(V->ne[0] == 512); + ggml_sycl_flash_attn_ext_tile_case<576, 512>(ctx, dst); + } break; + default: { + GGML_ABORT("Unsupported head size"); + } break; + } +} diff --git a/ggml/src/ggml-sycl/fattn-tile.hpp b/ggml/src/ggml-sycl/fattn-tile.hpp new file mode 100644 index 0000000000..29fd0f8c9e --- /dev/null +++ b/ggml/src/ggml-sycl/fattn-tile.hpp @@ -0,0 +1,1338 @@ +#include +#include +#include "dpct/helper.hpp" +#include "common.hpp" +#include "fattn-common.hpp" + +#include +#include + +namespace syclex = sycl::ext::oneapi::experimental; + +#define GGML_SYCL_FATTN_TILE_CONFIG_CASE(DKQ_, DV_, ncols_, nthreads, occupancy, nbatch_fa, nbatch_K) \ + if (DKQ == (DKQ_) && DV == (DV_) && ncols == (ncols_)) { \ + static_assert((nthreads) <= 512, "bad nthreads"); \ + static_assert((occupancy) <= 8, "bad occupancy"); \ + static_assert((nbatch_fa) <= 256, "bad nbatch_fa"); \ + static_assert((nbatch_K) <= 256, "bad nbatch_K"); \ + return ((nthreads) << 0) | ((occupancy) << 10) | ((nbatch_fa) << 14) | ((nbatch_K) << 23); \ + } \ + +static constexpr uint32_t ggml_sycl_fattn_tile_get_config_fp16(const int DKQ, const int DV, const int ncols) { + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 64, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 64, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 64, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 64, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 64, 40) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 64, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 128, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 256, 2, 64, 64) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 2, 64, 2, 64, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 4, 128, 2, 64, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 8, 256, 2, 64, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 16, 256, 2, 64, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 32, 256, 2, 64, 72) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 64, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 64, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 64, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 64, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 64, 40) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 64, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 64, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 64, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 64, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 64, 48) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 64, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 64, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 64, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 64, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 64, 56) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 2, 64, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 8, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2, 64, 64) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 2, 64, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 4, 128, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 8, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2, 64, 64) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 4, 128, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 8, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2, 64, 64) + + return 0; +} + +static constexpr uint32_t ggml_sycl_fattn_tile_get_config_fp32(const int DKQ, const int DV, const int ncols) { + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 32, 40) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 128, 3, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 128, 3, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 128, 3, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 128, 3, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 256, 2, 64, 64) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 2, 64, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 4, 128, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 8, 256, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 16, 256, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 32, 256, 2, 32, 72) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 32, 40) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 32, 48) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 32, 56) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 2, 128, 3, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 3, 32, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 8, 128, 3, 64, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 16, 128, 3, 32, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2, 64, 64) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 2, 128, 3, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 4, 128, 3, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 8, 256, 2, 32, 256) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2, 32, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2, 32, 64) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 4, 128, 2, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 8, 256, 2, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2, 32, 64) + + return 0; +} + +static constexpr uint32_t ggml_sycl_fattn_tile_get_config_amd(const int DKQ, const int DV, const int ncols) { + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 64, 256, 2, 32, 40) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 64, 3, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 128, 3, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 128, 2, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 256, 2, 128, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 64, 256, 2, 64, 64) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 2, 64, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 4, 128, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 8, 256, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 16, 256, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 32, 256, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 64, 256, 2, 32, 72) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 64, 256, 2, 32, 40) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 64, 256, 2, 32, 48) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 64, 256, 2, 32, 56) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 2, 256, 2, 128, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 2, 64, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 8, 256, 2, 64, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 2, 64, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 64, 256, 2, 64, 32) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 2, 256, 2, 128, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 4, 256, 2, 64, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 8, 256, 2, 64, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 2, 32, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 2, 32, 128) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 4, 128, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 8, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 32, 512, 1, 128, 64) + + return 0; +} + +static constexpr uint32_t ggml_sycl_fattn_tile_get_config_amd_rdna(const int DKQ, const int DV, const int ncols) { + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 2, 64, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 4, 128, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 8, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 16, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 32, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 40, 40, 64, 256, 2, 32, 40) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 2, 64, 8, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 4, 64, 8, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 8, 128, 5, 128, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 16, 128, 5, 128, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 32, 128, 4, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 64, 64, 64, 128, 5, 64, 64) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 2, 64, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 4, 128, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 8, 256, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 16, 256, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 32, 256, 2, 32, 72) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 72, 72, 64, 256, 2, 32, 72) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 2, 64, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 4, 128, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 8, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 16, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 32, 256, 2, 32, 40) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 80, 80, 64, 256, 2, 32, 40) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 2, 64, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 4, 128, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 8, 256, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 16, 256, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 32, 256, 2, 32, 48) + GGML_SYCL_FATTN_TILE_CONFIG_CASE( 96, 96, 64, 256, 2, 32, 48) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 2, 64, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 4, 128, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 8, 256, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 16, 256, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 32, 256, 2, 32, 56) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(112, 112, 64, 256, 2, 32, 56) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 2, 64, 8, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 4, 128, 8, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 8, 128, 8, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 16, 256, 3, 128, 128) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 32, 256, 3, 128, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(128, 128, 64, 256, 3, 64, 64) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 2, 64, 8, 32, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 4, 128, 6, 32, 256) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 8, 128, 6, 32, 256) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 16, 256, 5, 32, 256) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(256, 256, 32, 256, 3, 64, 128) + + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 4, 128, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 8, 256, 2, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 16, 256, 4, 64, 64) + GGML_SYCL_FATTN_TILE_CONFIG_CASE(576, 512, 32, 256, 2, 128, 64) + + return 0; +} + +static constexpr uint32_t ggml_sycl_fattn_tile_get_config(const int DKQ, const int DV, const int ncols, const int cc) { + if(fast_fp16_available(cc)) + return ggml_sycl_fattn_tile_get_config_fp16(DKQ, DV, ncols); + else + return ggml_sycl_fattn_tile_get_config_fp32(DKQ, DV, ncols); +} + +static constexpr uint32_t ggml_sycl_fattn_tile_get_config(const int DKQ, const int DV, const int ncols) { +#ifdef SYCL_FAST_FP16 + return ggml_sycl_fattn_tile_get_config_fp16(DKQ, DV, ncols); +#else + return ggml_sycl_fattn_tile_get_config_fp32(DKQ, DV, ncols); +#endif // SYCL_FAST_FP16 +} + +static int ggml_sycl_fattn_tile_get_nthreads(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_sycl_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 0) & ((1 << 10) - 1); +} + +static constexpr int ggml_sycl_fattn_tile_get_nthreads(const int DKQ, const int DV, const int ncols) { + return (ggml_sycl_fattn_tile_get_config(DKQ, DV, ncols) >> 0) & ((1 << 10) - 1); +} + +static int ggml_sycl_fattn_tile_get_occupancy(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_sycl_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 10) & ((1 << 4) - 1); +} + +static constexpr int ggml_sycl_fattn_tile_get_occupancy(const int DKQ, const int DV, const int ncols) { + return (ggml_sycl_fattn_tile_get_config(DKQ, DV, ncols) >> 10) & ((1 << 4) - 1); +} + +static int ggml_sycl_fattn_tile_get_nbatch_fa(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_sycl_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 14) & ((1 << 9) - 1); +} + +static constexpr int ggml_sycl_fattn_tile_get_nbatch_fa(const int DKQ, const int DV, const int ncols) { + return (ggml_sycl_fattn_tile_get_config(DKQ, DV, ncols) >> 14) & ((1 << 9) - 1); +} + +static int ggml_sycl_fattn_tile_get_nbatch_K(const int DKQ, const int DV, const int ncols, const int cc) { + return (ggml_sycl_fattn_tile_get_config(DKQ, DV, ncols, cc) >> 23) & ((1 << 9) - 1); +} + +static constexpr int ggml_sycl_fattn_tile_get_nbatch_K(const int DKQ, const int DV, const int ncols) { + return (ggml_sycl_fattn_tile_get_config(DKQ, DV, ncols) >> 23) & ((1 << 9) - 1); +} + +template +static __dpct_inline__ void flash_attn_tile_load_tile(const sycl::half2 * const __restrict__ KV, + sycl::half2 * const __restrict__ tile_KV, + const int stride_KV, + const int i_sup) { + constexpr int cpy_nb = ggml_sycl_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + auto load = [&] (const int n) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int stride_j = warp_size >> n; + + if (stride_j == 0) { + return; + } + + const int j0_start = stride_j == warp_size ? 0 : ((J/2)/cpy_ne) - ((J/2)/cpy_ne) % (2*stride_j); + const int j0_stop = ((J/2)/cpy_ne) - ((J/2)/cpy_ne) % (1*stride_j); + const int stride_i = warp_size / stride_j; + + if (j0_start == j0_stop) { + return; + } + +#pragma unroll + for (int i0 = 0; i0 < I; i0 += nwarps*stride_i) { + const int i = i0 + item_ct1.get_local_id(1) * stride_i + + (stride_j == warp_size ? 0 : item_ct1.get_local_id(2) / stride_j); + + if (i0 + nwarps*stride_i <= I || i < I) { +#pragma unroll + for (int j0 = j0_start; j0 < j0_stop; j0 += stride_j) { + const int j = j0 * cpy_ne + (stride_j == warp_size ? item_ct1.get_local_id(2) : + item_ct1.get_local_id(2) % stride_j) * + cpy_ne; + + const __dpct_align__(16) sycl::half2 zero[cpy_ne] = { + { 0.0f, 0.0f } + }; + ggml_sycl_memcpy_1( + tile_KV + i*(J/2 + J_padding) + j, + !oob_check || i < i_sup ? KV + i*stride_KV + j : zero); + } + } + } + }; + // 1: max 64*16=512 bytes, 512 half + // 2: max 32*16=512 bytes, 256 half + // 3: max 16*16=256 bytes, 128 half + // 4: max 8*16=128 bytes, 64 half + // 5: max 4*16= 64 bytes, 32 half + // 6: max 2*16= 32 bytes, 16 half + // 7: max 1*16= 16 bytes, 8 half + static_assert(J % 8 == 0, "bad J"); + static_assert((J/2) % cpy_ne == 0, "bad J"); + ggml_sycl_unroll<7>{}(load); +} + +template +static __dpct_inline__ void flash_attn_tile_load_tile(const sycl::half2 * const __restrict__ KV, + float * const __restrict__ tile_KV, + const int stride_KV, + const int i_sup) { + constexpr int cpy_nb = ggml_sycl_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + auto load = [&] (const int n) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int stride_j = warp_size >> n; + + if (stride_j == 0) { + return; + } + + const int j0_start = stride_j == warp_size ? 0 : (J/cpy_ne) - (J/cpy_ne) % (2*stride_j); + const int j0_stop = (J/cpy_ne) - (J/cpy_ne) % (1*stride_j); + const int stride_i = warp_size / stride_j; + + if (j0_start == j0_stop) { + return; + } + +#pragma unroll + for (int i0 = 0; i0 < I; i0 += nwarps*stride_i) { + const int i = i0 + item_ct1.get_local_id(1) * stride_i + + (stride_j == warp_size ? 0 : item_ct1.get_local_id(2) / stride_j); + + if (i0 + nwarps*stride_i <= I || i < I) { +#pragma unroll + for (int j0 = j0_start; j0 < j0_stop; j0 += stride_j) { + const int j = j0 * (cpy_ne / 2) + (stride_j == warp_size ? item_ct1.get_local_id(2) : + item_ct1.get_local_id(2) % stride_j) * + (cpy_ne / 2); + + const sycl::half2 zero[cpy_ne / 2] = { + { 0.0f, 0.0f } + }; + __dpct_align__(16) sycl::half2 tmp_h2[cpy_ne / 2]; + ggml_sycl_memcpy_1( + tmp_h2, !oob_check || i < i_sup ? KV + i*stride_KV + j : zero); + + __dpct_align__(16) sycl::float2 tmp_f2[cpy_ne / 2]; +#pragma unroll + for (int l = 0; l < cpy_ne/2; ++l) { + tmp_f2[l] = tmp_h2[l].template convert(); + } + ggml_sycl_memcpy_1(tile_KV + i*(J + J_padding) + 2*j, tmp_f2); + } + } + } + }; + // 1: max 32*16=512 bytes, 128 float + // 2: max 16*16=256 bytes, 64 float + // 3: max 8*16=128 bytes, 32 float + // 4: max 4*16= 64 bytes, 16 float + // 5: max 2*16= 32 bytes, 8 float + static_assert(J % 8 == 0, "bad J"); + static_assert(J % cpy_ne == 0, "bad J"); + ggml_sycl_unroll<5>{}(load); +} + +// Function that performs a single iteration in for the KQ matrix multiplication: +template +static __dpct_inline__ void flash_attn_tile_iter_KQ(T_vec_dot * const Q_tmp, + const sycl::half2 * const __restrict__ K_h2, + T_vec_dot * const KV_tmp, + const int stride_K2, + const int k_VKQ_0, + const int k_VKQ_sup, + const int k_KQ_0, + float * KQ_acc) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + constexpr int cpy_nb = ggml_sycl_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int ncols = ncols1*ncols2; + constexpr int cpw = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp + constexpr int np = nwarps > ncols ? nwarps/ncols : 1; // number of parallel warps per Q column + + flash_attn_tile_load_tile + (K_h2 + int64_t(k_VKQ_0)*stride_K2 + k_KQ_0/2, KV_tmp, stride_K2, k_VKQ_sup); + item_ct1.barrier(); + +#ifdef SYCL_FAST_FP16 + static_assert((nbatch_K/2) % cpy_ne == 0, "bad nbatch_K"); +#pragma unroll + for (int k_KQ_1 = 0; k_KQ_1 < nbatch_K/2; k_KQ_1 += cpy_ne) { + __dpct_align__(16) sycl::half2 K_k[nbatch_fa / (np * warp_size)][cpy_ne]; + __dpct_align__(16) sycl::half2 Q_k[cpw][cpy_ne]; +#else + static_assert(nbatch_K % cpy_ne == 0, "bad nbatch_K"); +#pragma unroll + for (int k_KQ_1 = 0; k_KQ_1 < nbatch_K; k_KQ_1 += cpy_ne) { + __dpct_align__(16) float K_k[nbatch_fa/(np*warp_size)][cpy_ne]; + __dpct_align__(16) float Q_k[cpw][cpy_ne]; +#endif // SYCL_FAST_FP16 + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) { + const int i_KQ = i_KQ_0 + (item_ct1.get_local_id(1) % np) * warp_size + item_ct1.get_local_id(2); + +#ifdef SYCL_FAST_FP16 + ggml_sycl_memcpy_1(&K_k[i_KQ_0/(np*warp_size)], &KV_tmp[i_KQ*(nbatch_K/2 + cpy_ne) + k_KQ_1]); +#else + ggml_sycl_memcpy_1(&K_k[i_KQ_0/(np*warp_size)], &KV_tmp[i_KQ*(nbatch_K + cpy_ne) + k_KQ_1]); +#endif // SYCL_FAST_FP16 + } +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (item_ct1.get_local_id(1) / np) * cpw; + +#ifdef SYCL_FAST_FP16 + ggml_sycl_memcpy_1(&Q_k[jc0], &Q_tmp[jc*(DKQ/2) + k_KQ_0/2 + k_KQ_1]); +#else + ggml_sycl_memcpy_1(&Q_k[jc0], &Q_tmp[jc* DKQ + k_KQ_0 + k_KQ_1]); +#endif // SYCL_FAST_FP16 + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) { +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { +#pragma unroll + for (int k = 0; k < cpy_ne; ++k) { + ggml_sycl_mad(KQ_acc[i_KQ_0/(np*warp_size)*cpw + jc0], K_k[i_KQ_0/(np*warp_size)][k], Q_k[jc0][k]); + } + } + } + } + + if (k_KQ_0 + nbatch_K < DKQ) { + item_ct1.barrier(); // Sync not needed on last iteration. + } +} + +// Function that performs a single iteration of the main loop over up to nbatch_fa tokens. +template +/* +The total declared local variable size in device function flash_attn_tile_iter exceeds 128 bytes and may cause high register pressure. Consult with your hardware vendor to find the total register size available and adjust the code, or use smaller sub-group size to avoid high register pressure. +*/ +static __dpct_inline__ void flash_attn_tile_iter(T_vec_dot * const Q_tmp, + const sycl::half2 * const __restrict__ K_h2, + const sycl::half2 * const __restrict__ V_h2, + const sycl::half * const __restrict__ mask, + const sycl::uint3 ne01, + const float logit_softcap, + const float slope, + T_KQ * const KQ, + T_vec_dot * const KV_tmp, + const int stride_K2, + const int stride_V2, + const int stride_mask, + float * const KQ_max, + float * const KQ_sum, + T_acc * const VKQ, + const int k_VKQ_0, + const int k_VKQ_max, + const int col_Q_0, + float * KQ_max_new_shared) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + constexpr int cpy_nb = ggml_sycl_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int ncols = ncols1*ncols2; + constexpr int cpw = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp + constexpr int np = nwarps > ncols ? nwarps/ncols : 1; // number of parallel warps per Q column + + constexpr int DVp = (DV + 2*warp_size - 1) & ~(2*warp_size - 1); // DV padded to multiple of 2*warp_size. + +#ifdef SYCL_FAST_FP16 + constexpr int KQ_cs = cpw < 2*cpy_ne ? cpw : 2*cpy_ne; +#else + constexpr int KQ_cs = cpw < 1*cpy_ne ? cpw : 1*cpy_ne; +#endif // SYCL_FAST_FP16 + static_assert(cpw % KQ_cs == 0, "bad KQ_cs"); + const int k_VKQ_sup = k_VKQ_max - k_VKQ_0; // k supremum, only smaller k values have valid KV data + + float KQ_max_new[cpw]; +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + KQ_max_new[jc0] = KQ_max[jc0]; + } + + float KQ_acc[nbatch_fa/(np*warp_size) * cpw] = {0.0f}; // Accumulators for KQ matrix multiplication. + + // KQ = K @ Q matrix multiplication: + constexpr int nbatch_K_last = DKQ % nbatch_K; +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < DKQ - nbatch_K_last; k_KQ_0 += nbatch_K) { + flash_attn_tile_iter_KQ( + Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, KQ_acc); + } + if (nbatch_K_last > 0) { + constexpr int k_KQ_0 = DKQ - nbatch_K_last; + flash_attn_tile_iter_KQ( + Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, KQ_acc); + } + + // Apply logit softcap + mask, update KQ_max: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int j = fastmodulo(col_Q_0 + (jc0 + (item_ct1.get_local_id(1) / np) * cpw) / ncols2, ne01); + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) { + const int i_KQ = i_KQ_0 + (item_ct1.get_local_id(1) % np) * warp_size + item_ct1.get_local_id(2); + +#if defined(SYCL_FAST_FP16) && !defined(GGML_SYCL_F16) + // Without the v_dot2_f32_f16 instruction there is a higher risk of numerical overflow in the KQ calculation. + // Therefore, scale down Q values and apply the inverse scale the FP32 KQ values afterwards again. + KQ_acc[i_KQ_0/(np*warp_size)*cpw + jc0] *= 4.0f; +#endif // defined(SYCL_FAST_FP16) && !defined(GGML_SYCL_F16) + + if (use_logit_softcap) { + KQ_acc[(i_KQ_0 / (np * warp_size)) * cpw + jc0] = + logit_softcap * sycl::tanh((float) KQ_acc[(i_KQ_0 / (np * warp_size)) * cpw + jc0]); + } + + if (!oob_check || i_KQ < k_VKQ_sup) { + KQ_acc[(i_KQ_0 / (np * warp_size)) * cpw + jc0] += + (ncols2 > 1 || mask) ? slope * sycl::vec(mask[j * stride_mask + k_VKQ_0 + i_KQ]) + .convert()[0] : + 0.0f; + + KQ_max_new[jc0] = + sycl::fmax((float) KQ_max_new[jc0], + (float) (KQ_acc[(i_KQ_0 / (np * warp_size)) * cpw + jc0] + FATTN_KQ_MAX_OFFSET)); + } + } + + KQ_max_new[jc0] = warp_reduce_max(KQ_max_new[jc0]); + } + + if constexpr (np == 1) { + item_ct1.barrier(); + } else { + static_assert(cpw == 1, "bad cpw"); + + if (item_ct1.get_local_id(2) == 0) { + KQ_max_new_shared[item_ct1.get_local_id(1)] = KQ_max_new[0]; + } + item_ct1.barrier(); + KQ_max_new[0] = KQ_max_new_shared[(item_ct1.get_local_id(1) & ~(np - 1)) + item_ct1.get_local_id(2) % np]; + KQ_max_new[0] = warp_reduce_max(KQ_max_new[0]); + } + + // Calculate KQ softmax, write to shared KQ buffer, re-scale VKQ accumulators: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; jc0 += KQ_cs) { +#ifdef SYCL_FAST_FP16 + __dpct_align__(16) sycl::half tmp[nbatch_fa / (np * warp_size)][KQ_cs]; +#else + __dpct_align__(16) float tmp[nbatch_fa/(np*warp_size)][KQ_cs]; +#endif // SYCL_FAST_FP16 + +#pragma unroll + for (int jc1 = 0; jc1 < KQ_cs; ++jc1) { + const int jc = jc0 + jc1; + + const float KQ_max_scale = sycl::native::exp((float) (KQ_max[jc] - KQ_max_new[jc])); + KQ_max[jc] = KQ_max_new[jc]; + + float KQ_sum_add = 0.0f; +#pragma unroll + for (int i0 = 0; i0 < nbatch_fa; i0 += np*warp_size) { + const float val = + !oob_check || i0 + (item_ct1.get_local_id(1) % np) * warp_size + item_ct1.get_local_id(2) < + static_cast(k_VKQ_sup) ? + sycl::native::exp((float) (KQ_acc[(i0 / (np * warp_size)) * cpw + jc] - KQ_max[jc])) : + 0.0f; + KQ_sum_add += val; + tmp[i0/(np*warp_size)][jc1] = val; + } + KQ_sum[jc] = KQ_sum[jc]*KQ_max_scale + KQ_sum_add; + +#ifdef SYCL_FAST_FP16 + const sycl::half2 KQ_max_scale_h2 = sycl::half2(KQ_max_scale, KQ_max_scale); +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size].x() *= KQ_max_scale_h2.x(); + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size].y() *= KQ_max_scale_h2.y(); + } +#else +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size].x() *= KQ_max_scale; + VKQ[jc*((DVp/2)/warp_size) + i0/warp_size].y() *= KQ_max_scale; + } +#endif // SYCL_FAST_FP16 + } + +#pragma unroll + for (int i0 = 0; i0 < nbatch_fa; i0 += np*warp_size) { + const int i = i0 + (item_ct1.get_local_id(1) % np) * warp_size + item_ct1.get_local_id(2); + + ggml_sycl_memcpy_1( + KQ + (jc0 / KQ_cs + (item_ct1.get_local_id(1) / np) * (cpw / KQ_cs)) * (nbatch_fa * KQ_cs) + i * KQ_cs, + tmp[i0 / (np * warp_size)]); + } + } + + // VKQ = V @ KQ matrix multiplication: + static_assert(DV <= DKQ, "bad DV"); + static_assert(DV % nbatch_K == 0 || (nbatch_K % 3 == 0 && DV % (nbatch_K*2/3) == 0), "bad nbatch_K"); + constexpr int nbatch_V = (DV % nbatch_K == 0 ? nbatch_K : nbatch_K*2/3) * nbatch_fa / DV; // Number of V columns that fit in SRAM for K. + static_assert(nbatch_fa % nbatch_V == 0, "bad nbatch_V"); + static_assert(nbatch_V % np == 0, "bad nbatch_V"); +#pragma unroll + for (int k0 = 0; k0 < nbatch_fa; k0 += nbatch_V) { + flash_attn_tile_load_tile + (V_h2 + int64_t(k_VKQ_0 + k0)*stride_V2, KV_tmp, stride_V2, k_VKQ_sup - k0); + item_ct1.barrier(); + +#ifdef SYCL_FAST_FP16 +#pragma unroll + for (int k1 = 0; k1 < nbatch_V; k1 += np) { + __dpct_align__(16) sycl::half2 V_k[(DVp / 2) / warp_size]; + __dpct_align__(16) sycl::half2 KQ_k[cpw]; + + constexpr int cpy_ne_D = cpy_ne/2 < (DVp/2)/warp_size ? cpy_ne/2 : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + ggml_sycl_memcpy_1(&V_k[i0 / warp_size], + &KV_tmp[(k1 + item_ct1.get_local_id(1) % np) * (DV / 2) + i0 + + item_ct1.get_local_id(2) * cpy_ne_D]); + } +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; jc_VKQ_0 += KQ_cs) { + const int jc_KQ = jc_VKQ_0 / KQ_cs + (item_ct1.get_local_id(1) / np) * (cpw / KQ_cs); + + __dpct_align__(16) sycl::half tmp[KQ_cs]; + ggml_sycl_memcpy_1( + &tmp, KQ + jc_KQ * (nbatch_fa * KQ_cs) + (k0 + k1 + item_ct1.get_local_id(1) % np) * KQ_cs); +#pragma unroll + for (int jc_VKQ_1 = 0; jc_VKQ_1 < KQ_cs; ++jc_VKQ_1) { + KQ_k[jc_VKQ_0 + jc_VKQ_1] = sycl::half2(tmp[jc_VKQ_1]); + } + } + +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; ++jc_VKQ_0) { + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size].x() += + V_k[i0/warp_size].x()*KQ_k[jc_VKQ_0].x(); + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size].y() += + V_k[i0/warp_size].y()*KQ_k[jc_VKQ_0].y(); + } + } + } +#else +#pragma unroll + for (int k1 = 0; k1 < nbatch_V; k1 += np) { + __dpct_align__(16) sycl::float2 V_k[(DVp/2)/warp_size]; + __dpct_align__(16) float KQ_k[cpw]; + + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + ggml_sycl_memcpy_1(&V_k[i0/(2*warp_size)], &KV_tmp[(k1 + item_ct1.get_local_id(1) % np)*DV + i0 + item_ct1.get_local_id(2)*cpy_ne_D]); + } +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; jc_VKQ_0 += KQ_cs) { + const int jc_KQ = jc_VKQ_0/KQ_cs + (item_ct1.get_local_id(1) / np)*(cpw/KQ_cs); + + ggml_sycl_memcpy_1( + &KQ_k[jc_VKQ_0], KQ + jc_KQ*(nbatch_fa*KQ_cs) + (k0 + k1 + item_ct1.get_local_id(1) % np)*KQ_cs); + } + +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { +#pragma unroll + for (int jc_VKQ_0 = 0; jc_VKQ_0 < cpw; ++jc_VKQ_0) { + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size].x() += V_k[i0/warp_size].x()*KQ_k[jc_VKQ_0]; + VKQ[jc_VKQ_0*((DVp/2)/warp_size) + i0/warp_size].y() += V_k[i0/warp_size].y()*KQ_k[jc_VKQ_0]; + } + } + } +#endif // SYCL_FAST_FP16 + item_ct1.barrier(); + } +} + +template // D == head size +/* +The total declared local variable size in device function flash_attn_tile exceeds 128 bytes and may cause high register pressure. Consult with your hardware vendor to find the total register size available and adjust the code, or use smaller sub-group size to avoid high register pressure. +*/ +static void flash_attn_tile(const char * Q, + const char * K, + const char * V, + const char * mask, + const char * sinks, + const int * KV_max, + float * dst, + sycl::float2 * dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const float logit_softcap, + const int32_t ne00, + const sycl::uint3 ne01, + const int32_t ne02, + const int32_t ne03, + const int32_t nb01, + const int32_t nb02, + const int32_t nb03, + const int32_t ne10, + const int32_t ne11, + const int32_t ne12, + const int32_t ne13, + const int32_t nb11, + const int32_t nb12, + const int64_t nb13, + const int32_t nb21, + const int32_t nb22, + const int64_t nb23, + const int32_t ne31, + const int32_t ne32, + const int32_t ne33, + const int32_t nb31, + const int32_t nb32, + const int64_t nb33) { +#ifdef SYCL_FLASH_ATTN + // Skip unused kernel variants for faster compilation: + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + if ((use_logit_softcap && !(DV == 128 || DV == 256))) { + GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, + max_bias, m0, m1, n_head_log2, logit_softcap, + ne00, ne01, ne02, ne03, + nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb11, nb12, nb13, + nb21, nb22, nb23, + ne31, ne32, ne33, + nb31, nb32, nb33); + return; + } + + static_assert(ggml_sycl_fattn_tile_get_config(DKQ, DV, ncols1*ncols2) != 0, "kernel config not defined"); + + constexpr int ncols = ncols1*ncols2; + + constexpr int nwarps = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, ncols1*ncols2) / warp_size; + constexpr int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, ncols1*ncols2); + constexpr int nbatch_K = ggml_sycl_fattn_tile_get_nbatch_K (DKQ, DV, ncols1*ncols2); + + // In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int col_Q_0 = item_ct1.get_group(2) * ncols1; // Index of the first Q column for this SYCL block to work on. + + const int sequence = item_ct1.get_group(0) / (ne02 / ncols2); + const int head0 = item_ct1.get_group(0) * ncols2 - sequence * ne02; // == item_ct1.get_group(0) % (ne02/ncols2) + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float * Q_f = (const float *) (Q + nb03*sequence + nb02* head0); + const sycl::half2 * K_h2 = (const sycl::half2 *) (K + nb13 * sequence + nb12 * (head0 / gqa_ratio)); + const sycl::half2 * V_h2 = + (const sycl::half2 *) (V + nb23 * sequence + nb22 * (head0 / gqa_ratio)); // K and V have same shape + + const sycl::half * maskh = mask ? (const sycl::half *) (mask + nb33 * (sequence % ne33)) : nullptr; + + const int stride_K2 = nb11 / sizeof(sycl::half2); + const int stride_V2 = nb21 / sizeof(sycl::half2); + const int stride_mask = nb31 / sizeof(sycl::half); + + const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head0, n_head_log2, m0, m1) : 1.0f; + + constexpr int cpy_nb = ggml_sycl_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int cpw = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp. + constexpr int np = nwarps > ncols ? nwarps/ncols : 1; // Number of parallel warps per Q column. + + static_assert(cpw == 1 || np == 1, "bad cpw / np"); + static_assert(nbatch_fa % (np*warp_size) == 0, "nbatch_fa % (np*warp_size) != 0"); + + constexpr int DKQp = (DKQ + 2*warp_size - 1) & ~(2*warp_size - 1); // DKQ padded to multiple of 2*warp_size. + constexpr int DVp = (DV + 2*warp_size - 1) & ~(2*warp_size - 1); // DV padded to multiple of 2*warp_size. + + // Q_tmp == SRAM buffer to hold Q data for the entire lifetime of the kernel. + // KV_tmp == SRAM buffer to hold fragments of K/V data while iterating over ne11. + // KV_tmp is padded to avoid memory conflicts for K (cpy_ne) and OOB accesses for V (DVp-DV). + // KQ == SRAM buffer to hold KQ fragments between KQ and VKQ matrix multiplications. + // VKQ == Accumulators in registers for the final VKQ result. + + +#ifdef SYCL_FAST_FP16 + constexpr size_t lsm_size1 = ncols * DKQ/2 ; + constexpr size_t lsm_size2 = nbatch_fa * (nbatch_K/2 + cpy_ne) + DVp-DV ; + constexpr size_t lsm_size3 = ncols * nbatch_fa; + constexpr size_t lsm_size4 = nwarps; + + constexpr size_t local_share_mem_size = lsm_size1 * sizeof(sycl::half2) + + lsm_size2 * sizeof(sycl::half2) + + lsm_size3 * sizeof(sycl::half) + + lsm_size4 * sizeof(float); + + syclex::work_group_static lsm; + + sycl::half2 *Q_tmp = (sycl::half2 *)&lsm; + sycl::half2 *KV_tmp = (sycl::half2*)(Q_tmp +lsm_size1); + sycl::half *KQ = (sycl::half *)(KV_tmp+lsm_size2); + float *KQ_max_new_shared = (float *)(KQ+lsm_size3); + + __dpct_align__(16) sycl::half2 VKQ[cpw * ((DVp / 2) / warp_size)] = { + { 0.0f, 0.0f } + }; +#else + constexpr size_t lsm_size1 = ncols * DKQ ; + constexpr size_t lsm_size2 = nbatch_fa * (nbatch_K + cpy_ne) + DVp-DV; + constexpr size_t lsm_size3 = ncols * nbatch_fa; + constexpr size_t lsm_size4 = nwarps; + + constexpr size_t local_share_mem_size = (lsm_size1 + lsm_size2 +lsm_size3 + lsm_size4) * sizeof(float); + + syclex::work_group_static lsm; + + float *Q_tmp = (float *)&lsm; + float *KV_tmp = Q_tmp +lsm_size1; + float *KQ = KV_tmp+lsm_size2; + float *KQ_max_new_shared = KQ+lsm_size3; + + __dpct_align__(16) sycl::float2 VKQ[cpw * ((DVp/2)/warp_size)] = {{0.0f, 0.0f}}; + + +#endif // SYCL_FAST_FP16 + + float KQ_max[cpw] = {}; + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + KQ_max[j0/nwarps] = -FLT_MAX/2.0f; + } + float KQ_sum[cpw] = {0.0f}; + + // Load Q data, convert to FP16 if fast: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (item_ct1.get_local_id(1) / np) * cpw; + + const int j = jc / ncols2; + const int c = jc % ncols2; + + constexpr int cpy_ne_D = cpy_ne < DKQp/warp_size ? cpy_ne : DKQp/warp_size; + +#pragma unroll + for (int i0 = 0; i0 < DKQp; i0 += np*warp_size*cpy_ne_D) { + if (i0 + np * warp_size * cpy_ne_D <= DKQ || + i0 + (item_ct1.get_local_id(1) % np) * (warp_size * cpy_ne_D) + item_ct1.get_local_id(2) * cpy_ne_D < + DKQ) { + __dpct_align__(16) float tmp_f[cpy_ne_D] = { 0.0f }; + ggml_sycl_memcpy_1( + tmp_f, &Q_f[c * (nb02 / sizeof(float)) + fastmodulo(col_Q_0 + j, ne01) * (nb01 / sizeof(float)) + + i0 + (item_ct1.get_local_id(1) % np) * (warp_size * cpy_ne_D) + + item_ct1.get_local_id(2) * cpy_ne_D]); + +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + tmp_f[i1] *= scale; + } + +#ifdef SYCL_FAST_FP16 + __dpct_align__(16) sycl::half2 tmp_h2[cpy_ne_D / 2]; +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; i1 += 2) { + tmp_h2[i1/2] = make_half2(tmp_f[i1 + 0], tmp_f[i1 + 1]); +#if defined(SYCL_FAST_FP16) && !defined(GGML_SYCL_F16) + // Without the v_dot2_f32_f16 instruction there is a higher risk of numerical overflow in the KQ calculation. + // Therefore, scale down Q values and apply the inverse scale the FP32 KQ values afterwards again. + tmp_h2[i1 / 2] *= sycl::half2(0.25f, 0.25f); +#endif // defined(SYCL_FAST_FP16) && !defined(GGML_SYCL_F16) + } + ggml_sycl_memcpy_1( + &Q_tmp[jc * (DKQ / 2) + i0 / 2 + (item_ct1.get_local_id(1) % np) * (warp_size * cpy_ne_D / 2) + + item_ct1.get_local_id(2) * (cpy_ne_D / 2)], + tmp_h2); +#else + ggml_sycl_memcpy_1( + &Q_tmp[jc* DKQ + i0 + (item_ct1.get_local_id(1) % np)*(warp_size*cpy_ne_D) + item_ct1.get_local_id(2)* cpy_ne_D], + tmp_f); +#endif // SYCL_FAST_FP16 + } + } + } + + item_ct1.barrier(); + + // Main loop over KV cache: + const int k_VKQ_max = KV_max ? KV_max[sequence * item_ct1.get_group_range(2) + item_ct1.get_group(2)] : ne11; + if (ncols2 == 1) { + // Branch with out-of-bounds checks. + int k_VKQ_0 = item_ct1.get_group(1) * nbatch_fa; + while (k_VKQ_0 < k_VKQ_max - nbatch_fa) { + constexpr bool oob_check = false; + flash_attn_tile_iter(Q_tmp, K_h2, V_h2, maskh, ne01, logit_softcap, slope, KQ, KV_tmp, stride_K2, + stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max, col_Q_0, + KQ_max_new_shared); + k_VKQ_0 += item_ct1.get_group_range(1) * nbatch_fa; + } + if (k_VKQ_0 < k_VKQ_max) { + constexpr bool oob_check = true; + flash_attn_tile_iter(Q_tmp, K_h2, V_h2, maskh, ne01, logit_softcap, slope, KQ, KV_tmp, stride_K2, + stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max, col_Q_0, + KQ_max_new_shared); + } + } else { + // Branch without out-of-bounds checks. + for (int k_VKQ_0 = item_ct1.get_group(1) * nbatch_fa; k_VKQ_0 < k_VKQ_max; + k_VKQ_0 += item_ct1.get_group_range(1) * nbatch_fa) { + + constexpr bool oob_check = false; + flash_attn_tile_iter(Q_tmp, K_h2, V_h2, maskh, ne01, logit_softcap, slope, KQ, KV_tmp, stride_K2, + stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max, col_Q_0, + KQ_max_new_shared); + } + } + +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + KQ_sum[jc0] = warp_reduce_sum(KQ_sum[jc0]); + } + + if constexpr (np > 1) { + static_assert(cpw == 1, "bad cpw"); + static_assert(nbatch_fa*nbatch_K >= nwarps*DVp, "KV_tmp too small"); + +#ifdef SYCL_FAST_FP16 + sycl::half2 * VKQ_combine = (sycl::half2 *) KV_tmp; +#else + float * VKQ_combine = (float *) KV_tmp; +#endif // SYCL_FAST_FP16 + + float * KQ_sum_combine = (float *) Q_tmp; + + if (item_ct1.get_local_id(1) % np != 0) { + +#ifdef SYCL_FAST_FP16 + constexpr int cpy_ne_D = cpy_ne < (DVp/2)/warp_size ? cpy_ne : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + ggml_sycl_memcpy_1( + &VKQ_combine[item_ct1.get_local_id(1) * (DVp / 2) + i0 + item_ct1.get_local_id(2) * cpy_ne_D], + &VKQ[i0 / warp_size]); + } +#else + + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; + +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + ggml_sycl_memcpy_1( + &VKQ_combine[item_ct1.get_local_id(1)*DVp + i0 + item_ct1.get_local_id(2)*cpy_ne_D], ((const float *) VKQ) + i0/warp_size); + } +#endif // SYCL_FAST_FP16 + + if (item_ct1.get_local_id(2) == 0) { + KQ_sum_combine[item_ct1.get_local_id(1)] = KQ_sum[0]; + } + return; + } + + item_ct1.barrier(); + +#pragma unroll + for (int ip = 1; ip < np; ++ip) { +#ifdef SYCL_FAST_FP16 + constexpr int cpy_ne_D = cpy_ne < (DVp/2)/warp_size ? cpy_ne : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + __dpct_align__(16) sycl::half2 tmp[cpy_ne_D]; + ggml_sycl_memcpy_1(tmp, &VKQ_combine[(item_ct1.get_local_id(1) + ip) * (DVp / 2) + i0 + + item_ct1.get_local_id(2) * cpy_ne_D]); +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + VKQ[i0/warp_size + i1] += tmp[i1]; + } + } +#else + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + __dpct_align__(16) float tmp[cpy_ne_D]; + ggml_sycl_memcpy_1(tmp, &VKQ_combine[(item_ct1.get_local_id(1) + ip)*DVp + i0 + item_ct1.get_local_id(2)*cpy_ne_D]); +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + ((float *)VKQ)[i0/warp_size + i1] += tmp[i1]; + } + } +#endif // SYCL_FAST_FP16 + + KQ_sum[0] += KQ_sum_combine[item_ct1.get_local_id(1) + ip]; + } + } + + // Attention sink: adjust KQ max and sum only for the first of all parallel blocks: + if (sinks && item_ct1.get_group(1) == 0) { +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (item_ct1.get_local_id(1) / np) * cpw; + const float sink = ((const float *) sinks)[head0 + jc % ncols2]; + + float KQ_max_new_j = sycl::fmax((float) KQ_max[jc0], sink); + const float KQ_max_scale = sycl::native::exp((float) (KQ_max[jc0] - KQ_max_new_j)); + KQ_max[jc0] = KQ_max_new_j; + + const float val = sycl::native::exp((float) (sink - KQ_max[jc0])); + KQ_sum[jc0] = KQ_sum[jc0]*KQ_max_scale + val; + +#ifdef SYCL_FAST_FP16 + const sycl::half2 KQ_max_scale_h2 = sycl::half2(KQ_max_scale, KQ_max_scale); +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size] *= KQ_max_scale_h2; + } +#else +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size) { + VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size].x() *= KQ_max_scale; + VKQ[jc0*((DVp/2)/warp_size) + i0/warp_size].y() *= KQ_max_scale; + } +#endif // SYCL_FAST_FP16 + } + } + + // Write back results: +#pragma unroll + for (int jc0 = 0; jc0 < cpw; ++jc0) { + const int jc = jc0 + (item_ct1.get_local_id(1) / np) * cpw; + + const int j = jc / ncols2; + const int c = jc % ncols2; + + if (ncols1 > 1 && col_Q_0 + j >= int(ne01.z())) { + return; + } + + const float scale = item_ct1.get_group_range(1) == 1 ? 1.0f / KQ_sum[jc0] : 1.0f; + + const int j_dst_unrolled = + ((sequence * int(ne01.z()) + col_Q_0 + j) * ne02 + head0 + c) * item_ct1.get_group_range(1) + + item_ct1.get_group(1); + +#ifdef SYCL_FAST_FP16 + constexpr int cpy_ne_D = cpy_ne/2 < (DVp/2)/warp_size ? cpy_ne/2 : (DVp/2)/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp/2; i0 += warp_size*cpy_ne_D) { + __dpct_align__(16) sycl::float2 tmp[cpy_ne_D]; +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D; ++i1) { + tmp[i1] = VKQ[jc0 * ((DVp / 2) / warp_size) + i0 / warp_size + i1] + .template convert(); + tmp[i1].x() *= scale; + tmp[i1].y() *= scale; + } + if (i0 + warp_size * cpy_ne_D <= DV / 2 || i0 + item_ct1.get_local_id(2) * cpy_ne_D < DV / 2) { + ggml_sycl_memcpy_1( + &dst[j_dst_unrolled * DV + 2 * i0 + item_ct1.get_local_id(2) * (2 * cpy_ne_D)], tmp); + } + } +#else + constexpr int cpy_ne_D = cpy_ne < DVp/warp_size ? cpy_ne : DVp/warp_size; +#pragma unroll + for (int i0 = 0; i0 < DVp; i0 += warp_size*cpy_ne_D) { + if (i0 + warp_size*cpy_ne_D <= DV || i0 + item_ct1.get_local_id(2)*cpy_ne_D < DV) { +#pragma unroll + for (int i1 = 0; i1 < cpy_ne_D/2; ++i1) { + VKQ[jc0*((DVp/2)/warp_size) + i0/(2*warp_size) + i1].x() *= scale; + VKQ[jc0*((DVp/2)/warp_size) + i0/(2*warp_size) + i1].y() *= scale; + } + ggml_sycl_memcpy_1( + &dst[j_dst_unrolled*DV + i0 + item_ct1.get_local_id(2)*cpy_ne_D], + &VKQ[jc0*((DVp/2)/warp_size) + i0/(2*warp_size)]); + } + } +#endif // SYCL_FAST_FP16 + + if (item_ct1.get_group_range(1) != 1 && item_ct1.get_local_id(2) == 0) { + dst_meta[j_dst_unrolled] = make_float2(KQ_max[jc0], KQ_sum[jc0]); + } + } +#else + GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, + max_bias, m0, m1, n_head_log2, logit_softcap, + ne00, ne01, ne02, ne03, + nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb11, nb12, nb13, + nb21, nb22, nb23, + ne31, ne32, ne33, + nb31, nb32, nb33); +#endif // SYCL_FLASH_ATTN +} + +template +static void launch_fattn_tile_switch_ncols1(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + + const int id = ggml_sycl_get_device(); + const int cc = ggml_sycl_info().devices[id].cc; + const int warp_size = WARP_32_SIZE; //can't support WARP_16_SIZE + + constexpr size_t nbytes_shared = 0; + + if constexpr (DV <= 256) { + if (Q->ne[1] > 16/ncols2) { + constexpr int cols_per_block = 32; + const int nwarps = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + launch_fattn, warp_size> + (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false); + return; + } + } + + if (Q->ne[1] > 8/ncols2) { + constexpr int cols_per_block = 16; + const int nwarps = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + launch_fattn, warp_size> + (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false); + return; + } + + if constexpr (ncols2 <= 8) { + if (Q->ne[1] > 4/ncols2) { + constexpr int cols_per_block = 8; + const int nwarps = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + launch_fattn, warp_size> + (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false); + return; + } + } + + if constexpr (ncols2 <= 4) { + if (Q->ne[1] > 2/ncols2) { + constexpr int cols_per_block = 4; + const int nwarps = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + launch_fattn, warp_size> + (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false); + return; + } + } + + if constexpr (ncols2 <= 2) { + constexpr int cols_per_block = 2; + const int nwarps = ggml_sycl_fattn_tile_get_nthreads (DKQ, DV, cols_per_block, cc) / warp_size; + const int nbatch_fa = ggml_sycl_fattn_tile_get_nbatch_fa(DKQ, DV, cols_per_block, cc); + launch_fattn, warp_size> + (ctx, dst, nwarps, nbytes_shared, nbatch_fa, true, true, false); + return; + } + + GGML_ABORT("fatal error"); +} + +template +static void launch_fattn_tile_switch_ncols2(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * mask = dst->src[3]; + + float max_bias = 0.0f; + memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float)); + + GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); + const int gqa_ratio = Q->ne[2] / K->ne[2]; + + // On NVIDIA (Pascal and older) the GQA optimizations seem to be detrimental in some cases. + // However, for DKQ == 576, DV == 512 only the kernel variant with GQA optimizations is implemented. + //const bool nvidia = GGML_SYCL_CC_IS_NVIDIA(ggml_sycl_info().devices[ggml_sycl_get_device()].cc); + const int gqa_limit = gqa_ratio <= 4 && DV <= 256 ? 16 : INT_MAX; + const bool use_gqa_opt = mask && max_bias == 0.0f && Q->ne[1] <= gqa_limit && K->ne[1] % FATTN_KQ_STRIDE == 0; + + if constexpr (DV == 512) { + if (use_gqa_opt && gqa_ratio % 16 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + if (use_gqa_opt && gqa_ratio % 4 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + } + + if constexpr (DV <= 256) { + if (use_gqa_opt && gqa_ratio % 8 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + + if (use_gqa_opt && gqa_ratio % 4 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + + if (use_gqa_opt && gqa_ratio % 2 == 0) { + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + + launch_fattn_tile_switch_ncols1(ctx, dst); + return; + } + GGML_ABORT("fatal error"); +} + +template +void ggml_sycl_flash_attn_ext_tile_case(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + + float logit_softcap; + memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); + + if (logit_softcap == 0.0f) { + constexpr bool use_logit_softcap = false; + launch_fattn_tile_switch_ncols2(ctx, dst); + } else { + constexpr bool use_logit_softcap = true; + launch_fattn_tile_switch_ncols2(ctx, dst); + } +} + +void ggml_sycl_flash_attn_ext_tile(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +#define DECL_FATTN_TILE_CASE(DKQ, DV) \ + template void ggml_sycl_flash_attn_ext_tile_case \ + (ggml_backend_sycl_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_TILE_CASE( 40, 40); +extern DECL_FATTN_TILE_CASE( 64, 64); +extern DECL_FATTN_TILE_CASE( 72, 72); +extern DECL_FATTN_TILE_CASE( 80, 80); +extern DECL_FATTN_TILE_CASE( 96, 96); +extern DECL_FATTN_TILE_CASE(112, 112); +extern DECL_FATTN_TILE_CASE(128, 128); +extern DECL_FATTN_TILE_CASE(256, 256); +extern DECL_FATTN_TILE_CASE(576, 512); + diff --git a/ggml/src/ggml-sycl/fattn-vec.hpp b/ggml/src/ggml-sycl/fattn-vec.hpp new file mode 100644 index 0000000000..48c389052f --- /dev/null +++ b/ggml/src/ggml-sycl/fattn-vec.hpp @@ -0,0 +1,667 @@ +#ifndef GGML_SYCL_FATTN_VEC_HPP +#define GGML_SYCL_FATTN_VEC_HPP + +#include +#include +#include +#include + +#include "dpct/helper.hpp" +#include "common.hpp" +#include "ggml.h" +#include "fattn-common.hpp" +#include +#include + +namespace syclex = sycl::ext::oneapi::experimental; + +static int ggml_sycl_fattn_vec_get_nthreads_host(const int cc) { + return 128; + GGML_UNUSED(cc); +} + +static constexpr int ggml_sycl_fattn_vec_get_nthreads_device() { + return 128; +} + +// Currenlty llvm with the amdgcn target dose not support unrolling loops +// that contain a break that can not be resolved at compile time. +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wpass-failed" +#endif // __clang__ + +template // D == head size +static void flash_attn_ext_vec(const char* __restrict__ Q, + const char* __restrict__ K, + const char* __restrict__ V, + const char* __restrict__ mask, + const char* __restrict__ sinks, + const int* __restrict__ KV_max, + float* __restrict__ dst, + sycl::float2* __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const float logit_softcap, + const int32_t ne00, + const sycl::uint3 ne01, + const int32_t ne02, + const int32_t ne03, + const int32_t nb01, + const int32_t nb02, + const int32_t nb03, + const int32_t ne10, + const int32_t ne11, + const int32_t ne12, + const int32_t ne13, + const int32_t nb11, + const int32_t nb12, + const int64_t nb13, + const int32_t nb21, + const int32_t nb22, + const int64_t nb23, + const int32_t ne31, + const int32_t ne32, + const int32_t ne33, + const int32_t nb31, + const int32_t nb32, + const int64_t nb33) { +#ifdef SYCL_FLASH_ATTN + // Skip unused kernel variants for faster compilation: + + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + if (use_logit_softcap && !(D == 128 || D == 256)) { + GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, + max_bias, m0, m1, n_head_log2, logit_softcap, + ne00, ne01, ne02, ne03, + nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb11, nb12, nb13, + nb21, nb22, nb23, + ne31, ne32, ne33, + nb31, nb32, nb33); + return; + } + + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + constexpr int cpy_nb = ggml_sycl_get_max_cpy_bytes(); + constexpr int cpy_ne = cpy_nb / 4; + + constexpr int nthreads_KQ_q = (D/4 < warp_size ? D/4 : warp_size); + constexpr int nthreads_V_q = (D/4 < warp_size ? D/4 : warp_size); + + constexpr int nthreads = ggml_sycl_fattn_vec_get_nthreads_device(); + constexpr int nthreads_KQ = type_K == GGML_TYPE_F16 ? 128 / cpy_nb : nthreads_KQ_q; + constexpr int nthreads_V = type_V == GGML_TYPE_F16 ? 128 / cpy_nb : nthreads_V_q; + + static_assert(warp_size % nthreads_KQ == 0, "bad nthreads_K"); + static_assert(warp_size % nthreads_V == 0, "bad nthreads_V"); + + constexpr int V_rows_per_thread = type_V == GGML_TYPE_F16 ? 2*cpy_ne : 4; + constexpr int V_cols_per_iter = warp_size / nthreads_V; + + constexpr vec_dot_KQ_t vec_dot_KQ = get_vec_dot_KQ(); + constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16; +#ifdef GGML_SYCL_F16 + constexpr dequantize_V_t dequantize_V = get_dequantize_V(); +#else + constexpr dequantize_V_t dequantize_V = get_dequantize_V(); +#endif // GGML_SYCL_F16 + + const int ic0 = item_ct1.get_group(2) * ncols; // Index of the Q/QKV column to work on. + + const int sequence = item_ct1.get_group(0) / ne02; + const int head = item_ct1.get_group(0) - sequence * ne02; + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + Q += nb03*sequence + nb02* head + nb01*ic0; + K += nb13*sequence + nb12*(head / gqa_ratio); + V += nb23*sequence + nb22*(head / gqa_ratio); + + const sycl::half * maskh = (const sycl::half *) (mask + nb33 * (sequence % ne33) + nb31 * ic0); + + const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1); + + static_assert(D % (2*warp_size) == 0, "D not divisible by 2*warp_size == 64."); + constexpr int nwarps = nthreads / warp_size; + const int tid = warp_size * item_ct1.get_local_id(1) + item_ct1.get_local_id(2); + __builtin_assume(tid < nthreads); + + constexpr int ne_KQ = ncols*D; + constexpr int ne_combine = nwarps*V_cols_per_iter*D; + + constexpr size_t lsm_size1 = ncols * warp_size; + constexpr size_t lsm_size2 = ncols * warp_size; +#ifdef GGML_SYCL_F16 + sycl::half2 VKQ[ncols][(D / 2) / nthreads_V] = { { { 0.0f, 0.0f } } }; + constexpr size_t lsm_size3 = (ne_KQ > ne_combine ? ne_KQ : ne_combine); + constexpr size_t local_share_mem_size = (lsm_size1 + lsm_size2)*sizeof(float) + lsm_size3*sizeof(sycl::half); + + syclex::work_group_static lsm; + + float *KQ_max_shared = (float *)&lsm; + float *KQ_sum_shared = KQ_max_shared+lsm_size1; + sycl::half* KQ = (sycl::half*)(KQ_sum_shared + lsm_size2); + + +#else + sycl::float2 VKQ[ncols][(D/2)/nthreads_V] = {{{0.0f, 0.0f}}}; + + constexpr size_t lsm_size3 = (ne_KQ > ne_combine ? ne_KQ : ne_combine); + constexpr size_t local_share_mem_size = (lsm_size1 + lsm_size2 + lsm_size3)*sizeof(float); + + + syclex::work_group_static lsm; + float *KQ_max_shared = (float *)&lsm; + float *KQ_sum_shared = KQ_max_shared+lsm_size1; + float* KQ = KQ_sum_shared + lsm_size2; + +#endif // GGML_SYCL_F16 + + float KQ_max[ncols]; + float KQ_sum[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ_max[j] = -FLT_MAX/2.0f; + KQ_sum[j] = 0.0f; + } + + // Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers: +#ifdef GGML_SYCL_F16 + sycl::half2 Q_reg[ncols][(D / 2) / nthreads_KQ] = {{{0.0f, 0.0f}}}; // Will be initialized completely. +#else + sycl::float2 Q_reg[ncols][(D/2)/nthreads_KQ] = {{{0.0f, 0.0f}}}; // May be only partially initialized. +#endif // GGML_SYCL_F16 + int Q_i32[ncols][1 > D/(sizeof(int)*nthreads_KQ) ? 1 : D/(sizeof(int)*nthreads_KQ)]; + sycl::float2 Q_ds[ncols][1 > D / (sizeof(int) * nthreads_KQ) ? 1 : D / (sizeof(int) * nthreads_KQ)]; + if constexpr (Q_q8_1) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + item_ct1.get_local_id(1); + + if (j0 + nwarps > ncols && j >= ncols) { + break; + } + + // Reuse KQ as temporary storage for converting Q to q8_1: + int * tmp_q_i32 = (int *) &KQ[j*D]; + sycl::float2 * tmp_q_ds = (sycl::float2 *) (tmp_q_i32 + D / sizeof(int)); + + // Set memory to zero if out of bounds: + if (ncols > 1 && ic0 + j >= int(ne01.z())) { +#pragma unroll + for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += warp_size) { + const int i = i0 + item_ct1.get_local_id(2); + + if (i0 + warp_size <= int(D/sizeof(int)) || i < int(D/sizeof(int))) { + tmp_q_i32[i] = 0; + } + } + if (item_ct1.get_local_id(2) < D/QK8_1) { + tmp_q_ds[item_ct1.get_local_id(2)] = sycl::float2(0.0f, 0.0f); + } + } else { + const float * Q_f = (const float *) (Q + j*nb01); + constexpr int nthreads_quantize = D/sizeof(int) < warp_size ? D/sizeof(int) : warp_size; +#pragma unroll + for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += nthreads_quantize) { + quantize_q8_1_to_shared + (Q_f + i0*sizeof(int), scale, tmp_q_i32 + i0, tmp_q_ds + i0/QI8_1); + } + } + } + + + item_ct1.barrier(sycl::access::fence_space::local_space); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + int * tmp_q_i32 = (int *) &KQ[j*D]; + sycl::float2 * tmp_q_ds = (sycl::float2 *) (tmp_q_i32 + D / sizeof(int)); + +#pragma unroll + for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += nthreads_KQ) { + const int i = + i0 + (nthreads_KQ == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_KQ); + + Q_i32[j][i0/nthreads_KQ] = tmp_q_i32[i]; + Q_ds[j][i0/nthreads_KQ] = tmp_q_ds[i/QI8_1]; + } + } + + item_ct1.barrier(sycl::access::fence_space::local_space); + + } else { +#ifdef GGML_SYCL_F16 + const sycl::half2 scale_h2 = sycl::half2(scale, scale); +#pragma unroll + for (int j = 0; j < ncols; ++j) { + const sycl::float2 * Q_j = (const sycl::float2 *) (Q + j * nb01); +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += nthreads_KQ*cpy_ne) { + const int i = i0 + (nthreads_KQ == warp_size ? item_ct1.get_local_id(2) : + item_ct1.get_local_id(2) % nthreads_KQ) * + cpy_ne; + + sycl::float2 tmp[cpy_ne] = { + { 0.0f, 0.0f } + }; + if (ncols == 1 || ic0 + j < int(ne01.z())) { + ggml_sycl_memcpy_1(tmp, &Q_j[i]); + ggml_sycl_memcpy_1(tmp + cpy_ne/2, &Q_j[i + cpy_ne/2]); + } +#pragma unroll + for (int i1 = 0; i1 < cpy_ne; ++i1) { + Q_reg[j][i0 / nthreads_KQ + i1] = sycl::half2(tmp[i1].x(), tmp[i1].y()); + } + } +#pragma unroll + for (int k = 0; k < (D/2)/nthreads_KQ; ++k) { + Q_reg[j][k] *= scale_h2; + } + } +#else +#pragma unroll + for (int j = 0; j < ncols; ++j) { + const sycl::float2 * Q_j = (const sycl::float2 *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += nthreads_KQ*cpy_ne) { + const int i = i0 + (nthreads_KQ == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_KQ)*cpy_ne; + if (ncols == 1 || ic0 + j < int(ne01.z())) { + ggml_sycl_memcpy_1(&Q_reg[j][i0/nthreads_KQ], &Q_j[i]); + ggml_sycl_memcpy_1(&Q_reg[j][i0/nthreads_KQ + cpy_ne/2], &Q_j[i + cpy_ne/2]); + } + } +#pragma unroll + for (int k = 0; k < (D/2)/nthreads_KQ; ++k) { + Q_reg[j][k].x() *= scale; + Q_reg[j][k].y() *= scale; + } + } +#endif // GGML_SYCL_F16 + } + + const int k_VKQ_max = KV_max ? KV_max[sequence * item_ct1.get_group_range(2) + item_ct1.get_group(2)] : ne11; + K += item_ct1.get_group(1) * nthreads * nb11; + V += item_ct1.get_group(1) * nthreads * nb21; + maskh += item_ct1.get_group(1) * nthreads; + for (int k_VKQ_0 = item_ct1.get_group(1) * nthreads; k_VKQ_0 < k_VKQ_max; + k_VKQ_0 += item_ct1.get_group_range(1) * nthreads, + // Increment pointers after each loop: + K += item_ct1.get_group_range(1) * nthreads * nb11, V += item_ct1.get_group_range(1) * nthreads * nb21, + maskh += item_ct1.get_group_range(1) * nthreads) { + // Calculate KQ tile and keep track of new maximum KQ values: + float KQ_reg[ncols]={}; // KQ in registers. + float KQ_max_new[ncols]={}; + + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ_max_new[j] = KQ_max[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < nthreads_KQ; ++i_KQ_0) { + const int i_KQ = item_ct1.get_local_id(1) * warp_size + + (nthreads_KQ == warp_size ? 0 : (item_ct1.get_local_id(2) & ~(nthreads_KQ - 1))) + i_KQ_0; + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float sum = vec_dot_KQ(K + i_KQ*nb11, Q_reg[j], Q_i32[j], Q_ds[j]); + sum = warp_reduce_sum(sum); + + if (use_logit_softcap) { + sum = logit_softcap * sycl::tanh(sum); + } + if (mask) { + sum += slope * sycl::vec(maskh[j * ne11 + i_KQ]) + .convert()[0]; + } + + KQ_max_new[j] = sycl::fmax((float) KQ_max_new[j], sum); + + if (int(nthreads_KQ == warp_size ? item_ct1.get_local_id(2) + : item_ct1.get_local_id(2) % + nthreads_KQ) == i_KQ_0) { + KQ_reg[j] = sum; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { +#pragma unroll + for (int offset = nthreads_KQ; offset < warp_size; offset <<= 1) { + KQ_max_new[j] = sycl::fmax( + (float)KQ_max_new[j], + (float)dpct::permute_sub_group_by_xor( + sycl::ext::oneapi::this_work_item::get_sub_group(), + KQ_max_new[j], + offset, + warp_size)); + } + const float KQ_max_scale = sycl::native::exp((float) (KQ_max[j] - KQ_max_new[j])); + KQ_max[j] = KQ_max_new[j]; + + KQ_reg[j] = sycl::native::exp((float) (KQ_reg[j] - KQ_max[j])); + KQ_sum[j] = KQ_sum[j]*KQ_max_scale + KQ_reg[j]; + KQ[j*nthreads + tid] = KQ_reg[j]; + +#ifdef GGML_SYCL_F16 + const sycl::half2 KQ_max_scale_h2 = sycl::half2(KQ_max_scale, KQ_max_scale); +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) { + VKQ[j][i_VKQ_0/nthreads_V] *= KQ_max_scale_h2; + } +#else +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) { + VKQ[j][i_VKQ_0/nthreads_V].x() *= KQ_max_scale; + VKQ[j][i_VKQ_0/nthreads_V].y() *= KQ_max_scale; + } +#endif // GGML_SYCL_F16 + } + + sycl::group_barrier(sycl::ext::oneapi::this_work_item::get_sub_group()); + +#pragma unroll + for (int k0 = 0; k0 < warp_size; k0 += V_cols_per_iter) { + const int k = item_ct1.get_local_id(1) * warp_size + k0 + + (nthreads_V == warp_size ? 0 : item_ct1.get_local_id(2) / nthreads_V); + +#ifdef GGML_SYCL_F16 + sycl::half2 KQ_k[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ_k[j] = sycl::half2(KQ[j * nthreads + k]); + } +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V*V_rows_per_thread/2) { + sycl::half2 tmp[V_rows_per_thread / 2]; + dequantize_V(V + k * nb21, tmp, + 2 * i_VKQ_0 + (nthreads_V == warp_size ? item_ct1.get_local_id(2) : + item_ct1.get_local_id(2) % nthreads_V) * + V_rows_per_thread); +#pragma unroll + for (int i_VKQ_1 = 0; i_VKQ_1 < V_rows_per_thread/2; ++i_VKQ_1) { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j][i_VKQ_0/nthreads_V + i_VKQ_1] += tmp[i_VKQ_1]*KQ_k[j]; + } + } + } +#else + float KQ_k[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ_k[j] = KQ[j*nthreads + k]; + } +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V*V_rows_per_thread/2) { + sycl::float2 tmp[V_rows_per_thread/2]; + dequantize_V(V + k*nb21, tmp, + 2*i_VKQ_0 + (nthreads_V == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_V)*V_rows_per_thread); +#pragma unroll + for (int i_VKQ_1 = 0; i_VKQ_1 < V_rows_per_thread/2; ++i_VKQ_1) { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j][i_VKQ_0/nthreads_V + i_VKQ_1].x() += tmp[i_VKQ_1].x()*KQ_k[j]; + VKQ[j][i_VKQ_0/nthreads_V + i_VKQ_1].y() += tmp[i_VKQ_1].y()*KQ_k[j]; + } + } + } +#endif // GGML_SYCL_F16 + } + } + + if (sinks && item_ct1.get_group(1) == 0) { + const float sink = ((const float *) sinks)[head]; + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + item_ct1.get_local_id(1); + + if (j0 + nwarps > ncols && j >= ncols) { + break; + } + const float kqmax_new_j = sycl::fmax(sink, (float) KQ_max[j]); + const float KQ_max_scale = sycl::native::exp((float) (KQ_max[j] - kqmax_new_j)); + KQ_max[j] = kqmax_new_j; + + KQ_sum[j] = KQ_sum[j] * KQ_max_scale + + (item_ct1.get_local_id(2) == 0 ? sycl::native::exp((float) (sink - KQ_max[j])) : 0.0f); +#ifdef GGML_SYCL_F16 + const sycl::half2 KQ_max_scale_h2 = sycl::half2(KQ_max_scale, KQ_max_scale); +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) { + VKQ[j][i_VKQ_0/nthreads_V] *= KQ_max_scale_h2; + } +#else +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) { + VKQ[j][i_VKQ_0/nthreads_V].x() *= KQ_max_scale; + VKQ[j][i_VKQ_0/nthreads_V].y() *= KQ_max_scale; + } +#endif // GGML_SYCL_F16 + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (item_ct1.get_local_id(1) == 0) { + KQ_max_shared[j*warp_size+item_ct1.get_local_id(2)] = -FLT_MAX / 2.0f; + KQ_sum_shared[j*warp_size+item_ct1.get_local_id(2)] = 0.0f; + } + } + + item_ct1.barrier(sycl::access::fence_space::local_space); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (item_ct1.get_local_id(2) == 0) { + KQ_max_shared[j*warp_size+item_ct1.get_local_id(1)] = KQ_max[j]; + } + } + + + item_ct1.barrier(sycl::access::fence_space::local_space); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + if (ncols > 1 && ic0 + j_VKQ >= int(ne01.z())) { + break; + } + + float kqmax_new = KQ_max_shared[j_VKQ*warp_size+item_ct1.get_local_id(2)]; + kqmax_new = warp_reduce_max(kqmax_new); + const float kqmax_scale = sycl::native::exp((float) (KQ_max[j_VKQ] - kqmax_new)); + KQ_max[j_VKQ] = kqmax_new; + +#ifdef GGML_SYCL_F16 + sycl::half2 * VKQ_tmp = (sycl::half2 *) KQ + item_ct1.get_local_id(1) * (V_cols_per_iter * D / 2) + + (nthreads_V == warp_size ? 0 : item_ct1.get_local_id(2) / nthreads_V) * (D / 2); + + const sycl::half2 kqmax_scale_h2 = sycl::half2(kqmax_scale, kqmax_scale); +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) { + VKQ[j_VKQ][i_VKQ_0/nthreads_V] *= kqmax_scale_h2; + } +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V*V_rows_per_thread/2) { + const int i_VKQ = + i_VKQ_0 + (nthreads_V == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_V) * + (V_rows_per_thread / 2); + + ggml_sycl_memcpy_1(VKQ_tmp + i_VKQ, + &VKQ[j_VKQ][i_VKQ_0 / nthreads_V]); + } +#else + sycl::float2 * VKQ_tmp = (sycl::float2 *) KQ + item_ct1.get_local_id(1)*(V_cols_per_iter*D/2) + + (nthreads_V == warp_size ? 0 : item_ct1.get_local_id(2) / nthreads_V)*(D/2); +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) { + VKQ[j_VKQ][i_VKQ_0/nthreads_V].x() *= kqmax_scale; + VKQ[j_VKQ][i_VKQ_0/nthreads_V].y() *= kqmax_scale; + } +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V*V_rows_per_thread/2) { + const int i_VKQ = i_VKQ_0 + (nthreads_V == warp_size ? item_ct1.get_local_id(2) : item_ct1.get_local_id(2) % nthreads_V)*(V_rows_per_thread/2); + + ggml_sycl_memcpy_1(VKQ_tmp + i_VKQ, &VKQ[j_VKQ][i_VKQ_0/nthreads_V]); + ggml_sycl_memcpy_1(VKQ_tmp + i_VKQ + V_rows_per_thread/4, &VKQ[j_VKQ][i_VKQ_0/nthreads_V + V_rows_per_thread/4]); + } +#endif // GGML_SYCL_F16 + + KQ_sum[j_VKQ] *= kqmax_scale; + KQ_sum[j_VKQ] = warp_reduce_sum(KQ_sum[j_VKQ]); + if (item_ct1.get_local_id(2) == 0) { + KQ_sum_shared[j_VKQ*warp_size+item_ct1.get_local_id(1)] = KQ_sum[j_VKQ]; + } + + item_ct1.barrier(sycl::access::fence_space::local_space); + + + if (nthreads <= D || tid < D) { + KQ_sum[j_VKQ] = KQ_sum_shared[j_VKQ*warp_size+item_ct1.get_local_id(2)]; + KQ_sum[j_VKQ] = warp_reduce_sum(KQ_sum[j_VKQ]); + +#pragma unroll + for (int i0 = 0; i0 < D; i0 += nthreads) { + float dst_val = 0; +#pragma unroll + for (int w = 0; w < nwarps; ++w) { +#pragma unroll + for (int v = 0; v < V_cols_per_iter; ++v) { + dst_val += float(KQ[w*V_cols_per_iter*D + v*D + i0 + tid]); + } + } + if (item_ct1.get_group_range(1) == 1) { + dst_val /= KQ_sum[j_VKQ]; + } + dst[(((sequence * int(ne01.z()) + ic0 + j_VKQ) * ne02 + head) * item_ct1.get_group_range(1) + + item_ct1.get_group(1)) * + D + + i0 + tid] = dst_val; + } + } + + if (j_VKQ < ncols-1) { + item_ct1.barrier(sycl::access::fence_space::local_space); + } + + } + + if (item_ct1.get_group_range(1) != 1 && tid < ncols && (ncols == 1 || ic0 + tid < int(ne01.z()))) { + dst_meta[((sequence * int(ne01.z()) + ic0 + tid) * ne02 + head) * item_ct1.get_group_range(1) + + item_ct1.get_group(1)] = make_float2(KQ_max[tid], KQ_sum[tid]); + } +#else + GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, + max_bias, m0, m1, n_head_log2, logit_softcap, + ne00, ne01, ne02, ne03, + nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb11, nb12, nb13, + nb21, nb22, nb23, + ne31, ne32, ne33, + nb31, nb32, nb33); + +#endif // SYCL_FLASH_ATTN +} +#ifdef __clang__ +#pragma clang diagnostic pop +#endif // __clang__ + + +template +void ggml_sycl_flash_attn_ext_vec_case_impl(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + + const int warp_size = WARP_16_SIZE; //better performance than WARP_32_SIZE + + const int cc = ggml_sycl_info().devices[ggml_sycl_get_device()].cc; + + const int nthreads = ggml_sycl_fattn_vec_get_nthreads_host(cc); + const int nwarps = nthreads / warp_size; + + const bool need_f16_K = type_K == GGML_TYPE_F16; + const bool need_f16_V = type_V == GGML_TYPE_F16; + constexpr size_t nbytes_shared = 0; + + launch_fattn, warp_size>( + ctx, dst, nwarps, nbytes_shared, D, need_f16_K, need_f16_V, false); +} + +template +void ggml_sycl_flash_attn_ext_vec_case(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + + float logit_softcap; + memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + if (logit_softcap == 0.0f) { + constexpr bool use_logit_softcap = false; + ggml_sycl_flash_attn_ext_vec_case_impl(ctx, dst); + } else { + constexpr bool use_logit_softcap = true; + ggml_sycl_flash_attn_ext_vec_case_impl(ctx, dst); + } + return; + } + + constexpr int cols_per_block = 2; + if (logit_softcap == 0.0f) { + constexpr bool use_logit_softcap = false; + ggml_sycl_flash_attn_ext_vec_case_impl(ctx, dst); + } else { + constexpr bool use_logit_softcap = true; + ggml_sycl_flash_attn_ext_vec_case_impl(ctx, dst); + } +} + +#define DECL_FATTN_VEC_CASE(D, type_K, type_V) \ + template void ggml_sycl_flash_attn_ext_vec_case \ + (ggml_backend_sycl_context & ctx, ggml_tensor * dst) \ + +#define EXTERN_DECL_FATTN_VEC_CASES(D, type_K) \ + extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_F16); \ + extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q4_0); \ + extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q4_1); \ + extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q5_0); \ + extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q5_1); \ + extern DECL_FATTN_VEC_CASE(D, type_K, GGML_TYPE_Q8_0); \ + +EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_F16) +EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q4_0) +EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q4_1) +EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q5_0) +EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q5_1) +EXTERN_DECL_FATTN_VEC_CASES( 64, GGML_TYPE_Q8_0) + +EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_F16) +EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q4_0) +EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q4_1) +EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q5_0) +EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q5_1) +EXTERN_DECL_FATTN_VEC_CASES(128, GGML_TYPE_Q8_0) + +EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_F16) +EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q4_0) +EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q4_1) +EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q5_0) +EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q5_1) +EXTERN_DECL_FATTN_VEC_CASES(256, GGML_TYPE_Q8_0) + +#endif // GGML_SYCL_FATTN_VEC_HPP diff --git a/ggml/src/ggml-sycl/fattn.cpp b/ggml/src/ggml-sycl/fattn.cpp new file mode 100644 index 0000000000..c276ed8982 --- /dev/null +++ b/ggml/src/ggml-sycl/fattn.cpp @@ -0,0 +1,225 @@ +// +// MIT license +// Copyright (C) 2025 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + + +#include +#include "dpct/helper.hpp" +#include "common.hpp" +#include "fattn-common.hpp" +#include "fattn-tile.hpp" +#include "fattn-vec.hpp" +#include "fattn.hpp" + + +#define FATTN_VEC_CASE(D, type_K, type_V) \ + { \ + const bool type_K_okay = K->type == (type_K) || (K->type == GGML_TYPE_F32 && (type_K) == GGML_TYPE_F16); \ + const bool type_V_okay = V->type == (type_V) || (V->type == GGML_TYPE_F32 && (type_V) == GGML_TYPE_F16); \ + if (Q->ne[0] == (D) && type_K_okay && type_V_okay) { \ + ggml_sycl_flash_attn_ext_vec_case(ctx, dst); \ + return; \ + } \ + } \ + +#define FATTN_VEC_CASES_ALL_D(type_K, type_V) \ + FATTN_VEC_CASE( 64, type_K, type_V) \ + FATTN_VEC_CASE(128, type_K, type_V) \ + FATTN_VEC_CASE(256, type_K, type_V) \ + +static void ggml_sycl_flash_attn_ext_vec(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + ggml_tensor * Q = dst->src[0]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + +#ifdef GGML_SYCL_FA_ALL_QUANTS + FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_F16) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_F16) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_F16) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_F16) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_F16) + + FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q4_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q4_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q4_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q4_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q4_0) + + FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q4_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q4_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q4_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q4_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q4_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q4_1) + + FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q5_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q5_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q5_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q5_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q5_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q5_0) + + FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q5_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q5_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q5_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q5_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q5_1) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q5_1) + + FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q8_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q8_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q8_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q8_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q8_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) +#else + FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) +#endif // GGML_SYCL_FA_ALL_QUANTS + + GGML_ABORT("Not match KV type in vec"); +} + +// Best FlashAttention kernel for a specific GPU: +enum best_fattn_kernel { + BEST_FATTN_KERNEL_NONE = 0, + BEST_FATTN_KERNEL_VEC = 100, + BEST_FATTN_KERNEL_TILE = 200, +}; + +static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const ggml_tensor * dst) { + GGML_UNUSED(device); +#ifndef SYCL_FLASH_ATTN + GGML_UNUSED(dst); + return BEST_FATTN_KERNEL_NONE; +#endif// SYCL_FLASH_ATTN + + if(!g_ggml_sycl_enable_flash_attention) return BEST_FATTN_KERNEL_NONE; + + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + const ggml_tensor * mask = dst->src[3]; + + const int gqa_ratio = Q->ne[2] / K->ne[2]; + GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); + + float max_bias = 0.0f; + memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float)); + + bool gqa_opt_applies = gqa_ratio >= 2 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0; + for (const ggml_tensor * t : {Q, K, V, mask}) { + if (t == nullptr || ggml_is_quantized(t->type)) { + continue; + } + for (size_t i = 1; i < GGML_MAX_DIMS; ++i) { + if (t->nb[i] % 16 != 0) { + gqa_opt_applies = false; + break; + } + } + } + + switch (K->ne[0]) { + case 40: + case 64: + case 72: + case 80: + case 96: + case 128: + case 112: + case 256: + if (V->ne[0] != K->ne[0]) { + return BEST_FATTN_KERNEL_NONE; + } + break; + case 576: + if (V->ne[0] != 512) { + return BEST_FATTN_KERNEL_NONE; + } + if (!gqa_opt_applies) { + return BEST_FATTN_KERNEL_NONE; + } + break; + default: + return BEST_FATTN_KERNEL_NONE; + } + +#ifndef GGML_SYCL_FA_ALL_QUANTS + if (K->type != V->type) { + return BEST_FATTN_KERNEL_NONE; + } +#endif // GGML_SYCL_FA_ALL_QUANTS + + switch (K->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + break; + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: +#ifndef GGML_SYCL_FA_ALL_QUANTS + return BEST_FATTN_KERNEL_NONE; +#endif // GGML_SYCL_FA_ALL_QUANTS + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q8_0: + break; + default: + return BEST_FATTN_KERNEL_NONE; + } + + if (mask && mask->ne[2] != 1) { + return BEST_FATTN_KERNEL_NONE; + } + + // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes: + const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0; + + // Todo: Use the XMX kernel if possible: + + // If there are no tensor cores available, use the generic tile kernel: + if (can_use_vector_kernel) { + if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) { + if (Q->ne[1] == 1) { + if (!gqa_opt_applies) { + return BEST_FATTN_KERNEL_VEC; + } + } + } else { + if (Q->ne[1] <= 2) { + return BEST_FATTN_KERNEL_VEC; + } + } + } + return BEST_FATTN_KERNEL_TILE; +} + +void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + ggml_sycl_set_device(ctx.device); + switch (ggml_sycl_get_best_fattn_kernel(ggml_sycl_get_device(), dst)) { + case BEST_FATTN_KERNEL_NONE: + GGML_ABORT("Not support Flash-Attention"); + case BEST_FATTN_KERNEL_TILE: + ggml_sycl_flash_attn_ext_tile(ctx, dst); + break; + case BEST_FATTN_KERNEL_VEC: + ggml_sycl_flash_attn_ext_vec(ctx, dst); + break; + } +} + +bool ggml_sycl_flash_attn_ext_supported(int device, const ggml_tensor * dst) { + return ggml_sycl_get_best_fattn_kernel(device, dst) != BEST_FATTN_KERNEL_NONE; +} diff --git a/ggml/src/ggml-sycl/fattn.hpp b/ggml/src/ggml-sycl/fattn.hpp new file mode 100644 index 0000000000..f2a8ffc97d --- /dev/null +++ b/ggml/src/ggml-sycl/fattn.hpp @@ -0,0 +1,22 @@ +// +// MIT license +// Copyright (C) 2025 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_FATTN_HPP +#define GGML_SYCL_FATTN_HPP + +#include "common.hpp" + +void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +bool ggml_sycl_flash_attn_ext_supported(int device, const ggml_tensor * dst); + +#endif // GGML_SYCL_FATTN_HPP diff --git a/ggml/src/ggml-sycl/gated_delta_net.cpp b/ggml/src/ggml-sycl/gated_delta_net.cpp new file mode 100644 index 0000000000..648455c134 --- /dev/null +++ b/ggml/src/ggml-sycl/gated_delta_net.cpp @@ -0,0 +1,309 @@ +#include +#include "dpct/helper.hpp" +#include "common.hpp" +#include "ggml.h" +#include "gated_delta_net.hpp" +#include + + +template +void gated_delta_net_sycl(const float * q, + const float * k, + const float * v, + const float * g, + const float * beta, + const float * curr_state, + float * dst, + int64_t H, + int64_t n_tokens, + int64_t n_seqs, + int64_t sq1, + int64_t sq2, + int64_t sq3, + int64_t sv1, + int64_t sv2, + int64_t sv3, + int64_t sb1, + int64_t sb2, + int64_t sb3, + const sycl::uint3 neqk1_magic, + const sycl::uint3 rq3_magic, + float scale) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const uint32_t h_idx = item_ct1.get_group(2); + const uint32_t sequence = item_ct1.get_group(1); + // each warp owns one column, using warp-level primitives to reduce across rows + const int lane = item_ct1.get_local_id(2); + const int col = item_ct1.get_group(0) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); + + const uint32_t iq1 = fastmodulo(h_idx, neqk1_magic); + const uint32_t iq3 = fastdiv(sequence, rq3_magic); + + const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs; + float * attn_data = dst; + float * state = dst + attn_score_elems; + + const int64_t state_offset = (sequence * H + h_idx) * S_v * S_v; + state += state_offset; + curr_state += state_offset; + attn_data += (sequence * n_tokens * H + h_idx) * S_v; + + constexpr int warp_size = ggml_sycl_get_physical_warp_size() < S_v ? ggml_sycl_get_physical_warp_size() : S_v; + static_assert(S_v % warp_size == 0, "S_v must be a multiple of warp_size"); + constexpr int rows_per_lane = (S_v + warp_size - 1) / warp_size; + float s_shard[rows_per_lane]; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + s_shard[r] = curr_state[col * S_v + i]; + } + + for (int t = 0; t < n_tokens; t++) { + const float * q_t = q + iq3 * sq3 + t * sq2 + iq1 * sq1; + const float * k_t = k + iq3 * sq3 + t * sq2 + iq1 * sq1; + const float * v_t = v + sequence * sv3 + t * sv2 + h_idx * sv1; + + const int64_t gb_offset = sequence * sb3 + t * sb2 + h_idx * sb1; + const float * beta_t = beta + gb_offset; + const float * g_t = g + gb_offset * (KDA ? S_v : 1); + + const float beta_val = *beta_t; + + if constexpr (!KDA) { + const float g_val = sycl::native::exp(*g_t); + + // kv[col] = (S^T @ k)[col] = sum_i S[i][col] * k[i] + float kv_shard = 0.0f; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + kv_shard += s_shard[r] * k_t[i]; + } + float kv_col = warp_reduce_sum(kv_shard); + + // delta[col] = (v[col] - g * kv[col]) * beta + float delta_col = (v_t[col] - g_val * kv_col) * beta_val; + + // fused: S[i][col] = g * S[i][col] + k[i] * delta[col] + // attn[col] = (S^T @ q)[col] = sum_i S[i][col] * q[i] + float attn_partial = 0.0f; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + s_shard[r] = g_val * s_shard[r] + k_t[i] * delta_col; + attn_partial += s_shard[r] * q_t[i]; + } + + float attn_col = warp_reduce_sum(attn_partial); + + if (lane == 0) { + attn_data[col] = attn_col * scale; + } + } else { + // kv[col] = sum_i g[i] * S[i][col] * k[i] + float kv_shard = 0.0f; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + kv_shard += sycl::native::exp(g_t[i]) * s_shard[r] * k_t[i]; + } + + float kv_col = warp_reduce_sum(kv_shard); + + // delta[col] = (v[col] - kv[col]) * beta + float delta_col = (v_t[col] - kv_col) * beta_val; + + // fused: S[i][col] = g[i] * S[i][col] + k[i] * delta[col] + // attn[col] = (S^T @ q)[col] = sum_i S[i][col] * q[i] + float attn_partial = 0.0f; +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + s_shard[r] = sycl::native::exp(g_t[i]) * s_shard[r] + k_t[i] * delta_col; + attn_partial += s_shard[r] * q_t[i]; + } + + float attn_col = warp_reduce_sum(attn_partial); + + if (lane == 0) { + attn_data[col] = attn_col * scale; + } + } + + attn_data += S_v * H; + } + + // Write state back to global memory +#pragma unroll + for (int r = 0; r < rows_per_lane; r++) { + const int i = r * warp_size + lane; + state[col * S_v + i] = s_shard[r]; + } +} + +template +static void launch_gated_delta_net(const float * q_d, + const float * k_d, + const float * v_d, + const float * g_d, + const float * b_d, + const float * s_d, + float * dst_d, + int64_t S_v, + int64_t H, + int64_t n_tokens, + int64_t n_seqs, + int64_t sq1, + int64_t sq2, + int64_t sq3, + int64_t sv1, + int64_t sv2, + int64_t sv3, + int64_t sb1, + int64_t sb2, + int64_t sb3, + int64_t neqk1, + int64_t rq3, + float scale, + dpct::queue_ptr stream) { + //TODO: Add chunked kernel for even faster pre-fill + const int warp_size = ggml_sycl_info().devices[ggml_sycl_get_device()].warp_size; + + const int num_warps = 4; + dpct::dim3 grid_dims(H, n_seqs, (S_v + num_warps - 1) / num_warps); + dpct::dim3 block_dims(warp_size <= S_v ? warp_size : S_v, num_warps, 1); + + const sycl::uint3 neqk1_magic = init_fastdiv_values(neqk1); + const sycl::uint3 rq3_magic = init_fastdiv_values(rq3); + + int cc = ggml_sycl_info().devices[ggml_sycl_get_device()].cc; + + switch (S_v) { + case 16: + { + constexpr int sv = 16; + stream->parallel_for(sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + gated_delta_net_sycl(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, n_tokens, + n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, sb1, sb2, + sb3, neqk1_magic, rq3_magic, scale); + }); + } + break; + case 32: + { + constexpr int sv = 32; + stream->parallel_for(sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + gated_delta_net_sycl(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, n_tokens, + n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, sb1, sb2, + sb3, neqk1_magic, rq3_magic, scale); + }); + } + break; + case 64: { + { + constexpr int sv = 64; + stream->parallel_for(sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + gated_delta_net_sycl( + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, n_tokens, n_seqs, sq1, sq2, + sq3, sv1, sv2, sv3, sb1, sb2, sb3, neqk1_magic, rq3_magic, scale); + }); + } + break; + } + case 128: { + { + constexpr int sv = 128; + stream->parallel_for(sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + gated_delta_net_sycl( + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, n_tokens, n_seqs, sq1, sq2, + sq3, sv1, sv2, sv3, sb1, sb2, sb3, neqk1_magic, rq3_magic, scale); + }); + } + break; + } + default: + GGML_ABORT("fatal error"); + break; + } +} + +void ggml_sycl_op_gated_delta_net(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + ggml_tensor * src_q = dst->src[0]; + ggml_tensor * src_k = dst->src[1]; + ggml_tensor * src_v = dst->src[2]; + ggml_tensor * src_g = dst->src[3]; + ggml_tensor * src_beta = dst->src[4]; + ggml_tensor * src_state = dst->src[5]; + + GGML_TENSOR_LOCALS(int64_t, neq, src_q, ne); + GGML_TENSOR_LOCALS(size_t , nbq, src_q, nb); + GGML_TENSOR_LOCALS(int64_t, nek, src_k, ne); + GGML_TENSOR_LOCALS(size_t , nbk, src_k, nb); + GGML_TENSOR_LOCALS(int64_t, nev, src_v, ne); + GGML_TENSOR_LOCALS(size_t, nbv, src_v, nb); + GGML_TENSOR_LOCALS(size_t, nbb, src_beta, nb); + + const int64_t S_v = nev0; + const int64_t H = nev1; + const int64_t n_tokens = nev2; + const int64_t n_seqs = nev3; + + const bool kda = (src_g->ne[0] == S_v); + + GGML_ASSERT(neq1 == nek1); + const int64_t neqk1 = neq1; + + const int64_t rq3 = nev3 / neq3; + + const float * q_d = (const float *) src_q->data; + const float * k_d = (const float *) src_k->data; + const float * v_d = (const float *) src_v->data; + const float * g_d = (const float *) src_g->data; + const float * b_d = (const float *) src_beta->data; + + const float * s_d = (const float *) src_state->data; + float * dst_d = (float *) dst->data; + + GGML_ASSERT(ggml_is_contiguous_rows(src_q)); + GGML_ASSERT(ggml_is_contiguous_rows(src_k)); + GGML_ASSERT(ggml_is_contiguous_rows(src_v)); + GGML_ASSERT(ggml_are_same_stride(src_q, src_k)); + GGML_ASSERT(src_g->ne[0] == 1 || kda); + GGML_ASSERT(ggml_is_contiguous(src_g)); + GGML_ASSERT(ggml_is_contiguous(src_beta)); + GGML_ASSERT(ggml_is_contiguous(src_state)); + + // strides in floats (beta strides used for both g and beta offset computation) + const int64_t sq1 = nbq1 / sizeof(float); + const int64_t sq2 = nbq2 / sizeof(float); + const int64_t sq3 = nbq3 / sizeof(float); + const int64_t sv1 = nbv1 / sizeof(float); + const int64_t sv2 = nbv2 / sizeof(float); + const int64_t sv3 = nbv3 / sizeof(float); + const int64_t sb1 = nbb1 / sizeof(float); + const int64_t sb2 = nbb2 / sizeof(float); + const int64_t sb3 = nbb3 / sizeof(float); + + const float scale = 1.0f / sqrtf((float) S_v); + + dpct::queue_ptr stream = ctx.stream(); + + if (kda) { + launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, + S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, + sb1, sb2, sb3, neqk1, rq3, scale, stream); + } else { + launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, + S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, + sb1, sb2, sb3, neqk1, rq3, scale, stream); + } +} + +void ggml_sycl_gated_delta_net(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/6); + ggml_sycl_op_gated_delta_net(ctx, dst); +} diff --git a/ggml/src/ggml-sycl/gated_delta_net.hpp b/ggml/src/ggml-sycl/gated_delta_net.hpp new file mode 100644 index 0000000000..a3308ee876 --- /dev/null +++ b/ggml/src/ggml-sycl/gated_delta_net.hpp @@ -0,0 +1,8 @@ +#pragma once + +#include +#include "dpct/helper.hpp" +#include "common.hpp" +#include "ggml.h" + +void ggml_sycl_gated_delta_net(ggml_backend_sycl_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 0614d7e8f3..1281970584 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -35,6 +35,7 @@ #endif #include +#include "ggml.h" #include "ggml-sycl.h" #include "ggml-impl.h" #include "ggml-backend-impl.h" @@ -43,17 +44,18 @@ #include "ggml-sycl/backend.hpp" #include "ggml-sycl/common.hpp" #include "ggml-sycl/element_wise.hpp" +#include "ggml-sycl/gated_delta_net.hpp" +#include "ggml-sycl/gemm.hpp" +#include "ggml-sycl/getrows.hpp" #include "ggml-sycl/norm.hpp" #include "ggml-sycl/presets.hpp" -#include "ggml-sycl/gemm.hpp" +#include "ggml-sycl/quantize.hpp" +#include "ggml-sycl/repeat_back.hpp" #include "ggml-sycl/set_rows.hpp" #include "ggml-sycl/set.hpp" -#include "ggml-sycl/sycl_hw.hpp" -#include "ggml-sycl/getrows.hpp" -#include "ggml-sycl/repeat_back.hpp" -#include "ggml-sycl/quantize.hpp" #include "ggml-sycl/ssm_conv.hpp" -#include "ggml.h" +#include "ggml-sycl/sycl_hw.hpp" + static bool g_sycl_loaded = false; int g_ggml_sycl_debug = 0; @@ -62,6 +64,8 @@ int g_ggml_sycl_disable_graph = 0; int g_ggml_sycl_disable_dnn = 0; int g_ggml_sycl_prioritize_dmmv = 0; int g_ggml_sycl_use_async_mem_op = 0; +int g_ggml_sycl_enable_flash_attention = 1; + static ggml_sycl_device_info ggml_sycl_init() { ggml_sycl_device_info info = {}; @@ -94,11 +98,14 @@ static ggml_sycl_device_info ggml_sycl_init() { info.devices[i].cc = 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - info.devices[i].nsm = prop.get_max_compute_units(); + info.devices[i].nsm = prop.get_max_compute_units() / 16; //16: Number of Xe Cores info.devices[i].opt_feature.reorder = device.ext_oneapi_architecture_is(syclex::arch_category::intel_gpu); info.devices[i].smpbo = prop.get_local_mem_size(); + info.devices[i].warp_size = WARP_SIZE; info.max_work_group_sizes[i] = prop.get_max_work_group_size(); + info.devices[i].max_wg_per_cu = info.max_work_group_sizes[i] / prop.get_max_compute_units(); + } for (int id = 0; id < info.device_count; ++id) { @@ -211,7 +218,37 @@ static void ggml_check_sycl() try { g_ggml_sycl_disable_graph = get_sycl_env("GGML_SYCL_DISABLE_GRAPH", 1); g_ggml_sycl_disable_dnn = get_sycl_env("GGML_SYCL_DISABLE_DNN", 0); g_ggml_sycl_prioritize_dmmv = get_sycl_env("GGML_SYCL_PRIORITIZE_DMMV", 0); + +#ifdef SYCL_FLASH_ATTN + g_ggml_sycl_enable_flash_attention = get_sycl_env("GGML_SYCL_ENABLE_FLASH_ATTN", 1); +#else + g_ggml_sycl_enable_flash_attention = 0; +#endif + GGML_SYCL_DEBUG("[SYCL] call ggml_check_sycl\n"); + + GGML_LOG_INFO("Build with Macros:\n"); +#if defined(GGML_SYCL_FORCE_MMQ) + GGML_LOG_INFO(" GGML_SYCL_FORCE_MMQ: yes\n"); +#else + GGML_LOG_INFO(" GGML_SYCL_FORCE_MMQ: no\n"); +#endif +#if defined(GGML_SYCL_F16) + GGML_LOG_INFO(" GGML_SYCL_F16: yes\n"); +#else + GGML_LOG_INFO(" GGML_SYCL_F16: no\n"); +#endif +#if defined(GGML_SYCL_GRAPH) + GGML_LOG_INFO(" GGML_SYCL_GRAPH: yes\n"); +#else + GGML_LOG_INFO(" GGML_SYCL_GRAPH: no\n"); +#endif +#if defined(GGML_SYCL_DNNL) + GGML_LOG_INFO(" GGML_SYCL_DNNL: yes\n"); +#else + GGML_LOG_INFO(" GGML_SYCL_DNNL: no\n"); +#endif + GGML_LOG_INFO("Running with Environment Variables:\n"); GGML_LOG_INFO(" GGML_SYCL_DEBUG: %d\n", g_ggml_sycl_debug); GGML_LOG_INFO(" GGML_SYCL_DISABLE_OPT: %d\n", g_ggml_sycl_disable_optimize); @@ -226,16 +263,12 @@ static void ggml_check_sycl() try { GGML_LOG_INFO(" GGML_SYCL_DISABLE_DNN: DNN disabled by compile flag\n"); #endif GGML_LOG_INFO(" GGML_SYCL_PRIORITIZE_DMMV: %d\n", g_ggml_sycl_prioritize_dmmv); - GGML_LOG_INFO("Build with Macros:\n"); -#if defined(GGML_SYCL_FORCE_MMQ) - GGML_LOG_INFO(" GGML_SYCL_FORCE_MMQ: yes\n"); + +#ifdef SYCL_FLASH_ATTN + GGML_LOG_INFO(" GGML_SYCL_ENABLE_FLASH_ATTN: %d\n", g_ggml_sycl_enable_flash_attention); #else - GGML_LOG_INFO(" GGML_SYCL_FORCE_MMQ: no\n"); -#endif -#if defined(GGML_SYCL_F16) - GGML_LOG_INFO(" GGML_SYCL_F16: yes\n"); -#else - GGML_LOG_INFO(" GGML_SYCL_F16: no\n"); + GGML_LOG_INFO(" GGML_SYCL_ENABLE_FLASH_ATTN: %d disabled by compile flag\n", + g_ggml_sycl_enable_flash_attention); #endif /* NOT REMOVE, keep it for next optimize for XMX. @@ -3012,7 +3045,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons } #if GGML_SYCL_DNNL - // oneDNN handles strided data and does not need overhead of get_to_fp16_nc_sycl + // oneDNN handles strided data and does not need overhead of ggml_get_to_fp16_nc_sycl const int64_t ne_src1 = src1->nb[last_str] * src1->ne[last_dim] / type_size_src1; src1_f16_alloc.alloc(ne_src1); const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type, dst); @@ -3021,7 +3054,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons # else const int64_t ne_src1 = ggml_nelements(src1); src1_f16_alloc.alloc(ne_src1); - const to_fp16_nc_sycl_t to_fp16_nc_sycl = get_to_fp16_nc_sycl(src1->type); + const to_fp16_nc_sycl_t to_fp16_nc_sycl = ggml_get_to_fp16_nc_sycl(src1->type); GGML_ASSERT(to_fp16_nc_sycl != nullptr); to_fp16_nc_sycl(src1_f16, src1_f16_alloc.get(), ne10, ne11, ne12, ne13, s11, s12, s13, queue); #endif @@ -4116,6 +4149,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_ROPE: ggml_sycl_rope(ctx, dst); break; + case GGML_OP_ROPE_BACK: + ggml_sycl_rope_back(ctx, dst); + break; case GGML_OP_IM2COL: ggml_sycl_im2col(ctx, dst); break; @@ -4149,6 +4185,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_GATED_LINEAR_ATTN: ggml_sycl_op_gated_linear_attn(ctx, dst); break; + case GGML_OP_GATED_DELTA_NET: + ggml_sycl_gated_delta_net(ctx, dst); + break; case GGML_OP_SSM_CONV: ggml_sycl_ssm_conv(ctx, dst); break; @@ -4158,6 +4197,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_ARANGE: ggml_sycl_arange(ctx, dst); break; + case GGML_OP_FLASH_ATTN_EXT: + ggml_sycl_flash_attn_ext(ctx, dst); + break; default: return false; } @@ -4819,6 +4861,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g return max_bias == 0.0f; } case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: case GGML_OP_IM2COL: return true; case GGML_OP_UPSCALE: @@ -4840,8 +4883,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g k > 0 && k <= 32; } case GGML_OP_POOL_2D: - case GGML_OP_ACC: return true; + case GGML_OP_ACC: + return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]); case GGML_OP_PAD: // TODO: add circular padding support for syscl, see https://github.com/ggml-org/llama.cpp/pull/16985 if (ggml_get_op_params_i32(op, 8) != 0) { @@ -4853,6 +4897,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: case GGML_OP_GATED_LINEAR_ATTN: + case GGML_OP_GATED_DELTA_NET: return true; case GGML_OP_SSM_CONV: return op->type == GGML_TYPE_F32 && @@ -4862,6 +4907,8 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g return op->type == GGML_TYPE_F32; case GGML_OP_ARANGE: return op->type == GGML_TYPE_F32; + case GGML_OP_FLASH_ATTN_EXT: + return ggml_sycl_flash_attn_ext_supported(device, op); default: return false; } diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp index 00702b5d09..09fce1280a 100644 --- a/ggml/src/ggml-sycl/norm.cpp +++ b/ggml/src/ggml-sycl/norm.cpp @@ -202,47 +202,34 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const int6 } } -static void l2_norm_f32(const float* x, float* dst, const int ncols, const float eps, - const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) { - const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + - item_ct1.get_local_id(1); - const int tid = item_ct1.get_local_id(2); - const int nthreads = item_ct1.get_local_range(2); - const int nwarps = nthreads / WARP_SIZE; +template +static void l2_norm_f32(const float * x, float * dst, const int ncols, + const int64_t stride_row, const int64_t stride_channel, + const int64_t stride_sample, const float eps, + const sycl::nd_item<3>& item_ct1, float* s_sum, const int block_size) { + const int nrows = item_ct1.get_group_range(2); + const int nchannels = item_ct1.get_group_range(1); + + const int row = item_ct1.get_group(2); + const int channel = item_ct1.get_group(1); + const int sample = item_ct1.get_group(0); + const int tid = item_ct1.get_local_id(2); + + x += sample*stride_sample + channel*stride_channel + row*stride_row; + dst += ((sample*nchannels + channel)*nrows + row)*ncols; + float tmp = 0.0f; // partial sum for thread in warp for (int col = tid; col < ncols; col += block_size) { - const float xi = x[row * ncols + col]; + const float xi = x[col]; tmp += xi * xi; } - // sum up partial sums - tmp = warp_reduce_sum(tmp, item_ct1); - if (block_size > WARP_SIZE) { - - int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; - int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; - if (lane_id == 0) { - s_sum[warp_id] = tmp; - } - /* - DPCT1118:3: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - item_ct1.barrier(sycl::access::fence_space::local_space); - size_t nreduce = nwarps / WARP_SIZE; - tmp = 0.f; - for (size_t i = 0; i < nreduce; i += 1) - { - tmp += s_sum[lane_id + i * WARP_SIZE]; - } - tmp = warp_reduce_sum(tmp, item_ct1); - } - - const float scale = sycl::rsqrt(sycl::max(tmp, eps * eps)); + tmp = block_reduce(tmp, s_sum, block_size); + const float scale = sycl::rsqrt(sycl::fmax(tmp, eps * eps)); for (int col = tid; col < ncols; col += block_size) { - dst[row * ncols + col] = scale * x[row * ncols + col]; + dst[col] = scale * x[col]; } } @@ -369,42 +356,50 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const } } -static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols, - const int nrows, const float eps, - queue_ptr stream, int device) { - // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE); +template +static void l2_norm_f32_sycl(const float * x, + float * dst, + const int ncols, + const int nrows, + const int nchannels, + const int nsamples, + const int64_t stride_row, + const int64_t stride_channel, + const int64_t stride_sample, + const float eps, + queue_ptr stream, + int device) { + const dpct::dim3 blocks_num(nrows, nchannels, nsamples); + if (ncols < 1024) { - const sycl::range<3> block_dims(1, 1, WARP_SIZE); + const dpct::dim3 block_dims(warp_size, 1, 1); stream->submit([&](sycl::handler& cgh) { cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + sycl::nd_range<3>(blocks_num * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - l2_norm_f32(x, dst, ncols, eps, item_ct1, - nullptr, WARP_SIZE); + [[sycl::reqd_sub_group_size(warp_size)]] { + l2_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, + nullptr, warp_size); }); }); } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; - assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0); + assert(work_group_size % (warp_size * warp_size) == 0); const sycl::range<3> block_dims(1, 1, work_group_size); - /* - DPCT1049:19: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ + int lsm_size = block_dims[2] > warp_size ? work_group_size / warp_size * sizeof(float): 0; stream->submit([&](sycl::handler& cgh) { - sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE), + sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(lsm_size), cgh); + cgh.parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + sycl::nd_range<3>(blocks_num * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) - [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - l2_norm_f32(x, dst, ncols, eps, item_ct1, - get_pointer(s_sum_acc_ct1), work_group_size); + [[sycl::reqd_sub_group_size(warp_size)]] { + l2_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, + eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size); }); }); } @@ -634,21 +629,28 @@ void ggml_sycl_op_rms_norm_back(ggml_backend_sycl_context & ctx, ggml_tensor * d } void ggml_sycl_op_l2_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { + const ggml_tensor * src0 = dst->src[0]; + const float * src0_d = (const float *) src0->data; + float * dst_d = (float *) dst->data; + dpct::queue_ptr stream = ctx.stream(); - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); - dpct::queue_ptr main_stream = ctx.stream(); - SYCL_CHECK(ggml_sycl_set_device(ctx.device)); - - const int64_t ne00 = dst->src[0]->ne[0]; - const int64_t nrows = ggml_nrows(dst->src[0]); - const float * src0_dd = static_cast(dst->src[0]->data); - float * dst_dd = static_cast(dst->data); + GGML_TENSOR_UNARY_OP_LOCALS; float eps; memcpy(&eps, dst->op_params, sizeof(float)); + GGML_ASSERT(eps >= 0.0f); - l2_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream, ctx.device); + const size_t ts0 = ggml_type_size(src0->type); + GGML_ASSERT(nb00 == ts0); + const int64_t s01 = nb01 / ts0; + const int64_t s02 = nb02 / ts0; + const int64_t s03 = nb03 / ts0; + /*support both WARP_SIZE or WARP_32_SIZE in code + choose by hardware for better performance + */ + l2_norm_f32_sycl(src0_d, dst_d, ne00, ne01, ne02, ne03, s01, s02, s03, eps, stream, ctx.device); } diff --git a/ggml/src/ggml-sycl/presets.hpp b/ggml/src/ggml-sycl/presets.hpp index b651737423..dc4dad1d37 100644 --- a/ggml/src/ggml-sycl/presets.hpp +++ b/ggml/src/ggml-sycl/presets.hpp @@ -73,4 +73,7 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA #define MUL_MAT_SRC1_COL_STRIDE 128 #define QK_WARP_SIZE 32 +#define WARP_32_SIZE 32 +#define WARP_16_SIZE 16 + #endif // GGML_SYCL_PRESETS_HPP diff --git a/ggml/src/ggml-sycl/quants.hpp b/ggml/src/ggml-sycl/quants.hpp index d0d5ac9a4e..14490fea5b 100644 --- a/ggml/src/ggml-sycl/quants.hpp +++ b/ggml/src/ggml-sycl/quants.hpp @@ -29,7 +29,7 @@ namespace ggml_sycl_reordered { // [qs0, qs1, qs2, ..., qsN] [d0, d1, d2, ..., dN] // // Notes: out-of-bounds qs will run into d values -// Aligment relies on the allocated size of qs +// Alignment relies on the allocated size of qs template struct block_q_t; diff --git a/ggml/src/ggml-sycl/rope.cpp b/ggml/src/ggml-sycl/rope.cpp index aeaa58b95b..9d83a1e9fa 100644 --- a/ggml/src/ggml-sycl/rope.cpp +++ b/ggml/src/ggml-sycl/rope.cpp @@ -1,4 +1,5 @@ #include "rope.hpp" +#include "convert.hpp" #include "ggml-sycl/common.hpp" #include "ggml.h" @@ -15,366 +16,489 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) { return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y)); } -// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn -// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. -static void rope_yarn( - float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta) { - // Get n-d rotational scaling corrected for extrapolation +template +static void rope_yarn(const float theta_extrap, const float freq_scale, + const rope_corr_dims corr_dims, const int64_t i0, + const float ext_factor, float mscale, float &cos_theta, + float &sin_theta) { float theta_interp = freq_scale * theta_extrap; float theta = theta_interp; if (ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor; + float ramp_mix = + rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor; theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - // Get n-d magnitude scaling corrected for interpolation mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale); } - *cos_theta = sycl::cos(theta) * mscale; - *sin_theta = sycl::sin(theta) * mscale; + cos_theta = sycl::cos(theta) * mscale; + sin_theta = sycl::sin(theta) * mscale; + if (!forward) { + sin_theta *= -1.0f; + } } -template -static void rope_norm(const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, - const int32_t * pos, float freq_scale, float ext_factor, float attn_factor, - const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors, - const sycl::nd_item<3> & item_ct1) { - const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + item_ct1.get_local_id(1)); +template +static void rope_norm(const T *x, D *dst, const int ne00, const int ne01, + const int ne02, const int s01, const int s02, + const int s03, const int s1, const int s2, const int s3, + const int n_dims, const int32_t *pos, + const float freq_scale, const float ext_factor, + const float attn_factor, const rope_corr_dims corr_dims, + const float theta_scale, const float *freq_factors, + const int64_t *row_indices, const int set_rows_stride) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); - if (i0 >= ne0) { + if (i0 >= ne00) { return; } - const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + item_ct1.get_local_id(2); + const int row_dst = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); - const int row0 = row % ne1; - const int channel0 = row / ne1; + const uint32_t i3 = row_dst / (ne01 * ne02); + const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01; + const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01; - const int i = row * ne0 + i0; - const int i2 = channel0 * s2 + row0 * s1 + i0; + int idst = i0 + i1 * s1 + i2 * s2 + i3 * s3; + const int ix = i0 + i1 * s01 + i2 * s02 + i3 * s03; + if (set_rows_stride != 0) { + idst = i1 * s1 + i0; + idst += row_indices[i2] * set_rows_stride; + } + + const auto &store_coaelsced = [&](float x0, float x1) { + if constexpr (std::is_same_v) { + sycl::float2 v = sycl::float2(x0, x1); + ggml_sycl_memcpy_1<8>(dst + idst, &v); + } else if constexpr (std::is_same_v) { + sycl::half2 v = sycl::half2(x0, x1); + ggml_sycl_memcpy_1<4>(dst + idst, &v); + } + }; if (i0 >= n_dims) { - *reinterpret_cast *>(dst + i) = *reinterpret_cast *>(x + i2); + store_coaelsced(x[ix + 0], x[ix + 1]); return; } - const float theta_base = pos[channel0] * sycl::pow(theta_scale, i0 / 2.0f); + const float theta_base = pos[i2] * dpct::pow(theta_scale, i0 / 2.0f); const float freq_factor = has_ff ? freq_factors[i0 / 2] : 1.0f; float cos_theta; float sin_theta; - rope_yarn(theta_base / freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta_base / freq_factor, freq_scale, corr_dims, i0, + ext_factor, attn_factor, cos_theta, sin_theta); - const float x0 = x[i2 + 0]; - const float x1 = x[i2 + 1]; + const float x0 = x[ix + 0]; + const float x1 = x[ix + 1]; - dst[i + 0] = x0 * cos_theta - x1 * sin_theta; - dst[i + 1] = x0 * sin_theta + x1 * cos_theta; + store_coaelsced(x0 * cos_theta - x1 * sin_theta, + x0 * sin_theta + x1 * cos_theta); } -template -static void rope_neox(const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, - const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor, - const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors, - const sycl::nd_item<3> & item_ct1) { - const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + item_ct1.get_local_id(1)); +template +static void rope_neox(const T *x, D *dst, const int ne00, const int ne01, + const int ne02, const int s01, const int s02, + const int s03, const int s1, const int s2, const int s3, + const int n_dims, const int32_t *pos, + const float freq_scale, const float ext_factor, + const float attn_factor, const rope_corr_dims corr_dims, + const float theta_scale, const float *freq_factors, + const int64_t *row_indices, const int set_rows_stride) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); - if (i0 >= ne0) { + if (i0 >= ne00) { return; } - const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + item_ct1.get_local_id(2); + const int row_dst = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); - const int row0 = row % ne1; - const int channel0 = row / ne1; + const uint32_t i3 = row_dst / (ne01 * ne02); + const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01; + const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01; - const int i = row * ne0 + i0 / 2; - const int i2 = channel0 * s2 + row0 * s1 + i0 / 2; + int idst = i0 / 2 + i1 * s1 + i2 * s2 + i3 * s3; + const int ix = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03; + + if (set_rows_stride != 0) { + idst = i1 * s1 + i0 / 2; + idst += row_indices[i2] * set_rows_stride; + } if (i0 >= n_dims) { - *reinterpret_cast *>(dst + i + i0 / 2) = *reinterpret_cast *>(x + i2 + i0 / 2); + dst[idst + i0 / 2 + 0] = ggml_sycl_cast(x[ix + i0 / 2 + 0]); + dst[idst + i0 / 2 + 1] = ggml_sycl_cast(x[ix + i0 / 2 + 1]); + return; } - const float theta_base = pos[channel0] * sycl::pow(theta_scale, i0 / 2.0f); + const float theta_base = pos[i2] * dpct::pow(theta_scale, i0 / 2.0f); const float freq_factor = has_ff ? freq_factors[i0 / 2] : 1.0f; float cos_theta; float sin_theta; - rope_yarn(theta_base / freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta_base / freq_factor, freq_scale, corr_dims, i0, + ext_factor, attn_factor, cos_theta, sin_theta); - const float x0 = x[i2 + 0]; - const float x1 = x[i2 + n_dims / 2]; + const float x0 = x[ix + 0]; + const float x1 = x[ix + n_dims / 2]; - dst[i + 0] = x0 * cos_theta - x1 * sin_theta; - dst[i + n_dims / 2] = x0 * sin_theta + x1 * cos_theta; + dst[idst + 0] = ggml_sycl_cast(x0 * cos_theta - x1 * sin_theta); + dst[idst + n_dims / 2] = ggml_sycl_cast(x0 * sin_theta + x1 * cos_theta); } -template -static void rope_multi(const T * x, T * dst, const int ne0, const int ne1, const int ne2, const size_t s1, - const size_t s2, const int n_dims, const int32_t * pos, const float freq_scale, - const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims, - const float theta_scale, const float * freq_factors, const mrope_sections sections, - const bool is_imrope, const sycl::nd_item<3> & item_ct1) { - // get index pos - const int i0 = 2 * (item_ct1.get_group(1) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1)); - if (i0 >= ne0) { +template +static void rope_multi(const T *x, T *dst, const int ne00, const int ne01, + const int ne02, const int s01, const int s02, + const int s03, const int s1, const int s2, const int s3, + const int n_dims, const int32_t *pos, + const float freq_scale, const float ext_factor, + const float attn_factor, const rope_corr_dims corr_dims, + const float theta_scale, const float *freq_factors, + const mrope_sections sections, const bool is_imrope) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); + + if (i0 >= ne00) { return; } - const int row_dst = (item_ct1.get_group(2) * item_ct1.get_local_range(2)) + item_ct1.get_local_id(2); - const int row_x = row_dst % ne1; - const int channel_x = row_dst / ne1; - const int idst = (row_dst * ne0) + (i0 / 2); - const size_t ix = ((size_t) channel_x * s2) + ((size_t) row_x * s1) + (i0 / 2); + const int row_dst = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + const uint32_t i3 = row_dst / (ne01 * ne02); + const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01; + const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01; + + int idst = i0 / 2 + i1 * s1 + i2 * s2 + i3 * s3; + const int ix = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03; if (i0 >= n_dims) { - *reinterpret_cast *>(dst + idst + i0 / 2) = *reinterpret_cast *>(x + i0 / 2 + ix); + dst[idst + i0 / 2 + 0] = x[ix + i0 / 2 + 0]; + dst[idst + i0 / 2 + 1] = x[ix + i0 / 2 + 1]; + return; } - const int sect_dims = sections.v[0] + sections.v[1] + sections.v[2] + sections.v[3]; + const int sect_dims = + sections.v[0] + sections.v[1] + sections.v[2] + sections.v[3]; const int sec_w = sections.v[1] + sections.v[0]; const int sector = (i0 / 2) % sect_dims; - float theta_base = 0.0; if (is_imrope) { - if (sector % 3 == 1 && sector < 3 * sections.v[1]) { - theta_base = pos[channel_x + ne2 * 1]*sycl::pow(theta_scale, i0/2.0f); - } else if (sector % 3 == 2 && sector < 3 * sections.v[2]) { - theta_base = pos[channel_x + ne2 * 2]*sycl::pow(theta_scale, i0/2.0f); - } else if (sector % 3 == 0 && sector < 3 * sections.v[0]) { - theta_base = pos[channel_x]*sycl::pow(theta_scale, i0/2.0f); + if (sector % 3 == 1 && sector < 3 * sections.v[1]) { // h + theta_base = pos[i2 + ne02 * 1] * dpct::pow(theta_scale, i0 / 2.0f); + } else if (sector % 3 == 2 && sector < 3 * sections.v[2]) { // w + theta_base = pos[i2 + ne02 * 2] * dpct::pow(theta_scale, i0 / 2.0f); + } else if (sector % 3 == 0 && sector < 3 * sections.v[0]) { // t + theta_base = pos[i2] * dpct::pow(theta_scale, i0 / 2.0f); } else { - theta_base = pos[channel_x + ne2 * 3]*sycl::pow(theta_scale, i0/2.0f); + theta_base = pos[i2 + ne02 * 3] * dpct::pow(theta_scale, i0 / 2.0f); } } else { if (sector < sections.v[0]) { - theta_base = pos[channel_x]*sycl::pow(theta_scale, i0/2.0f); - } - else if (sector >= sections.v[0] && sector < sec_w) { - theta_base = pos[channel_x + ne2 * 1]*sycl::pow(theta_scale, i0/2.0f); - } - else if (sector >= sec_w && sector < sec_w + sections.v[2]) { - theta_base = pos[channel_x + ne2 * 2]*sycl::pow(theta_scale, i0/2.0f); - } - else if (sector >= sec_w + sections.v[2]) { - theta_base = pos[channel_x + ne2 * 3]*sycl::pow(theta_scale, i0/2.0f); + theta_base = pos[i2] * dpct::pow(theta_scale, i0 / 2.0f); + } else if (sector >= sections.v[0] && sector < sec_w) { + theta_base = pos[i2 + ne02 * 1] * dpct::pow(theta_scale, i0 / 2.0f); + } else if (sector >= sec_w && sector < sec_w + sections.v[2]) { + theta_base = pos[i2 + ne02 * 2] * dpct::pow(theta_scale, i0 / 2.0f); + } else if (sector >= sec_w + sections.v[2]) { + theta_base = pos[i2 + ne02 * 3] * dpct::pow(theta_scale, i0 / 2.0f); } } const float freq_factor = has_ff ? freq_factors[i0 / 2] : 1.0f; - float cos_theta; - float sin_theta; - rope_yarn(theta_base / freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); - const float x0 = x[ix + 0]; - const float x1 = x[ix + n_dims/2]; - // store results in dst - dst[idst + 0] = x0 * cos_theta - x1 * sin_theta; - dst[idst + n_dims/2] = x0 * sin_theta + x1 * cos_theta; + float cos_theta; + float sin_theta; + + rope_yarn(theta_base / freq_factor, freq_scale, corr_dims, i0, + ext_factor, attn_factor, cos_theta, sin_theta); + + const float x0 = x[ix + 0]; + const float x1 = x[ix + n_dims / 2]; + + dst[idst + 0] = x0 * cos_theta - x1 * sin_theta; + dst[idst + n_dims / 2] = x0 * sin_theta + x1 * cos_theta; } +template +static void rope_vision(const T *x, T *dst, const int ne00, const int ne01, + const int ne02, const int s01, const int s02, + const int s03, const int s1, const int s2, const int s3, + const int n_dims, const int32_t *pos, + const float freq_scale, const float ext_factor, + const float attn_factor, const rope_corr_dims corr_dims, + const float theta_scale, const float *freq_factors, + const mrope_sections sections) { + auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); - -template -static void rope_vision(const T * x, T * dst, const int ne0, const int ne1, const int ne2, const size_t s1, - const size_t s2, const int n_dims, const int32_t * pos, const float freq_scale, - const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims, - const float theta_scale, const float * freq_factors, const mrope_sections sections, - const sycl::nd_item<3> & item_ct1) { - // get index pos - const int i0 = 2 * (item_ct1.get_group(1) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1)); - if (i0 >= ne0) { + if (i0 >= ne00) { return; } - const int row_dst = (item_ct1.get_group(2) * item_ct1.get_local_range(2)) + item_ct1.get_local_id(2); - const int row_x = row_dst % ne1; - const int channel_x = row_dst / ne1; - const int idst = (row_dst * ne0) + (i0 / 2); - const size_t ix = ((size_t) channel_x * s2) + ((size_t) row_x * s1) + (i0 / 2); + + const int row_dst = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + const uint32_t i3 = row_dst / (ne01 * ne02); + const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01; + const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01; + + int idst = i0 / 2 + i1 * s1 + i2 * s2 + i3 * s3; + const int ix = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03; const int sect_dims = sections.v[0] + sections.v[1]; - const int sector = (i0 / 2) % sect_dims; + const int sec_w = sections.v[1] + sections.v[0]; + const int sector = (i0 / 2) % sect_dims; - float theta_base = 0.0f; + float theta_base = 0.0; if (sector < sections.v[0]) { const int p = sector; - theta_base = pos[channel_x] * sycl::pow(theta_scale, (float) p); - } else { + theta_base = pos[i2] * dpct::pow(theta_scale, p); + } else if (sector >= sections.v[0] && sector < sec_w) { const int p = sector - sections.v[0]; - theta_base = pos[channel_x + ne2] * sycl::pow(theta_scale, (float) p); + theta_base = pos[i2 + ne02] * dpct::pow(theta_scale, p); } const float freq_factor = has_ff ? freq_factors[i0 / 2] : 1.0f; - float cos_theta; - float sin_theta; - rope_yarn(theta_base / freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + + float cos_theta; + float sin_theta; + + rope_yarn(theta_base / freq_factor, freq_scale, corr_dims, i0, + ext_factor, attn_factor, cos_theta, sin_theta); + const float x0 = x[ix + 0]; const float x1 = x[ix + n_dims]; - // store results in dst - dst[idst + 0] = x0 * cos_theta - x1 * sin_theta; + dst[idst + 0] = x0 * cos_theta - x1 * sin_theta; dst[idst + n_dims] = x0 * sin_theta + x1 * cos_theta; } -template -static void rope_norm_sycl(const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, - const int n_dims, int nr, const int32_t * pos, const float freq_scale, const float freq_base, - const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims, - const float * freq_factors, queue_ptr stream) { - GGML_ASSERT(ne0 % 2 == 0); - const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); - const int num_blocks_x = ceil_div(ne0, (2 * SYCL_ROPE_BLOCK_SIZE)); - const sycl::range<3> block_nums(1, num_blocks_x, nr); +template +static void +rope_norm_sycl(const T *x, D *dst, const int ne00, const int ne01, + const int ne02, const int s01, const int s02, const int s03, + const int s1, const int s2, const int s3, const int n_dims, + const int nr, const int32_t *pos, const float freq_scale, + const float freq_base, const float ext_factor, + const float attn_factor, const rope_corr_dims corr_dims, + const float *freq_factors, const int64_t *row_indices, + const int set_rows_stride, dpct::queue_ptr stream) { + GGML_ASSERT(ne00 % 2 == 0); + const dpct::dim3 block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); + const int n_blocks_x = + (ne00 + 2 * SYCL_ROPE_BLOCK_SIZE - 1) / (2 * SYCL_ROPE_BLOCK_SIZE); + const dpct::dim3 block_nums(nr, n_blocks_x, 1); const float theta_scale = powf(freq_base, -2.0f / n_dims); - dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - if (freq_factors == nullptr) { - /* - DPCT1049:40: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - rope_norm(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, - theta_scale, freq_factors, item_ct1); - }); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + rope_norm( + x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, + pos, freq_scale, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors, row_indices, set_rows_stride); + }); } else { - /* - DPCT1049:41: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - rope_norm(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, - theta_scale, freq_factors, item_ct1); - }); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + rope_norm( + x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, + pos, freq_scale, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors, row_indices, set_rows_stride); + }); } } -template -static void rope_neox_sycl(const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, - const int n_dims, const int nr, const int32_t * pos, const float freq_scale, - const float freq_base, const float ext_factor, const float attn_factor, - const rope_corr_dims corr_dims, const float * freq_factors, queue_ptr stream) { - GGML_ASSERT(ne0 % 2 == 0); - const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); - const int num_blocks_x = ceil_div(ne0, (2 * SYCL_ROPE_BLOCK_SIZE)); - const sycl::range<3> block_nums(1, num_blocks_x, nr); +template +static void +rope_neox_sycl(const T *x, D *dst, const int ne00, const int ne01, + const int ne02, const int s01, const int s02, const int s03, + const int s1, const int s2, const int s3, const int n_dims, + const int nr, const int32_t *pos, const float freq_scale, + const float freq_base, const float ext_factor, + const float attn_factor, const rope_corr_dims corr_dims, + const float *freq_factors, const int64_t *row_indices, + const int set_rows_stride, dpct::queue_ptr stream) { + GGML_ASSERT(ne00 % 2 == 0); + const dpct::dim3 block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); + const int n_blocks_x = + (ne00 + 2 * SYCL_ROPE_BLOCK_SIZE - 1) / (2 * SYCL_ROPE_BLOCK_SIZE); + const dpct::dim3 block_nums(nr, n_blocks_x, 1); const float theta_scale = powf(freq_base, -2.0f / n_dims); - dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - if (freq_factors == nullptr) { - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, - theta_scale, freq_factors, item_ct1); - }); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + rope_neox( + x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, + pos, freq_scale, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors, row_indices, set_rows_stride); + }); } else { - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, - theta_scale, freq_factors, item_ct1); - }); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + rope_neox( + x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, + pos, freq_scale, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors, row_indices, set_rows_stride); + }); } } -template -static void rope_multi_sycl(const T * x, T * dst, const int ne0, const int ne1, const int ne2, const size_t s1, - const size_t s2, const int n_dims, const int nr, const int32_t * pos, - const float freq_scale, const float freq_base, const float ext_factor, - const float attn_factor, const rope_corr_dims corr_dims, const float * freq_factors, - const mrope_sections sections, const bool is_imrope, queue_ptr stream) { - GGML_ASSERT(ne0 % 2 == 0); - const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); - const int n_blocks_y = ceil_div(ne0, (2 * SYCL_ROPE_BLOCK_SIZE)); - const sycl::range<3> grid_dims(1, n_blocks_y, nr); - const sycl::nd_range<3> nd_range(grid_dims * block_dims, block_dims); +template +static void +rope_multi_sycl(const T *x, T *dst, const int ne00, const int ne01, + const int ne02, const int s01, const int s02, const int s03, + const int s1, const int s2, const int s3, const int n_dims, + const int nr, const int32_t *pos, const float freq_scale, + const float freq_base, const float ext_factor, + const float attn_factor, const rope_corr_dims corr_dims, + const float *freq_factors, const mrope_sections sections, + const bool is_imrope, dpct::queue_ptr stream) { + GGML_ASSERT(ne00 % 2 == 0); + const dpct::dim3 block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); + const int n_blocks_x = + (ne00 + 2 * SYCL_ROPE_BLOCK_SIZE - 1) / (2 * SYCL_ROPE_BLOCK_SIZE); + const dpct::dim3 block_nums(nr, n_blocks_x, 1); + + const float theta_scale = powf(freq_base, -2.0f / n_dims); - const float theta_scale = std::pow(freq_base, -2.0f / n_dims); - // Add FP16 capability check if T could be sycl::half - if constexpr (std::is_same_v) { - dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - } - // launch kernel if (freq_factors == nullptr) { - stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) { - rope_multi(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, - corr_dims, theta_scale, freq_factors, sections, is_imrope, item_ct1); - }); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + rope_multi( + x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, + pos, freq_scale, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors, sections, is_imrope); + }); } else { - stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) { - rope_multi(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, - corr_dims, theta_scale, freq_factors, sections, is_imrope, item_ct1); - }); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + rope_multi( + x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, + pos, freq_scale, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors, sections, is_imrope); + }); } } +template +static void +rope_vision_sycl(const T *x, T *dst, const int ne00, const int ne01, + const int ne02, const int s01, const int s02, const int s03, + const int s1, const int s2, const int s3, const int n_dims, + const int nr, const int32_t *pos, const float freq_scale, + const float freq_base, const float ext_factor, + const float attn_factor, const rope_corr_dims corr_dims, + const float *freq_factors, const mrope_sections sections, + dpct::queue_ptr stream) { + GGML_ASSERT(ne00 % 2 == 0); + const dpct::dim3 block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); + const int n_blocks_x = + (ne00 + 2 * SYCL_ROPE_BLOCK_SIZE - 1) / (2 * SYCL_ROPE_BLOCK_SIZE); + const dpct::dim3 block_nums(nr, n_blocks_x, 1); + const float theta_scale = powf(freq_base, -2.0f / n_dims); - -// rope vision -template -static void rope_vision_sycl(const T * x, T * dst, const int ne0, const int ne1, const int ne2, const size_t s1, - const size_t s2, const int n_dims, const int nr, const int32_t * pos, - const float freq_scale, const float freq_base, const float ext_factor, - const float attn_factor, const rope_corr_dims corr_dims, const float * freq_factors, - const mrope_sections sections, queue_ptr stream) { - GGML_ASSERT(ne0 % 2 == 0); - const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); - const int n_blocks_y = ceil_div(ne0, (2 * SYCL_ROPE_BLOCK_SIZE)); - const sycl::range<3> grid_dims(1, n_blocks_y, nr); - const sycl::nd_range<3> nd_range(grid_dims * block_dims, block_dims); - - const float theta_scale = std::pow(freq_base, -2.0f / n_dims); - // Add FP16 capability check if T could be sycl::half - if constexpr (std::is_same_v) { - dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 }); - } - // launch kernel if (freq_factors == nullptr) { - stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) { - rope_vision(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, - corr_dims, theta_scale, freq_factors, sections, item_ct1); - }); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + rope_vision( + x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, + pos, freq_scale, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors, sections); + }); } else { - stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) { - rope_vision(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, - corr_dims, theta_scale, freq_factors, sections, item_ct1); - }); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + GGML_UNUSED(item_ct1); + rope_vision( + x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, + pos, freq_scale, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors, sections); + }); } } -inline void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, ggml_tensor *dst) { +template +void ggml_sycl_op_rope_impl(ggml_backend_sycl_context &ctx, ggml_tensor *dst, + const ggml_tensor *set_rows = nullptr) { + const ggml_tensor *src0 = dst->src[0]; + const ggml_tensor *src1 = dst->src[1]; + const ggml_tensor *src2 = dst->src[2]; - GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16); - GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); - GGML_ASSERT(dst->src[0]->type == dst->type); - const int64_t ne00 = dst->src[0]->ne[0]; // head dims - const int64_t ne01 = dst->src[0]->ne[1]; // num heads - const int64_t ne02 = dst->src[0]->ne[2]; // num heads - const int64_t nr = ggml_nrows(dst->src[0]); + const float *src0_d = (const float *)src0->data; + const float *src1_d = (const float *)src1->data; - const size_t s01 = dst->src[0]->nb[1] / ggml_type_size(dst->src[0]->type); - const size_t s02 = dst->src[0]->nb[2] / ggml_type_size(dst->src[0]->type); + void *dst_d = dst->data; + const int64_t *row_indices = nullptr; + ggml_type dst_type = dst->type; + int set_rows_stride = 0; + if (set_rows != nullptr) { + GGML_ASSERT(forward); + dst_d = set_rows->data; + row_indices = (const int64_t *)set_rows->src[1]->data; + dst_type = set_rows->type; + set_rows_stride = set_rows->nb[1] / ggml_type_size(set_rows->type); + } + dpct::queue_ptr stream = ctx.stream(); - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - //const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + GGML_ASSERT(src0->type == dst->type || + (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16)); + + const int64_t ne00 = src0->ne[0]; // head dims + const int64_t ne01 = src0->ne[1]; // num heads + const int64_t ne02 = src0->ne[2]; // num heads + const int64_t nr = ggml_nrows(src0); + + const size_t s01 = src0->nb[1] / ggml_type_size(src0->type); + const size_t s02 = src0->nb[2] / ggml_type_size(src0->type); + const size_t s03 = src0->nb[3] / ggml_type_size(src0->type); + + const size_t s1 = dst->nb[1] / ggml_type_size(dst->type); + const size_t s2 = dst->nb[2] / ggml_type_size(dst->type); + const size_t s3 = dst->nb[3] / ggml_type_size(dst->type); + + const int n_dims = ((int32_t *)dst->op_params)[1]; + const int mode = ((int32_t *)dst->op_params)[2]; + const int n_ctx_orig = ((int32_t *)dst->op_params)[4]; mrope_sections sections; - // RoPE alteration for extended context float freq_base; float freq_scale; float ext_factor; @@ -382,13 +506,13 @@ inline void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, ggml_tensor *dst) float beta_fast; float beta_slow; - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - memcpy(§ions.v, (int32_t *) dst->op_params + 11, sizeof(int)*4); + memcpy(&freq_base, (int32_t *)dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *)dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *)dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *)dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *)dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *)dst->op_params + 10, sizeof(float)); + memcpy(§ions.v, (int32_t *)dst->op_params + 11, sizeof(int) * 4); const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE; @@ -396,82 +520,122 @@ inline void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, ggml_tensor *dst) const bool is_vision = mode == GGML_ROPE_TYPE_VISION; if (is_mrope) { - GGML_ASSERT(sections.v[0] > 0 || sections.v[1] > 0 || sections.v[2] > 0); + GGML_ASSERT(sections.v[0] > 0 || sections.v[1] > 0 || + sections.v[2] > 0); } if (is_vision) { - GGML_ASSERT(n_dims == ne00/2); + GGML_ASSERT(n_dims == ne00 / 2); } - const int32_t * pos = (const int32_t *) dst->src[1]->data; + const int32_t *pos = (const int32_t *)src1_d; - const float * freq_factors = nullptr; - if (dst->src[2] != nullptr) { - freq_factors = (const float *) dst->src[2]->data; + const float *freq_factors = nullptr; + if (src2 != nullptr) { + freq_factors = (const float *)src2->data; } rope_corr_dims corr_dims; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v); - - dpct::queue_ptr main_stream = ctx.stream(); - SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, + beta_slow, corr_dims.v); // compute if (is_neox) { GGML_SYCL_DEBUG("%s: neox path\n", __func__); - if (dst->src[0]->type == GGML_TYPE_F32) { - rope_neox_sycl((const float *) dst->src[0]->data, (float *) dst->data, ne00, ne01, s01, s02, n_dims, nr, - pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, main_stream); - } else if (dst->src[0]->type == GGML_TYPE_F16) { - rope_neox_sycl((const sycl::half *) dst->src[0]->data, (sycl::half *) dst->data, ne00, ne01, s01, s02, - n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, - main_stream); + if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) { + rope_neox_sycl( + (const float *)src0_d, (float *)dst_d, ne00, ne01, ne02, s01, + s02, s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base, + ext_factor, attn_factor, corr_dims, freq_factors, row_indices, + set_rows_stride, stream); + } else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) { + rope_neox_sycl( + (const float *)src0_d, (sycl::half *)dst_d, ne00, ne01, ne02, + s01, s02, s03, s1, s2, s3, n_dims, nr, pos, freq_scale, + freq_base, ext_factor, attn_factor, corr_dims, freq_factors, + row_indices, set_rows_stride, stream); + } else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) { + rope_neox_sycl( + (const sycl::half *)src0_d, (sycl::half *)dst_d, ne00, ne01, + ne02, s01, s02, s03, s1, s2, s3, n_dims, nr, pos, freq_scale, + freq_base, ext_factor, attn_factor, corr_dims, freq_factors, + row_indices, set_rows_stride, stream); } else { - GGML_ABORT("fatal error"); + GGML_ABORT("Fatal error: Tensor type unsupported!"); } } else if (is_mrope && !is_vision) { GGML_SYCL_DEBUG("%s: mrope path\n", __func__); - if (dst->src[0]->type == GGML_TYPE_F16) { - rope_multi_sycl((const sycl::half *)dst->src[0]->data, (sycl::half *)dst->data, ne00, ne01, ne02, s01, - s02, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims, - freq_factors, sections, is_imrope, main_stream); - } else if (dst->src[0]->type == GGML_TYPE_F32) { - rope_multi_sycl((const float *) dst->src[0]->data, (float *) dst->data, ne00, ne01, ne02, s01, s02, n_dims, - nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, - is_imrope, main_stream); + if (src0->type == GGML_TYPE_F32) { + rope_multi_sycl((const float *)src0_d, (float *)dst_d, + ne00, ne01, ne02, s01, s02, s03, s1, s2, + s3, n_dims, nr, pos, freq_scale, freq_base, + ext_factor, attn_factor, corr_dims, + freq_factors, sections, is_imrope, stream); + } else if (src0->type == GGML_TYPE_F16) { + rope_multi_sycl( + (const sycl::half *)src0_d, (sycl::half *)dst_d, ne00, ne01, + ne02, s01, s02, s03, s1, s2, s3, n_dims, nr, pos, freq_scale, + freq_base, ext_factor, attn_factor, corr_dims, freq_factors, + sections, is_imrope, stream); } else { GGML_ABORT("Fatal error: Tensor type unsupported!"); } } else if (is_vision) { GGML_SYCL_DEBUG("%s: vision path\n", __func__); - if (dst->src[0]->type == GGML_TYPE_F16) { - rope_vision_sycl((const sycl::half *) dst->src[0]->data, (sycl::half *) dst->data, ne00, ne01, ne02, s01, - s02, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims, - freq_factors, sections, main_stream); - } else if (dst->src[0]->type == GGML_TYPE_F32) { - rope_vision_sycl((const float *) dst->src[0]->data, (float *) dst->data, ne00, ne01, ne02, s01, s02, n_dims, - nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, - main_stream); + if (src0->type == GGML_TYPE_F32) { + rope_vision_sycl( + (const float *)src0_d, (float *)dst_d, ne00, ne01, ne02, s01, + s02, s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base, + ext_factor, attn_factor, corr_dims, freq_factors, sections, + stream); + } else if (src0->type == GGML_TYPE_F16) { + rope_vision_sycl( + (const sycl::half *)src0_d, (sycl::half *)dst_d, ne00, ne01, + ne02, s01, s02, s03, s1, s2, s3, n_dims, nr, pos, freq_scale, + freq_base, ext_factor, attn_factor, corr_dims, freq_factors, + sections, stream); } else { GGML_ABORT("Fatal error: Tensor type unsupported!"); } } else { GGML_SYCL_DEBUG("%s: norm path\n", __func__); - if (dst->src[0]->type == GGML_TYPE_F32) { - rope_norm_sycl((const float *) dst->src[0]->data, (float *) dst->data, ne00, ne01, s01, s02, n_dims, nr, - pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, main_stream); - } else if (dst->src[0]->type == GGML_TYPE_F16) { - rope_norm_sycl((const sycl::half *) dst->src[0]->data, (sycl::half *) dst->data, ne00, ne01, s01, s02, - n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, - main_stream); + if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) { + rope_norm_sycl( + (const float *)src0_d, (float *)dst_d, ne00, ne01, ne02, s01, + s02, s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base, + ext_factor, attn_factor, corr_dims, freq_factors, row_indices, + set_rows_stride, stream); + } else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) { + rope_norm_sycl( + (const float *)src0_d, (sycl::half *)dst_d, ne00, ne01, ne02, + s01, s02, s03, s1, s2, s3, n_dims, nr, pos, freq_scale, + freq_base, ext_factor, attn_factor, corr_dims, freq_factors, + row_indices, set_rows_stride, stream); + } else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) { + rope_norm_sycl( + (const sycl::half *)src0_d, (sycl::half *)dst_d, ne00, ne01, + ne02, s01, s02, s03, s1, s2, s3, n_dims, nr, pos, freq_scale, + freq_base, ext_factor, attn_factor, corr_dims, freq_factors, + row_indices, set_rows_stride, stream); } else { - GGML_ABORT("fatal error"); + GGML_ABORT("Fatal error: Tensor type unsupported!"); } } } -void ggml_sycl_rope(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { +void ggml_sycl_rope(ggml_backend_sycl_context &ctx, ggml_tensor *dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/3); - ggml_sycl_op_rope(ctx, dst); + + ggml_sycl_op_rope_impl(ctx, dst); } +void ggml_sycl_rope_back(ggml_backend_sycl_context &ctx, ggml_tensor *dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/3); + ggml_sycl_op_rope_impl(ctx, dst); +} + +void ggml_sycl_rope_fused(ggml_backend_sycl_context &ctx, ggml_tensor *rope, + ggml_tensor *set_rows) { + scope_op_debug_print scope_dbg_print(__func__, rope, /*num_src=*/3); + ggml_sycl_op_rope_impl(ctx, rope, set_rows); +} diff --git a/ggml/src/ggml-sycl/rope.hpp b/ggml/src/ggml-sycl/rope.hpp index 8c7141aac5..b95a585808 100644 --- a/ggml/src/ggml-sycl/rope.hpp +++ b/ggml/src/ggml-sycl/rope.hpp @@ -15,6 +15,12 @@ #include "common.hpp" +#define SYCL_ROPE_BLOCK_SIZE 256 + void ggml_sycl_rope(ggml_backend_sycl_context & ctx, ggml_tensor *dst); +void ggml_sycl_rope_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +void ggml_sycl_rope_fused(ggml_backend_sycl_context & ctx, ggml_tensor * dst, ggml_tensor * set_rows); + #endif // GGML_SYCL_ROPE_HPP diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp index b41124acc1..fdf9b843e0 100644 --- a/ggml/src/ggml-sycl/softmax.cpp +++ b/ggml/src/ggml-sycl/softmax.cpp @@ -37,7 +37,7 @@ struct soft_max_params { }; // When ncols_template == 0 the bounds for the loops in this function are not known and can't be unrolled. -// As we want to keep pragma unroll for all other cases we supress the clang transformation warning here. +// As we want to keep pragma unroll for all other cases we suppress the clang transformation warning here. #ifdef __clang__ #pragma clang diagnostic push #pragma clang diagnostic ignored "-Wpass-failed" @@ -102,7 +102,7 @@ static void soft_max_f32(const float * x, max_val = sycl::max(max_val, val); } // find the max value in the block - max_val = warp_reduce_max(max_val); + max_val = warp_reduce_max(max_val); if (block_size > WARP_SIZE) { if (warp_id == 0) { @@ -116,7 +116,7 @@ static void soft_max_f32(const float * x, item_ct1.barrier(); max_val = buf_iw[lane_id]; - max_val = warp_reduce_max(max_val); + max_val = warp_reduce_max(max_val); } float tmp = 0.0f; // partial sum @@ -133,7 +133,7 @@ static void soft_max_f32(const float * x, vals[col] = val; } // find the sum of exps in the block - tmp = warp_reduce_sum(tmp); + tmp = warp_reduce_sum(tmp); if (block_size > WARP_SIZE) { item_ct1.barrier(); if (warp_id == 0) { @@ -153,7 +153,7 @@ static void soft_max_f32(const float * x, for (size_t i = 1; i < nreduce; i += 1) { tmp += buf_iw[lane_id + i * WARP_SIZE]; } - tmp = warp_reduce_sum(tmp); + tmp = warp_reduce_sum(tmp); } if (sinks) { tmp += sycl::native::exp(sinks[i02] - max_val); @@ -191,7 +191,7 @@ static void soft_max_back_f32(const float *grad, const float *dstf, float *dst, dgf_dot += dstf[col]*grad[col]; } - dgf_dot = warp_reduce_sum(dgf_dot); + dgf_dot = warp_reduce_sum(dgf_dot); for (int col = tid; col < ncols; col += WARP_SIZE) { dst[col] = scale * (grad[col] - dgf_dot) * dstf[col]; diff --git a/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq112-dv112.cpp b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq112-dv112.cpp new file mode 100644 index 0000000000..5c06d42fdb --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq112-dv112.cpp @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.hpp" + +DECL_FATTN_TILE_CASE(112, 112); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq128-dv128.cpp b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq128-dv128.cpp new file mode 100644 index 0000000000..f74e1202b8 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq128-dv128.cpp @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.hpp" + +DECL_FATTN_TILE_CASE(128, 128); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq256-dv256.cpp b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq256-dv256.cpp new file mode 100644 index 0000000000..b574fe9308 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq256-dv256.cpp @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.hpp" + +DECL_FATTN_TILE_CASE(256, 256); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq40-dv40.cpp b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq40-dv40.cpp new file mode 100644 index 0000000000..8c8fb692c4 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq40-dv40.cpp @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.hpp" + +DECL_FATTN_TILE_CASE(40, 40); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq576-dv512.cpp b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq576-dv512.cpp new file mode 100644 index 0000000000..f218552e85 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq576-dv512.cpp @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.hpp" + +DECL_FATTN_TILE_CASE(576, 512); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq64-dv64.cpp b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq64-dv64.cpp new file mode 100644 index 0000000000..99303a53a3 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq64-dv64.cpp @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.hpp" + +DECL_FATTN_TILE_CASE(64, 64); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq72-dv72.cpp b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq72-dv72.cpp new file mode 100644 index 0000000000..50592768af --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq72-dv72.cpp @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.hpp" + +DECL_FATTN_TILE_CASE(72, 72); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq80-dv80.cpp b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq80-dv80.cpp new file mode 100644 index 0000000000..74f1ea5e90 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq80-dv80.cpp @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.hpp" + +DECL_FATTN_TILE_CASE(80, 80); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq96-dv96.cpp b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq96-dv96.cpp new file mode 100644 index 0000000000..cefb46dddc --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-tile-instance-dkq96-dv96.cpp @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-tile.hpp" + +DECL_FATTN_TILE_CASE(96, 96); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-f16.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-f16.cpp new file mode 100644 index 0000000000..32cf4f2859 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-f16.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q4_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q4_0.cpp new file mode 100644 index 0000000000..a61a19021b --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q4_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q4_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q4_1.cpp new file mode 100644 index 0000000000..63b74fb347 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q4_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q5_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q5_0.cpp new file mode 100644 index 0000000000..46e2d9853c --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q5_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q5_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q5_1.cpp new file mode 100644 index 0000000000..7aabb6ff6e --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q5_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q8_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q8_0.cpp new file mode 100644 index 0000000000..148ea217f6 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-f16-q8_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-f16.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-f16.cpp new file mode 100644 index 0000000000..4b169dbcdb --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-f16.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_F16); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q4_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q4_0.cpp new file mode 100644 index 0000000000..79f530b181 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q4_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q4_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q4_1.cpp new file mode 100644 index 0000000000..2f7db51ce8 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q4_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q5_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q5_0.cpp new file mode 100644 index 0000000000..9e3bf0b14a --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q5_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q5_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q5_1.cpp new file mode 100644 index 0000000000..18081879ce --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q5_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q8_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q8_0.cpp new file mode 100644 index 0000000000..1c387b0d87 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_0-q8_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-f16.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-f16.cpp new file mode 100644 index 0000000000..f005b3762c --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-f16.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_F16); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q4_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q4_0.cpp new file mode 100644 index 0000000000..3553b1cdd1 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q4_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q4_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q4_1.cpp new file mode 100644 index 0000000000..687ec56711 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q4_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q5_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q5_0.cpp new file mode 100644 index 0000000000..2663bfe746 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q5_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q5_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q5_1.cpp new file mode 100644 index 0000000000..641b7c7ae2 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q5_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q8_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q8_0.cpp new file mode 100644 index 0000000000..3d3181d471 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q4_1-q8_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-f16.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-f16.cpp new file mode 100644 index 0000000000..85d5026ad4 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-f16.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_F16); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q4_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q4_0.cpp new file mode 100644 index 0000000000..1e81401a2c --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q4_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q4_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q4_1.cpp new file mode 100644 index 0000000000..54251473f9 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q4_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q5_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q5_0.cpp new file mode 100644 index 0000000000..d418c1fb21 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q5_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q5_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q5_1.cpp new file mode 100644 index 0000000000..0f26cfabd0 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q5_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q8_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q8_0.cpp new file mode 100644 index 0000000000..4fb9872351 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_0-q8_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-f16.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-f16.cpp new file mode 100644 index 0000000000..85b79cd197 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-f16.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_F16); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q4_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q4_0.cpp new file mode 100644 index 0000000000..7348323b28 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q4_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q4_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q4_1.cpp new file mode 100644 index 0000000000..f19af2aa0b --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q4_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q5_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q5_0.cpp new file mode 100644 index 0000000000..d7075bac60 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q5_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q5_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q5_1.cpp new file mode 100644 index 0000000000..627f9a5775 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q5_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q8_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q8_0.cpp new file mode 100644 index 0000000000..23304eecd3 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q5_1-q8_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-f16.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-f16.cpp new file mode 100644 index 0000000000..95acb5d4fb --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-f16.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q8_0, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q8_0, GGML_TYPE_F16); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q4_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q4_0.cpp new file mode 100644 index 0000000000..5e88f4bab8 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q4_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q4_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q4_1.cpp new file mode 100644 index 0000000000..69f297feb0 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q4_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q5_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q5_0.cpp new file mode 100644 index 0000000000..455842a942 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q5_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q5_1.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q5_1.cpp new file mode 100644 index 0000000000..f7ef739157 --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q5_1.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); diff --git a/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q8_0.cpp b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q8_0.cpp new file mode 100644 index 0000000000..1c633bdf2f --- /dev/null +++ b/ggml/src/ggml-sycl/template-instances/fattn-vec-instance-q8_0-q8_0.cpp @@ -0,0 +1,7 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec.hpp" + +DECL_FATTN_VEC_CASE( 64, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); +DECL_FATTN_VEC_CASE(256, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp index 43482b3672..9a267d85a0 100644 --- a/ggml/src/ggml-sycl/vecdotq.hpp +++ b/ggml/src/ggml-sycl/vecdotq.hpp @@ -650,6 +650,19 @@ static __dpct_inline__ float vec_dot_q8_0_q8_1_impl(const int *v, const int *u, return d8_0*d8_1 * sumi; } +template +static __dpct_inline__ T vec_dot_q8_0_q8_1_impl(const int * v, const int * u, const T & d8_0, const T & d8_1) { + int sumi = 0; + +#pragma unroll + for (int i = 0; i < vdr; ++i) { + // SIMD dot product of quantized values + sumi = ggml_sycl_dp4a(v[i], u[i], sumi); + } + + return d8_0*d8_1 * ((T) sumi); +} + template static __dpct_inline__ float vec_dot_q8_1_q8_1_impl(const int *v, const int *u, const sycl::half2 &dm8, diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp index cc879e51d0..03a037f1cb 100644 --- a/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp @@ -7,9 +7,21 @@ #include +static uint32_t validate_graph_operation(size_t cgraph_size, uint32_t shmem_res_id, const char * operation) { + if (cgraph_size == 0) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Zero-size computation graph\n", operation); + return 1; + } + + // place-holder: validate that the size of shmem_res_id is <= cgraph_size + // need to add another method in the Virgl->APIR callback interface + GGML_UNUSED(shmem_res_id); + + return 0; // Valid +} + uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { GGML_UNUSED(ctx); - GGML_UNUSED(enc); static bool async_backend_initialized = false; static bool async_backend; @@ -34,10 +46,26 @@ uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, v size_t cgraph_size; apir_decode_size_t(dec, &cgraph_size); + if (validate_graph_operation(cgraph_size, shmem_res_id, __func__) != 0) { + apir_decoder_set_fatal(dec); + return 1; + } + apir_decoder secondary_dec = apir_new_decoder((const char *) shmem_data, cgraph_size); ggml_cgraph * cgraph = apir_decode_ggml_cgraph(&secondary_dec, cgraph_size); + if (!cgraph || apir_decoder_get_fatal(&secondary_dec)) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Failed to deserialize computation graph\n", __func__); + return 1; + } + + if (cgraph->n_nodes < 0 || cgraph->n_leafs < 0) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid negative node/leaf count: nodes=%d leafs=%d\n", __func__, + cgraph->n_nodes, cgraph->n_leafs); + return 1; + } + ggml_status status; #if APIR_BACKEND_CHECK_SUPPORTS_OP == 1 for (int idx = 0; idx < cgraph->n_nodes; idx++) { @@ -45,7 +73,8 @@ uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, v if (dev->iface.supports_op(dev, op)) { continue; } - GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Graph node %d (%s) not supported by the backend\n", idx, ggml_op_desc(op)); + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Graph node %d (%s) not supported by the backend\n", __func__, idx, + ggml_op_desc(op)); status = GGML_STATUS_ABORTED; apir_encode_ggml_status(enc, &status); @@ -53,9 +82,17 @@ uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, v return 0; } #endif + + // Check if backend is properly initialized + if (!bck) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Backend not initialized (bck is null)\n", __func__); + + return 1; + } + status = bck->iface.graph_compute(bck, cgraph); - if (async_backend) { + if (async_backend && bck->iface.synchronize) { bck->iface.synchronize(bck); } diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp index d55eec2761..c66dbaa9e8 100644 --- a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp @@ -85,7 +85,19 @@ uint32_t backend_buffer_type_get_alloc_size(apir_encoder * enc, apir_decoder * d const ggml_tensor * op = apir_decode_ggml_tensor_inplace(dec); - size_t value = buft->iface.get_alloc_size(buft, op); + // Check for decode error + if (op == nullptr) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Failed to decode tensor\n", __func__); + apir_decoder_set_fatal(dec); + return 1; + } + + size_t value; + if (buft->iface.get_alloc_size) { + value = buft->iface.get_alloc_size(buft, op); + } else { + value = ggml_nbytes(op); // Default fallback + } apir_encode_size_t(enc, &value); diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp index 8cc063ff0a..3ade8d99b4 100644 --- a/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp @@ -6,11 +6,26 @@ #include +static uint32_t validate_buffer_operation(size_t offset, size_t size, const char * operation) { + // Only check for critical integer overflow - no arbitrary size limits + if (offset > SIZE_MAX - size) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Integer overflow in offset+size: %zu + %zu\n", operation, offset, size); + return 1; + } + + return 0; // Valid +} + uint32_t backend_buffer_get_base(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { GGML_UNUSED(ctx); ggml_backend_buffer_t buffer; buffer = apir_decode_ggml_buffer(dec); + if (!buffer || apir_decoder_get_fatal(dec)) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); + return 1; + } + uintptr_t base = (uintptr_t) buffer->iface.get_base(buffer); apir_encode_uintptr_t(enc, &base); @@ -24,6 +39,11 @@ uint32_t backend_buffer_set_tensor(apir_encoder * enc, apir_decoder * dec, virgl ggml_backend_buffer_t buffer; buffer = apir_decode_ggml_buffer(dec); + if (!buffer || apir_decoder_get_fatal(dec)) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); + return 1; + } + ggml_tensor * tensor; // safe to remove the const qualifier here tensor = (ggml_tensor *) (uintptr_t) apir_decode_ggml_tensor(dec); @@ -37,6 +57,10 @@ uint32_t backend_buffer_set_tensor(apir_encoder * enc, apir_decoder * dec, virgl size_t size; apir_decode_size_t(dec, &size); + if (validate_buffer_operation(offset, size, __func__) != 0) { + return 1; + } + void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); if (!shmem_data) { @@ -56,6 +80,11 @@ uint32_t backend_buffer_get_tensor(apir_encoder * enc, apir_decoder * dec, virgl ggml_backend_buffer_t buffer; buffer = apir_decode_ggml_buffer(dec); + if (!buffer || apir_decoder_get_fatal(dec)) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); + return 1; + } + const ggml_tensor * tensor; // safe to remove the const qualifier here tensor = apir_decode_ggml_tensor(dec); @@ -69,6 +98,10 @@ uint32_t backend_buffer_get_tensor(apir_encoder * enc, apir_decoder * dec, virgl size_t size; apir_decode_size_t(dec, &size); + if (validate_buffer_operation(offset, size, __func__) != 0) { + return 1; + } + void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); if (!shmem_data) { GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__); @@ -86,6 +119,11 @@ uint32_t backend_buffer_cpy_tensor(apir_encoder * enc, apir_decoder * dec, virgl ggml_backend_buffer_t buffer; buffer = apir_decode_ggml_buffer(dec); + if (!buffer || apir_decoder_get_fatal(dec)) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); + return 1; + } + const ggml_tensor * src; // safe to remove the const qualifier here src = apir_decode_ggml_tensor(dec); @@ -105,6 +143,11 @@ uint32_t backend_buffer_clear(apir_encoder * enc, apir_decoder * dec, virgl_apir ggml_backend_buffer_t buffer; buffer = apir_decode_ggml_buffer(dec); + if (!buffer || apir_decoder_get_fatal(dec)) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); + return 1; + } + uint8_t value; apir_decode_uint8_t(dec, &value); @@ -120,6 +163,11 @@ uint32_t backend_buffer_free_buffer(apir_encoder * enc, apir_decoder * dec, virg ggml_backend_buffer_t buffer; buffer = apir_decode_ggml_buffer(dec); + if (!buffer || apir_decoder_get_fatal(dec)) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); + return 1; + } + if (!apir_untrack_backend_buffer(buffer)) { GGML_LOG_WARN(GGML_VIRTGPU_BCK "%s: unknown buffer %p\n", __func__, (void *) buffer); return 1; diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp b/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp index 64152eef0d..c80e4aabe1 100644 --- a/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp @@ -1,6 +1,6 @@ #include "backend-dispatched.h" -#include "backend-virgl-apir.h" +#include "backend-virgl-apir.h" #include "ggml-backend-impl.h" #include "ggml-backend.h" #include "ggml-impl.h" @@ -28,19 +28,24 @@ uint32_t backend_dispatch_initialize(void * ggml_backend_reg_fct_p) { return APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED; } - if (!reg->iface.get_device_count(reg)) { - GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed: no device found\n", __func__); + size_t device_count = reg->iface.get_device_count(reg); + if (!device_count) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: no device found\n", __func__); return APIR_BACKEND_INITIALIZE_NO_DEVICE; } dev = reg->iface.get_device(reg, 0); if (!dev) { - GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed: no device received\n", __func__); + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: failed to get device\n", __func__); return APIR_BACKEND_INITIALIZE_NO_DEVICE; } bck = dev->iface.init_backend(dev, NULL); + if (!bck) { + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed\n", __func__); + return APIR_BACKEND_INITIALIZE_BACKEND_INIT_FAILED; + } return APIR_BACKEND_INITIALIZE_SUCCESS; } diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h b/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h index 481d7f3150..3dc334e4ce 100644 --- a/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h @@ -32,64 +32,6 @@ uint32_t backend_buffer_free_buffer(apir_encoder * enc, apir_decoder * dec, virg /* backend */ uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx); -static inline const char * backend_dispatch_command_name(ApirBackendCommandType type) { - switch (type) { - /* device */ - case APIR_COMMAND_TYPE_DEVICE_GET_DEVICE_COUNT: - return "backend_device_get_device_count"; - case APIR_COMMAND_TYPE_DEVICE_GET_COUNT: - return "backend_device_get_count"; - case APIR_COMMAND_TYPE_DEVICE_GET_NAME: - return "backend_device_get_name"; - case APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION: - return "backend_device_get_description"; - case APIR_COMMAND_TYPE_DEVICE_GET_TYPE: - return "backend_device_get_type"; - case APIR_COMMAND_TYPE_DEVICE_GET_MEMORY: - return "backend_device_get_memory"; - case APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP: - return "backend_device_supports_op"; - case APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE: - return "backend_device_get_buffer_type"; - case APIR_COMMAND_TYPE_DEVICE_GET_PROPS: - return "backend_device_get_props"; - case APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR: - return "backend_device_buffer_from_ptr"; - /* buffer-type */ - case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME: - return "backend_buffer_type_get_name"; - case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT: - return "backend_buffer_type_get_alignment"; - case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE: - return "backend_buffer_type_get_max_size"; - case APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST: - return "backend_buffer_type_is_host (DEPRECATED)"; - case APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER: - return "backend_buffer_type_alloc_buffer"; - case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE: - return "backend_buffer_type_get_alloc_size"; - /* buffer */ - case APIR_COMMAND_TYPE_BUFFER_GET_BASE: - return "backend_buffer_get_base"; - case APIR_COMMAND_TYPE_BUFFER_SET_TENSOR: - return "backend_buffer_set_tensor"; - case APIR_COMMAND_TYPE_BUFFER_GET_TENSOR: - return "backend_buffer_get_tensor"; - case APIR_COMMAND_TYPE_BUFFER_CPY_TENSOR: - return "backend_buffer_cpy_tensor"; - case APIR_COMMAND_TYPE_BUFFER_CLEAR: - return "backend_buffer_clear"; - case APIR_COMMAND_TYPE_BUFFER_FREE_BUFFER: - return "backend_buffer_free_buffer"; - /* backend */ - case APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE: - return "backend_backend_graph_compute"; - - default: - return "unknown"; - } -} - extern "C" { static const backend_dispatch_t apir_backend_dispatch_table[APIR_BACKEND_DISPATCH_TABLE_COUNT] = { diff --git a/ggml/src/ggml-virtgpu/backend/backend-dispatched.h b/ggml/src/ggml-virtgpu/backend/backend-dispatched.h index 10311631d4..740ee9e3ff 100644 --- a/ggml/src/ggml-virtgpu/backend/backend-dispatched.h +++ b/ggml/src/ggml-virtgpu/backend/backend-dispatched.h @@ -1,5 +1,6 @@ #pragma once +// clang-format off #include #include @@ -10,6 +11,7 @@ #include "shared/apir_backend.h" #include "shared/apir_cs.h" #include "shared/apir_cs_ggml.h" +// clang-format on #define GGML_VIRTGPU_BCK "ggml-virtgpu-backend: " diff --git a/ggml/src/ggml-virtgpu/backend/backend-virgl-apir.h b/ggml/src/ggml-virtgpu/backend/backend-virgl-apir.h index 44b347f853..c65a01cdf9 100644 --- a/ggml/src/ggml-virtgpu/backend/backend-virgl-apir.h +++ b/ggml/src/ggml-virtgpu/backend/backend-virgl-apir.h @@ -19,7 +19,7 @@ struct virgl_apir_callbacks { }; extern "C" { -ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks *virgl_cbs); +ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks * virgl_cbs); void apir_backend_deinit(uint32_t virgl_ctx_id); uint32_t apir_backend_dispatcher(uint32_t virgl_ctx_id, virgl_apir_callbacks * virgl_cbs, diff --git a/ggml/src/ggml-virtgpu/backend/backend.cpp b/ggml/src/ggml-virtgpu/backend/backend.cpp index d93414a078..535a05f3e6 100644 --- a/ggml/src/ggml-virtgpu/backend/backend.cpp +++ b/ggml/src/ggml-virtgpu/backend/backend.cpp @@ -1,6 +1,5 @@ #include "backend-dispatched.h" #include "backend-virgl-apir.h" - #include "shared/api_remoting.h" #include "shared/apir_backend.h" #include "shared/apir_cs.h" @@ -17,10 +16,10 @@ #define GGML_DEFAULT_BACKEND_REG "ggml_backend_init" static void * backend_library_handle = NULL; -static FILE * apir_logfile = NULL; +static FILE * apir_logfile = NULL; static void log_to_file_callback(enum ggml_log_level level, const char * text, void * user_data) { - FILE * logfile = (FILE *)user_data; + FILE * logfile = (FILE *) user_data; fprintf(logfile, "[%d] %s", level, text); fflush(logfile); } @@ -48,9 +47,9 @@ void apir_backend_deinit(uint32_t virgl_ctx_id) { } #define APIR_GGML_LIBRARY_PATH_KEY "ggml.library.path" -#define APIR_GGML_LIBRARY_REG_KEY "ggml.library.reg" +#define APIR_GGML_LIBRARY_REG_KEY "ggml.library.reg" -ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks *virgl_cbs) { +ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks * virgl_cbs) { const char * dlsym_error; const char * apir_log_to_file = getenv(APIR_LLAMA_CPP_LOG_TO_FILE_ENV); @@ -63,15 +62,13 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct } } - const char * library_name = virgl_cbs->get_config(virgl_ctx_id, APIR_GGML_LIBRARY_PATH_KEY); + const char * library_name = virgl_cbs->get_config(virgl_ctx_id, APIR_GGML_LIBRARY_PATH_KEY); const char * virgl_library_reg = virgl_cbs->get_config(virgl_ctx_id, APIR_GGML_LIBRARY_REG_KEY); - const char * library_reg = virgl_library_reg ? virgl_library_reg : GGML_DEFAULT_BACKEND_REG; + const char * library_reg = virgl_library_reg ? virgl_library_reg : GGML_DEFAULT_BACKEND_REG; if (!library_name) { - GGML_LOG_ERROR(GGML_VIRTGPU_BCK - "%s: cannot open the GGML library: env var '%s' not defined\n", - __func__, APIR_LLAMA_CPP_GGML_LIBRARY_PATH_ENV); - + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: cannot open the GGML library: env var '%s' not defined\n", __func__, + APIR_LLAMA_CPP_GGML_LIBRARY_PATH_ENV); return APIR_LOAD_LIBRARY_ENV_VAR_MISSING; } @@ -79,16 +76,14 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct backend_library_handle = dlopen(library_name, RTLD_LAZY); if (!backend_library_handle) { - GGML_LOG_ERROR(GGML_VIRTGPU_BCK - "%s: cannot open the GGML library: %s\n", __func__, dlerror()); + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: cannot open the GGML library: %s\n", __func__, dlerror()); return APIR_LOAD_LIBRARY_CANNOT_OPEN; } if (!library_reg) { - GGML_LOG_ERROR(GGML_VIRTGPU_BCK - "%s: cannot register the GGML library: env var '%s' not defined\n", - __func__, APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV); + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: cannot register the GGML library: env var '%s' not defined\n", __func__, + APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV); return APIR_LOAD_LIBRARY_ENV_VAR_MISSING; } @@ -96,11 +91,9 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct void * ggml_backend_reg_fct = dlsym(backend_library_handle, library_reg); dlsym_error = dlerror(); if (dlsym_error) { - GGML_LOG_ERROR(GGML_VIRTGPU_BCK - "%s: cannot find the GGML backend registration symbol '%s' (from %s): %s\n", + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: cannot find the GGML backend registration symbol '%s' (from %s): %s\n", __func__, library_reg, APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV, dlsym_error); - return APIR_LOAD_LIBRARY_SYMBOL_MISSING; } @@ -132,13 +125,12 @@ uint32_t apir_backend_dispatcher(uint32_t virgl_ctx_id, virgl_apir_context ctx = { .ctx_id = virgl_ctx_id, - .iface = virgl_cbs, + .iface = virgl_cbs, }; if (cmd_type >= APIR_BACKEND_DISPATCH_TABLE_COUNT) { - GGML_LOG_ERROR(GGML_VIRTGPU_BCK - "%s: Received an invalid dispatch index (%d >= %d)\n", - __func__, cmd_type, APIR_BACKEND_DISPATCH_TABLE_COUNT); + GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Received an invalid dispatch index (%d >= %d)\n", __func__, cmd_type, + APIR_BACKEND_DISPATCH_TABLE_COUNT); return APIR_BACKEND_FORWARD_INDEX_INVALID; } diff --git a/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h b/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h index f19a5d12d1..6bf97e8a3a 100644 --- a/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h +++ b/ggml/src/ggml-virtgpu/backend/shared/api_remoting.h @@ -16,28 +16,32 @@ enum ApirCommandType { APIR_COMMAND_TYPE_LOADLIBRARY = 1, APIR_COMMAND_TYPE_FORWARD = 2, - APIR_COMMAND_TYPE_LENGTH = 3, + APIR_COMMAND_TYPE_LENGTH = 3, }; typedef uint64_t ApirCommandFlags; enum ApirLoadLibraryReturnCode { APIR_LOAD_LIBRARY_SUCCESS = 0, + // these error codes are returned by the Virglrenderer APIR component APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR = 1, APIR_LOAD_LIBRARY_ALREADY_LOADED = 2, APIR_LOAD_LIBRARY_ENV_VAR_MISSING = 3, APIR_LOAD_LIBRARY_CANNOT_OPEN = 4, APIR_LOAD_LIBRARY_SYMBOL_MISSING = 5, - APIR_LOAD_LIBRARY_INIT_BASE_INDEX = 6, // anything above this is a APIR backend library initialization return code + // any value greater than this is an APIR *backend library* initialization return code + APIR_LOAD_LIBRARY_INIT_BASE_INDEX = 6, }; enum ApirForwardReturnCode { - APIR_FORWARD_SUCCESS = 0, - APIR_FORWARD_NO_DISPATCH_FCT = 1, - APIR_FORWARD_TIMEOUT = 2, - - APIR_FORWARD_BASE_INDEX = 3, // anything above this is a APIR backend library forward return code -} ; + APIR_FORWARD_SUCCESS = 0, + // these error codes are returned by the Virglrenderer APIR component + APIR_FORWARD_NO_DISPATCH_FCT = 1, + APIR_FORWARD_TIMEOUT = 2, + APIR_FORWARD_FAILED_TO_SYNC_STREAMS = 3, + // any value greater than this index an APIR *backend library* forward return code + APIR_FORWARD_BASE_INDEX = 4, +}; __attribute__((unused)) static inline const char * apir_command_name(ApirCommandType type) { switch (type) { @@ -82,6 +86,7 @@ __attribute__((unused)) static const char * apir_forward_error(ApirForwardReturn APIR_FORWARD_ERROR(APIR_FORWARD_SUCCESS); APIR_FORWARD_ERROR(APIR_FORWARD_NO_DISPATCH_FCT); APIR_FORWARD_ERROR(APIR_FORWARD_TIMEOUT); + APIR_FORWARD_ERROR(APIR_FORWARD_FAILED_TO_SYNC_STREAMS); APIR_FORWARD_ERROR(APIR_FORWARD_BASE_INDEX); return "Unknown APIR_COMMAND_TYPE_FORWARD error"; diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_backend.gen.h b/ggml/src/ggml-virtgpu/backend/shared/apir_backend.gen.h index d214b6f2a9..520ac9c729 100644 --- a/ggml/src/ggml-virtgpu/backend/shared/apir_backend.gen.h +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_backend.gen.h @@ -34,3 +34,61 @@ typedef enum ApirBackendCommandType { // last command_type index + 1 APIR_BACKEND_DISPATCH_TABLE_COUNT = 23, } ApirBackendCommandType; + +static inline const char * apir_dispatch_command_name(ApirBackendCommandType type) { + switch (type) { + /* device */ + case APIR_COMMAND_TYPE_DEVICE_GET_DEVICE_COUNT: + return "device_get_device_count"; + case APIR_COMMAND_TYPE_DEVICE_GET_COUNT: + return "device_get_count"; + case APIR_COMMAND_TYPE_DEVICE_GET_NAME: + return "device_get_name"; + case APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION: + return "device_get_description"; + case APIR_COMMAND_TYPE_DEVICE_GET_TYPE: + return "device_get_type"; + case APIR_COMMAND_TYPE_DEVICE_GET_MEMORY: + return "device_get_memory"; + case APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP: + return "device_supports_op"; + case APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE: + return "device_get_buffer_type"; + case APIR_COMMAND_TYPE_DEVICE_GET_PROPS: + return "device_get_props"; + case APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR: + return "device_buffer_from_ptr"; + /* buffer-type */ + case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME: + return "buffer_type_get_name"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT: + return "buffer_type_get_alignment"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE: + return "buffer_type_get_max_size"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST: + return "buffer_type_is_host"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER: + return "buffer_type_alloc_buffer"; + case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE: + return "buffer_type_get_alloc_size"; + /* buffer */ + case APIR_COMMAND_TYPE_BUFFER_GET_BASE: + return "buffer_get_base"; + case APIR_COMMAND_TYPE_BUFFER_SET_TENSOR: + return "buffer_set_tensor"; + case APIR_COMMAND_TYPE_BUFFER_GET_TENSOR: + return "buffer_get_tensor"; + case APIR_COMMAND_TYPE_BUFFER_CPY_TENSOR: + return "buffer_cpy_tensor"; + case APIR_COMMAND_TYPE_BUFFER_CLEAR: + return "buffer_clear"; + case APIR_COMMAND_TYPE_BUFFER_FREE_BUFFER: + return "buffer_free_buffer"; + /* backend */ + case APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE: + return "backend_graph_compute"; + + default: + return "unknown"; + } +} diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_backend.h b/ggml/src/ggml-virtgpu/backend/shared/apir_backend.h index f3efa52c72..da1e21b5b2 100644 --- a/ggml/src/ggml-virtgpu/backend/shared/apir_backend.h +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_backend.h @@ -14,7 +14,7 @@ #define APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED 6 #define APIR_BACKEND_INITIALIZE_ALREADY_INITED 7 #define APIR_BACKEND_INITIALIZE_NO_DEVICE 8 - +#define APIR_BACKEND_INITIALIZE_BACKEND_INIT_FAILED 9 // new entries here need to be added to the apir_backend_initialize_error function below @@ -39,6 +39,10 @@ static const char * apir_backend_initialize_error(int code) { APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_MISSING_BACKEND_SYMBOLS); APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_MISSING_GGML_SYMBOLS); APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_BACKEND_FAILED); + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED); + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_ALREADY_INITED); + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_NO_DEVICE); + APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_BACKEND_INIT_FAILED); return "Unknown APIR_BACKEND_INITIALIZE error:/"; diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h b/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h index 1bc3a5f685..64bf2ec960 100644 --- a/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_cs.h @@ -13,7 +13,6 @@ struct apir_encoder { const char * start; const char * end; bool fatal; - }; struct apir_decoder { @@ -28,8 +27,8 @@ struct apir_decoder { static apir_decoder apir_new_decoder(const char * ptr, size_t size) { apir_decoder dec = { - .cur = ptr, - .end = ptr + size, + .cur = ptr, + .end = ptr + size, .fatal = false, }; @@ -79,10 +78,7 @@ static inline bool apir_decoder_get_fatal(const apir_decoder * dec) { * encode peek */ -static inline bool apir_decoder_peek_internal(apir_decoder * dec, - size_t size, - void * val, - size_t val_size) { +static inline bool apir_decoder_peek_internal(apir_decoder * dec, size_t size, void * val, size_t val_size) { assert(val_size <= size); if (unlikely(size > (size_t) (dec->end - dec->cur))) { @@ -332,8 +328,7 @@ static inline void apir_decode_char_array(apir_decoder * dec, char * val, size_t static inline void * apir_decoder_alloc_array(size_t size, size_t count) { size_t alloc_size; if (unlikely(__builtin_mul_overflow(size, count, &alloc_size))) { - GGML_LOG_ERROR("%s: overflow in array allocation of %zu * %zu bytes\n", - __func__, size, count); + GGML_LOG_ERROR("%s: overflow in array allocation of %zu * %zu bytes\n", __func__, size, count); return NULL; } @@ -352,20 +347,19 @@ static inline void apir_decode_bool_t(apir_decoder * dec, bool * val) { /* apir_buffer_type_host_handle_t */ -static inline void apir_encode_apir_buffer_type_host_handle_t(apir_encoder * enc, +static inline void apir_encode_apir_buffer_type_host_handle_t(apir_encoder * enc, const apir_buffer_type_host_handle_t * val) { apir_encode(enc, sizeof(apir_buffer_type_host_handle_t), val, sizeof(apir_buffer_type_host_handle_t)); } -static inline void apir_decode_apir_buffer_type_host_handle_t(apir_decoder * dec, +static inline void apir_decode_apir_buffer_type_host_handle_t(apir_decoder * dec, apir_buffer_type_host_handle_t * val) { apir_decode(dec, sizeof(apir_buffer_type_host_handle_t), val, sizeof(apir_buffer_type_host_handle_t)); } /* apir_buffer_host_handle_t */ -static inline void apir_encode_apir_buffer_host_handle_t(apir_encoder * enc, - const apir_buffer_host_handle_t * val) { +static inline void apir_encode_apir_buffer_host_handle_t(apir_encoder * enc, const apir_buffer_host_handle_t * val) { apir_encode(enc, sizeof(apir_buffer_host_handle_t), val, sizeof(apir_buffer_host_handle_t)); } diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h index 289f4b77d7..fabe3e401c 100644 --- a/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h @@ -1,11 +1,10 @@ -#include "ggml-impl.h" #include "apir_cs.h" #include "apir_cs_rpc.h" +#include "ggml-impl.h" // ggml_buffer_to_apir_host_handle(ggml_backend_buffer_t buffer); -static inline void apir_encode_ggml_buffer_host_handle(apir_encoder * enc, - const apir_buffer_host_handle_t * handle); +static inline void apir_encode_ggml_buffer_host_handle(apir_encoder * enc, const apir_buffer_host_handle_t * handle); static inline ggml_backend_buffer_t apir_decode_ggml_buffer(apir_decoder * dec); @@ -22,8 +21,7 @@ static inline apir_rpc_tensor * apir_decode_apir_rpc_tensor_inplace(apir_decoder return (apir_rpc_tensor *) (uintptr_t) apir_decoder_use_inplace(dec, apir_rpc_tensor_size); } -static inline apir_rpc_tensor * apir_decode_apir_rpc_tensor_array_inplace(apir_decoder * dec, - uint32_t n_tensors) { +static inline apir_rpc_tensor * apir_decode_apir_rpc_tensor_array_inplace(apir_decoder * dec, uint32_t n_tensors) { size_t apir_rpc_tensor_size = sizeof(apir_rpc_tensor) * n_tensors; return (apir_rpc_tensor *) (uintptr_t) apir_decoder_use_inplace(dec, apir_rpc_tensor_size); @@ -45,9 +43,9 @@ static inline const ggml_tensor * apir_decode_ggml_tensor(apir_decoder * dec) { } ggml_init_params params{ - /*.mem_size =*/ ggml_tensor_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, + /*.mem_size =*/ggml_tensor_overhead(), + /*.mem_buffer =*/NULL, + /*.no_alloc =*/true, }; ggml_context * ctx = ggml_init(params); @@ -105,6 +103,19 @@ static inline ggml_backend_buffer_t apir_decode_ggml_buffer(apir_decoder * dec) apir_decoder_read(dec, buffer_ptr_size, &buffer, buffer_ptr_size); + // SECURITY: Validate buffer handle against tracked buffers to prevent + // guest VM from providing arbitrary host memory addresses + if (buffer) { + extern std::unordered_set backend_buffers; + if (backend_buffers.find(buffer) == backend_buffers.end()) { + GGML_LOG_WARN("ggml-virtgpu-backend: %s: Invalid buffer handle from guest: %p\n", __func__, + (void *) buffer); + // Set fatal flag to prevent further processing with invalid handle + apir_decoder_set_fatal(dec); + return NULL; + } + } + return buffer; } diff --git a/ggml/src/ggml-virtgpu/backend/shared/apir_cs_rpc.h b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_rpc.h index f681798952..4cb2f047d1 100644 --- a/ggml/src/ggml-virtgpu/backend/shared/apir_cs_rpc.h +++ b/ggml/src/ggml-virtgpu/backend/shared/apir_cs_rpc.h @@ -1,3 +1,6 @@ +#pragma once + +// clang-format off #include "ggml.h" #include "ggml-backend-impl.h" @@ -5,6 +8,7 @@ #include #include #include +// clang-format on // ggml_tensor is serialized into apir_rpc_tensor struct apir_rpc_tensor { diff --git a/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp b/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp index c493a8e2ae..8fa20ff43b 100644 --- a/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp +++ b/ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp @@ -34,6 +34,7 @@ static ggml_backend_buffer_t ggml_backend_remoting_buffer_type_alloc_buffer(ggml static const char * ggml_backend_remoting_buffer_type_get_name(ggml_backend_buffer_type_t buft) { virtgpu * gpu = BUFT_TO_GPU(buft); + // Return the prefixed name that was built once during initialization return gpu->cached_buffer_type.name; } @@ -53,9 +54,8 @@ static size_t ggml_backend_remoting_buffer_type_get_alloc_size(ggml_backend_buff const ggml_tensor * tensor) { virtgpu * gpu = BUFT_TO_GPU(buft); - if (tensor->buffer == NULL - || !tensor->buffer->context - || !buft->device->iface.supports_buft(buft->device, tensor->buffer->buft)) { + if (tensor->buffer == NULL || !tensor->buffer->context || + !buft->device->iface.supports_buft(buft->device, tensor->buffer->buft)) { return ggml_nbytes(tensor); } diff --git a/ggml/src/ggml-virtgpu/ggml-backend-device.cpp b/ggml/src/ggml-virtgpu/ggml-backend-device.cpp index c7d2881058..ec8156bb86 100644 --- a/ggml/src/ggml-virtgpu/ggml-backend-device.cpp +++ b/ggml/src/ggml-virtgpu/ggml-backend-device.cpp @@ -3,6 +3,7 @@ static const char * ggml_backend_remoting_device_get_name(ggml_backend_dev_t dev) { virtgpu * gpu = DEV_TO_GPU(dev); + // Return the prefixed name that was built once during initialization return gpu->cached_device_info.name; } @@ -22,7 +23,7 @@ static enum ggml_backend_dev_type ggml_backend_remoting_device_get_type(ggml_bac static void ggml_backend_remoting_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { virtgpu * gpu = DEV_TO_GPU(dev); - *free = gpu->cached_device_info.memory_free; + *free = gpu->cached_device_info.memory_free; *total = gpu->cached_device_info.memory_total; } @@ -72,7 +73,7 @@ static void ggml_backend_remoting_device_get_props(ggml_backend_dev_t dev, ggml_ ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_type(ggml_backend_dev_t dev) { virtgpu * gpu = DEV_TO_GPU(dev); - static std::atomic initialized = false; + static std::atomic initialized = false; static ggml_backend_buffer_type buft; if (!initialized) { @@ -95,7 +96,7 @@ ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_type(ggml_bac static ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_from_ptr_type(ggml_backend_dev_t dev) { virtgpu * gpu = DEV_TO_GPU(dev); - static std::atomic initialized = false; + static std::atomic initialized = false; static ggml_backend_buffer_type buft; if (!initialized) { diff --git a/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp b/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp index 2d02cfec1d..a4df5956aa 100644 --- a/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp +++ b/ggml/src/ggml-virtgpu/ggml-backend-reg.cpp @@ -7,8 +7,8 @@ void ggml_virtgpu_cleanup(virtgpu * gpu); static virtgpu * apir_initialize() { - static virtgpu * gpu = NULL; - static std::atomic initialized = false; + static virtgpu * gpu = NULL; + static std::atomic initialized = false; if (initialized) { // fast track @@ -31,29 +31,53 @@ static virtgpu * apir_initialize() { } // Pre-fetch and cache all device information, it will not change - gpu->cached_device_info.description = apir_device_get_description(gpu); + gpu->cached_device_info.description = apir_device_get_description(gpu); if (!gpu->cached_device_info.description) { GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu device description", __func__); } - gpu->cached_device_info.name = apir_device_get_name(gpu); - if (!gpu->cached_device_info.name) { - GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu device name", __func__); - } gpu->cached_device_info.device_count = apir_device_get_count(gpu); gpu->cached_device_info.type = apir_device_get_type(gpu); - apir_device_get_memory(gpu, - &gpu->cached_device_info.memory_free, - &gpu->cached_device_info.memory_total); + { + // Get the remote name and create prefixed version + char * rmt_device_name = apir_device_get_name(gpu); + if (!rmt_device_name) { + GGML_ABORT(GGML_VIRTGPU "%s: failed to get the virtgpu device name", __func__); + } + + size_t device_name_len = strlen(rmt_device_name) + 11; // "[virtgpu] " + null terminator + gpu->cached_device_info.name = (char *) malloc(device_name_len); + if (!gpu->cached_device_info.name) { + free(rmt_device_name); + GGML_ABORT(GGML_VIRTGPU "%s: failed to allocate memory for prefixed device name", __func__); + } + snprintf(gpu->cached_device_info.name, device_name_len, "[virtgpu] %s", rmt_device_name); + free(rmt_device_name); + } + + apir_device_get_memory(gpu, &gpu->cached_device_info.memory_free, &gpu->cached_device_info.memory_total); apir_buffer_type_host_handle_t buft_host_handle = apir_device_get_buffer_type(gpu); gpu->cached_buffer_type.host_handle = buft_host_handle; - gpu->cached_buffer_type.name = apir_buffer_type_get_name(gpu, buft_host_handle); - if (!gpu->cached_buffer_type.name) { - GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu buffer type name", __func__); + { + // Get the remote name and create prefixed version + char * rmt_name = apir_buffer_type_get_name(gpu, buft_host_handle); + if (!rmt_name) { + GGML_ABORT(GGML_VIRTGPU "%s: failed to get the virtgpu buffer type name", __func__); + } + + size_t prefixed_len = strlen(rmt_name) + 11; // "[virtgpu] " + null terminator + gpu->cached_buffer_type.name = (char *) malloc(prefixed_len); + if (!gpu->cached_buffer_type.name) { + free(rmt_name); + GGML_ABORT(GGML_VIRTGPU "%s: failed to allocate memory for prefixed buffer type name", __func__); + } + snprintf(gpu->cached_buffer_type.name, prefixed_len, "[virtgpu] %s", rmt_name); + free(rmt_name); } - gpu->cached_buffer_type.alignment = apir_buffer_type_get_alignment(gpu, buft_host_handle); - gpu->cached_buffer_type.max_size = apir_buffer_type_get_max_size(gpu, buft_host_handle); + + gpu->cached_buffer_type.alignment = apir_buffer_type_get_alignment(gpu, buft_host_handle); + gpu->cached_buffer_type.max_size = apir_buffer_type_get_max_size(gpu, buft_host_handle); initialized = true; } @@ -98,7 +122,7 @@ static void ggml_backend_remoting_reg_init_devices(ggml_backend_reg_t reg) { static std::atomic initialized = false; if (initialized) { - return; // fast track + return; // fast track } { diff --git a/ggml/src/ggml-virtgpu/ggml-backend.cpp b/ggml/src/ggml-virtgpu/ggml-backend.cpp index 5cd6c0c060..a63ee2b9d2 100644 --- a/ggml/src/ggml-virtgpu/ggml-backend.cpp +++ b/ggml/src/ggml-virtgpu/ggml-backend.cpp @@ -1,5 +1,5 @@ -#include "ggml-remoting.h" #include "../../include/ggml-virtgpu.h" +#include "ggml-remoting.h" static const char * ggml_backend_remoting_get_name(ggml_backend_t backend) { UNUSED(backend); diff --git a/ggml/src/ggml-virtgpu/ggml-remoting.h b/ggml/src/ggml-virtgpu/ggml-remoting.h index 0876640867..4f70326bee 100644 --- a/ggml/src/ggml-virtgpu/ggml-remoting.h +++ b/ggml/src/ggml-virtgpu/ggml-remoting.h @@ -9,7 +9,7 @@ #include #define GGML_VIRTGPU_NAME "ggml-virtgpu" -#define GGML_VIRTGPU "ggml-virtgpu: " +#define GGML_VIRTGPU "ggml-virtgpu: " // USE_ALWAYS_TRUE_SUPPORTS_OP: 1 is fast, 0 avoid micro-benchmark crashes diff --git a/ggml/src/ggml-virtgpu/include/apir_hw.h b/ggml/src/ggml-virtgpu/include/apir_hw.h index 33af045ca2..7d6ea2265d 100644 --- a/ggml/src/ggml-virtgpu/include/apir_hw.h +++ b/ggml/src/ggml-virtgpu/include/apir_hw.h @@ -3,7 +3,7 @@ #include struct virgl_renderer_capset_apir { - uint32_t apir_version; - uint32_t supports_blob_resources; - uint32_t reserved[4]; // For future expansion + uint32_t apir_version; + uint32_t supports_blob_resources; + uint32_t reserved[4]; // For future expansion }; diff --git a/ggml/src/ggml-virtgpu/regenerate_remoting.py b/ggml/src/ggml-virtgpu/regenerate_remoting.py index aeb48a4087..dae75fd1c8 100755 --- a/ggml/src/ggml-virtgpu/regenerate_remoting.py +++ b/ggml/src/ggml-virtgpu/regenerate_remoting.py @@ -145,8 +145,31 @@ class RemotingCodebaseGenerator: enum_lines.append(f" APIR_BACKEND_DISPATCH_TABLE_COUNT = {total_count},") enum_lines.append("} ApirBackendCommandType;") + # Generate function name mapping + func_lines = [] + func_lines.append("static inline const char * apir_dispatch_command_name(ApirBackendCommandType type) {") + func_lines.append(" switch (type) {") + + current_group = None + for func in functions: + # Add comment for new group + if func['group_name'] != current_group: + func_lines.append(f" /* {func['group_description']} */") + current_group = func['group_name'] + + # Generate clean function name without backend_ prefix + clean_name = f"{func['group_name']}_{func['function_name']}" + func_lines.append(f" case {func['enum_name']}:") + func_lines.append(f" return \"{clean_name}\";") + + func_lines.append("") + func_lines.append(" default:") + func_lines.append(" return \"unknown\";") + func_lines.append(" }") + func_lines.append("}") + # Full header template - header_content = NL.join(enum_lines) + "\n" + header_content = NL.join(enum_lines) + "\n\n" + NL.join(func_lines) + "\n" return header_content @@ -170,19 +193,6 @@ class RemotingCodebaseGenerator: decl_lines.append(f"{signature} {func['backend_function']}({params});") - # Switch cases - switch_lines = [] - current_group = None - - for func in functions: - if func['group_name'] != current_group: - switch_lines.append(f" /* {func['group_description']} */") - current_group = func['group_name'] - - deprecated = " (DEPRECATED)" if func['deprecated'] else "" - - switch_lines.append(f" case {func['enum_name']}: return \"{func['backend_function']}{deprecated}\";") - # Dispatch table table_lines = [] current_group = None @@ -201,15 +211,6 @@ class RemotingCodebaseGenerator: {NL.join(decl_lines)} -static inline const char *backend_dispatch_command_name(ApirBackendCommandType type) -{{ - switch (type) {{ -{NL.join(switch_lines)} - - default: return "unknown"; - }} -}} - extern "C" {{ static const backend_dispatch_t apir_backend_dispatch_table[APIR_BACKEND_DISPATCH_TABLE_COUNT] = {{ {NL.join(table_lines)} diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp index 07d9a66849..4593690c63 100644 --- a/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp @@ -17,8 +17,8 @@ ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) { size_t cgraph_size = apir_serialize_ggml_cgraph(cgraph, cgraph_data); virtgpu_shmem temp_shmem; // Local storage for large buffers - virtgpu_shmem * shmem = &temp_shmem; - bool using_shared_shmem = false; + virtgpu_shmem * shmem = &temp_shmem; + bool using_shared_shmem = false; if (cgraph_size <= gpu->data_shmem.mmap_size) { // Lock mutex before using shared data_shmem buffer @@ -26,7 +26,7 @@ ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) { GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__); } using_shared_shmem = true; - shmem = &gpu->data_shmem; + shmem = &gpu->data_shmem; } else if (virtgpu_shmem_create(gpu, cgraph_size, shmem)) { GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__); } diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp index cab74fd170..38f8ec945e 100644 --- a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp @@ -62,7 +62,9 @@ size_t apir_buffer_type_get_max_size(virtgpu * gpu, apir_buffer_type_host_handle return max_size; } -apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle, size_t size) { +apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, + apir_buffer_type_host_handle_t host_handle, + size_t size) { apir_encoder * encoder; apir_decoder * decoder; ApirForwardReturnCode ret; @@ -84,7 +86,9 @@ apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, apir_buffer_t return buffer_context; } -size_t apir_buffer_type_get_alloc_size(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle, const ggml_tensor * op) { +size_t apir_buffer_type_get_alloc_size(virtgpu * gpu, + apir_buffer_type_host_handle_t host_handle, + const ggml_tensor * op) { apir_encoder * encoder; apir_decoder * decoder; ApirForwardReturnCode ret; diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp index 86eee358cf..228284f4a4 100644 --- a/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp @@ -35,8 +35,8 @@ void apir_buffer_set_tensor(virtgpu * gpu, apir_encode_ggml_tensor(encoder, tensor); virtgpu_shmem temp_shmem; // Local storage for large buffers - virtgpu_shmem * shmem = &temp_shmem; - bool using_shared_shmem = false; + virtgpu_shmem * shmem = &temp_shmem; + bool using_shared_shmem = false; if (size <= gpu->data_shmem.mmap_size) { // Lock mutex before using shared data_shmem buffer @@ -44,7 +44,7 @@ void apir_buffer_set_tensor(virtgpu * gpu, GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__); } using_shared_shmem = true; - shmem = &gpu->data_shmem; + shmem = &gpu->data_shmem; } else if (virtgpu_shmem_create(gpu, size, shmem)) { GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__); @@ -86,8 +86,8 @@ void apir_buffer_get_tensor(virtgpu * gpu, apir_encode_ggml_tensor(encoder, tensor); virtgpu_shmem temp_shmem; // Local storage for large buffers - virtgpu_shmem * shmem = &temp_shmem; - bool using_shared_shmem = false; + virtgpu_shmem * shmem = &temp_shmem; + bool using_shared_shmem = false; if (size <= gpu->data_shmem.mmap_size) { // Lock mutex before using shared data_shmem buffer @@ -95,7 +95,7 @@ void apir_buffer_get_tensor(virtgpu * gpu, GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__); } using_shared_shmem = true; - shmem = &gpu->data_shmem; + shmem = &gpu->data_shmem; } else if (virtgpu_shmem_create(gpu, size, shmem)) { GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__); diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp b/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp index 4b6b8f527b..9f513c138d 100644 --- a/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp @@ -26,7 +26,7 @@ char * apir_device_get_name(virtgpu * gpu) { REMOTE_CALL(gpu, encoder, decoder, ret); const size_t string_size = apir_decode_array_size_unchecked(decoder); - char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); + char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); if (!string) { GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device name buffer\n", __func__); return NULL; @@ -173,7 +173,7 @@ apir_buffer_context_t apir_device_buffer_from_ptr(virtgpu * gpu, size_t size, si REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR); if (virtgpu_shmem_create(gpu, size, &buffer_context.shmem)) { - GGML_ABORT(GGML_VIRTGPU "Couldn't allocate the guest-host shared buffer"); + GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate %ldb of guest-host shared buffer", __func__, size); } apir_encode_virtgpu_shmem_res_id(encoder, buffer_context.shmem.res_id); diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h b/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h index f23c75bb96..4d0b6e05c7 100644 --- a/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h +++ b/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h @@ -1,29 +1,36 @@ -#include "virtgpu.h" +#pragma once +// clang-format off +#include "virtgpu.h" #include "ggml-remoting.h" #include "backend/shared/apir_backend.h" #include "backend/shared/apir_cs_ggml.h" - #include "ggml-backend-impl.h" +// clang-format on -#define REMOTE_CALL_PREPARE(gpu_dev_name, encoder_name, apir_command_type__) \ - do { \ - int32_t forward_flag = (int32_t) apir_command_type__; \ - encoder_name = remote_call_prepare(gpu_dev_name, APIR_COMMAND_TYPE_FORWARD, forward_flag); \ - if (!encoder_name) { \ - GGML_ABORT(GGML_VIRTGPU "%s: failed to prepare the remote call encoder", __func__); \ - } \ +#define REMOTE_CALL_PREPARE(gpu_dev_name, encoder_name, apir_command_type__) \ + int32_t REMOTE_CALL_PREPARE_forward_flag = (int32_t) apir_command_type__; \ + const char * REMOTE_CALL_PREPARE_command_name = apir_dispatch_command_name(apir_command_type__); \ + do { \ + encoder_name = remote_call_prepare(gpu_dev_name, APIR_COMMAND_TYPE_FORWARD, REMOTE_CALL_PREPARE_forward_flag); \ + if (!encoder_name) { \ + GGML_ABORT(GGML_VIRTGPU "%s: failed to prepare the remote call encoder", __func__); \ + } \ } while (0) -#define REMOTE_CALL(gpu_dev_name, encoder_name, decoder_name, ret_name) \ - do { \ - ret_name = (ApirForwardReturnCode) remote_call(gpu_dev_name, encoder_name, &decoder_name, 0, NULL); \ - if (!decoder_name) { \ - GGML_ABORT(GGML_VIRTGPU "%s: failed to kick the remote call", __func__); \ - } \ - if (ret_name < APIR_FORWARD_BASE_INDEX) { \ - GGML_ABORT(GGML_VIRTGPU "%s: failed to forward the API call: %s: code %d", __func__, \ - apir_forward_error(ret_name), ret_name); \ - } \ - ret_name = (ApirForwardReturnCode) (ret_name - APIR_FORWARD_BASE_INDEX); \ +#define REMOTE_CALL(gpu_dev_name, encoder_name, decoder_name, ret_name) \ + do { \ + ret_name = (ApirForwardReturnCode) remote_call(gpu_dev_name, encoder_name, &decoder_name, 0, NULL); \ + if (!decoder_name) { \ + GGML_ABORT(GGML_VIRTGPU "%s: failed to kick the remote call", __func__); \ + } \ + if (ret_name < APIR_FORWARD_BASE_INDEX) { \ + GGML_ABORT(GGML_VIRTGPU "%s: failed to forward the API call: %s: code %d", __func__, \ + apir_forward_error(ret_name), ret_name); \ + } \ + ret_name = (ApirForwardReturnCode) (ret_name - APIR_FORWARD_BASE_INDEX); \ + if (ret_name != 0) { \ + GGML_ABORT(GGML_VIRTGPU "backend function '%s' failed (return code: %d)", \ + REMOTE_CALL_PREPARE_command_name, ret_name); \ + } \ } while (0) diff --git a/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h b/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h index fe4cae2025..44b0ad1ffa 100644 --- a/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h +++ b/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h @@ -20,6 +20,7 @@ apir_buffer_context_t apir_device_buffer_from_ptr(struct virtgpu * gpu, char * apir_buffer_type_get_name(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle); size_t apir_buffer_type_get_alignment(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle); size_t apir_buffer_type_get_max_size(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle); +/* apir_buffer_type_is_host is deprecated. */ apir_buffer_context_t apir_buffer_type_alloc_buffer(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle, size_t size); diff --git a/ggml/src/ggml-virtgpu/virtgpu.cpp b/ggml/src/ggml-virtgpu/virtgpu.cpp index 1e650dc65b..a84a77399d 100644 --- a/ggml/src/ggml-virtgpu/virtgpu.cpp +++ b/ggml/src/ggml-virtgpu/virtgpu.cpp @@ -53,9 +53,9 @@ static int virtgpu_handshake(virtgpu * gpu) { if (!decoder) { GGML_ABORT(GGML_VIRTGPU - "%s: failed to initiate the communication with the virglrenderer library. " - "Most likely, the wrong virglrenderer library was loaded in the hypervisor.", - __func__); + "%s: failed to initiate the communication with the virglrenderer library. " + "Most likely, the wrong virglrenderer library was loaded in the hypervisor.", + __func__); return 1; } @@ -65,8 +65,7 @@ static int virtgpu_handshake(virtgpu * gpu) { uint32_t host_minor; if (ret_magic != APIR_HANDSHAKE_MAGIC) { - GGML_ABORT(GGML_VIRTGPU - "%s: handshake with the virglrenderer failed (code=%d | %s)", __func__, ret_magic, + GGML_ABORT(GGML_VIRTGPU "%s: handshake with the virglrenderer failed (code=%d | %s)", __func__, ret_magic, apir_backend_initialize_error(ret_magic)); } else { apir_decode_uint32_t(decoder, &host_major); @@ -140,15 +139,13 @@ static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) { "Make sure virglrenderer is correctly configured by the hypervisor. (%s) ", __func__, apir_load_library_error(ret)); } else { - GGML_ABORT(GGML_VIRTGPU - "%s: virglrenderer could not load the API Remoting backend library. (%s - code %d)", __func__, - apir_load_library_error(ret), ret); + GGML_ABORT(GGML_VIRTGPU "%s: virglrenderer could not load the API Remoting backend library. (%s - code %d)", + __func__, apir_load_library_error(ret), ret); } return ret; } - GGML_LOG_INFO(GGML_VIRTGPU - "%s: virglrenderer successfully loaded the API Remoting backend library.\n", __func__); + GGML_LOG_INFO(GGML_VIRTGPU "%s: virglrenderer successfully loaded the API Remoting backend library.\n", __func__); ApirLoadLibraryReturnCode apir_ret = (ApirLoadLibraryReturnCode) (ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX); @@ -158,10 +155,11 @@ static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) { "Make sure virglrenderer is correctly configured by the hypervisor. (%s)", __func__, apir_load_library_error(apir_ret)); } else if (apir_ret == APIR_LOAD_LIBRARY_SYMBOL_MISSING) { - GGML_ABORT(GGML_VIRTGPU - "%s: the API Remoting backend library couldn't load the GGML backend library, some symbols are missing. " - "Make sure virglrenderer is correctly configured by the hypervisor. (%s)", - __func__, apir_load_library_error(apir_ret)); + GGML_ABORT( + GGML_VIRTGPU + "%s: the API Remoting backend library couldn't load the GGML backend library, some symbols are missing. " + "Make sure virglrenderer is correctly configured by the hypervisor. (%s)", + __func__, apir_load_library_error(apir_ret)); } else if (apir_ret < APIR_LOAD_LIBRARY_INIT_BASE_INDEX) { GGML_ABORT(GGML_VIRTGPU "%s: the API Remoting backend library couldn't load the GGML backend library: apir code=%d | %s)", @@ -169,8 +167,8 @@ static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) { } else { uint32_t lib_ret = apir_ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX; GGML_ABORT(GGML_VIRTGPU - "%s: the API Remoting backend library initialize its backend library: apir code=%d)", __func__, - lib_ret); + "%s: the API Remoting backend library failed to initialize its backend library: apir code=%d)", + __func__, lib_ret); } return ret; } @@ -184,55 +182,49 @@ virtgpu * create_virtgpu() { // Initialize mutex to protect shared data_shmem buffer if (mtx_init(&gpu->data_shmem_mutex, mtx_plain) != thrd_success) { delete gpu; - GGML_ABORT(GGML_VIRTGPU - "%s: failed to initialize data_shmem mutex", __func__); + GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize data_shmem mutex", __func__); return NULL; } if (virtgpu_open(gpu) != APIR_SUCCESS) { - GGML_LOG_ERROR(GGML_VIRTGPU - "%s: failed to open the virtgpu device\n", __func__); + GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to open the virtgpu device\n", __func__); return NULL; } if (virtgpu_init_capset(gpu) != APIR_SUCCESS) { if (gpu->use_apir_capset) { GGML_ABORT(GGML_VIRTGPU - "%s: failed to initialize the virtgpu APIR capset. Make sure that the virglrenderer library supports it.", __func__); + "%s: failed to initialize the virtgpu APIR capset. Make sure that the virglrenderer library " + "supports it.", + __func__); } else { - GGML_ABORT(GGML_VIRTGPU - "%s: failed to initialize the virtgpu Venus capset", __func__); + GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu Venus capset", __func__); } return NULL; } if (virtgpu_init_context(gpu) != APIR_SUCCESS) { - GGML_ABORT(GGML_VIRTGPU - "%s: failed to initialize the GPU context", __func__); + GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the GPU context", __func__); return NULL; } if (virtgpu_shmem_create(gpu, SHMEM_REPLY_SIZE, &gpu->reply_shmem)) { - GGML_ABORT(GGML_VIRTGPU - "%s: failed to create the shared reply memory pages", __func__); + GGML_ABORT(GGML_VIRTGPU "%s: failed to create the shared reply memory pages", __func__); return NULL; } if (virtgpu_shmem_create(gpu, SHMEM_DATA_SIZE, &gpu->data_shmem)) { - GGML_ABORT(GGML_VIRTGPU - "%s: failed to create the shared data memory pages", __func__); + GGML_ABORT(GGML_VIRTGPU "%s: failed to create the shared data memory pages", __func__); return NULL; } if (virtgpu_handshake(gpu)) { - GGML_ABORT(GGML_VIRTGPU - "%s: failed to handshake with the virglrenderer library", __func__); + GGML_ABORT(GGML_VIRTGPU "%s: failed to handshake with the virglrenderer library", __func__); return NULL; } if (virtgpu_load_library(gpu) != APIR_LOAD_LIBRARY_SUCCESS) { - GGML_ABORT(GGML_VIRTGPU - "%s: failed to load the backend library", __func__); + GGML_ABORT(GGML_VIRTGPU "%s: failed to load the backend library", __func__); return NULL; } @@ -243,8 +235,7 @@ static virt_gpu_result_t virtgpu_open(virtgpu * gpu) { drmDevicePtr devs[8]; int count = drmGetDevices2(0, devs, ARRAY_SIZE(devs)); if (count < 0) { - GGML_LOG_ERROR(GGML_VIRTGPU - "%s: failed to enumerate DRM devices\n", __func__); + GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to enumerate DRM devices\n", __func__); return APIR_ERROR_INITIALIZATION_FAILED; } @@ -266,19 +257,17 @@ static virt_gpu_result_t virtgpu_open_device(virtgpu * gpu, const drmDevicePtr d int fd = open(node_path, O_RDWR | O_CLOEXEC); if (fd < 0) { - GGML_ABORT(GGML_VIRTGPU - "%s: failed to open %s", __func__, node_path); + GGML_ABORT(GGML_VIRTGPU "%s: failed to open %s", __func__, node_path); return APIR_ERROR_INITIALIZATION_FAILED; } drmVersionPtr version = drmGetVersion(fd); if (!version || strcmp(version->name, "virtio_gpu") || version->version_major != 0) { if (version) { - GGML_LOG_ERROR(GGML_VIRTGPU - "%s: unknown DRM driver %s version %d\n", __func__, version->name, version->version_major); + GGML_LOG_ERROR(GGML_VIRTGPU "%s: unknown DRM driver %s version %d\n", __func__, version->name, + version->version_major); } else { - GGML_LOG_ERROR(GGML_VIRTGPU - "%s: failed to get DRM driver version\n", __func__); + GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to get DRM driver version\n", __func__); } if (version) { @@ -322,9 +311,8 @@ static virt_gpu_result_t virtgpu_init_capset(virtgpu * gpu) { virtgpu_ioctl_get_caps(gpu, gpu->capset.id, gpu->capset.version, &gpu->capset.data, sizeof(gpu->capset.data)); if (ret) { - GGML_LOG_ERROR(GGML_VIRTGPU - "%s: failed to get APIR v%d capset: %s\n", - __func__, gpu->capset.version, strerror(errno)); + GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to get APIR v%d capset: %s\n", __func__, gpu->capset.version, + strerror(errno)); return APIR_ERROR_INITIALIZATION_FAILED; } @@ -547,13 +535,10 @@ static void log_call_duration(long long call_duration_ns, const char * name) { double call_duration_s = (double) call_duration_ns / 1e9; // 1 second = 1e9 nanoseconds if (call_duration_s > 1) { - GGML_LOG_INFO(GGML_VIRTGPU - "waited %.2fs for the %s host reply...\n", call_duration_s, name); + GGML_LOG_INFO(GGML_VIRTGPU "waited %.2fs for the %s host reply...\n", call_duration_s, name); } else if (call_duration_ms > 1) { - GGML_LOG_INFO(GGML_VIRTGPU - "waited %.2fms for the %s host reply...\n", call_duration_ms, name); + GGML_LOG_INFO(GGML_VIRTGPU "waited %.2fms for the %s host reply...\n", call_duration_ms, name); } else { - GGML_LOG_INFO(GGML_VIRTGPU - "waited %lldns for the %s host reply...\n", call_duration_ns, name); + GGML_LOG_INFO(GGML_VIRTGPU "waited %lldns for the %s host reply...\n", call_duration_ns, name); } } diff --git a/ggml/src/ggml-virtgpu/virtgpu.h b/ggml/src/ggml-virtgpu/virtgpu.h index 68e0f3a376..f82d8fb50b 100644 --- a/ggml/src/ggml-virtgpu/virtgpu.h +++ b/ggml/src/ggml-virtgpu/virtgpu.h @@ -1,5 +1,6 @@ #pragma once +// clang-format off #include "virtgpu-utils.h" #include "virtgpu-shm.h" #include "virtgpu-apir.h" @@ -23,20 +24,21 @@ #include "apir_hw.h" #include #include "venus_hw.h" +// clang-format on #ifndef VIRTGPU_DRM_CAPSET_APIR // Will be defined include/drm/virtgpu_drm.h when // https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590/diffs // is merged -#define VIRTGPU_DRM_CAPSET_APIR 10 +# define VIRTGPU_DRM_CAPSET_APIR 10 #endif // Mesa/Virlgrenderer Venus internal. Only necessary during the // Venus->APIR transition in Virglrenderer #define VENUS_COMMAND_TYPE_LENGTH 331 -#ifndef VIRTGPU_DRM_CAPSET_VENUS // only available with Linux >= v6.16 -#define VIRTGPU_DRM_CAPSET_VENUS 4 +#ifndef VIRTGPU_DRM_CAPSET_VENUS // only available with Linux >= v6.16 +# define VIRTGPU_DRM_CAPSET_VENUS 4 #endif typedef uint32_t virgl_renderer_capset; diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt index de01336cd3..715a263a6d 100644 --- a/ggml/src/ggml-vulkan/CMakeLists.txt +++ b/ggml/src/ggml-vulkan/CMakeLists.txt @@ -90,7 +90,7 @@ if (Vulkan_FOUND) target_include_directories(ggml-vulkan PRIVATE ${CMAKE_CURRENT_BINARY_DIR}) # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build - # Posssibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector + # Possibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector if (MSVC AND CMAKE_CXX_COMPILER_ID STREQUAL "Clang") add_compile_definitions(_ITERATOR_DEBUG_LEVEL=0) endif() diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index a8840a0773..7092361d2e 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -27,6 +27,7 @@ DispatchLoaderDynamic & ggml_vk_default_dispatcher(); #include #include #include +#include #include #include #include @@ -188,6 +189,11 @@ struct ggml_backend_vk_buffer_type_context { struct vk_queue; +struct vk_command_buffer { + vk::CommandBuffer buf; + bool in_use = false; +}; + // Stores command pool/buffers. There's an instance of this // for each (context,queue) pair and for each (device,queue) pair. struct vk_command_pool { @@ -195,10 +201,16 @@ struct vk_command_pool { void destroy(vk::Device& device); vk::CommandPool pool; - uint32_t cmd_buffer_idx; - std::vector cmd_buffers; + // Using deque so the pointers to command buffers + // remain valid even if we add more + std::deque cmd_buffers; vk_queue *q; + + size_t buffers_in_use() const { + return std::count_if(cmd_buffers.begin(), cmd_buffers.end(), + [](const auto& cb) { return cb.in_use; }); + } }; // Prevent simultaneous submissions to the same queue. @@ -403,19 +415,20 @@ enum FaCodePath { }; struct vk_fa_pipeline_state { - vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, bool small_cache, FaCodePath path, bool aligned, bool f32acc, uint32_t flags) - : HSK(HSK), HSV(HSV), small_rows(small_rows), small_cache(small_cache), path(path), aligned(aligned), f32acc(f32acc), flags(flags) {} - uint32_t HSK, HSV; - bool small_rows, small_cache; + uint32_t Br, Bc; + uint32_t D_split, row_split; + bool shmem_staging; FaCodePath path; + uint32_t workgroup_size, subgroup_size; bool aligned; bool f32acc; uint32_t flags; + uint32_t limit_occupancy_shmem; bool operator<(const vk_fa_pipeline_state &b) const { - return std::tie(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, flags) < - std::tie(b.HSK, b.HSV, b.small_rows, b.small_cache, b.path, b.aligned, b.f32acc, b.flags); + return std::tie(HSK, HSV, Br, Bc, D_split, row_split, shmem_staging, path, workgroup_size, subgroup_size, aligned, f32acc, flags, limit_occupancy_shmem) < + std::tie(b.HSK, b.HSV, b.Br, b.Bc, b.D_split, b.row_split, b.shmem_staging, b.path, b.workgroup_size, b.subgroup_size, b.aligned, b.f32acc, b.flags, b.limit_occupancy_shmem); } }; @@ -589,6 +602,7 @@ struct vk_device_struct { vk_queue transfer_queue; bool single_queue; bool support_async; + bool async_use_transfer_queue; uint32_t subgroup_size; uint32_t subgroup_size_log2; uint32_t shader_core_count; @@ -742,6 +756,7 @@ struct vk_device_struct { // [src/dst 0=fp32,1=fp16] vk_pipeline pipeline_exp[2]; + vk_pipeline pipeline_elu[2]; vk_pipeline pipeline_gelu[2]; vk_pipeline pipeline_gelu_erf[2]; vk_pipeline pipeline_gelu_quick[2]; @@ -760,6 +775,7 @@ struct vk_device_struct { vk_pipeline pipeline_ceil[2]; vk_pipeline pipeline_floor[2]; vk_pipeline pipeline_trunc[2]; + vk_pipeline pipeline_sgn[2]; vk_pipeline pipeline_add1_f16_f16; vk_pipeline pipeline_add1_f16_f32; @@ -809,6 +825,8 @@ struct vk_device_struct { vk_pipeline pipeline_pool2d_f32; vk_pipeline pipeline_rwkv_wkv6_f32; vk_pipeline pipeline_rwkv_wkv7_f32; + // [size_idx][kda] where size_idx: 0=d32, 1=d64, 2=d128 + vk_pipeline pipeline_gated_delta_net[3][2]; vk_pipeline pipeline_ssm_scan_f32_d128; vk_pipeline pipeline_ssm_scan_f32_d256; vk_pipeline pipeline_ssm_conv_f32; @@ -874,10 +892,12 @@ struct vk_device_struct { }; void vk_command_pool::init(vk_device& device, vk_queue *q_) { - cmd_buffer_idx = 0; + cmd_buffers.clear(); q = q_; - vk::CommandPoolCreateInfo command_pool_create_info(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), q->queue_family_index); + vk::CommandPoolCreateInfo command_pool_create_info( + vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT | VK_COMMAND_POOL_CREATE_RESET_COMMAND_BUFFER_BIT), + q->queue_family_index); pool = device->device.createCommandPool(command_pool_create_info); } @@ -925,6 +945,7 @@ struct vk_subbuffer { struct vk_event { vk::Event event; vk::Fence fence; + vk_command_buffer* cmd_buffer = nullptr; }; struct vk_semaphore { @@ -933,7 +954,7 @@ struct vk_semaphore { }; struct vk_submission { - vk::CommandBuffer buffer; + vk_command_buffer* buffer = nullptr; std::vector wait_semaphores; std::vector signal_semaphores; }; @@ -1435,6 +1456,18 @@ struct vk_op_rwkv_wkv7_push_constants { uint32_t C; uint32_t H; }; +struct vk_op_gated_delta_net_push_constants { + uint32_t H; + uint32_t n_tokens; + uint32_t n_seqs; + uint32_t s_off; + uint32_t sq1, sq2, sq3; + uint32_t sv1, sv2, sv3; + uint32_t sb1, sb2, sb3; + uint32_t neq1, rq3; + float scale; +}; + struct vk_op_ssm_scan_push_constants { uint32_t nb02, nb03, nb12, nb13; uint32_t nb21, nb22, nb31; @@ -1656,6 +1689,7 @@ static bool vk_perf_logger_concurrent = false; static bool vk_enable_sync_logger = false; // number of calls between perf logger prints static uint32_t vk_perf_logger_frequency = 1; +static std::string vk_pipeline_stats_filter; class vk_perf_logger { public: @@ -1856,6 +1890,10 @@ struct ggml_backend_vk_context { vk_context_ref compute_ctx; + vk_context_ref transfer_ctx; + vk_semaphore transfer_semaphore; + uint64_t transfer_semaphore_last_submitted {}; + std::vector tensor_ctxs; std::vector descriptor_pools; @@ -1864,6 +1902,7 @@ struct ggml_backend_vk_context { uint32_t pipeline_descriptor_set_requirements {}; vk_command_pool compute_cmd_pool; + vk_command_pool transfer_cmd_pool; // number of additional consecutive nodes that are being fused with the // node currently being processed @@ -2172,7 +2211,32 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin executableInfo.pipeline = pipeline->pipeline; auto statistics = device->device.getPipelineExecutableStatisticsKHR(executableInfo); + + bool print_stats = !vk_pipeline_stats_filter.empty() && + pipeline->name.find(vk_pipeline_stats_filter) != std::string::npos; + if (print_stats) { + std::cerr << "ggml_vulkan: pipeline stats for " << pipeline->name << ":" << std::endl; + } + for (auto & s : statistics) { + if (print_stats) { + std::cerr << "ggml_vulkan: " << s.name.data() << ": "; + switch (s.format) { + case vk::PipelineExecutableStatisticFormatKHR::eBool32: + std::cerr << (s.value.b32 ? "true" : "false"); + break; + case vk::PipelineExecutableStatisticFormatKHR::eInt64: + std::cerr << s.value.i64; + break; + case vk::PipelineExecutableStatisticFormatKHR::eUint64: + std::cerr << s.value.u64; + break; + case vk::PipelineExecutableStatisticFormatKHR::eFloat64: + std::cerr << s.value.f64; + break; + } + std::cerr << std::endl; + } // "Register Count" is reported by NVIDIA drivers. if (strcmp(s.name, "Register Count") == 0) { VK_LOG_DEBUG(pipeline->name << " " << s.name << ": " << s.value.u64 << " registers"); @@ -2248,25 +2312,15 @@ static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx } } -static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_device& device, vk_command_pool& p) { +static vk_command_buffer* ggml_vk_create_cmd_buffer(vk_device& device, vk_command_pool& p) { VK_LOG_DEBUG("ggml_vk_create_cmd_buffer()"); - - if (p.cmd_buffers.size() > p.cmd_buffer_idx) { - // Reuse command buffer - return p.cmd_buffers[p.cmd_buffer_idx++]; - } - vk::CommandBufferAllocateInfo command_buffer_alloc_info( p.pool, vk::CommandBufferLevel::ePrimary, 1); const std::vector cmd_buffers = device->device.allocateCommandBuffers(command_buffer_alloc_info); - auto buf = cmd_buffers.front(); - - p.cmd_buffers.push_back(buf); - p.cmd_buffer_idx++; - - return buf; + p.cmd_buffers.push_back({ cmd_buffers.front(), true }); + return &p.cmd_buffers[p.cmd_buffers.size()-1]; } static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { @@ -2333,7 +2387,7 @@ static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { tl_wait_semaphores[idx].data(), stage_flags[idx].data(), 1, - &submission.buffer, + &submission.buffer->buf, (uint32_t) submission.signal_semaphores.size(), tl_signal_semaphores[idx].data(), }; @@ -2457,7 +2511,11 @@ static void ggml_vk_command_pool_cleanup(vk_device& device, vk_command_pool& p) // Requires command buffers to be done device->device.resetCommandPool(p.pool); - p.cmd_buffer_idx = 0; + // Don't clear the command buffers and mark them as not in use. + // This allows us to reuse them + for (auto& cmd_buffer : p.cmd_buffers) { + cmd_buffer.in_use = false; + } } static void ggml_vk_queue_command_pools_cleanup(vk_device& device) { @@ -2466,10 +2524,10 @@ static void ggml_vk_queue_command_pools_cleanup(vk_device& device) { // Arbitrary frequency to cleanup/reuse command buffers static constexpr uint32_t cleanup_frequency = 10; - if (device->compute_queue.cmd_pool.cmd_buffer_idx >= cleanup_frequency) { + if (device->compute_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) { ggml_vk_command_pool_cleanup(device, device->compute_queue.cmd_pool); } - if (device->transfer_queue.cmd_pool.cmd_buffer_idx >= cleanup_frequency) { + if (device->transfer_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) { ggml_vk_command_pool_cleanup(device, device->transfer_queue.cmd_pool); } } @@ -2717,7 +2775,7 @@ static void ggml_vk_sync_buffers(ggml_backend_vk_context* ctx, vk_context& subct ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false; } - subctx->s->buffer.pipelineBarrier( + subctx->s->buffer->buf.pipelineBarrier( subctx->p->q->stage_flags, subctx->p->q->stage_flags, {}, @@ -2733,7 +2791,7 @@ static void ggml_vk_sync_buffers(ggml_backend_vk_context* ctx, vk_context& subct static void ggml_vk_set_event(vk_context& ctx, vk::Event& event) { VK_LOG_DEBUG("ggml_vk_set_event()"); - ctx->s->buffer.setEvent( + ctx->s->buffer->buf.setEvent( event, ctx->p->q->stage_flags ); @@ -2745,7 +2803,7 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector&& events return; } - ctx->s->buffer.waitEvents( + ctx->s->buffer->buf.waitEvents( events, ctx->p->q->stage_flags, ctx->p->q->stage_flags, @@ -2755,78 +2813,218 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector&& events ); } -// number of rows/cols for flash attention shader -static constexpr uint32_t flash_attention_num_small_rows = 32; -static constexpr uint32_t scalar_flash_attention_num_small_rows = 1; +struct vk_fa_tuning_params { + FaCodePath path; + uint32_t workgroup_size; + uint32_t subgroup_size; + uint32_t block_rows; + uint32_t block_cols; + uint32_t d_split; + uint32_t row_split; + bool shmem_staging; + bool disable_subgroups; + uint32_t limit_occupancy_shmem; -static uint32_t get_fa_scalar_num_large_rows(uint32_t hsk, uint32_t hsv, bool small_cache) { - if (hsv >= 192) { - return 2; - } else if ((hsv | hsk) & 8 || small_cache) { - return 4; - } else { - return 8; + void print() const { + std::cerr << "path=" << path << " workgroup_size=" << workgroup_size << " subgroup_size=" << subgroup_size << + " block_rows=" << block_rows << " block_cols=" << block_cols << " d_split=" << d_split << + " row_split=" << row_split << " shmem_staging=" << shmem_staging << " disable_subgroups=" << disable_subgroups << + " limit_occupancy_shmem=" << limit_occupancy_shmem << std::endl; } -} +}; -// The FA coopmat1 shader assumes 16x16x16 matrix multiply support. -// 128 threads split into four subgroups, each subgroup does 1/4 -// of the Bc dimension. -static constexpr uint32_t coopmat1_flash_attention_num_large_rows = 16; -static constexpr uint32_t scalar_flash_attention_Bc = 64; -static constexpr uint32_t scalar_flash_attention_workgroup_size = 128; +static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc); +static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc); -static uint32_t get_fa_num_small_rows(FaCodePath path) { - if (path == FA_COOPMAT2) { - return flash_attention_num_small_rows; +static vk_fa_tuning_params get_fa_tuning_params_scalar(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type kv_type, bool f32acc) { + GGML_UNUSED(kv_type); + + vk_fa_tuning_params result{}; + result.path = FA_SCALAR; + + if (device->vendor_id == VK_VENDOR_ID_INTEL) { + // Disable subgroup use due to performance issues when enforcing subgroup sizes + result.subgroup_size = 32; + result.disable_subgroups = true; + } else if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != AMD_GCN) { + result.subgroup_size = n_rows < 4 ? 32 : device->subgroup_size; } else { - return scalar_flash_attention_num_small_rows; + result.subgroup_size = device->subgroup_size; } -} -static std::array fa_rows_cols(FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache) { - GGML_UNUSED(clamp); + // Row split splits the workgroup so that synchronization only has to happen within subgroups, which avoids barriers + uint32_t row_split_max_hsk = 64; + if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != AMD_GCN && !device->uma) { + row_split_max_hsk = n_rows <= 8 ? 64 : 128; + } + result.row_split = (n_rows < 4 || hsk <= row_split_max_hsk) ? 1 : 4; - if (path == FA_SCALAR) { - if (small_rows) { - return {scalar_flash_attention_num_small_rows, 64}; + if (result.subgroup_size > 32 && (n_rows < 4 || hsk < (result.row_split == 1 ? 128 : 64))) { + result.workgroup_size = result.subgroup_size * 2; + } else { + result.workgroup_size = result.subgroup_size * 4; + } + + const uint32_t D = hsk | hsv; + + const bool reduce_block_rows = D & 8 || n_kv < 1024 || device->vendor_id == VK_VENDOR_ID_INTEL; + + if (n_rows == 1) { + result.block_rows = 1; + result.block_cols = 64; + } else { + // row_split 1 means higher register use per row, so block size has to be adjusted + if (result.row_split == 1) { + result.block_rows = n_rows == 2 ? 2 : ((n_rows <= 4 || reduce_block_rows) ? 4 : 8); } else { - if ((hsv | hsk) & 8) { - // HSV/HSK not being a multiple of 16 makes D_split smaller, which makes cols_per_iter - // larger, and Bc needs to be >= cols_per_thread. 64 is large enough, 32 is not. - return {get_fa_scalar_num_large_rows(hsk, hsv, small_cache), 64}; - } else { - return {get_fa_scalar_num_large_rows(hsk, hsv, small_cache), 32}; - } + result.block_rows = n_rows <= 4 ? 4 : ((n_rows <= 8 || reduce_block_rows) ? 8 : 16); } + + result.block_cols = (D & 8) ? 64 : 32; + } + + const uint32_t D_lsb = D ^ (D & (D-1)); // extract lowest set bit + + result.d_split = std::min(std::min(result.subgroup_size, 8u), D_lsb / 4); + + result.shmem_staging = (device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 && hsv < 256) ? 1 : 0; + + if (!reduce_block_rows && !ggml_vk_flash_attn_scalar_shmem_support(device, result, hsk, hsv, f32acc)) { + result.block_rows /= 2; + } + + // On AMD RDNA, for small head sizes and big batch size the shader uses few registers, so too many subgroups get scheduled + // at once and end up thrashing the cache. Fix this by setting a large (unused) shmem buffer that reduces occupancy. + // This targets an occupancy of 4 subgroups per SIMD. + if (device->vendor_id == VK_VENDOR_ID_AMD && device->properties.limits.maxComputeSharedMemorySize == 65536) { + if (device->architecture != AMD_GCN && n_rows >= 64 && hsk <= 128) { + // 30kb target for hsk > 64, 26kb for <= 64 due to smaller workgroup size + // Values are guessed, tested on RDNA2 + result.limit_occupancy_shmem = (hsk <= 64 ? 26 : 30) * 1024 / 4 / 4; + } else if (device->architecture == AMD_GCN && n_rows <= 8 && hsk >= 256) { + // Same thing for GCN, with an occupancy target of 2 subgroups per SIMD. + // Here low-batch FA with large head size is affected. + // n_rows < 4 switch because workgroup size switches from 128 to 256 there. + result.limit_occupancy_shmem = (n_rows < 4 ? 14 : 26) * 1024 / 4 / 4; + } + } + + return result; +} + +static vk_fa_tuning_params get_fa_tuning_params_coopmat1(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type kv_type, bool f32acc) { + GGML_UNUSED(n_rows); + GGML_UNUSED(n_kv); + GGML_UNUSED(kv_type); + GGML_UNUSED(f32acc); + + vk_fa_tuning_params result{}; + result.path = FA_COOPMAT1; + + const uint32_t D = hsk | hsv; + + const uint32_t coopmat_block_rows = 16; + const uint32_t coopmat_block_cols = 16; + + const uint32_t num_subgroups = 4; + + result.block_rows = coopmat_block_rows; + result.block_cols = coopmat_block_cols * num_subgroups; + result.row_split = num_subgroups; + result.subgroup_size = device->subgroup_size; + result.workgroup_size = num_subgroups * result.subgroup_size; + + const uint32_t D_lsb = D ^ (D & (D-1)); // extract lowest set bit + result.d_split = std::min(std::min(result.subgroup_size, 8u), D_lsb / 4); + + result.shmem_staging = (device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 && hsv < 256) ? 1 : 0; + + return result; +} + +static vk_fa_tuning_params get_fa_tuning_params_coopmat2(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type kv_type, bool f32acc) { + GGML_UNUSED(n_kv); + GGML_UNUSED(f32acc); + + vk_fa_tuning_params result{}; + result.path = FA_COOPMAT2; + + const uint32_t D = hsk | hsv; + + const bool small_rows = n_rows < 32; + + if (small_rows) { + result.block_rows = 32; + result.block_cols = 32; + } else if (ggml_is_quantized(kv_type) || hsk >= 256 || hsv >= 256) { + result.block_rows = (hsk >= 512 || hsv >= 512) ? 32 : 64; + result.block_cols = 32; + } else { + result.block_rows = 64; + result.block_cols = 64; + } + + result.subgroup_size = device->subgroup_size; + result.workgroup_size = (small_rows && (D % 32) == 0) ? 256 : 128; + + return result; +} + +static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type kv_type, bool f32acc) { + FaCodePath path = device->coopmat2 ? FA_COOPMAT2 : + device->coopmat1_fa_support ? FA_COOPMAT1 : FA_SCALAR; + + if (path == FA_COOPMAT1 && device->architecture == vk_device_architecture::NVIDIA_TURING) { + // Nvidia compiler bug, see https://github.com/ggml-org/llama.cpp/pull/19075#issuecomment-3820716090 + path = FA_SCALAR; } if (path == FA_COOPMAT1) { - if (small_rows) { - return {scalar_flash_attention_num_small_rows, scalar_flash_attention_Bc}; - } else { - return {coopmat1_flash_attention_num_large_rows, scalar_flash_attention_Bc}; + bool shape_ok = (f32acc && device->coopmat_support_16x16x16_f32acc) || + (!f32acc && device->coopmat_support_16x16x16_f16acc); + const vk_fa_tuning_params params = get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, kv_type, f32acc); + bool shmem_ok = ggml_vk_flash_attn_coopmat_shmem_support(device, params, hsk, hsv, f32acc); + + if (!shape_ok || !shmem_ok) { + path = FA_SCALAR; } } - // small rows, large cols - if (small_rows) { - return {get_fa_num_small_rows(FA_COOPMAT2), 32}; + // scalar is faster than coopmat when N==1 + if (n_rows == 1 && (path == FA_COOPMAT1 || path == FA_COOPMAT2)) { + path = FA_SCALAR; } - // small cols to reduce register count - if (ggml_is_quantized(type) || hsk >= 256 || hsv >= 256) { - if (hsk >= 512 || hsv >= 512) { - return {32, 32}; - } else { - return {64, 32}; - } + switch (path) { + case FA_SCALAR: + return get_fa_tuning_params_scalar(device, hsk, hsv, n_rows, n_kv, kv_type, f32acc); + case FA_COOPMAT1: + return get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, kv_type, f32acc); + case FA_COOPMAT2: + return get_fa_tuning_params_coopmat2(device, hsk, hsv, n_rows, n_kv, kv_type, f32acc); + default: + throw std::runtime_error("unsupported FaCodePath"); } - return {64, 64}; } -static uint32_t fa_align(FaCodePath path, uint32_t hsk, uint32_t hsv, ggml_type type, bool small_rows, bool small_cache) { - return fa_rows_cols(path, hsk, hsv, 0, type, small_rows, small_cache)[1]; +static vk_fa_pipeline_state get_fa_pipeline_state(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool aligned, bool f32acc, + bool use_mask, bool use_mask_opt, bool use_logit_softcap) { + const bool old_amd_windows = device->vendor_id == VK_VENDOR_ID_AMD && device->driver_id == vk::DriverId::eAmdProprietary && + (device->architecture == AMD_GCN || device->architecture == AMD_RDNA1 || device->architecture == AMD_RDNA2); + + uint32_t flags = (use_mask_opt ? 1 : 0) | + (use_mask ? 2 : 0) | + (use_logit_softcap ? 4 : 0) | + (old_amd_windows ? 8 : 0); + + const uint32_t subgroup_size = params.disable_subgroups ? 0 : params.subgroup_size; + + return vk_fa_pipeline_state{hsk, hsv, params.block_rows, params.block_cols, params.d_split, params.row_split, params.shmem_staging, params.path, params.workgroup_size, subgroup_size, aligned, f32acc, flags, params.limit_occupancy_shmem}; +} + +static std::vector get_fa_spec_constants(const vk_fa_pipeline_state& state) { + return {state.workgroup_size, state.Br, state.Bc, state.HSK, state.HSV, !state.aligned, state.D_split, + state.row_split, state.subgroup_size, state.shmem_staging ? 1u : 0u, state.flags, state.limit_occupancy_shmem}; } static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vector& warptile, bool mul_mat_id, ggml_type src0_type) { @@ -3193,76 +3391,43 @@ static void ggml_vk_load_shaders(vk_device& device) { align, disable_robustness, require_full_subgroups, required_subgroup_size); }; - auto const &fa_wg_denoms = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache) -> std::array { - return {fa_rows_cols(path, hsk, hsv, clamp, type, small_rows, small_cache)[0], 1, 1}; - }; - - auto const &fa_spec_constants = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache, uint32_t flags) -> std::vector { - // For large number of rows, 128 invocations seems to work best. - // For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we - // can't use 256 for D==80. - // For scalar, use 128 (arbitrary) - // The same D_split value is used for both HSK and HSV, so just base it on the union of the LSBs. - const uint32_t D = (hsk|hsv); - auto rows_cols = fa_rows_cols(path, hsk, hsv, clamp, type, small_rows, small_cache); - - uint32_t wg_size; - switch (path) { - case FA_COOPMAT2: - wg_size = ((small_rows && (D % 32) == 0) ? 256 : 128); - break; - case FA_COOPMAT1: - wg_size = (rows_cols[1] / 16) * device->subgroup_size; // enough subgroups for Bc/MatBc - break; - default: - wg_size = scalar_flash_attention_workgroup_size; - break; - } - - // D_split can't be larger than a subgroup because we use subgroupShuffle to reduce it. - // D_split can't be larger than the LSB of D divided by 4 due to vectorization in the shader. - const uint32_t D_lsb = D ^ (D & (D-1)); - uint32_t D_split = std::min(std::min(device->subgroup_size, 8u), D_lsb / 4); - - // Nvidia prefers shared memory use to load large tiles of K. - // Switch to loading from global memory when it would use too much shared memory. - // AMD prefers loading K directly from global memory - const uint32_t k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 ? 1 : 0; - - return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split, device->subgroup_size, k_load_shmem, flags}; - }; - #define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \ for (auto &fa : device->pipeline_flash_attn_f32_f16[TYPE]) { \ - uint32_t HSK = fa.first.HSK; \ - uint32_t HSV = fa.first.HSV; \ - bool small_rows = fa.first.small_rows; \ - bool small_cache = fa.first.small_cache; \ FaCodePath path = fa.first.path; \ + uint32_t Br = fa.first.Br; \ + uint32_t Bc = fa.first.Bc; \ bool aligned = fa.first.aligned; \ bool f32acc = fa.first.f32acc; \ - uint32_t flags = fa.first.flags; \ + uint32_t fa_sgs = fa.first.subgroup_size; \ + bool fa_ds = fa.first.subgroup_size == 0; \ if (path == FAPATH) { \ if (aligned) { \ if (f32acc) { \ - ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,flags), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \ + ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, get_fa_spec_constants(fa.first), Bc, true, (!fa_ds && (FAPATH!=FA_COOPMAT2)), ((!fa_ds && (FAPATH!=FA_COOPMAT2)) ? fa_sgs : 0)); \ } else { \ - ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,flags), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \ + ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, get_fa_spec_constants(fa.first), Bc, true, (!fa_ds && (FAPATH!=FA_COOPMAT2)), ((!fa_ds && (FAPATH!=FA_COOPMAT2)) ? fa_sgs : 0)); \ } \ } else { \ if (f32acc) { \ - ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,flags), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \ + ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, get_fa_spec_constants(fa.first), 1, true, (!fa_ds && (FAPATH!=FA_COOPMAT2)), ((!fa_ds && (FAPATH!=FA_COOPMAT2)) ? fa_sgs : 0)); \ } else { \ - ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,flags), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \ + ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, get_fa_spec_constants(fa.first), 1, true, (!fa_ds && (FAPATH!=FA_COOPMAT2)), ((!fa_ds && (FAPATH!=FA_COOPMAT2)) ? fa_sgs : 0)); \ } \ } \ } \ } - CREATE_FA(GGML_TYPE_F32, f32, FA_SCALAR, ) - CREATE_FA(GGML_TYPE_F16, f16, FA_SCALAR, ) - CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_SCALAR, ) - CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_SCALAR, ) + if (device->fp16) { + CREATE_FA(GGML_TYPE_F32, f32, FA_SCALAR, ) + CREATE_FA(GGML_TYPE_F16, f16, FA_SCALAR, ) + CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_SCALAR, ) + CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_SCALAR, ) + } else { + CREATE_FA(GGML_TYPE_F32, f32, FA_SCALAR, _fp32) + CREATE_FA(GGML_TYPE_F16, f16, FA_SCALAR, _fp32) + CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_SCALAR, _fp32) + CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_SCALAR, _fp32) + } #if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) if (device->coopmat1_fa_support) { CREATE_FA(GGML_TYPE_F32, f32, FA_COOPMAT1, _cm1) @@ -3780,10 +3945,12 @@ static void ggml_vk_load_shaders(vk_device& device) { && !device->coopmat_bf16_support #endif ) { + const uint32_t s_warptile_wm = device->subgroup_size == 8 ? 8 : 32; + // use scalar tile sizes l_warptile = { 128, 128, 128, 16, subgroup_size_8 * 2, 64, 2, 4, 4, 1, subgroup_size_8 }; m_warptile = { 128, 64, 64, 16, subgroup_size_8, 32, 2, 4, 2, 1, subgroup_size_8 }; - s_warptile = { subgroup_size_16, 32, 32, 16, 32, 32, 2, 2, 2, 1, subgroup_size_8 }; + s_warptile = { subgroup_size_32, 32, 32, 16, s_warptile_wm, 32, 2, 2, 2, 1, subgroup_size_8 }; l_wg_denoms = {128, 128, 1 }; m_wg_denoms = { 64, 64, 1 }; @@ -4231,6 +4398,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); \ ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + CREATE_UNARY(elu) CREATE_UNARY(gelu) CREATE_UNARY(gelu_erf) CREATE_UNARY(gelu_quick) @@ -4249,6 +4417,7 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_UNARY(ceil) CREATE_UNARY(floor) CREATE_UNARY(trunc) + CREATE_UNARY(sgn) #undef CREATE_UNARY #define CREATE_UNARY_RTE(name) \ @@ -4413,6 +4582,23 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv7_f32, "rwkv_wkv7_f32", rwkv_wkv7_f32_len, rwkv_wkv7_f32_data, "main", 8, sizeof(vk_op_rwkv_wkv7_push_constants), {1, 1, 1}, {device->subgroup_size}, 1); + { + const uint32_t gdn_sizes[] = {32, 64, 128}; + const char * gdn_names[][2] = { + {"gated_delta_net_f32_d32", "gated_delta_net_f32_d32_kda"}, + {"gated_delta_net_f32_d64", "gated_delta_net_f32_d64_kda"}, + {"gated_delta_net_f32_d128", "gated_delta_net_f32_d128_kda"}, + }; + for (uint32_t si = 0; si < 3; si++) { + for (uint32_t kda = 0; kda < 2; kda++) { + ggml_vk_create_pipeline(device, device->pipeline_gated_delta_net[si][kda], + gdn_names[si][kda], gated_delta_net_f32_len, gated_delta_net_f32_data, + "main", 7, sizeof(vk_op_gated_delta_net_push_constants), + {1, 1, 1}, {gdn_sizes[si], kda}, 1); + } + } + } + if (device->subgroup_arithmetic && device->subgroup_require_full_support) { ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d128, "ssm_scan_128_f32", ssm_scan_subgroup_f32_len, ssm_scan_subgroup_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {128, device->subgroup_size}, 1, true, true); ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d256, "ssm_scan_256_f32", ssm_scan_subgroup_f32_len, ssm_scan_subgroup_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {256, device->subgroup_size}, 1, true, true); @@ -4421,7 +4607,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d256, "ssm_scan_256_f32", ssm_scan_f32_len, ssm_scan_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {256, device->subgroup_size, 16}, 1, true, true); } - ggml_vk_create_pipeline(device, device->pipeline_ssm_conv_f32, "ssm_conv_f32", ssm_conv_f32_len, ssm_conv_f32_data, "main", 3, sizeof(vk_op_ssm_conv_push_constants), {32, 1, 1}, {32}, 1); + ggml_vk_create_pipeline(device, device->pipeline_ssm_conv_f32, "ssm_conv_f32", ssm_conv_f32_len, ssm_conv_f32_data, "main", 3, sizeof(vk_op_ssm_conv_push_constants), {32, 16, 1}, {32, 16}, 1); ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); @@ -4533,6 +4719,7 @@ static void ggml_vk_load_shaders(vk_device& device) { } static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch); +static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev); static vk_device ggml_vk_get_device(size_t idx) { VK_LOG_DEBUG("ggml_vk_get_device(" << idx << ")"); @@ -4749,6 +4936,8 @@ static vk_device ggml_vk_get_device(size_t idx) { device->shader_core_count = sm_props.shaderSMCount; } else if (amd_shader_core_properties2) { device->shader_core_count = amd_shader_core_properties2_props.activeComputeUnitCount; + } else if (device->vendor_id == VK_VENDOR_ID_INTEL) { + device->shader_core_count = ggml_vk_intel_shader_core_count(device->physical_device); } else { device->shader_core_count = 0; } @@ -4792,8 +4981,10 @@ static vk_device ggml_vk_get_device(size_t idx) { std::vector queue_family_props = device->physical_device.getQueueFamilyProperties(); // Try to find a non-graphics compute queue and transfer-focused queues - const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); - const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); + // On AMD, the graphics queue seems to be faster, so don't avoid it + const vk::QueueFlagBits graphics_flag = device->vendor_id == VK_VENDOR_ID_AMD ? (vk::QueueFlagBits)0 : vk::QueueFlagBits::eGraphics; + const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, graphics_flag, -1, 1); + const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | graphics_flag, compute_queue_family_index, 1); const float priorities[] = { 1.0f, 1.0f }; device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; @@ -4968,11 +5159,7 @@ static vk_device ggml_vk_get_device(size_t idx) { #if defined(VK_KHR_cooperative_matrix) device->coopmat_support = device->coopmat_support && coopmat_features.cooperativeMatrix; - - // coopmat1 fa shader currently assumes 32 invocations per subgroup - device->coopmat1_fa_support = device->coopmat_support && device->subgroup_require_full_support && - device->subgroup_size_control && device->subgroup_min_size <= 32 && - device->subgroup_max_size >= 32; + device->coopmat1_fa_support = device->coopmat_support && device->subgroup_require_full_support; #endif if (coopmat2_support) { @@ -5259,10 +5446,14 @@ static vk_device ggml_vk_get_device(size_t idx) { if (!device->single_queue) { const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true); + + device->async_use_transfer_queue = (getenv("GGML_VK_ASYNC_USE_TRANSFER_QUEUE") != nullptr); } else { // TODO: Use pointer or reference to avoid copy device->transfer_queue.copyFrom(device->compute_queue); device->transfer_queue.cmd_pool.init(device, &device->transfer_queue); + + device->async_use_transfer_queue = false; } device->buffer_type = { @@ -5540,6 +5731,10 @@ static void ggml_vk_instance_init() { vk_perf_logger_concurrent = getenv("GGML_VK_PERF_LOGGER_CONCURRENT") != nullptr; vk_enable_sync_logger = getenv("GGML_VK_SYNC_LOGGER") != nullptr; vk_memory_logger_enabled = getenv("GGML_VK_MEMORY_LOGGER") != nullptr; + const char* GGML_VK_PIPELINE_STATS = getenv("GGML_VK_PIPELINE_STATS"); + if (GGML_VK_PIPELINE_STATS != nullptr) { + vk_pipeline_stats_filter = GGML_VK_PIPELINE_STATS; + } const char* GGML_VK_PERF_LOGGER_FREQUENCY = getenv("GGML_VK_PERF_LOGGER_FREQUENCY"); if (GGML_VK_PERF_LOGGER_FREQUENCY != nullptr) { @@ -5732,6 +5927,15 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->almost_ready_fence = ctx->device->device.createFence({}); ctx->compute_cmd_pool.init(ctx->device, &ctx->device->compute_queue); + if (ctx->device->async_use_transfer_queue) { + vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; + vk::SemaphoreCreateInfo ci{}; + ci.setPNext(&tci); + ctx->transfer_semaphore.s = ctx->device->device.createSemaphore(ci); + ctx->transfer_semaphore.value = 0; + + ctx->transfer_cmd_pool.init(ctx->device, &ctx->device->transfer_queue); + } if (vk_perf_logger_enabled) { ctx->perf_logger = std::unique_ptr(new vk_perf_logger()); @@ -6184,13 +6388,24 @@ static vk_subbuffer ggml_vk_tensor_subbuffer( return vk_subbuffer{buffer, offset, size}; } +// Get a command buffer from pool. Create a new one if no reusable buffer is available +static vk_command_buffer* ggml_vk_get_or_create_cmd_buffer(vk_device& device, vk_command_pool& pool) { + for (auto& cmd_buffer : pool.cmd_buffers) { + if (!cmd_buffer.in_use) { + cmd_buffer.in_use = true; + return &cmd_buffer; + } + } + return ggml_vk_create_cmd_buffer(device, pool); +} + static vk_submission ggml_vk_begin_submission(vk_device& device, vk_command_pool& p, bool one_time = true) { vk_submission s; - s.buffer = ggml_vk_create_cmd_buffer(device, p); + s.buffer = ggml_vk_get_or_create_cmd_buffer(device, p); if (one_time) { - s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit }); + s.buffer->buf.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit }); } else { - s.buffer.begin({ vk::CommandBufferUsageFlags{} }); + s.buffer->buf.begin({ vk::CommandBufferUsageFlags{} }); } return s; @@ -6243,18 +6458,18 @@ static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context& vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() }; ctx->device->device.updateDescriptorSets({ write_descriptor_set }, {}); - subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size(push_constants), push_constant_data(push_constants)); - subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline); - subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, + subctx->s->buffer->buf.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size(push_constants), push_constant_data(push_constants)); + subctx->s->buffer->buf.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline); + subctx->s->buffer->buf.bindDescriptorSets(vk::PipelineBindPoint::eCompute, pipeline->layout, 0, { descriptor_set }, {}); - subctx->s->buffer.dispatch(wg0, wg1, wg2); + subctx->s->buffer->buf.dispatch(wg0, wg1, wg2); } static void ggml_vk_end_submission(vk_submission& s, std::vector wait_semaphores, std::vector signal_semaphores) { - s.buffer.end(); + s.buffer->buf.end(); s.wait_semaphores = std::move(wait_semaphores); s.signal_semaphores = std::move(signal_semaphores); @@ -6266,7 +6481,7 @@ static void ggml_vk_ctx_end(vk_context& ctx) { return; } - ctx->s->buffer.end(); + ctx->s->buffer->buf.end(); ctx->s = nullptr; } @@ -6280,6 +6495,47 @@ static void ggml_vk_ctx_begin(vk_device& device, vk_context& subctx) { subctx->s = subctx->seqs[subctx->seqs.size() - 1].data(); } +static vk_context ggml_vk_get_compute_ctx(ggml_backend_vk_context * ctx) { + if (!ctx->compute_ctx.expired()) { + return ctx->compute_ctx.lock(); + } + + vk_context result = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); + + ctx->compute_ctx = result; + ggml_vk_ctx_begin(ctx->device, result); + + if (ctx->device->async_use_transfer_queue && ctx->transfer_semaphore_last_submitted < ctx->transfer_semaphore.value) { + result->s->wait_semaphores.push_back(ctx->transfer_semaphore); + ctx->transfer_semaphore_last_submitted = ctx->transfer_semaphore.value; + } + + return result; +} + +// Submit any pending transfer queue work and signal the transfer semaphore. +// The next compute context created via ggml_vk_get_compute_ctx will wait on this semaphore. +// Returns true if work was submitted. +static bool ggml_vk_submit_transfer_ctx(ggml_backend_vk_context * ctx) { + if (!ctx->device->async_use_transfer_queue || ctx->transfer_ctx.expired()) { + return false; + } + + vk_context cpy_ctx = ctx->transfer_ctx.lock(); + ggml_vk_ctx_end(cpy_ctx); + + for (auto& cpy : cpy_ctx->in_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + + ctx->transfer_semaphore.value++; + cpy_ctx->seqs.back().back().signal_semaphores.push_back(ctx->transfer_semaphore); + + ggml_vk_submit(cpy_ctx, {}); + ctx->transfer_ctx.reset(); + return true; +} + static size_t ggml_vk_align_size(size_t width, size_t align) { VK_LOG_DEBUG("ggml_vk_align_size(" << width << ", " << align << ")"); return CEIL_DIV(width, align) * align; @@ -6379,7 +6635,7 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont } ggml_vk_sync_buffers(ctx, subctx); - subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + subctx->s->buffer->buf.copyBuffer(buf->buffer, dst->buffer, slices); return; } @@ -6394,7 +6650,7 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont VkBufferCopy buf_copy{ 0, offset, copy_size }; ggml_vk_sync_buffers(ctx, subctx); - vkCmdCopyBuffer(subctx->s->buffer, (VkBuffer)staging->buffer, (VkBuffer)dst->buffer, 1, &buf_copy); + vkCmdCopyBuffer(subctx->s->buffer->buf, (VkBuffer)staging->buffer, (VkBuffer)dst->buffer, 1, &buf_copy); for (uint64_t i3 = 0; i3 < ne3; i3++) { for (uint64_t i2 = 0; i2 < ne2; i2++) { @@ -6443,7 +6699,7 @@ static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz } ggml_vk_sync_buffers(nullptr, subctx); - subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + subctx->s->buffer->buf.copyBuffer(buf->buffer, dst->buffer, slices); return true; } VK_LOG_DEBUG("STAGING"); @@ -6465,7 +6721,7 @@ static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz copy_size}; ggml_vk_sync_buffers(nullptr, subctx); - vkCmdCopyBuffer(subctx->s->buffer, (VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, 1, &buf_copy); + vkCmdCopyBuffer(subctx->s->buffer->buf, (VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, 1, &buf_copy); if (width == spitch) { deferred_memcpy((uint8_t *)staging_buffer->ptr, src, width * height, &subctx->in_memcpys); @@ -6551,7 +6807,7 @@ static bool ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size if (buf != nullptr) { // Memory is pinned, use as staging buffer ggml_vk_sync_buffers(nullptr, subctx); - subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices); + subctx->s->buffer->buf.copyBuffer(src->buffer, buf->buffer, slices); return true; } @@ -6569,7 +6825,7 @@ static bool ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size vk_buffer& staging_buffer = src->device->sync_staging; ggml_vk_sync_buffers(nullptr, subctx); - subctx->s->buffer.copyBuffer(src->buffer, staging_buffer->buffer, slices); + subctx->s->buffer->buf.copyBuffer(src->buffer, staging_buffer->buffer, slices); deferred_memcpy(dst, staging_buffer->ptr, copy_size, &subctx->out_memcpys); return true; @@ -6616,7 +6872,7 @@ static void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t ds VkBufferCopy bc{ src_offset, dst_offset, size }; - vkCmdCopyBuffer(ctx->s->buffer, (VkBuffer)src->buffer, (VkBuffer)dst->buffer, 1, &bc); + vkCmdCopyBuffer(ctx->s->buffer->buf, (VkBuffer)src->buffer, (VkBuffer)dst->buffer, 1, &bc); } static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { @@ -6654,7 +6910,7 @@ static void ggml_vk_buffer_memset_async(vk_context& ctx, vk_buffer& dst, size_t } // Fall back to GPU fillBuffer for non-UMA or non-host-visible buffers - ctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); + ctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c); } static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, size_t size) { @@ -6669,7 +6925,7 @@ static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, siz std::lock_guard guard(dst->device->mutex); vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool); ggml_vk_ctx_begin(dst->device, subctx); - subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); + subctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c); ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, dst->device->fence); @@ -7373,6 +7629,18 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_ return false; } + if (device->driver_id == vk::DriverId::eIntelProprietaryWindows) { + // Intel Windows proprietary driver tuning + switch (src0_type) { + case GGML_TYPE_MXFP4: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + return false; + default: + return true; + } + } + switch (src0_type) { // From tests on A770 Linux, may need more tuning case GGML_TYPE_Q4_0: @@ -8419,21 +8687,27 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx } } -static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const uint32_t hsk, uint32_t hsv, bool small_cache) { +static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc) { + GGML_UNUSED(f32acc); // Needs to be kept up to date on shader changes - GGML_UNUSED(hsv); - const uint32_t wg_size = scalar_flash_attention_workgroup_size; - const uint32_t Br = get_fa_scalar_num_large_rows(hsk, hsv, small_cache); - const uint32_t Bc = scalar_flash_attention_Bc; + const uint32_t wg_size = params.workgroup_size; + const uint32_t Br = params.block_rows; + const uint32_t Bc = params.block_cols; + const uint32_t float_type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float); + + // tmpsh is overestimated slightly const uint32_t tmpsh = wg_size * sizeof(float); - const uint32_t tmpshv4 = wg_size * 4 * sizeof(float); + const uint32_t tmpshv4 = wg_size * 4 * float_type_size; - const uint32_t masksh = Bc * Br * sizeof(float); + const uint32_t masksh = Bc * (Br + 1) * float_type_size; - const uint32_t Qf = Br * (hsk / 4 + 2) * 4 * sizeof(float); + const uint32_t Qf = Br * (hsk / 4 + 1) * 4 * float_type_size; - const uint32_t total_size = tmpsh + tmpshv4 + masksh + Qf; + const uint32_t D = std::max(hsk, hsv); + const uint32_t kvsh = params.shmem_staging ? Bc * (D / 4 + 1) * 4 * float_type_size : 4 * float_type_size; + + const uint32_t total_size = tmpsh + tmpshv4 + masksh + Qf + kvsh; const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; VK_LOG_DEBUG("ggml_vk_flash_attn_scalar_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", total_size=" << total_size << ", supported=" << supported); @@ -8441,18 +8715,17 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con return supported; } -static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type kv_type) { +static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc) { // Needs to be kept up to date on shader changes - GGML_UNUSED(hsv); - const auto rows_cols = fa_rows_cols(FA_COOPMAT1, hsk, hsv, 0, kv_type, false, false); - const uint32_t Br = rows_cols[0]; - const uint32_t Bc = rows_cols[1]; + const uint32_t Br = params.block_rows; + const uint32_t Bc = params.block_cols; const uint32_t MatBr = 16, MatBc = 16; const uint32_t row_split = Bc / MatBc; const uint32_t hsk_pad = ROUNDUP_POW2(hsk, 16); + const uint32_t hsv_pad = ROUNDUP_POW2(hsv, 16); const uint32_t acctype = f32acc ? 4 : 2; const uint32_t f16vec4 = 8; @@ -8468,17 +8741,19 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co const uint32_t sfshstride = (hsk <= 128) ? (Br + 8) : Br; const uint32_t sfsh = Bc * sfshstride * acctype; - const bool k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256; - const uint32_t kshstride = (k_load_shmem ? hsk_pad : MatBr) / 4 + 2; + const uint32_t kvshstride = (params.shmem_staging ? std::max(hsk_pad, hsv_pad) : MatBr) / 4 + 2; const uint32_t vsh_stride = MatBc / 4 * row_split; - const uint32_t ksh = ((kshstride >= vsh_stride) ? (Bc * kshstride) : (Bc * vsh_stride)) * f16vec4; + const uint32_t ksh = ((kvshstride >= vsh_stride) ? (Bc * kvshstride) : (Bc * vsh_stride)) * f16vec4; + + const uint32_t osh_stride = params.row_split * MatBr / 4; + const uint32_t pvsh = MatBc * osh_stride * f16vec4; const uint32_t slope = Br * acctype; - const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + slope; + const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + pvsh + slope; const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; - VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", kv_type=" << kv_type << ", total_size=" << total_size << ", supported=" << supported); + VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", total_size=" << total_size << ", supported=" << supported); return supported; } @@ -8536,48 +8811,18 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx assert(q->type == GGML_TYPE_F32); assert(k->type == v->type); - FaCodePath path = ctx->device->coopmat2 ? FA_COOPMAT2 : - ctx->device->coopmat1_fa_support ? FA_COOPMAT1 : FA_SCALAR; - - if (path == FA_COOPMAT1 && ctx->device->architecture == vk_device_architecture::NVIDIA_TURING) { - // Nvidia compiler bug, see https://github.com/ggml-org/llama.cpp/pull/19075#issuecomment-3820716090 - path = FA_SCALAR; - } - - if (path == FA_COOPMAT1) { - const bool coopmat_shape_supported = (dst->op_params[3] == GGML_PREC_F32 && ctx->device->coopmat_support_16x16x16_f32acc) || - (dst->op_params[3] != GGML_PREC_F32 && ctx->device->coopmat_support_16x16x16_f16acc); - - const bool coopmat_shmem_supported = ggml_vk_flash_attn_coopmat_shmem_support(ctx->device, HSK, HSV, dst->op_params[3] == GGML_PREC_F32, k->type); - - if (!coopmat_shape_supported || !coopmat_shmem_supported) { - path = FA_SCALAR; - } - } - uint32_t gqa_ratio = 1; uint32_t qk_ratio = neq2 / nek2; uint32_t workgroups_x = (uint32_t)neq1; uint32_t workgroups_y = (uint32_t)neq2; uint32_t workgroups_z = (uint32_t)neq3; - const bool small_cache = nek1 < 1024; + const bool f32acc = !ctx->device->fp16 || dst->op_params[3] == GGML_PREC_F32; // For scalar/coopmat1 FA, we can use the "large" size to accommodate qga. // For coopmat2 FA, we always use the small size (which is still pretty large for gqa). - uint32_t max_gqa; - switch (path) { - case FA_SCALAR: - case FA_COOPMAT1: - // We may switch from coopmat1 to scalar, so use the scalar limit for both - max_gqa = get_fa_scalar_num_large_rows(HSK, HSV, small_cache); - break; - case FA_COOPMAT2: - max_gqa = get_fa_num_small_rows(FA_COOPMAT2); - break; - default: - GGML_ASSERT(0); - } + vk_fa_tuning_params tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, 512, KV, k->type, f32acc); + const uint32_t max_gqa = std::min(tuning_params.block_rows, 32u); if (N <= 8 && qk_ratio > 1 && qk_ratio <= max_gqa && qk_ratio * nek2 == neq2 && nek2 == nev2 && nem2 <= 1) { @@ -8589,24 +8834,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx workgroups_y /= gqa_ratio; } - bool small_rows = N <= get_fa_num_small_rows(path); - - // coopmat1 does not actually support "small rows" (it needs 16 rows). - // So use scalar instead. - if (small_rows && path == FA_COOPMAT1) { - path = FA_SCALAR; - } - - // scalar is faster than coopmat2 when N==1 - if (N == 1 && path == FA_COOPMAT2) { - path = FA_SCALAR; - } - - // with large hsk/hsv, scalar path may need to use small_rows to fit in shared memory - if (path == FA_SCALAR && - !ggml_vk_flash_attn_scalar_shmem_support(ctx->device, HSK, HSV, small_cache)) { - small_rows = true; - } + tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, N, KV, k->type, f32acc); const uint32_t q_stride = (uint32_t)(nbq1 / ggml_type_size(q->type)); uint32_t k_stride = (uint32_t)(nbk1 / ggml_type_size(k->type)); @@ -8620,18 +8848,16 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx v_stride /= 4; } - uint32_t alignment = fa_align(path, HSK, HSV, k->type, small_rows, small_cache); + const uint32_t alignment = tuning_params.block_cols; bool aligned = (KV % alignment) == 0 && // the "aligned" shader variant will forcibly align strides, for performance (q_stride & 7) == 0 && (k_stride & 7) == 0 && (v_stride & 7) == 0; // Need to use the coopmat2 variant that clamps loads when HSK/HSV aren't sufficiently aligned. - if (((HSK | HSV) % 16) != 0 && path == FA_COOPMAT2) { + if (((HSK | HSV) % 16) != 0 && tuning_params.path == FA_COOPMAT2) { aligned = false; } - bool f32acc = path == FA_SCALAR || dst->op_params[3] == GGML_PREC_F32; - float scale = 1.0f; float max_bias = 0.0f; float logit_softcap = 0.0f; @@ -8645,13 +8871,9 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx } // Only use mask opt when the mask is fairly large. This hasn't been tuned extensively. - bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768; - - uint32_t flags = (use_mask_opt ? 1 : 0) | - (mask != nullptr ? 2 : 0) | - (logit_softcap != 0 ? 4 : 0); - - vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, flags); + bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768 && nem0 >= tuning_params.block_cols * 16; + vk_fa_pipeline_state fa_pipeline_state = get_fa_pipeline_state(ctx->device, tuning_params, HSK, HSV, aligned, f32acc, + mask != nullptr, use_mask_opt, logit_softcap != 0); vk_pipeline pipeline = nullptr; @@ -8673,22 +8895,35 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx uint32_t split_kv = KV; uint32_t split_k = 1; + // Intel Alchemist prefers more workgroups + const uint32_t shader_core_count_multiplier = (ctx->device->vendor_id == VK_VENDOR_ID_INTEL && ctx->device->architecture != INTEL_XE2) ? 2 : 1; + // Use a placeholder core count if one isn't available. split_k is a big help for perf. - const uint32_t shader_core_count = ctx->device->shader_core_count ? ctx->device->shader_core_count : 16; + const uint32_t shader_core_count = ctx->device->shader_core_count ? ctx->device->shader_core_count * shader_core_count_multiplier : 16; + + const uint32_t Br = fa_pipeline_state.Br; + const uint32_t Bc = fa_pipeline_state.Bc; + + GGML_ASSERT(Br == pipeline->wg_denoms[0]); + const uint32_t Tr = CEIL_DIV(N, Br); // Try to use split_k when KV is large enough to be worth the overhead. - // Must either be a single batch or be using gqa, we can't mix the two. - if (workgroups_x <= pipeline->wg_denoms[0] && (workgroups_x == 1 || gqa_ratio > 1)) { - // Try to run two workgroups per SM. + if (gqa_ratio > 1 && workgroups_x <= Br) { split_k = shader_core_count * 2 / (workgroups_x * workgroups_y * workgroups_z); - if (split_k > 1) { - // Try to evenly split KV into split_k chunks, but it needs to be a multiple - // of "align", so recompute split_k based on that. - split_kv = ROUNDUP_POW2(std::max(1u, KV / split_k), alignment); - split_k = CEIL_DIV(KV, split_kv); + } else if (gqa_ratio <= 1) { + uint32_t total_wgs_no_split = Tr * workgroups_y * workgroups_z; + if (total_wgs_no_split < shader_core_count * 2) { + split_k = shader_core_count * 2 / total_wgs_no_split; } } + if (split_k > 1) { + // Try to evenly split KV into split_k chunks, but it needs to be a multiple + // of "align", so recompute split_k based on that. + split_kv = ROUNDUP_POW2(std::max(1u, KV / split_k), alignment); + split_k = CEIL_DIV(KV, split_kv); + } + // Reserve space for split_k temporaries. For each split x batch, we need to store the O matrix (D x ne1) // and the per-row m and L values (ne1 rows). We store all the matrices first, followed by the rows. // For matrices, the order is (inner to outer) [HSV, ne1, k, ne2, ne3]. @@ -8702,10 +8937,6 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx ggml_vk_preallocate_buffers(ctx, subctx); } - auto rows_cols = fa_rows_cols(path, HSK, HSV, !aligned, k->type, small_rows, small_cache); - const uint32_t Br = rows_cols[0]; - const uint32_t Bc = rows_cols[1]; - const uint32_t mask_opt_num_dwords = CEIL_DIV(nem0, 16 * Bc); const uint64_t mask_opt_size = sizeof(uint32_t) * mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2 * nem3; @@ -8785,15 +9016,21 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx if (ctx->prealloc_split_k_need_sync) { ggml_vk_sync_buffers(ctx, subctx); } - workgroups_x *= pipeline->wg_denoms[0]; + + // We reuse workgroups_x to mean the number of splits, so we need to + // cancel out the divide by wg_denoms[0]. + uint32_t dispatch_x; + if (gqa_ratio > 1) { + workgroups_x *= pipeline->wg_denoms[0]; + dispatch_x = split_k * workgroups_x; + } else { + dispatch_x = Tr * split_k * pipeline->wg_denoms[0]; + } + vk_subbuffer split_k_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {q_buf, k_buf, v_buf, mask_buf, sinks_buf, split_k_buf, mask_opt_buf}, - // We only use split_k when group query attention is enabled, which means - // there's no more than one tile of rows (i.e. workgroups_x would have been - // one). We reuse workgroups_x to mean the number of splits, so we need to - // cancel out the divide by wg_denoms[0]. - pc, { split_k * workgroups_x, workgroups_y, workgroups_z }); + pc, { dispatch_x, workgroups_y, workgroups_z }); ggml_vk_sync_buffers(ctx, subctx); const vk_op_flash_attn_split_k_reduce_push_constants pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, split_k, (sinks != nullptr) }; @@ -9059,6 +9296,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const switch (ggml_get_unary_op(dst)) { case GGML_UNARY_OP_EXP: return ctx->device->pipeline_exp[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_ELU: + return ctx->device->pipeline_elu[dst->type == GGML_TYPE_F16]; case GGML_UNARY_OP_SILU: return ctx->device->pipeline_silu[dst->type == GGML_TYPE_F16]; case GGML_UNARY_OP_GELU: @@ -9095,6 +9334,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_floor[dst->type == GGML_TYPE_F16]; case GGML_UNARY_OP_TRUNC: return ctx->device->pipeline_trunc[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_SGN: + return ctx->device->pipeline_sgn[dst->type == GGML_TYPE_F16]; default: break; } @@ -9288,6 +9529,20 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_rwkv_wkv7_f32; } return nullptr; + case GGML_OP_GATED_DELTA_NET: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + const uint32_t S_v = dst->src[2]->ne[0]; + const uint32_t kda = (dst->src[3]->ne[0] == (int64_t)S_v) ? 1 : 0; + uint32_t si; + switch (S_v) { + case 32: si = 0; break; + case 64: si = 1; break; + case 128: si = 2; break; + default: return nullptr; + } + return ctx->device->pipeline_gated_delta_net[si][kda]; + } + return nullptr; case GGML_OP_SSM_SCAN: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { const uint32_t d_state = src0->ne[0]; @@ -10118,6 +10373,59 @@ static void ggml_vk_rwkv_wkv7(ggml_backend_vk_context * ctx, vk_context& subctx, ); } +static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { + const ggml_tensor * src_q = dst->src[0]; + const ggml_tensor * src_v = dst->src[2]; + const ggml_tensor * src_beta = dst->src[4]; + + GGML_ASSERT(dst->buffer != nullptr); + + const uint32_t S_v = (uint32_t)src_v->ne[0]; + const uint32_t H = (uint32_t)src_v->ne[1]; + const uint32_t n_tokens = (uint32_t)src_v->ne[2]; + const uint32_t n_seqs = (uint32_t)src_v->ne[3]; + + const uint32_t s_off = S_v * H * n_tokens * n_seqs; + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op); + GGML_ASSERT(pipeline != nullptr); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst); + vk_subbuffer src_buf[6] = {}; + for (int i = 0; i < 6; i++) { + src_buf[i] = ggml_vk_tensor_subbuffer(ctx, dst->src[i]); + } + + const uint32_t sq1 = (uint32_t)(src_q->nb[1] / sizeof(float)); + const uint32_t sq2 = (uint32_t)(src_q->nb[2] / sizeof(float)); + const uint32_t sq3 = (uint32_t)(src_q->nb[3] / sizeof(float)); + const uint32_t sv1 = (uint32_t)(src_v->nb[1] / sizeof(float)); + const uint32_t sv2 = (uint32_t)(src_v->nb[2] / sizeof(float)); + const uint32_t sv3 = (uint32_t)(src_v->nb[3] / sizeof(float)); + const uint32_t sb1 = (uint32_t)(src_beta->nb[1] / sizeof(float)); + const uint32_t sb2 = (uint32_t)(src_beta->nb[2] / sizeof(float)); + const uint32_t sb3 = (uint32_t)(src_beta->nb[3] / sizeof(float)); + + const uint32_t neq1 = (uint32_t)src_q->ne[1]; + const uint32_t rq3 = (uint32_t)(src_v->ne[3] / src_q->ne[3]); + + const float scale = 1.0f / sqrtf((float)S_v); + const vk_op_gated_delta_net_push_constants pc = { + H, n_tokens, n_seqs, s_off, + sq1, sq2, sq3, + sv1, sv2, sv3, + sb1, sb2, sb3, + neq1, rq3, + scale + }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], dst_buf}, + pc, { H, n_seqs, 1u }); +} + static void ggml_vk_ssm_scan(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; @@ -12421,15 +12729,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr } } - vk_context compute_ctx; - - if (ctx->compute_ctx.expired()) { - compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); - } else { - compute_ctx = ctx->compute_ctx.lock(); - } + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); { // This logic detects dependencies between modes in the graph and calls ggml_vk_sync_buffers @@ -12500,7 +12800,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr if (vk_perf_logger_enabled && vk_perf_logger_concurrent) { ctx->query_node_idx[ctx->query_idx] = node_idx; - compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++); + compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++); } } // Add all fused nodes to the unsynchronized lists. @@ -12678,6 +12978,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr } switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_ELU: case GGML_UNARY_OP_EXP: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_GELU: @@ -12696,6 +12997,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr case GGML_UNARY_OP_CEIL: case GGML_UNARY_OP_FLOOR: case GGML_UNARY_OP_TRUNC: + case GGML_UNARY_OP_SGN: ggml_vk_unary(ctx, compute_ctx, src0, node); break; case GGML_UNARY_OP_XIELU: @@ -12840,6 +13142,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr break; + case GGML_OP_GATED_DELTA_NET: + ggml_vk_gated_delta_net(ctx, compute_ctx, node); + + break; + case GGML_OP_SSM_SCAN: ggml_vk_ssm_scan(ctx, compute_ctx, node); @@ -12947,6 +13254,9 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false; ggml_vk_command_pool_cleanup(ctx->device, ctx->compute_cmd_pool); + if (ctx->device->async_use_transfer_queue) { + ggml_vk_command_pool_cleanup(ctx->device, ctx->transfer_cmd_pool); + } for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) { ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s }); @@ -13008,6 +13318,11 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { ctx->descriptor_sets.clear(); ctx->compute_cmd_pool.destroy(ctx->device->device); + if (ctx->device->async_use_transfer_queue) { + ctx->device->device.destroySemaphore(ctx->transfer_semaphore.s); + + ctx->transfer_cmd_pool.destroy(ctx->device->device); + } if (vk_perf_logger_enabled) { ctx->perf_logger->print_timings(true); } @@ -13066,6 +13381,10 @@ static void ggml_backend_vk_buffer_memset_tensor(ggml_backend_buffer_t buffer, g ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; vk_buffer buf = buf_ctx->dev_buffer; + if (size == 0) { + return; + } + uint32_t val32 = (uint32_t)value * 0x01010101; ggml_vk_buffer_memset(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, val32, size); } @@ -13075,6 +13394,10 @@ static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; vk_buffer buf = buf_ctx->dev_buffer; + if (size == 0) { + return; + } + ggml_vk_buffer_write(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); } @@ -13082,12 +13405,20 @@ static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, cons VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; + if (size == 0) { + return; + } + vk_buffer buf = buf_ctx->dev_buffer; ggml_vk_buffer_read(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); } static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { + if (ggml_nbytes(src) == 0) { + return true; + } + if (ggml_backend_buffer_is_vk(src->buffer)) { ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context; ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; @@ -13277,36 +13608,44 @@ static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + if (size == 0) { + return; + } + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; - vk_context compute_ctx; + vk_context cpy_ctx; - if (ctx->compute_ctx.expired()) { - // Initialize new transfer context - compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); + if (ctx->device->async_use_transfer_queue) { + if (ctx->transfer_ctx.expired()) { + // Initialize new transfer context + cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); + ctx->transfer_ctx = cpy_ctx; + ggml_vk_ctx_begin(ctx->device, cpy_ctx); + } else { + cpy_ctx = ctx->transfer_ctx.lock(); + } } else { - compute_ctx = ctx->compute_ctx.lock(); + cpy_ctx = ggml_vk_get_compute_ctx(ctx); } vk_buffer buf = buf_ctx->dev_buffer; auto dst_offset = vk_tensor_offset(tensor) + tensor->view_offs + offset; - bool ret = ggml_vk_buffer_write_async(compute_ctx, buf, dst_offset, data, size); + bool ret = ggml_vk_buffer_write_async(cpy_ctx, buf, dst_offset, data, size); if (!ret) { ggml_vk_ensure_sync_staging_buffer(ctx, size); - ggml_vk_sync_buffers(nullptr, compute_ctx); + ggml_vk_sync_buffers(nullptr, cpy_ctx); vk::BufferCopy buffer_cpy; buffer_cpy.srcOffset = 0; buffer_cpy.dstOffset = dst_offset; buffer_cpy.size = size; - compute_ctx->s->buffer.copyBuffer(ctx->sync_staging->buffer, buf->buffer, { buffer_cpy }); - deferred_memcpy(ctx->sync_staging->ptr, data, size, &compute_ctx->in_memcpys); + cpy_ctx->s->buffer->buf.copyBuffer(ctx->sync_staging->buffer, buf->buffer, { buffer_cpy }); + deferred_memcpy(ctx->sync_staging->ptr, data, size, &cpy_ctx->in_memcpys); ggml_vk_synchronize(ctx); } } @@ -13316,18 +13655,13 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_ ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + if (size == 0) { + return; + } + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; - vk_context compute_ctx; - - if (ctx->compute_ctx.expired()) { - // Initialize new transfer context - compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); - } else { - compute_ctx = ctx->compute_ctx.lock(); - } + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); vk_buffer buf = buf_ctx->dev_buffer; @@ -13344,37 +13678,71 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_ buffer_cpy.dstOffset = 0; buffer_cpy.size = size; - compute_ctx->s->buffer.copyBuffer(buf->buffer, ctx->sync_staging->buffer, { buffer_cpy }); + compute_ctx->s->buffer->buf.copyBuffer(buf->buffer, ctx->sync_staging->buffer, { buffer_cpy }); deferred_memcpy(data, ctx->sync_staging->ptr, size, &compute_ctx->out_memcpys); ggml_vk_synchronize(ctx); } } -static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { - VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async()"); - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - if ((dst->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { - ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context; - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; +static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) { + VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async(" << src << " -> " << dst << ", size=" << ggml_nbytes(src) << ")"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend_dst->context; - vk_context compute_ctx; - - if (ctx->compute_ctx.expired()) { - // Initialize new transfer context - compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); - } else { - compute_ctx = ctx->compute_ctx.lock(); - } - - vk_buffer src_buf = src_buf_ctx->dev_buffer; - vk_buffer dst_buf = dst_buf_ctx->dev_buffer; - - ggml_vk_buffer_copy_async(compute_ctx, dst_buf, vk_tensor_offset(dst) + dst->view_offs, src_buf, vk_tensor_offset(src) + src->view_offs, ggml_nbytes(src)); + // Skip zero-size tensors + if (ggml_nbytes(src) == 0) { return true; } + if (dst->buffer->buft != ggml_backend_vk_get_default_buffer_type(backend_dst)) { + return false; + } + + ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; + vk_buffer dst_buf = dst_buf_ctx->dev_buffer; + + if (ggml_backend_buffer_is_vk(src->buffer)) { + ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context; + + // Async copy only works within the same device + if (src_buf_ctx->dev_buffer->device != dst_buf->device) { + return false; + } + + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); + + ggml_vk_buffer_copy_async(compute_ctx, dst_buf, vk_tensor_offset(dst) + dst->view_offs, + src_buf_ctx->dev_buffer, vk_tensor_offset(src) + src->view_offs, + ggml_nbytes(src)); + return true; + } + + if (ggml_backend_buffer_is_host(src->buffer)) { + vk_buffer pinned_buf = nullptr; + size_t pinned_offset = 0; + ggml_vk_host_get(ctx->device, src->data, pinned_buf, pinned_offset); + if (pinned_buf == nullptr) { + return false; + } + + vk_context cpy_ctx; + if (ctx->device->async_use_transfer_queue) { + if (ctx->transfer_ctx.expired()) { + cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); + ctx->transfer_ctx = cpy_ctx; + ggml_vk_ctx_begin(ctx->device, cpy_ctx); + } else { + cpy_ctx = ctx->transfer_ctx.lock(); + } + } else { + cpy_ctx = ggml_vk_get_compute_ctx(ctx); + } + + return ggml_vk_buffer_write_async(cpy_ctx, dst_buf, + vk_tensor_offset(dst) + dst->view_offs, + src->data, ggml_nbytes(src)); + } + + GGML_UNUSED(backend_src); return false; } @@ -13383,9 +13751,17 @@ static void ggml_vk_synchronize(ggml_backend_vk_context * ctx) { bool do_transfer = !ctx->compute_ctx.expired(); + if (ggml_vk_submit_transfer_ctx(ctx)) { + ctx->submit_pending = true; + } + vk_context compute_ctx; + vk_command_buffer* cmd_buf = nullptr; if (do_transfer) { compute_ctx = ctx->compute_ctx.lock(); + if (compute_ctx->s) { + cmd_buf = compute_ctx->s->buffer; + } ggml_vk_ctx_end(compute_ctx); @@ -13398,12 +13774,30 @@ static void ggml_vk_synchronize(ggml_backend_vk_context * ctx) { } if (ctx->submit_pending) { - { + if (ctx->device->async_use_transfer_queue && ctx->transfer_semaphore_last_submitted < ctx->transfer_semaphore.value) { + vk::TimelineSemaphoreSubmitInfo tl_info{ + 1, &ctx->transfer_semaphore.value, + 0, nullptr, + }; + vk::PipelineStageFlags stage = ctx->device->transfer_queue.stage_flags; + vk::SubmitInfo si{ + 1, &ctx->transfer_semaphore.s, &stage, + 0, nullptr, + 0, nullptr, + }; + si.setPNext(&tl_info); + std::lock_guard guard(queue_mutex); + ctx->device->compute_queue.queue.submit({ si }, ctx->fence); + ctx->transfer_semaphore_last_submitted = ctx->transfer_semaphore.value; + } else { std::lock_guard guard(queue_mutex); ctx->device->compute_queue.queue.submit({}, ctx->fence); } ggml_vk_wait_for_fence(ctx); ctx->submit_pending = false; + if (cmd_buf) { + cmd_buf->in_use = false; + } } if (do_transfer) { @@ -13710,12 +14104,11 @@ static bool ggml_vk_can_fuse_rope_set_rows(ggml_backend_vk_context * ctx, const return true; } -// Check whether the tensors overlap in memory but are not equal. -// Fusions can potenitally overwrite src tensors in ways that are not prevented -// by ggml-alloc. If the fusion is entirely elementwise, then it's OK for them -// to overlap if they are exactly equal. -// XXX TODO this check is probably missing from several fusion optimizations. -static bool ggml_vk_tensors_overlap_but_not_equal(const ggml_tensor * a, const ggml_tensor * b) { +// Check whether the tensors overlap in memory. +// Fusions can potentially overwrite src tensors in ways that are not prevented +// by ggml-alloc. If the fusion src is being applied in a way that's elementwise +// with the destination, then it's OK for them to overlap if they are exactly equal. +static bool ggml_vk_tensors_overlap(const ggml_tensor * a, const ggml_tensor * b, bool elementwise) { ggml_backend_vk_buffer_context * a_buf_ctx = (ggml_backend_vk_buffer_context *)a->buffer->context; vk_buffer a_buf = a_buf_ctx->dev_buffer; ggml_backend_vk_buffer_context * b_buf_ctx = (ggml_backend_vk_buffer_context *)b->buffer->context; @@ -13726,7 +14119,7 @@ static bool ggml_vk_tensors_overlap_but_not_equal(const ggml_tensor * a, const g auto b_base = vk_tensor_offset(b) + b->view_offs; auto b_size = ggml_nbytes(b); - if (a_base == b_base && a_size == b_size) { + if (elementwise && a_base == b_base && a_size == b_size) { return false; } @@ -13764,13 +14157,6 @@ static bool ggml_vk_can_fuse_rms_norm_mul_rope(ggml_backend_vk_context * ctx, co return false; } - // must not overwrite srcs in a way that's not elementwise - ggml_tensor *other_src = mul->src[0] == rms ? mul->src[1] : mul->src[0]; - if (ggml_vk_tensors_overlap_but_not_equal(rms->src[0], rope) || - ggml_vk_tensors_overlap_but_not_equal(other_src, rope)) { - return false; - } - // conditions for pipeline creation if (!(ctx->device->float_controls_rte_fp16 && sizeof(vk_op_rms_norm_mul_rope_push_constants) <= ctx->device->properties.limits.maxPushConstantsSize)) { @@ -13832,6 +14218,18 @@ static uint32_t ggml_vk_fuse_multi_add(ggml_backend_vk_context * ctx, const stru return num_adds; } +static int32_t find_first_set(uint32_t x) { + int32_t ret = 0; + if (!x) { + return -1; + } + while (!(x & 1)) { + x >>= 1; + ret++; + } + return ret; +} + static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)"); ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; @@ -13860,6 +14258,8 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg bool first_node_in_batch = true; // true if next node will be first node in a batch int submit_node_idx = 0; // index to first node in a batch + ggml_vk_submit_transfer_ctx(ctx); + vk_context compute_ctx; if (vk_perf_logger_enabled) { // allocate/resize the query pool @@ -13885,11 +14285,9 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg std::fill(ctx->query_node_idx.begin(), ctx->query_node_idx.end(), 0); GGML_ASSERT(ctx->compute_ctx.expired()); - compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); + compute_ctx = ggml_vk_get_compute_ctx(ctx); ctx->query_idx = 0; - compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++); + compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++); } ctx->prealloc_y_last_pipeline_used = nullptr; @@ -13897,13 +14295,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg if (ctx->prealloc_size_add_rms_partials) { ggml_vk_preallocate_buffers(ctx, nullptr); - if (ctx->compute_ctx.expired()) { - compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); - } else { - compute_ctx = ctx->compute_ctx.lock(); - } + compute_ctx = ggml_vk_get_compute_ctx(ctx); // initialize partial sums to zero. ggml_vk_buffer_memset_async(compute_ctx, ctx->prealloc_add_rms_partials, 0, 0, ctx->prealloc_size_add_rms_partials); ggml_vk_sync_buffers(ctx, compute_ctx); @@ -13930,6 +14322,12 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg total_mul_mat_bytes += bytes; } + // op_srcs_fused_elementwise indicates whether an op's srcs all contribute to + // the fused result in an elementwise-way. This affects whether the memory for + // the src is allowed to overlap the memory for the destination. + // The array is sized to handle the largest fusion (asserted later). + bool op_srcs_fused_elementwise[12]; + ctx->fused_topk_moe_mode = TOPK_MOE_COUNT; ctx->fused_topk_moe_scale = false; const char *fusion_string {}; @@ -13938,39 +14336,68 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg if (num_adds) { ctx->num_additional_fused_ops = num_adds - 1; fusion_string = "MULTI_ADD"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, true); } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD, GGML_OP_ADD })) { ctx->num_additional_fused_ops = 2; fusion_string = "MUL_MAT_ADD_ADD"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + op_srcs_fused_elementwise[2] = true; } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD })) { ctx->num_additional_fused_ops = 1; fusion_string = "MUL_MAT_ADD"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID, GGML_OP_MUL })) { ctx->num_additional_fused_ops = 2; fusion_string = "MUL_MAT_ID_ADD_ID_MUL"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + op_srcs_fused_elementwise[2] = true; } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID })) { ctx->num_additional_fused_ops = 1; fusion_string = "MUL_MAT_ID_ADD_ID"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_MUL })) { ctx->num_additional_fused_ops = 1; fusion_string = "MUL_MAT_ID_MUL"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; } else if (ggml_can_fuse_subgraph(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, { i + 4 }) && ggml_check_edges(cgraph, i, rms_norm_mul_rope_view_set_rows_edges) && ggml_vk_can_fuse_rms_norm_mul_rope(ctx, cgraph, i) && ggml_vk_can_fuse_rope_set_rows(ctx, cgraph, i + 2)) { ctx->num_additional_fused_ops = 4; fusion_string = "RMS_NORM_MUL_ROPE_VIEW_SET_ROWS"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = false; + op_srcs_fused_elementwise[2] = false; + op_srcs_fused_elementwise[3] = false; + op_srcs_fused_elementwise[4] = false; } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE })&& ggml_vk_can_fuse_rms_norm_mul_rope(ctx, cgraph, i)) { ctx->num_additional_fused_ops = 2; fusion_string = "RMS_NORM_MUL_ROPE"; + // rope is approximately elementwise - whole rows are done by a single workgroup and it's row-wise + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + op_srcs_fused_elementwise[2] = true; } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) { ctx->num_additional_fused_ops = 1; fusion_string = "RMS_NORM_MUL"; + // rms_norm is not elementwise, but whole rows must be consumed and the scale factor computed before + // they are overwritten, and one workgroup per row. So close enough. + op_srcs_fused_elementwise[0] = true; + op_srcs_fused_elementwise[1] = true; } else if (ggml_can_fuse_subgraph(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, { i + 2 }) && ggml_check_edges(cgraph, i, rope_view_set_rows_edges) && ggml_vk_can_fuse_rope_set_rows(ctx, cgraph, i)) { ctx->num_additional_fused_ops = 2; fusion_string = "ROPE_VIEW_SET_ROWS"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = false; + op_srcs_fused_elementwise[2] = false; } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax_norm, { i + 3, i + 9 }) && ggml_check_edges(cgraph, i, topk_moe_early_softmax_norm_edges) && ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX_NORM)) { @@ -13979,6 +14406,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg ctx->fused_ops_write_mask |= 1 << 3; ctx->fused_topk_moe_mode = TOPK_MOE_EARLY_SOFTMAX_NORM; fusion_string = "TOPK_MOE_EARLY_SOFTMAX_NORM"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_sigmoid_norm_bias, { i + 4, i + 10 }) && ggml_check_edges(cgraph, i, topk_moe_sigmoid_norm_bias_edges) && ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_SIGMOID_NORM_BIAS)) { @@ -13987,6 +14415,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg ctx->fused_ops_write_mask |= 1 << 4; ctx->fused_topk_moe_mode = TOPK_MOE_SIGMOID_NORM_BIAS; fusion_string = "TOPK_MOE_SIGMOID_NORM_BIAS"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax, { i + 3, i + 4 }) && ggml_check_edges(cgraph, i, topk_moe_early_softmax_edges) && ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX)) { @@ -13995,6 +14424,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg ctx->fused_ops_write_mask |= 1 << 3; ctx->fused_topk_moe_mode = TOPK_MOE_EARLY_SOFTMAX; fusion_string = "TOPK_MOE_EARLY_SOFTMAX"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_late_softmax, { i + 1, i + 5 }) && ggml_check_edges(cgraph, i, topk_moe_late_softmax_edges) && ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_LATE_SOFTMAX)) { @@ -14003,6 +14433,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg ctx->fused_ops_write_mask |= 1 << 1; ctx->fused_topk_moe_mode = TOPK_MOE_LATE_SOFTMAX; fusion_string = "TOPK_MOE_LATE_SOFTMAX"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); } if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) { // Look for an additional scale op to fuse - occurs in deepseek2 and nemotron3 nano. @@ -14010,11 +14441,73 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg ggml_can_fuse_subgraph(cgraph, i + ctx->num_additional_fused_ops, { GGML_OP_GET_ROWS, GGML_OP_SCALE }, { i + ctx->num_additional_fused_ops + 1 })) { ctx->fused_topk_moe_scale = true; ctx->num_additional_fused_ops++; + op_srcs_fused_elementwise[ctx->num_additional_fused_ops] = false; } } } + GGML_ASSERT(ctx->num_additional_fused_ops < (int)(sizeof(op_srcs_fused_elementwise) / sizeof(op_srcs_fused_elementwise[0]))); ctx->fused_ops_write_mask |= 1 << ctx->num_additional_fused_ops; + // Check whether fusion would overwrite src operands while they're still in use. + // If so, disable fusion. + if (ctx->num_additional_fused_ops) { + // There are up to two output nodes - topk_moe has two. + uint32_t bits = ctx->fused_ops_write_mask & ~(1 << ctx->num_additional_fused_ops); + ggml_tensor *output_nodes[2] {}; + output_nodes[0] = cgraph->nodes[i + ctx->num_additional_fused_ops]; + if (bits) { + int output_idx = find_first_set(bits); + GGML_ASSERT(bits == (1u << output_idx)); + output_nodes[1] = cgraph->nodes[i + output_idx]; + } + + bool need_disable = false; + + // topk_moe often overwrites the source, but for a given row all the src values are + // loaded before anything is stored. If there's only one row, this is safe, so treat + // this as a special case. + bool is_topk_moe_single_row = ctx->fused_topk_moe_mode != TOPK_MOE_COUNT && + ggml_nrows(cgraph->nodes[i]->src[0]) == 1; + + if (!is_topk_moe_single_row) { + for (int j = 0; j < 2; ++j) { + ggml_tensor *dst = output_nodes[j]; + if (!dst) { + continue; + } + // Loop over all srcs of all nodes in the fusion. If the src overlaps + // the destination and the src is not an intermediate node that's being + // elided, then disable fusion. + for (int k = 0; k <= ctx->num_additional_fused_ops; ++k) { + for (uint32_t s = 0; s < GGML_MAX_SRC; ++s) { + ggml_tensor *src = cgraph->nodes[i + k]->src[s]; + if (!src || src->op == GGML_OP_NONE) { + continue; + } + if (ggml_vk_tensors_overlap(src, dst, op_srcs_fused_elementwise[k])) { + bool found = false; + for (int n = 0; n < k; ++n) { + if (cgraph->nodes[i + n] == src) { + found = true; + break; + } + } + if (!found) { + need_disable = true; + } + } + } + } + } + } + if (need_disable) { + ctx->num_additional_fused_ops = 0; + ctx->fused_ops_write_mask = 1; + ctx->fused_topk_moe_mode = TOPK_MOE_COUNT; + ctx->fused_topk_moe_scale = false; + } + } + // Signal the almost_ready fence when the graph is mostly complete (< 20% remaining) bool almost_ready = (cgraph->n_nodes - i) < cgraph->n_nodes / 5; bool submit = (submitted_nodes >= nodes_per_submit) || @@ -14025,18 +14518,12 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg bool enqueued = ggml_vk_build_graph(ctx, cgraph, i, cgraph->nodes[submit_node_idx], submit_node_idx, i + ctx->num_additional_fused_ops >= last_node, almost_ready, submit); if (vk_perf_logger_enabled && enqueued) { - if (ctx->compute_ctx.expired()) { - compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); - } else { - compute_ctx = ctx->compute_ctx.lock(); - } + compute_ctx = ggml_vk_get_compute_ctx(ctx); if (!vk_perf_logger_concurrent) { // track a single node/fusion for the current query ctx->query_nodes[ctx->query_idx] = cgraph->nodes[i]; ctx->query_fusion_names[ctx->query_idx] = fusion_string; - compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++); + compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++); } else { // track a fusion string and number of fused ops for the current node_idx ctx->query_fusion_names[i] = fusion_string; @@ -14366,16 +14853,10 @@ static void ggml_backend_vk_event_record(ggml_backend_t backend, ggml_backend_ev ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; vk_event *vkev = (vk_event *)event->context; - vk_context compute_ctx; + ggml_vk_submit_transfer_ctx(ctx); - if (ctx->compute_ctx.expired()) { - // Initialize new transfer context - compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); - } else { - compute_ctx = ctx->compute_ctx.lock(); - } + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); + auto* cmd_buf = compute_ctx->s->buffer; // retrieve pointer before it gets reset // the backend interface doesn't have an explicit reset, so reset it here // before we record the command to set it @@ -14388,6 +14869,7 @@ static void ggml_backend_vk_event_record(ggml_backend_t backend, ggml_backend_ev ggml_vk_submit(compute_ctx, {vkev->fence}); ctx->submit_pending = true; + vkev->cmd_buffer = cmd_buf; ctx->compute_ctx.reset(); } @@ -14396,16 +14878,7 @@ static void ggml_backend_vk_event_wait(ggml_backend_t backend, ggml_backend_even ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; vk_event *vkev = (vk_event *)event->context; - vk_context compute_ctx; - - if (ctx->compute_ctx.expired()) { - // Initialize new transfer context - compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); - } else { - compute_ctx = ctx->compute_ctx.lock(); - } + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); ggml_vk_wait_events(compute_ctx, {vkev->event}); ggml_vk_ctx_end(compute_ctx); @@ -14418,7 +14891,7 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .free = */ ggml_backend_vk_free, /* .set_tensor_async = */ ggml_backend_vk_set_tensor_async, /* .get_tensor_async = */ ggml_backend_vk_get_tensor_async, - /* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async, + /* .cpy_tensor_async = */ ggml_backend_vk_cpy_tensor_async, /* .synchronize = */ ggml_backend_vk_synchronize, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, @@ -14649,6 +15122,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { case GGML_UNARY_OP_EXP: + case GGML_UNARY_OP_ELU: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_ERF: case GGML_UNARY_OP_GELU_QUICK: @@ -14667,6 +15141,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_UNARY_OP_CEIL: case GGML_UNARY_OP_FLOOR: case GGML_UNARY_OP_TRUNC: + case GGML_UNARY_OP_SGN: return ggml_is_contiguous(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && @@ -15083,6 +15558,19 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: return true; // all inputs are contiguous, see ggml.c + case GGML_OP_GATED_DELTA_NET: + { + const uint32_t S_v = op->src[2]->ne[0]; + if (S_v != 32 && S_v != 64 && S_v != 128) { + return false; + } + for (int i = 0; i < 6; i++) { + if (op->src[i] == nullptr || op->src[i]->type != GGML_TYPE_F32) { + return false; + } + } + return op->type == GGML_TYPE_F32; + } case GGML_OP_SSM_SCAN: { for (int i = 0; i < 6; i++) { @@ -15154,11 +15642,25 @@ static bool ggml_backend_vk_device_supports_buft(ggml_backend_dev_t dev, ggml_ba return buft_ctx->device->idx == ctx->device; } +static int64_t ggml_vk_get_op_batch_size(const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_GET_ROWS: + return 0; + case GGML_OP_MUL_MAT: + return op->ne[1]; + case GGML_OP_MUL_MAT_ID: + case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: + return op->ne[2]; + default: + return ggml_nrows(op); + } +} + static bool ggml_backend_vk_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { ggml_backend_vk_device_context * dev_ctx = (ggml_backend_vk_device_context *)dev->context; - return (op->ne[1] >= dev_ctx->op_offload_min_batch_size && op->op != GGML_OP_GET_ROWS) || - (op->ne[2] >= dev_ctx->op_offload_min_batch_size && op->op == GGML_OP_MUL_MAT_ID); + return ggml_vk_get_op_batch_size(op) >= dev_ctx->op_offload_min_batch_size; } static ggml_backend_event_t ggml_backend_vk_device_event_new(ggml_backend_dev_t dev) { @@ -15200,6 +15702,10 @@ static void ggml_backend_vk_device_event_synchronize(ggml_backend_dev_t dev, ggm vk_event *vkev = (vk_event *)event->context; VK_CHECK(device->device.waitForFences({ vkev->fence }, true, UINT64_MAX), "event_synchronize"); + // Finished using current command buffer so we flag for reuse + if (vkev->cmd_buffer) { + vkev->cmd_buffer->in_use = false; + } } static vk_buffer ggml_vk_buffer_from_host_ptr(vk_device & device, void * ptr, size_t size) { @@ -15418,6 +15924,46 @@ static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDevicePrope } } +static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev) { + VkPhysicalDeviceProperties2 props = vkdev.getProperties2(); + + if (props.properties.vendorID != VK_VENDOR_ID_INTEL) { + return 0; + } + + const uint32_t device_id = props.properties.deviceID; + + switch (device_id) { + case 0x56A6: // A310 + return 6; + case 0x5693: // A370M + case 0x56A5: // A380 + case 0x56B1: // Pro A40/A50 + return 8; + case 0x5697: // A530M + return 12; + case 0x5692: // A550M + case 0x56B3: // Pro A60 + return 16; + case 0x56A2: // A580 + return 24; + case 0x5691: // A730M + case 0x56A1: // A750 + return 28; + case 0x56A0: // A770 + case 0x5690: // A770M + return 32; + case 0xE212: // Pro B50 + return 16; + case 0xE20C: // B570 + return 18; + case 0xE20B: // B580 + return 20; + default: + return 0; + } +} + // checks #ifdef GGML_VULKAN_CHECK_RESULTS @@ -15631,7 +16177,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * tensor_clone = ggml_arange(ggml_ctx, start, stop, step); } else if (tensor->op == GGML_OP_FILL) { const float value = ggml_get_op_params_f32(tensor, 0); - tensor_clone = ggml_fill(ggml_ctx, tensor_clone, value); + tensor_clone = ggml_fill(ggml_ctx, src_clone[0], value); } else if (tensor->op == GGML_OP_SQR) { tensor_clone = ggml_sqr(ggml_ctx, src_clone[0]); } else if (tensor->op == GGML_OP_SQRT) { @@ -15718,6 +16264,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * case GGML_UNARY_OP_EXP: tensor_clone = ggml_exp(ggml_ctx, src_clone[0]); break; + case GGML_UNARY_OP_ELU: + tensor_clone = ggml_elu(ggml_ctx, src_clone[0]); + break; case GGML_UNARY_OP_SILU: tensor_clone = ggml_silu(ggml_ctx, src_clone[0]); break; @@ -15776,6 +16325,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * case GGML_UNARY_OP_TRUNC: tensor_clone = ggml_trunc(ggml_ctx, src_clone[0]); break; + case GGML_UNARY_OP_SGN: + tensor_clone = ggml_sgn(ggml_ctx, src_clone[0]); + break; default: std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; GGML_ABORT("fatal error"); @@ -15896,6 +16448,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * } else if (tensor->op == GGML_OP_RWKV_WKV7) { tensor_clone = ggml_rwkv_wkv7(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3], src_clone[4], src_clone[5], src_clone[6]); + } else if (tensor->op == GGML_OP_GATED_DELTA_NET) { + tensor_clone = ggml_gated_delta_net(ggml_ctx, src_clone[0], src_clone[1], + src_clone[2], src_clone[3], src_clone[4], src_clone[5]); } else if (tensor->op == GGML_OP_OPT_STEP_ADAMW) { src_clone[0]->flags = tensor->src[0]->flags; tensor_clone = ggml_opt_step_adamw(ggml_ctx, src_clone[0], src_clone[1], @@ -16094,7 +16649,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_cgraph * ggml_vk_print_graph_origin(tensor, done); } - if (avg_err > 0.5 || std::isnan(avg_err)) { + if (avg_err > 0.01 || std::isnan(avg_err)) { std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; if (src0 != nullptr) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/elu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/elu.comp new file mode 100644 index 0000000000..84dcbd8c88 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/elu.comp @@ -0,0 +1,27 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + float x = float(data_a[i]); + + if (x < 0.0f) { + x = exp(x) - 1; + } + + data_d[i] = D_TYPE(x); +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp index 0735f67854..11b7dce857 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp @@ -3,9 +3,13 @@ #extension GL_EXT_control_flow_attributes : enable #extension GL_EXT_shader_16bit_storage : require -#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require #extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#ifdef FLOAT16 +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_shader_subgroup_extended_types_float16 : require +#endif + #extension GL_KHR_shader_subgroup_shuffle : enable #extension GL_KHR_shader_subgroup_vote : enable @@ -15,8 +19,10 @@ const uint32_t HSK_per_thread = HSK / D_split; const uint32_t HSV_per_thread = HSV / D_split; -const uint32_t cols_per_iter = WorkGroupSize / D_split; +const uint32_t rows_per_thread = Br / row_split; +const uint32_t cols_per_iter = WorkGroupSize / D_split / row_split; const uint32_t cols_per_thread = Bc / cols_per_iter; +const uint32_t num_subgroups = SubGroupSize == 0 ? 0 : WorkGroupSize / SubGroupSize; layout (binding = 0) readonly buffer Q {float data_q[];}; @@ -27,20 +33,22 @@ layout (binding = 2) readonly buffer V {float16_t data_v[];}; layout (binding = 2) readonly buffer VV4 {f16vec4 data_vv4[];}; layout (binding = 3) readonly buffer M {float16_t data_m[];}; -// Store the output when doing grouped query attention. -// Rows index by Q's dimension 2, and the first N rows are valid. -D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N) -{ - uint32_t offset = (iq2 + r) * HSV + c; - data_o[o_offset + offset] = D_TYPE(elem); - return elem; -} +// If SubGroupSize is set to 0 then only use shmem reductions +const uint32_t tmpsh_size = (SubGroupSize > 0) ? (row_split == 1 ? num_subgroups * D_split : num_subgroups) : WorkGroupSize; +shared float tmpsh[tmpsh_size]; +shared FLOAT_TYPEV4 tmpshv4[tmpsh_size]; -shared FLOAT_TYPE tmpsh[WorkGroupSize]; -shared vec4 tmpshv4[WorkGroupSize]; +const uint32_t masksh_stride = Br + 1; +shared FLOAT_TYPE masksh[Bc * masksh_stride]; -shared float masksh[Bc][Br]; -shared vec4 Qf[Br][HSK / 4]; +const uint32_t qf_stride = HSK / 4 + 1; +shared FLOAT_TYPEV4 Qf[Br * qf_stride]; + +const uint32_t D = HSK > HSV ? HSK : HSV; +const uint32_t kvsh_stride = D / 4 + 1; +shared FLOAT_TYPEV4 kvsh[SHMEM_STAGING != 0 ? Bc * kvsh_stride : 1]; + +shared vec4 occupancy_limiter[LIMIT_OCCUPANCY_SHMEM > 0 ? LIMIT_OCCUPANCY_SHMEM : 1]; void main() { #ifdef NEEDS_INIT_IQ_SHMEM @@ -50,8 +58,24 @@ void main() { init_indices(); const uint32_t tid = gl_LocalInvocationIndex; + const uint32_t threads_per_rowgroup = gl_WorkGroupSize.x / row_split; + const uint32_t row_tid = gl_LocalInvocationIndex / threads_per_rowgroup; + const uint32_t rowgroup_tid = gl_LocalInvocationIndex % threads_per_rowgroup; const uint32_t d_tid = gl_LocalInvocationIndex % D_split; - const uint32_t col_tid = gl_LocalInvocationIndex / D_split; + const uint32_t col_tid = (gl_LocalInvocationIndex % threads_per_rowgroup) / D_split; + + if (LIMIT_OCCUPANCY_SHMEM > 0) { + // This just exists to avoid the occupancy_limiter array getting optimized out + occupancy_limiter[tid] = vec4(tid); + + barrier(); + + if (occupancy_limiter[tid] == vec4(99999.0)) { + data_ov4[0] = D_TYPEV4(occupancy_limiter[tid]); + } + } + +#define tile_row(r) (row_tid * rows_per_thread + (r)) uint32_t q_offset = gqa_iq1*p.nb01 + (iq2*p.nb02 + iq3*p.nb03) / 4; @@ -60,37 +84,37 @@ void main() { uint32_t r = (idx + tid) / (HSK / 4); if (r < Br && d < HSK / 4 && i * Br + r < N) { - Qf[r][d] = vec4(data_qv4[q_offset / 4 + (i * Br + r) * q_stride / 4 + d]) * p.scale; + Qf[r * qf_stride + d] = FLOAT_TYPEV4(data_qv4[q_offset / 4 + (i * Br + r) * q_stride / 4 + d] * p.scale); } } barrier(); - vec4 Of[Br][HSV_per_thread / 4]; + FLOAT_TYPEV4 Of[rows_per_thread][HSV_per_thread / 4]; [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - Of[r][d] = vec4(0.0); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d] = FLOAT_TYPEV4(0.0); } } - float Lf[Br], Mf[Br]; + float Lf[rows_per_thread], Mf[rows_per_thread]; // Use -FLT_MAX/2 rather than -inf to reduce the possibility of NaNs, e.g. when computing Mold-M. const float NEG_FLT_MAX_OVER_2 = uintBitsToFloat(0xFEFFFFFF); - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { Lf[r] = 0; Mf[r] = NEG_FLT_MAX_OVER_2; } - float slope[Br]; - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - slope[r] = 1.0; + ACC_TYPE slope[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + slope[r] = ACC_TYPE(1.0); } // ALiBi if (p.max_bias > 0.0f) { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - slope[r] = perElemOpComputeSlope(r, col_tid, ACC_TYPE(0), iq2); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + slope[r] = perElemOpComputeSlope(tile_row(r), col_tid, ACC_TYPE(0), iq2); } } @@ -113,75 +137,141 @@ void main() { uint32_t mask_opt = 0; uint32_t mask_opt_idx = ~0; + uint32_t mask_opt_bits = 0; [[dont_unroll]] for (uint32_t j = start_j; j < end_j; ++j) { + if (MASK_ENABLE) { + if (USE_MASK_OPT && mask_opt_idx != j / 16) { + mask_opt_idx = j / 16; + mask_opt = data_mask_opt[mo_offset + mask_opt_idx]; + } + mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3; + if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) { + // skip this block + continue; + } + // Only load if the block is not all zeros + if (mask_opt_bits != MASK_OPT_ALL_ZERO) { + bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0; - if (USE_MASK_OPT && mask_opt_idx != j / 16) { - mask_opt_idx = j / 16; - mask_opt = data_mask_opt[mo_offset + mask_opt_idx]; + float max_mask = NEG_FLT_MAX_OVER_2; + barrier(); + [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) { + uint32_t c = (idx + tid) % Bc; + uint32_t r = (idx + tid) / Bc; + if (idx + tid < Bc * Br) { + if ((!KV_bounds_check || j * Bc + c < KV) && (!nem1_bounds_check || i * Br + r < p.nem1)) { + FLOAT_TYPE m = FLOAT_TYPE(data_m[m_offset + (i * Br + r) * m_stride + (j * Bc + c)]); + masksh[c * masksh_stride + r] = m; + max_mask = max(max_mask, float(m)); + } else { + masksh[c * masksh_stride + r] = FLOAT_TYPE(0); + } + } + } + // skip the block if the mask is entirely -inf + bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2); + barrier(); + if (gl_SubgroupInvocationID == 0) { + tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f; + } + barrier(); + [[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) { + max_mask = max(max_mask, tmpsh[s]); + } + if (max_mask <= NEG_FLT_MAX_OVER_2) { + continue; + } + } } - uint32_t mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3; - if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) { - // skip this block - continue; - } - // Only load if the block is not all zeros - if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) { - bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0; - float max_mask = NEG_FLT_MAX_OVER_2; - [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) { - uint32_t c = (idx + tid) % Bc; - uint32_t r = (idx + tid) / Bc; - if (idx + tid < Bc * Br) { - if ((!KV_bounds_check || j * Bc + c < KV) && (!nem1_bounds_check || i * Br + r < p.nem1)) { - float m = float(data_m[m_offset + (i * Br + r) * m_stride + (j * Bc + c)]); - masksh[c][r] = m; - max_mask = max(max_mask, m); + ACC_TYPE Sf[rows_per_thread][cols_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + Sf[r][c] = ACC_TYPE(0.0); + } + } + + if (SHMEM_STAGING != 0) { + barrier(); + [[unroll]] for (uint32_t idx = 0; idx < Bc * HSK / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSK / 4); + uint32_t c = (idx + tid) / (HSK / 4); + if (idx + gl_WorkGroupSize.x <= Bc * HSK / 4 || c < Bc) { + FLOAT_TYPEV4 K_Tf = FLOAT_TYPEV4(0); + if (!KV_bounds_check || j * Bc + c < KV) { +#if BLOCK_SIZE > 1 + uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE + 4 * d; + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); +#else + K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]); +#endif + } + + kvsh[c * kvsh_stride + d] = K_Tf; + } + } + barrier(); + } + + // More d iterations means Q register caching becomes relevant + // Few iterations means the additional registers needed are worse than the speed-up from caching + if (HSK_per_thread / 4 > 4) { + [[unroll]] for (uint32_t d = 0; d < HSK_per_thread / 4; ++d) { + FLOAT_TYPEV4 Q_cache[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Q_cache[r] = Qf[tile_row(r) * qf_stride + d * D_split + d_tid]; + } + + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { + continue; + } + + FLOAT_TYPEV4 K_Tf; + if (SHMEM_STAGING != 0) { + K_Tf = kvsh[(c * cols_per_iter + col_tid) * kvsh_stride + (d * D_split + d_tid)]; } else { - masksh[c][r] = float(0); +#if BLOCK_SIZE > 1 + uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); +#else + K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * k_stride / 4 + d * D_split + d_tid]); +#endif + } + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Sf[r][c] += dot(ACC_TYPEV4(Q_cache[r]), ACC_TYPEV4(K_Tf)); } } } - // skip the block if the mask is entirely -inf - bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2); - barrier(); - if (gl_SubgroupInvocationID == 0) { - tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f; - } - barrier(); - [[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) { - max_mask = max(max_mask, tmpsh[s]); - } - if (max_mask <= NEG_FLT_MAX_OVER_2) { - continue; - } - } - - float Sf[Br][cols_per_thread]; - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + } else { [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { - Sf[r][c] = 0.0; - } - } + if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { + continue; + } - - [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { - if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { - continue; - } - [[unroll]] for (uint32_t d = 0; d < HSK_per_thread / 4; ++d) { + [[unroll]] for (uint32_t d = 0; d < HSK_per_thread / 4; ++d) { + FLOAT_TYPEV4 K_Tf; + if (SHMEM_STAGING != 0) { + K_Tf = kvsh[(c * cols_per_iter + col_tid) * kvsh_stride + (d * D_split + d_tid)]; + } else { #if BLOCK_SIZE > 1 - uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); - uint ib = coord / BLOCK_SIZE; - uint iqs = (coord % BLOCK_SIZE); - vec4 K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); + uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); #else - vec4 K_Tf = vec4(data_kv4[k_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * k_stride / 4 + d * D_split + d_tid]); + K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * k_stride / 4 + d * D_split + d_tid]); #endif - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - Sf[r][c] += dot(Qf[r][d * D_split + d_tid], K_Tf); + } + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Sf[r][c] += dot(ACC_TYPEV4(Qf[tile_row(r) * qf_stride + d * D_split + d_tid]), ACC_TYPEV4(K_Tf)); + } } } } @@ -189,89 +279,109 @@ void main() { [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { // Compute sum across the D_split [[unroll]] for (uint s = D_split / 2; s > 0; s >>= 1) { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { Sf[r][c] += subgroupShuffleXor(Sf[r][c], s); } } } if (LOGIT_SOFTCAP) { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { - Sf[r][c] = p.logit_softcap * tanh(Sf[r][c]); + Sf[r][c] = ACC_TYPE(p.logit_softcap * tanh(Sf[r][c])); } } } if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) { [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - float mvf = masksh[c * cols_per_iter + col_tid][r]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + FLOAT_TYPE mvf = masksh[(c * cols_per_iter + col_tid) * masksh_stride + tile_row(r)]; Sf[r][c] += slope[r]*mvf; } } - barrier(); } - float rowmaxf[Br], Pf[Br][cols_per_thread], rowsumf[Br], eMf[Br], Moldf[Br]; - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - rowmaxf[r] = NEG_FLT_MAX_OVER_2; + float eMf[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + float rowmaxf = NEG_FLT_MAX_OVER_2; [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { continue; } - rowmaxf[r] = max(rowmaxf[r], Sf[r][c]); + rowmaxf = max(rowmaxf, float(Sf[r][c])); } - Moldf[r] = Mf[r]; + float Moldf = Mf[r]; // M = max(rowmax, Mold) // P = e^(S - M) // eM = e^(Mold - M) - Mf[r] = max(rowmaxf[r], Moldf[r]); - [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { - Pf[r][c] = exp(Sf[r][c] - Mf[r]); - } - eMf[r] = exp(Moldf[r] - Mf[r]); - - // Compute sum across row of P - rowsumf[r] = 0.0; - [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { - if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { - continue; - } - rowsumf[r] += Pf[r][c]; - } - - Lf[r] = eMf[r]*Lf[r] + rowsumf[r]; + Mf[r] = max(rowmaxf, Moldf); + eMf[r] = exp(Moldf - Mf[r]); + Lf[r] = eMf[r]*Lf[r]; } [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - Of[r][d] = eMf[r] * Of[r][d]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d] = FLOAT_TYPE(eMf[r]) * Of[r][d]; } } + if (SHMEM_STAGING != 0) { + barrier(); + [[unroll]] for (uint32_t idx = 0; idx < Bc * HSV / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSV / 4); + uint32_t c = (idx + tid) / (HSV / 4); + if (idx + gl_WorkGroupSize.x <= Bc * HSV / 4 || c < Bc) { + FLOAT_TYPEV4 V_Tf = FLOAT_TYPEV4(0); + if (!KV_bounds_check || j * Bc + c < KV) { +#if BLOCK_SIZE > 1 + uint coord = (j * Bc + c) * v_stride * BLOCK_SIZE + 4 * d; + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + V_Tf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); +#else + V_Tf = FLOAT_TYPEV4(data_vv4[v_offset / 4 + (j * Bc + c) * v_stride / 4 + d]); +#endif + } + + kvsh[c * kvsh_stride + d] = V_Tf; + } + } + barrier(); + } + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { continue; } + + FLOAT_TYPE Pf[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Pf[r] = FLOAT_TYPE(exp(float(Sf[r][c]) - Mf[r])); + Lf[r] += Pf[r]; + } + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + FLOAT_TYPEV4 Vf; + if (SHMEM_STAGING != 0) { + Vf = kvsh[(c * cols_per_iter + col_tid) * kvsh_stride + (d * D_split + d_tid)]; + } else { #if BLOCK_SIZE > 1 - uint coord = (j * Bc + c * cols_per_iter + col_tid) * v_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); - uint ib = coord / BLOCK_SIZE; - uint iqs = (coord % BLOCK_SIZE); - vec4 Vf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); + uint coord = (j * Bc + c * cols_per_iter + col_tid) * v_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + Vf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); #else - vec4 Vf = vec4(data_vv4[v_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * v_stride / 4 + d * D_split + d_tid]); + Vf = FLOAT_TYPEV4(data_vv4[v_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * v_stride / 4 + d * D_split + d_tid]); #endif - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - Of[r][d] += Pf[r][c] * Vf; + } + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d] += FLOAT_TYPEV4(Pf[r] * Vf); } } } - - barrier(); } // prevent race on tmpsh @@ -279,58 +389,115 @@ void main() { // reduce across threads - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - float rowmaxf, eMf; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + float rowmaxf = Mf[r]; - tmpsh[tid] = Mf[r]; // Compute max across the row - barrier(); - [[unroll]] for (int s = int(gl_WorkGroupSize.x) / 2; s >= D_split; s >>= 1) { - if (tid < s) { - tmpsh[tid] = max(tmpsh[tid], tmpsh[tid + s]); + if (SubGroupSize > 0) { + [[unroll]] for (uint s = D_split; s < SubGroupSize; s *= 2) { + rowmaxf = max(rowmaxf, subgroupShuffleXor(rowmaxf, s)); } + if (row_split == 1) { + // Reduce inside workgroup with shmem + barrier(); + if (gl_SubgroupInvocationID == d_tid) { + tmpsh[gl_SubgroupID * D_split + d_tid] = rowmaxf; + } + barrier(); + rowmaxf = tmpsh[d_tid]; + [[unroll]] for (uint32_t s = 1; s < num_subgroups; ++s) { + rowmaxf = max(rowmaxf, tmpsh[s * D_split + d_tid]); + } + } + } else { barrier(); + tmpsh[tid] = rowmaxf; + barrier(); + [[unroll]] for (int s = int(threads_per_rowgroup) / 2; s >= D_split; s >>= 1) { + if (rowgroup_tid < s) { + tmpsh[tid] = max(tmpsh[tid], tmpsh[tid ^ s]); + } + barrier(); + } + rowmaxf = tmpsh[row_tid * threads_per_rowgroup + d_tid]; } - rowmaxf = tmpsh[d_tid]; - barrier(); float Moldf = Mf[r]; // M = max(rowmax, Mold) // eM = e^(Mold - M) Mf[r] = max(rowmaxf, Moldf); - eMf = exp(Moldf - Mf[r]); + float eMf = exp(Moldf - Mf[r]); Lf[r] = eMf*Lf[r]; - tmpsh[tid] = Lf[r]; - // Compute sum across the row - barrier(); - [[unroll]] for (int s = int(gl_WorkGroupSize.x) / 2; s >= D_split; s >>= 1) { - if (tid < s) { - tmpsh[tid] = tmpsh[tid] + tmpsh[tid + s]; + if (SubGroupSize > 0) { + [[unroll]] for (uint s = D_split; s < SubGroupSize; s *= 2) { + Lf[r] += subgroupShuffleXor(Lf[r], s); } + if (row_split == 1) { + barrier(); + if (gl_SubgroupInvocationID == d_tid) { + tmpsh[gl_SubgroupID * D_split + d_tid] = Lf[r]; + } + barrier(); + Lf[r] = tmpsh[d_tid]; + [[unroll]] for (uint32_t s = 1; s < num_subgroups; ++s) { + Lf[r] += tmpsh[s * D_split + d_tid]; + } + } + } else { barrier(); - } - Lf[r] = tmpsh[d_tid]; - barrier(); - - [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { - - Of[r][d] = eMf * Of[r][d]; - tmpshv4[tid] = Of[r][d]; - + tmpsh[tid] = Lf[r]; barrier(); - [[unroll]] for (int s = int(gl_WorkGroupSize.x) / 2; s >= D_split; s >>= 1) { - if (tid < s) { - Of[r][d] += tmpshv4[tid + s]; - tmpshv4[tid] = Of[r][d]; + [[unroll]] for (int s = int(threads_per_rowgroup) / 2; s >= D_split; s >>= 1) { + if (rowgroup_tid < s) { + tmpsh[tid] = tmpsh[tid] + tmpsh[tid ^ s]; } barrier(); } - Of[r][d] = tmpshv4[d_tid]; - barrier(); + Lf[r] = tmpsh[row_tid * threads_per_rowgroup + d_tid]; + } + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + Of[r][d] = FLOAT_TYPE(eMf) * Of[r][d]; + + if (SubGroupSize > 0) { + [[unroll]] for (uint s = D_split; s < SubGroupSize; s *= 2) { + if (!OLD_AMD_WINDOWS) { + Of[r][d] += subgroupShuffleXor(Of[r][d], s); + } else { + // Something about f16vec4 subgroupShuffleXor is broken on AMD Windows RDNA2 and below. + // Shuffle full vec4 as workaround. + // See https://github.com/ggml-org/llama.cpp/issues/19881#issuecomment-3958643697 + Of[r][d] += FLOAT_TYPEV4(subgroupShuffleXor(vec4(Of[r][d]), s)); + } + } + if (row_split == 1) { + barrier(); + if (gl_SubgroupInvocationID == d_tid) { + tmpshv4[gl_SubgroupID * D_split + d_tid] = Of[r][d]; + } + barrier(); + Of[r][d] = tmpshv4[d_tid]; + [[unroll]] for (uint32_t s = 1; s < num_subgroups; ++s) { + Of[r][d] += tmpshv4[s * D_split + d_tid]; + } + } + } else { + barrier(); + tmpshv4[tid] = Of[r][d]; + barrier(); + [[unroll]] for (int s = int(threads_per_rowgroup) / 2; s >= D_split; s >>= 1) { + if (rowgroup_tid < s) { + Of[r][d] += tmpshv4[tid ^ s]; + tmpshv4[tid] = Of[r][d]; + } + barrier(); + } + Of[r][d] = tmpshv4[row_tid * threads_per_rowgroup + d_tid]; + } } } @@ -338,33 +505,53 @@ void main() { // If there is split_k, then the split_k resolve shader does the final // division by L. Store the intermediate O value and per-row m and L values. if (p.k_num > 1) { - // note: O and Q have swapped coord 1,2. - uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + if (p.gqa_ratio > 1) { + // note: O and Q have swapped coord 1,2. + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)) / 4; - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - if (r < N) { - [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { - [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { - perElemOpGqaStore(r, 4*(d * D_split + d_tid) + comp, Of[r][d][comp], o_offset, iq2, N); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + if (row < N) { + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + gqaStore(row, d * D_split + d_tid, Of[r][d], o_offset, iq2, N); } } } - } - o_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - if (r < N) { - perElemOpStoreCol0(r, 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N); - perElemOpStoreCol0(r, 0u, ACC_TYPE(Mf[r]), o_offset + p.ne1, iq2, N); + o_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + if (row < N) { + perElemOpStoreCol0(row, 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N); + perElemOpStoreCol0(row, 0u, ACC_TYPE(Mf[r]), o_offset + p.ne1, iq2, N); + } + } + } else { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + const uint global_row = i * Br + row; + + if (global_row < N) { + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)) / 4; + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + data_ov4[o_offset + iq2 * HSV/4 + d * D_split + d_tid] = D_TYPEV4(Of[r][d]); + } + } + + if (global_row < N && d_tid == 0 && col_tid == 0) { + uint32_t lm_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)); + data_o[lm_offset + iq2] = D_TYPE(Lf[r]); + data_o[lm_offset + p.ne1 + iq2] = D_TYPE(Mf[r]); + } } } - return; } if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - float sink = perElemOpGetSink(r, 0u, ACC_TYPE(0), iq2); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + float sink = perElemOpGetSink(tile_row(r), 0u, ACC_TYPE(0), iq2); float ms = 1.0f; float vs = 1.0f; @@ -373,7 +560,7 @@ void main() { ms = exp(Mf[r] - sink); [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { - Of[r][d] *= ms; + Of[r][d] *= FLOAT_TYPE(ms); } } else { vs = exp(sink - Mf[r]); @@ -383,39 +570,37 @@ void main() { } } - float Lfrcp[Br]; - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + float Lfrcp[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { Lfrcp[r] = (Lf[r] == 0.0) ? 0.0 : (1.0 / Lf[r]); } [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - Of[r][d] *= Lfrcp[r]; -#if defined(ACC_TYPE_MAX) - Of[r][d] = clamp(Of[r][d], -vec4(ACC_TYPE_MAX), vec4(ACC_TYPE_MAX)); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d] *= FLOAT_TYPE(Lfrcp[r]); +#if defined(FLOAT_TYPE_MAX) + Of[r][d] = clamp(Of[r][d], -FLOAT_TYPE_MAX, FLOAT_TYPE_MAX); #endif } } - uint32_t o_offset = gqa_iq1*p.ne1*HSV + iq3*p.ne2*p.ne1*HSV; + uint32_t o_offset = (gqa_iq1*p.ne1*HSV + iq3*p.ne2*p.ne1*HSV) / 4; if (p.gqa_ratio > 1) { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - if (r < N) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + if (row < N) { [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { - [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { - perElemOpGqaStore(r, 4*(d * D_split + d_tid) + comp, Of[r][d][comp], o_offset, iq2, N); - } + gqaStore(row, d * D_split + d_tid, Of[r][d], o_offset, iq2, N); } } } } else { - [[unroll]] for (uint32_t r = 0; r < Br; ++r) { - if (i * Br + r < N) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + if (i * Br + row < N) { [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { - [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { - data_o[o_offset + iq2 * HSV + (i * Br + r) * p.ne1 * HSV + 4*(d * D_split + d_tid) + comp] = D_TYPE(Of[r][d][comp]); - } + data_ov4[o_offset + (iq2 * HSV + (i * Br + row) * p.ne1 * HSV) / 4 + d * D_split + d_tid] = D_TYPEV4(Of[r][d]); } } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl index 4142c1e6ea..172d38f034 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl @@ -1,20 +1,23 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -layout (constant_id = 0) const uint32_t WorkGroupSize = 128; -layout (constant_id = 1) const uint32_t Br = 1; -layout (constant_id = 2) const uint32_t Bc = 32; -layout (constant_id = 3) const uint32_t HSK = 32; -layout (constant_id = 4) const uint32_t HSV = 32; -layout (constant_id = 5) const uint32_t Clamp = 0; -layout (constant_id = 6) const uint32_t D_split = 16; -layout (constant_id = 7) const uint32_t SubGroupSize = 32; -layout (constant_id = 8) const uint32_t K_LOAD_SHMEM = 0; -layout (constant_id = 9) const uint32_t Flags = 0; +layout (constant_id = 0) const uint32_t WorkGroupSize = 128; +layout (constant_id = 1) const uint32_t Br = 1; +layout (constant_id = 2) const uint32_t Bc = 32; +layout (constant_id = 3) const uint32_t HSK = 32; +layout (constant_id = 4) const uint32_t HSV = 32; +layout (constant_id = 5) const uint32_t Clamp = 0; +layout (constant_id = 6) const uint32_t D_split = 16; +layout (constant_id = 7) const uint32_t row_split = 1; +layout (constant_id = 8) const uint32_t SubGroupSize = 32; +layout (constant_id = 9) const uint32_t SHMEM_STAGING = 0; +layout (constant_id = 10) const uint32_t Flags = 0; +layout (constant_id = 11) const uint32_t LIMIT_OCCUPANCY_SHMEM = 0; -const bool USE_MASK_OPT = (Flags & 1) != 0; -const bool MASK_ENABLE = (Flags & 2) != 0; -const bool LOGIT_SOFTCAP = (Flags & 4) != 0; +const bool USE_MASK_OPT = (Flags & 1) != 0; +const bool MASK_ENABLE = (Flags & 2) != 0; +const bool LOGIT_SOFTCAP = (Flags & 4) != 0; +const bool OLD_AMD_WINDOWS = (Flags & 8) != 0; // Round up head sizes to a multiple of 16, for coopmat1/coopmat2 paths const uint32_t HSK_pad = (HSK + 15) & ~15; @@ -69,6 +72,7 @@ layout (push_constant) uniform parameter { layout (binding = 4) readonly buffer S {float data_s[];}; layout (binding = 5) writeonly buffer O {D_TYPE data_o[];}; +layout (binding = 5) writeonly buffer OV4 {D_TYPEV4 data_ov4[];}; layout (binding = 6) readonly buffer MO {uint32_t data_mask_opt[];}; @@ -94,12 +98,12 @@ layout (binding = 2) readonly buffer V_PACKED16 {A_TYPE_PACKED16 v_data_packed16 #define BLOCK_SIZE 4 #define BLOCK_BYTE_SIZE 16 -vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { +FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { // iqs is currently always zero in the flash attention shaders if (binding_idx == BINDING_IDX_K) { - return k_packed.k_data_packed[a_offset + ib]; + return FLOAT_TYPEV4(k_packed.k_data_packed[a_offset + ib]); } else { - return v_packed.v_data_packed[a_offset + ib]; + return FLOAT_TYPEV4(v_packed.v_data_packed[a_offset + ib]); } } #endif @@ -107,7 +111,7 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { #if defined(DATA_A_Q4_0) #define BLOCK_BYTE_SIZE 18 -vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { +FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { if (binding_idx == BINDING_IDX_K) { uint vui_lo = uint(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]); uint vui_hi = uint(k_packed.k_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]); @@ -115,7 +119,7 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { vui_lo >>= shift; vui_hi >>= shift; - return float(k_packed.k_data_packed16[a_offset + ib].d) * (vec4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF) - 8.0f); + return FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].d) * (FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF) - FLOAT_TYPE(8.0f)); } else { uint vui_lo = uint(v_packed.v_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]); uint vui_hi = uint(v_packed.v_data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]); @@ -123,24 +127,24 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { vui_lo >>= shift; vui_hi >>= shift; - return float(v_packed.v_data_packed16[a_offset + ib].d) * (vec4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF) - 8.0f); + return FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].d) * (FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF) - FLOAT_TYPE(8.0f)); } } #endif #if defined(DATA_A_Q8_0) #define BLOCK_BYTE_SIZE 34 -vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { +FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { if (binding_idx == BINDING_IDX_K) { const i8vec2 v0 = unpack8(int32_t(k_packed.k_data_packed16[a_offset + ib].qs[iqs / 2])).xy; // vec4 used due to #12147 const i8vec2 v1 = unpack8(int32_t(k_packed.k_data_packed16[a_offset + ib].qs[iqs / 2 + 1])).xy; - return float(k_packed.k_data_packed16[a_offset + ib].d) * vec4(v0.x, v0.y, v1.x, v1.y); + return FLOAT_TYPE(k_packed.k_data_packed16[a_offset + ib].d) * FLOAT_TYPEV4(v0.x, v0.y, v1.x, v1.y); } else { const i8vec2 v0 = unpack8(int32_t(v_packed.v_data_packed16[a_offset + ib].qs[iqs / 2])).xy; // vec4 used due to #12147 const i8vec2 v1 = unpack8(int32_t(v_packed.v_data_packed16[a_offset + ib].qs[iqs / 2 + 1])).xy; - return float(v_packed.v_data_packed16[a_offset + ib].d) * vec4(v0.x, v0.y, v1.x, v1.y); + return FLOAT_TYPE(v_packed.v_data_packed16[a_offset + ib].d) * FLOAT_TYPEV4(v0.x, v0.y, v1.x, v1.y); } } #endif @@ -189,10 +193,16 @@ void init_indices() KV = p.KV; if (p.k_num > 1) { - i = 0; - // batch and split_k share gl_WorkGroupID.x - gqa_iq1 = gl_WorkGroupID.x / p.k_num; - split_k_index = gl_WorkGroupID.x % p.k_num; + if (p.gqa_ratio > 1) { + i = 0; + // batch and split_k share gl_WorkGroupID.x + gqa_iq1 = gl_WorkGroupID.x / p.k_num; + split_k_index = gl_WorkGroupID.x % p.k_num; + } else { + gqa_iq1 = 0; + split_k_index = gl_WorkGroupID.x % p.k_num; + i = gl_WorkGroupID.x / p.k_num; + } } else if (p.gqa_ratio > 1) { i = 0; gqa_iq1 = gl_WorkGroupID.x; @@ -244,3 +254,11 @@ void init_indices() // Bias applied to softmax to stay in fp16 range. // Based on ggml-cuda issue https://github.com/ggml-org/llama.cpp/issues/18606 const float FATTN_KQ_MAX_OFFSET = 3.0f*0.6931f; + +// Store the output when doing grouped query attention. +// Rows index by Q's dimension 2, and the first N rows are valid. +void gqaStore(const in uint32_t r, const in uint32_t c, const in FLOAT_TYPEV4 elems, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N) +{ + uint32_t offset = (iq2 + r) * HSV / 4 + c; + data_ov4[o_offset + offset] = D_TYPEV4(elems); +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp index 19630972da..526e8da384 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp @@ -19,7 +19,6 @@ const uint32_t MatBr = 16; const uint32_t MatBc = 16; -const uint32_t row_split = Bc / MatBc; const uint32_t rows_per_thread = Br / row_split; const uint32_t cols_per_iter = gl_WorkGroupSize.x / row_split; const uint32_t cols_per_thread = Bc / cols_per_iter; @@ -33,15 +32,6 @@ layout (binding = 2) readonly buffer V {float16_t data_v[];}; layout (binding = 2) readonly buffer VV4 {f16vec4 data_vv4[];}; layout (binding = 3) readonly buffer M {float16_t data_m[];}; -// Store the output when doing grouped query attention. -// Rows index by Q's dimension 2, and the first N rows are valid. -D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N) -{ - uint32_t offset = (iq2 + r) * HSV + c; - data_o[o_offset + offset] = D_TYPE(elem); - return elem; -} - shared float tmpsh[row_split]; const uint32_t qstride = HSK_pad / 4 + 2; // in units of f16vec4 @@ -54,10 +44,14 @@ shared f16vec4 Psh[Bc * psh_stride]; const uint32_t sfshstride = (HSK <= 128) ? (Br / 4 + 2) : Br / 4; shared ACC_TYPEV4 sfsh[Bc * sfshstride]; -const uint32_t kshstride = (K_LOAD_SHMEM != 0 ? HSK_pad : MatBr) / 4 + 2; // in units of f16vec4 +const uint32_t D_pad = HSK_pad > HSV_pad ? HSK_pad : HSV_pad; +const uint32_t kvsh_stride = (SHMEM_STAGING != 0 ? D_pad : MatBr) / 4 + 2; // in units of f16vec4 const uint v_cols = MatBc / 4 * row_split; // total cols, 4 vec4s per MatBc * number of subgroups const uint vsh_stride = v_cols; -shared f16vec4 ksh[(kshstride >= vsh_stride) ? (Bc * kshstride) : (Bc * vsh_stride)]; +shared f16vec4 kvsh[(kvsh_stride >= vsh_stride) ? (Bc * kvsh_stride) : (Bc * vsh_stride)]; + +const uint32_t osh_stride = row_split * MatBr / 4; +shared f16vec4 pvsh[MatBc * osh_stride]; shared ACC_TYPE slope[Br]; @@ -84,11 +78,6 @@ void main() { Qf[i + tid] = f16vec4(0); } } - [[unroll]] for (uint i = 0; i < Bc * kshstride; i += gl_WorkGroupSize.x) { - if (i + tid < Bc * kshstride) { - ksh[i + tid] = f16vec4(0); - } - } barrier(); } @@ -104,10 +93,10 @@ void main() { } barrier(); - ACC_TYPEV4 Of[rows_per_thread][d_per_thread]; + f16vec4 Of[rows_per_thread][d_per_thread]; [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { [[unroll]] for (uint32_t d = 0; d < d_per_thread; ++d) { - Of[r][d] = ACC_TYPEV4(0.0); + Of[r][d] = f16vec4(0.0); } } @@ -153,22 +142,22 @@ void main() { uint32_t mask_opt = 0; uint32_t mask_opt_idx = ~0; + uint32_t mask_opt_bits = 0; + f16vec4 mask_cache[Bc * Br / 4 / WorkGroupSize]; [[dont_unroll]] for (uint32_t j = start_j; j < end_j; ++j) { - f16vec4 mask_cache[Bc * Br / 4 / WorkGroupSize]; [[unroll]] for (uint32_t idx = 0; idx < mask_cache.length(); ++idx) { mask_cache[idx] = f16vec4(0); } if (MASK_ENABLE) { - if (USE_MASK_OPT && mask_opt_idx != j / 16) { mask_opt_idx = j / 16; mask_opt = data_mask_opt[mo_offset + mask_opt_idx]; } - uint32_t mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3; + mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3; if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) { // skip this block continue; @@ -231,24 +220,24 @@ void main() { } } - if (K_LOAD_SHMEM != 0) { - [[unroll]] for (uint32_t idx = 0; idx < Bc * HSK / 4; idx += gl_WorkGroupSize.x) { - uint32_t d = (idx + tid) % (HSK / 4); - uint32_t c = (idx + tid) / (HSK / 4); - if (c < Bc && d < HSK / 4) { + if (SHMEM_STAGING != 0) { + [[unroll]] for (uint32_t idx = 0; idx < Bc * HSK_pad / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSK_pad / 4); + uint32_t c = (idx + tid) / (HSK_pad / 4); + if (idx + gl_WorkGroupSize.x <= Bc * HSK_pad / 4 || c < Bc) { f16vec4 K_Tf = f16vec4(0); - if (!KV_bounds_check || j * Bc + c < KV) { + if ((!KV_bounds_check || j * Bc + c < KV) && (HSK == HSK_pad || d < HSK / 4)) { #if BLOCK_SIZE > 1 uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE + 4 * d; uint ib = coord / BLOCK_SIZE; uint iqs = (coord % BLOCK_SIZE); - K_Tf = f16vec4(dequantize4(ib, iqs, k_offset, BINDING_IDX_K)); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); #else K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]); #endif } - ksh[c * kshstride + d] = K_Tf; + kvsh[c * kvsh_stride + d] = K_Tf; } } barrier(); @@ -262,7 +251,11 @@ void main() { coopmat QMat; [[unroll]] for (uint32_t d = 0; d < HSK_pad / 16; ++d) { - if (K_LOAD_SHMEM == 0) { + // If SHMEM_STAGING is set, a Bc * HSK_pad size tile of K is loaded to shmem + // If not, f16 K is loaded directly from global memory if aligned, otherwise + // staged through a Bc * MatBr size staging buffer. + // If K is not type f16, then it is always staged for dequantization. + if (SHMEM_STAGING == 0) { #if BLOCK_SIZE == 1 if (KV_bounds_check || d * 16 + 16 > HSK) { #endif @@ -277,13 +270,13 @@ void main() { uint coord = (j * Bc + row) * k_stride * BLOCK_SIZE + d * 16 + col_vec * 4; uint ib = coord / BLOCK_SIZE; uint iqs = (coord % BLOCK_SIZE); - K_Tf = f16vec4(dequantize4(ib, iqs, k_offset, BINDING_IDX_K)); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); #else K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + row) * k_stride / 4 + d * 16 / 4 + col_vec]); #endif } - ksh[row * kshstride + col_vec] = K_Tf; + kvsh[row * kvsh_stride + col_vec] = K_Tf; } } barrier(); @@ -295,8 +288,8 @@ void main() { if (KV_bounds_check || d * 16 + 16 > HSK) #endif { - uint coord = (gl_SubgroupID * MatBc) * kshstride; - coopMatLoad(KMat, ksh, coord, kshstride, gl_CooperativeMatrixLayoutRowMajor); + uint coord = (gl_SubgroupID * MatBc) * kvsh_stride; + coopMatLoad(KMat, kvsh, coord, kvsh_stride, gl_CooperativeMatrixLayoutRowMajor); } #if BLOCK_SIZE == 1 else { @@ -305,8 +298,8 @@ void main() { } #endif } else { - uint coord = (gl_SubgroupID * MatBc) * kshstride + d * 16 / 4; - coopMatLoad(KMat, ksh, coord, kshstride, gl_CooperativeMatrixLayoutRowMajor); + uint coord = (gl_SubgroupID * MatBc) * kvsh_stride + d * 16 / 4; + coopMatLoad(KMat, kvsh, coord, kvsh_stride, gl_CooperativeMatrixLayoutRowMajor); } coopMatLoad(QMat, Qf, d * 16 / 4, qstride, gl_CooperativeMatrixLayoutColumnMajor); @@ -329,7 +322,7 @@ void main() { barrier(); } - if (MASK_ENABLE) { + if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) { [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) { uint32_t c = (idx + tid) / (Br / 4); uint32_t r = (idx + tid) % (Br / 4); @@ -374,7 +367,7 @@ void main() { [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { const uint d_local = d0 / threads_per_rowgroup; [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - Of[r][d_local] = ACC_TYPE(eMf[r]) * Of[r][d_local]; + Of[r][d_local] = float16_t(eMf[r]) * Of[r][d_local]; } } @@ -397,19 +390,47 @@ void main() { } } + if (SHMEM_STAGING != 0) { + [[unroll]] for (uint32_t idx = 0; idx < Bc * HSV_pad / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSV_pad / 4); + uint32_t c = (idx + tid) / (HSV_pad / 4); + if (idx + gl_WorkGroupSize.x <= Bc * HSV_pad / 4 || c < Bc) { + f16vec4 V_Tf = f16vec4(0); + if ((!KV_bounds_check || j * Bc + c < KV) && (HSV == HSV_pad || d < HSV / 4)) { +#if BLOCK_SIZE > 1 + uint coord = (j * Bc + c) * v_stride * BLOCK_SIZE + 4 * d; + uint ib = coord / BLOCK_SIZE; + uint iqs = (coord % BLOCK_SIZE); + V_Tf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); +#else + V_Tf = f16vec4(data_vv4[v_offset / 4 + (j * Bc + c) * v_stride / 4 + d]); +#endif + } + + kvsh[c * kvsh_stride + d] = V_Tf; + } + } + } + barrier(); + const uint num_hsv_tiles = (HSV + MatBc * row_split - 1) / (MatBc * row_split); // round up // Each subgroup handles HSV/4 columns [[unroll]] for (uint32_t hsv_tile = 0; hsv_tile < num_hsv_tiles; ++hsv_tile) { const uint hsv_offset = (hsv_tile * row_split + gl_SubgroupID) * 16; - SfMat = coopmat(0); + coopmat PVMat = coopmat(0); // Preload V tiles for [Bc, 16 * num subgroups] const uint v_rows = Bc; const uint v_total = v_rows * v_cols; const uint v_loads_per_thread = v_total / gl_WorkGroupSize.x; + // If SHMEM_STAGING is set, a Bc * HSV_pad size tile of V is loaded to shmem. + // If not, f16 V is loaded directly from global memory if aligned, otherwise + // staged through a Bc * MatBr size staging buffer. + // If V is not type f16, then it is always staged for dequantization. + if (SHMEM_STAGING == 0) { #if BLOCK_SIZE == 1 // For f16, only preload if not aligned if (KV_bounds_check) { @@ -428,44 +449,52 @@ void main() { if (!KV_bounds_check || (v_row < KV && v_col < HSV)) { #if BLOCK_SIZE > 1 - ksh[row * vsh_stride + col] = f16vec4(dequantize4(ib, iqs, v_offset, BINDING_IDX_V)); + kvsh[row * vsh_stride + col] = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); #else - ksh[row * vsh_stride + col] = data_vv4[(v_offset + v_row * v_stride + v_col) / 4]; + kvsh[row * vsh_stride + col] = data_vv4[(v_offset + v_row * v_stride + v_col) / 4]; #endif } else { - ksh[row * vsh_stride + col] = f16vec4(0.0f); + kvsh[row * vsh_stride + col] = f16vec4(0.0f); } } + #if BLOCK_SIZE == 1 } #endif - + } barrier(); - [[unroll]] for (uint32_t bc_chunk = 0; bc_chunk < Bc / MatBc; ++bc_chunk) { - coopMatLoad(KMat, Psh, bc_chunk * MatBc * psh_stride, psh_stride, gl_CooperativeMatrixLayoutColumnMajor); + const uint o_offset = gl_SubgroupID * MatBr / 4; + if (hsv_offset < HSV_pad) { + [[unroll]] for (uint32_t bc_chunk = 0; bc_chunk < Bc / MatBc; ++bc_chunk) { + coopMatLoad(KMat, Psh, bc_chunk * MatBc * psh_stride, psh_stride, gl_CooperativeMatrixLayoutColumnMajor); + + if (SHMEM_STAGING == 0) { #if BLOCK_SIZE == 1 - if (!KV_bounds_check) { - // F16 values can be loaded directly from global memory - const uint v_tile_row = j * Bc + bc_chunk * MatBc; - const uint v_tile_offset = v_offset / 4 + v_tile_row * v_stride / 4 + hsv_offset / 4; - coopMatLoad(QMat, data_vv4, v_tile_offset, v_stride / 4, gl_CooperativeMatrixLayoutRowMajor); - } else + if (!KV_bounds_check) { + // F16 values can be loaded directly from global memory + const uint v_tile_row = j * Bc + bc_chunk * MatBc; + const uint v_tile_offset = v_offset / 4 + v_tile_row * v_stride / 4 + hsv_offset / 4; + coopMatLoad(QMat, data_vv4, v_tile_offset, v_stride / 4, gl_CooperativeMatrixLayoutRowMajor); + } else #endif - { - const uint v_tile_offset = bc_chunk * MatBr * v_cols + gl_SubgroupID * (MatBc / 4); - coopMatLoad(QMat, ksh, v_tile_offset, vsh_stride, gl_CooperativeMatrixLayoutRowMajor); + { + const uint v_tile_offset = bc_chunk * MatBr * v_cols + gl_SubgroupID * (MatBc / 4); + coopMatLoad(QMat, kvsh, v_tile_offset, vsh_stride, gl_CooperativeMatrixLayoutRowMajor); + } + } else { + const uint v_tile_offset = bc_chunk * MatBc * kvsh_stride + (hsv_tile * row_split + gl_SubgroupID) * (MatBc / 4); + coopMatLoad(QMat, kvsh, v_tile_offset, kvsh_stride, gl_CooperativeMatrixLayoutRowMajor); + } + + PVMat = coopMatMulAdd(KMat, QMat, PVMat); } - SfMat = coopMatMulAdd(KMat, QMat, SfMat); + // Store PVMat to pvsh and load into Of + coopMatStore(PVMat, pvsh, o_offset, osh_stride, gl_CooperativeMatrixLayoutRowMajor); } - // Store SfMat to sfsh and load into Of - const uint osh_stride = row_split * MatBc / 4; - const uint o_offset = gl_SubgroupID * MatBc / 4; - coopMatStore(SfMat, sfsh, o_offset, osh_stride, gl_CooperativeMatrixLayoutRowMajor); - barrier(); const uint hsv_per_tile = row_split * MatBc; @@ -484,7 +513,7 @@ void main() { if (hsv_col >= hsv_base && hsv_col < hsv_base + hsv_per_tile && hsv_col < HSV) { const uint local_hsv = (hsv_col - hsv_base) / 4; - Of[r][d_local] += ACC_TYPEV4(sfsh[row * osh_stride + local_hsv]); + Of[r][d_local] += pvsh[row * osh_stride + local_hsv]; } } } @@ -500,27 +529,48 @@ void main() { // If there is split_k, then the split_k resolve shader does the final // division by L. Store the intermediate O value and per-row m and L values. if (p.k_num > 1) { - // note: O and Q have swapped coord 1,2. - uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + if (p.gqa_ratio > 1) { + // note: O and Q have swapped coord 1,2. + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)) / 4; - [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - if (tile_row(r) < N) { - [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { - const uint d = d0 + col_tid; - if (d >= HSV/4) break; - const uint d_local = d0 / threads_per_rowgroup; - [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { - perElemOpGqaStore(tile_row(r), 4 * d + comp, float(Of[r][d_local][comp]), o_offset, iq2, N); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + if (tile_row(r) < N) { + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d = d0 + col_tid; + if (d >= HSV/4) break; + const uint d_local = d0 / threads_per_rowgroup; + gqaStore(tile_row(r), d, Of[r][d_local], o_offset, iq2, N); } } } - } - o_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); - [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - if (tile_row(r) < N) { - perElemOpStoreCol0(tile_row(r), 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N); - perElemOpStoreCol0(tile_row(r), 0u, ACC_TYPE(Mf[r]), o_offset + p.ne1, iq2, N); + o_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + if (tile_row(r) < N) { + perElemOpStoreCol0(tile_row(r), 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N); + perElemOpStoreCol0(tile_row(r), 0u, ACC_TYPE(Mf[r]), o_offset + p.ne1, iq2, N); + } + } + } else { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + const uint global_row = i * Br + row; + + if (global_row < N) { + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)) / 4; + + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d = d0 + col_tid; + if (d >= HSV/4) break; + data_ov4[o_offset + iq2 * HSV/4 + d] = D_TYPEV4(Of[r][d/threads_per_rowgroup]); + } + } + + if (global_row < N && col_tid == 0) { + uint32_t lm_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)); + data_o[lm_offset + iq2] = D_TYPE(Lf[r]); + data_o[lm_offset + p.ne1 + iq2] = D_TYPE(Mf[r]); + } } } @@ -539,7 +589,7 @@ void main() { [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { const uint d_local = d0 / threads_per_rowgroup; - Of[r][d_local] *= ACC_TYPE(ms); + Of[r][d_local] *= float16_t(ms); } } else { vs = exp(sink - Mf[r]); @@ -557,14 +607,14 @@ void main() { [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { const uint d_local = d0 / threads_per_rowgroup; [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { - Of[r][d_local] *= ACC_TYPE(Lfrcp[r]); -#if defined(ACC_TYPE_MAX) - Of[r][d_local] = clamp(Of[r][d_local], -ACC_TYPE_MAX, ACC_TYPE_MAX); + Of[r][d_local] *= float16_t(Lfrcp[r]); +#if defined(FLOAT_TYPE_MAX) + Of[r][d_local] = clamp(Of[r][d_local], -FLOAT_TYPE_MAX, FLOAT_TYPE_MAX); #endif } } - uint32_t o_offset = gqa_iq1*p.ne1*HSV + iq3*p.ne2*p.ne1*HSV; + uint32_t o_offset = (gqa_iq1*p.ne1*HSV + iq3*p.ne2*p.ne1*HSV) / 4; if (p.gqa_ratio > 1) { [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { @@ -573,9 +623,7 @@ void main() { const uint d = d0 + col_tid; if (d >= HSV / 4) break; const uint d_local = d0 / threads_per_rowgroup; - [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { - perElemOpGqaStore(tile_row(r), 4 * d + comp, float(Of[r][d_local][comp]), o_offset, iq2, N); - } + gqaStore(tile_row(r), d, Of[r][d_local], o_offset, iq2, N); } } } @@ -586,9 +634,7 @@ void main() { const uint d = d0 + col_tid; if (d >= HSV / 4) break; const uint d_local = d0 / threads_per_rowgroup; - [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { - data_o[o_offset + iq2 * HSV + (i * Br + tile_row(r)) * p.ne1 * HSV + 4 * d + comp] = D_TYPE(Of[r][d_local][comp]); - } + data_ov4[o_offset + (iq2 * HSV + (i * Br + tile_row(r)) * p.ne1 * HSV) / 4 + d] = D_TYPEV4(Of[r][d_local]); } } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp index 853f17fa16..0ea181342c 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp @@ -72,6 +72,28 @@ D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TY return elem; } +// Store O values for non-GQA split_k. Rows are tokens, not heads. +D_TYPE perElemOpNonGqaSplitKStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t unused, const in uint32_t iq2, const in uint32_t N) { + uint32_t global_row = i * Br + r; + if (global_row < N && c < HSV) { + uint32_t o_off = HSV * p.ne1 + * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)); + data_o[o_off + iq2 * HSV + c] = D_TYPE(elem); + } + return elem; +} + +// Store L/M values for non-GQA split_k. +ACC_TYPE perElemOpNonGqaSplitKStoreCol0(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t lm_base, const in uint32_t iq2, const in uint32_t N) { + uint32_t global_row = i * Br + r; + if (global_row < N && c == 0) { + uint32_t lm_off = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + + p.ne1 * 2 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)); + data_o[lm_off + lm_base + iq2] = D_TYPE(elem); + } + return elem; +} + void main() { #ifdef NEEDS_INIT_IQ_SHMEM init_iq_shmem(gl_WorkGroupSize); @@ -290,13 +312,19 @@ void main() { if (p.k_num > 1) { coopmat O_D = coopmat(O); - // note: O and Q have swapped coord 1,2. - uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); - coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N); + if (p.gqa_ratio > 1) { + // note: O and Q have swapped coord 1,2. + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N); - o_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); - coopMatPerElementNV(L, L, perElemOpStoreCol0, o_offset, iq2, N); - coopMatPerElementNV(M, M, perElemOpStoreCol0, o_offset + p.ne1, iq2, N); + o_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + coopMatPerElementNV(L, L, perElemOpStoreCol0, o_offset, iq2, N); + coopMatPerElementNV(M, M, perElemOpStoreCol0, o_offset + p.ne1, iq2, N); + } else { + coopMatPerElementNV(O_D, O_D, perElemOpNonGqaSplitKStore, 0u, iq2, N); + coopMatPerElementNV(L, L, perElemOpNonGqaSplitKStoreCol0, 0u, iq2, N); + coopMatPerElementNV(M, M, perElemOpNonGqaSplitKStoreCol0, p.ne1, iq2, N); + } return; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp index 8c92c1adcd..0e41770806 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp @@ -33,6 +33,61 @@ layout (push_constant) uniform parameter { shared float minsh[NUM_SUBGROUPS]; shared float maxsh[NUM_SUBGROUPS]; +float FLT_MAX_OVER_2 = uintBitsToFloat(0x7EFFFFFF); + +void loadvec4(inout uint result, const uint i0, const uint i1, const uint i2, const uint i3, const bool need_bounds_check) { + const uint tid = gl_LocalInvocationIndex; + + [[unroll]] for (uint block_x = 0; block_x < 16; ++block_x) { + float min_v = FLT_MAX_OVER_2; + float max_v = -FLT_MAX_OVER_2; + [[unroll]] for (uint i = 0; i < Br * Bc / 4; i += BLOCK_SIZE) { + uint j0 = (i + tid) % (Bc / 4); + uint j1 = (i + tid) / (Bc / 4); + + j0 *= 4; + j0 += (i0 * 16 + block_x) * Bc; + j1 += i1 * Br; + + if (!need_bounds_check || j0 + 3 < nem0) { + vec4 f = vec4(data_av4[(j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3) / 4]); + [[unroll]] for (int c = 0; c < 4; ++c) { + min_v = min(min_v, f[c]); + max_v = max(max_v, f[c]); + } + } else { + [[unroll]] for (int c = 0; c < 4; ++c) { + if (j0 + c < nem0) { + float f = float(data_a[j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3]); + min_v = min(min_v, f); + max_v = max(max_v, f); + } + } + } + } + min_v = subgroupMin(min_v); + max_v = subgroupMax(max_v); + if (gl_SubgroupInvocationID == 0) { + minsh[gl_SubgroupID] = min_v; + maxsh[gl_SubgroupID] = max_v; + } + barrier(); + if (tid == 0) { + [[unroll]] for (uint i = 0; i < NUM_SUBGROUPS; ++i) { + min_v = min(min_v, minsh[i]); + max_v = max(max_v, maxsh[i]); + } + if (max_v <= -FLT_MAX_OVER_2) { + result |= 1 << (2*block_x); + } + if (min_v == 0.0f && max_v == 0.0f) { + result |= 2 << (2*block_x); + } + } + barrier(); + } +} + // For each Br x Bc block of the mask (input) buffer, read all values and check // if it's all -inf or all zero. Write out a two-bit code indicating which it is // (or zero for neither). Each workgroup processes 16 tiles and writes out a @@ -48,50 +103,15 @@ void main() { const uint i2 = gl_WorkGroupID.z % nem2; const uint i3 = gl_WorkGroupID.z / nem2; - float FLT_MAX_OVER_2 = uintBitsToFloat(0x7EFFFFFF); - uint result = 0; // Fast path for fully in-bounds blocks where we can do f16vec4 loads if ((nem0 % Bc) == 0 && (nem1 % Br) == 0 && ((Br * Bc) % (BLOCK_SIZE * 4)) == 0) { - [[unroll]] for (uint block_x = 0; block_x < 16; ++block_x) { - float min_v = FLT_MAX_OVER_2; - float max_v = -FLT_MAX_OVER_2; - [[unroll]] for (uint i = 0; i < Br * Bc / 4; i += BLOCK_SIZE) { - uint j0 = (i + tid) % (Bc / 4); - uint j1 = (i + tid) / (Bc / 4); - - j0 *= 4; - j0 += (i0 * 16 + block_x) * Bc; - j1 += i1 * Br; - - vec4 f = vec4(data_av4[(j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3) / 4]); - [[unroll]] for (int c = 0; c < 4; ++c) { - min_v = min(min_v, f[c]); - max_v = max(max_v, f[c]); - } - } - min_v = subgroupMin(min_v); - max_v = subgroupMax(max_v); - if (gl_SubgroupInvocationID == 0) { - minsh[gl_SubgroupID] = min_v; - maxsh[gl_SubgroupID] = max_v; - } - barrier(); - if (tid == 0) { - [[unroll]] for (uint i = 0; i < NUM_SUBGROUPS; ++i) { - min_v = min(min_v, minsh[i]); - max_v = max(max_v, maxsh[i]); - } - if (max_v <= -FLT_MAX_OVER_2) { - result |= 1 << (2*block_x); - } - if (min_v == 0.0f && max_v == 0.0f) { - result |= 2 << (2*block_x); - } - } - barrier(); + if ((i0 + 1) * 16 * Bc <= nem0) { + loadvec4(result, i0, i1, i2, i3, false); + } else { + loadvec4(result, i0, i1, i2, i3, true); } } else { [[unroll]] for (uint block_x = 0; block_x < 16; ++block_x) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp b/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp new file mode 100644 index 0000000000..f008859b99 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp @@ -0,0 +1,128 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require + +layout(constant_id = 0) const uint S_V = 128; +layout(constant_id = 1) const uint KDA = 0; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout(push_constant) uniform Parameters { + uint H; + uint n_tokens; + uint n_seqs; + uint s_off; + uint sq1, sq2, sq3; + uint sv1, sv2, sv3; + uint sb1, sb2, sb3; + uint neq1, rq3; + float scale; +}; + +layout(binding = 0) readonly buffer QBuf { FLOAT_TYPE data_q[]; }; +layout(binding = 1) readonly buffer KBuf { FLOAT_TYPE data_k[]; }; +layout(binding = 2) readonly buffer VBuf { FLOAT_TYPE data_v[]; }; +layout(binding = 3) readonly buffer GBuf { FLOAT_TYPE data_g[]; }; +layout(binding = 4) readonly buffer BetaBuf { FLOAT_TYPE data_beta[]; }; +layout(binding = 5) readonly buffer StateBuf { FLOAT_TYPE data_state[]; }; +layout(binding = 6) buffer DstBuf { FLOAT_TYPE data_dst[]; }; + +shared FLOAT_TYPE s_k[S_V]; +shared FLOAT_TYPE s_q[S_V]; +shared FLOAT_TYPE s_g[S_V]; // KDA only: cached exp(g[i]) + +void main() { + const uint head_id = gl_WorkGroupID.x; + const uint seq_id = gl_WorkGroupID.y; + const uint col = gl_LocalInvocationID.x; + + const uint iq1 = head_id % neq1; + const uint iq3 = seq_id / rq3; + + const uint state_size = S_V * S_V; + const uint state_base = (seq_id * H + head_id) * state_size; + + FLOAT_TYPE state[S_V]; + [[unroll]] for (uint i = 0; i < S_V; i++) { + state[i] = FLOAT_TYPE(data_state[state_base + col * S_V + i]); + } + + uint attn_off = (seq_id * n_tokens * H + head_id) * S_V; + + for (uint t = 0; t < n_tokens; t++) { + const uint q_off = iq3 * sq3 + t * sq2 + iq1 * sq1; + const uint k_off = q_off; + const uint v_off = seq_id * sv3 + t * sv2 + head_id * sv1; + + s_q[col] = FLOAT_TYPE(data_q[q_off + col]); + s_k[col] = FLOAT_TYPE(data_k[k_off + col]); + + const uint gb_off = seq_id * sb3 + t * sb2 + head_id * sb1; + + if (KDA != 0) { + const uint g_base = gb_off * S_V; + s_g[col] = exp(FLOAT_TYPE(data_g[g_base + col])); + } + + barrier(); + + const FLOAT_TYPE v_val = FLOAT_TYPE(data_v[v_off + col]); + const FLOAT_TYPE beta_val = FLOAT_TYPE(data_beta[gb_off]); + + if (KDA == 0) { + const FLOAT_TYPE g_val = exp(FLOAT_TYPE(data_g[gb_off])); + + FLOAT_TYPE kv_col = 0.0; + [[unroll]] for (uint i = 0; i < S_V; i += 4) { + kv_col += dot( + vec4(state[i], state[i+1], state[i+2], state[i+3]), + vec4(s_k[i], s_k[i+1], s_k[i+2], s_k[i+3]) + ); + } + + FLOAT_TYPE delta_col = (v_val - g_val * kv_col) * beta_val; + + FLOAT_TYPE attn_col = 0.0; + [[unroll]] for (uint i = 0; i < S_V; i += 4) { + vec4 sv = vec4(state[i], state[i+1], state[i+2], state[i+3]); + vec4 kv = vec4(s_k[i], s_k[i+1], s_k[i+2], s_k[i+3]); + sv = g_val * sv + kv * delta_col; + state[i] = sv.x; state[i+1] = sv.y; state[i+2] = sv.z; state[i+3] = sv.w; + + attn_col += dot(sv, vec4(s_q[i], s_q[i+1], s_q[i+2], s_q[i+3])); + } + + data_dst[attn_off + col] = attn_col * scale; + } else { + FLOAT_TYPE kv_col = 0.0; + [[unroll]] for (uint i = 0; i < S_V; i += 4) { + vec4 gv = vec4(s_g[i], s_g[i+1], s_g[i+2], s_g[i+3]); + vec4 sv = vec4(state[i], state[i+1], state[i+2], state[i+3]); + vec4 kv = vec4(s_k[i], s_k[i+1], s_k[i+2], s_k[i+3]); + kv_col += dot(gv * sv, kv); + } + + FLOAT_TYPE delta_col = (v_val - kv_col) * beta_val; + + FLOAT_TYPE attn_col = 0.0; + [[unroll]] for (uint i = 0; i < S_V; i += 4) { + vec4 gv = vec4(s_g[i], s_g[i+1], s_g[i+2], s_g[i+3]); + vec4 sv = vec4(state[i], state[i+1], state[i+2], state[i+3]); + vec4 kv = vec4(s_k[i], s_k[i+1], s_k[i+2], s_k[i+3]); + sv = gv * sv + kv * delta_col; + state[i] = sv.x; state[i+1] = sv.y; state[i+2] = sv.z; state[i+3] = sv.w; + + attn_col += dot(sv, vec4(s_q[i], s_q[i+1], s_q[i+2], s_q[i+3])); + } + + data_dst[attn_off + col] = attn_col * scale; + } + + attn_off += S_V * H; + barrier(); + } + + [[unroll]] for (uint i = 0; i < S_V; i++) { + data_dst[s_off + state_base + col * S_V + i] = state[i]; + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp index 7d0a1de0df..f9af46744d 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp @@ -36,7 +36,7 @@ void main() { barrier(); } - const FLOAT_TYPE scale = inversesqrt(max(sum[0], FLOAT_TYPE(p.param1))); + const FLOAT_TYPE scale = 1.0f / max(sqrt(sum[0]), FLOAT_TYPE(p.param1)); [[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) { data_d[i3*p.nb13 + i2*p.nb12 + i1*p.nb11 + i0] = D_TYPE(scale * FLOAT_TYPE(data_a[i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0])); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp index 79344d3300..23f3bd8d6d 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp @@ -377,6 +377,7 @@ void main() { [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) { coopMatStore(sums[cm_col * cms_per_row + cm_row], coopmat_stage, warp_i * TM * TN, TM, gl_CooperativeMatrixLayoutColumnMajor); + barrier(); [[unroll]] for (uint col = 0; col < TN; col += storestride) { const uint row_i = dc + cm_col * TN + col + store_c; if (row_i >= _ne1) break; @@ -387,6 +388,7 @@ void main() { data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr + cm_row * TM + store_r] = D_TYPE(coopmat_stage[warp_i * TM * TN + (col + store_c) * TM + store_r]); } } + barrier(); } } #else @@ -404,18 +406,22 @@ void main() { // Full coopMat is within bounds, but stride_d is not aligned coopMatStore(sums[cm_col * cms_per_row + cm_row], coopmat_stage, warp_i * TM * TN, TM, gl_CooperativeMatrixLayoutColumnMajor); + controlBarrier(gl_ScopeSubgroup, gl_ScopeSubgroup, gl_StorageSemanticsShared, gl_SemanticsAcquireRelease); [[unroll]] for (uint col = 0; col < TN; col += storestride) { data_d[offsets + (dc + cm_col * TN + col + store_c) * p.stride_d + dr + cm_row * TM + store_r] = D_TYPE(coopmat_stage[warp_i * TM * TN + (col + store_c) * TM + store_r]); } + controlBarrier(gl_ScopeSubgroup, gl_ScopeSubgroup, gl_StorageSemanticsShared, gl_SemanticsAcquireRelease); } else if (dr + cm_row * TM < p.M && dc + cm_col * TN < p.N) { // Partial coopMat is within bounds coopMatStore(sums[cm_col * cms_per_row + cm_row], coopmat_stage, warp_i * TM * TN, TM, gl_CooperativeMatrixLayoutColumnMajor); + controlBarrier(gl_ScopeSubgroup, gl_ScopeSubgroup, gl_StorageSemanticsShared, gl_SemanticsAcquireRelease); [[unroll]] for (uint col = 0; col < TN; col += storestride) { if (dr + cm_row * TM + store_r < p.M && dc + cm_col * TN + col + store_c < p.N) { data_d[offsets + (dc + cm_col * TN + col + store_c) * p.stride_d + dr + cm_row * TM + store_r] = D_TYPE(coopmat_stage[warp_i * TM * TN + (col + store_c) * TM + store_r]); } } + controlBarrier(gl_ScopeSubgroup, gl_ScopeSubgroup, gl_StorageSemanticsShared, gl_SemanticsAcquireRelease); } } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp index 717d124e01..497a18ff8a 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp @@ -167,7 +167,9 @@ void load_row_ids(uint expert_idx, bool nei0_is_pow2, uint ic) { uint id = ids[iter++]; uvec4 ballot = subgroupBallot(in_range && id == expert_idx); - ballots_sh[gl_SubgroupID] = ballot; + if (gl_SubgroupInvocationID == 0) { + ballots_sh[gl_SubgroupID] = ballot; + } barrier(); uint subgroup_base = 0; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_id_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_id_funcs.glsl index 743004ff8a..26c5c12a49 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_id_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_id_funcs.glsl @@ -43,7 +43,9 @@ void load_row_ids(uint expert_idx, bool nei0_is_pow2, uint ic) { uint id = ids[iter++]; uvec4 ballot = subgroupBallot(in_range && id == expert_idx); - ballots_sh[gl_SubgroupID] = ballot; + if (gl_SubgroupInvocationID == 0) { + ballots_sh[gl_SubgroupID] = ballot; + } barrier(); uint subgroup_base = 0; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/sgn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sgn.comp new file mode 100644 index 0000000000..a9c147bf9a --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/sgn.comp @@ -0,0 +1,21 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + data_d[i] = D_TYPE(sign(float(data_a[i]))); +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp b/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp index d62696bcfa..6802b1fc95 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp @@ -5,8 +5,9 @@ #include "types.glsl" layout(constant_id = 0) const uint BLOCK_SIZE = 32; +layout(constant_id = 1) const uint TOKENS_PER_WG = 16; -layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; +layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z = 1) in; layout(binding = 0) readonly buffer Src0 { float src0[]; }; layout(binding = 1) readonly buffer Src1 { float src1[]; }; @@ -20,25 +21,30 @@ layout(push_constant) uniform PushConstants { }; void main() { - const uint global_thread_id = gl_GlobalInvocationID.x; - const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_GlobalInvocationID.x; + const uint i2 = gl_WorkGroupID.y * TOKENS_PER_WG + gl_LocalInvocationID.y; const uint i3 = gl_WorkGroupID.z; - if (global_thread_id >= nr || i2 >= n_t || i3 >= n_s) { + if (i1 >= nr || i2 >= n_t || i3 >= n_s) { return; } - const uint i1 = global_thread_id; const uint src0_base = i3 * (nb02 / 4) + i2 + i1 * (nb01 / 4); const uint src1_base = i1 * (nb11 / 4); - const uint dst_idx = i3 * (dst_nb2 / 4) + i2 * (dst_nb1 / 4) + i1; float sum = 0.0; - [[unroll]] for (uint i0 = 0; i0 < nc; i0++) { - const uint src0_idx = src0_base + i0; - const uint src1_idx = src1_base + i0; - sum += src0[src0_idx] * src1[src1_idx]; + + if (nc == 4) { + sum = dot( + vec4(src0[src0_base], src0[src0_base + 1], src0[src0_base + 2], src0[src0_base + 3]), + vec4(src1[src1_base], src1[src1_base + 1], src1[src1_base + 2], src1[src1_base + 3]) + ); + } else { + [[unroll]] for (uint i0 = 0; i0 < nc; i0++) { + sum += src0[src0_base + i0] * src1[src1_base + i0]; + } } + const uint dst_idx = i3 * (dst_nb2 / 4) + i2 * (dst_nb1 / 4) + i1; dst[dst_idx] = sum; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 42ebc21e2a..4b00ba3deb 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -595,8 +595,6 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c } void process_shaders() { - std::map base_dict = {{"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}}; - // matmul for (const MatMulIdType& matmul_id_type : {MatMulIdType::NONE, MatMulIdType::DEFAULT, MatMulIdType::SUBGROUP}) { // No coopmats @@ -622,49 +620,63 @@ void process_shaders() { } } - // flash attention - for (const auto& f16acc : {false, true}) { - std::map fa_base_dict = base_dict; - fa_base_dict["ACC_TYPE"] = f16acc ? "float16_t" : "float"; - fa_base_dict["ACC_TYPEV4"] = f16acc ? "f16vec4" : "vec4"; - if (f16acc) { - fa_base_dict["ACC_TYPE_MAX"] = "float16_t(65504.0)"; + for (const bool& fp16 : {false, true}) { + std::map base_dict; + if (fp16) { + base_dict = {{"FLOAT_TYPE", "float16_t"}, {"FLOAT_TYPEV4", "f16vec4"}, {"FLOAT16", "1"}, {"FLOAT_TYPE_MAX", "float16_t(65504.0)"}}; + } else { + base_dict = {{"FLOAT_TYPE", "float"}, {"FLOAT_TYPEV4", "vec4"}}; } - for (const auto& tname : type_names) { - if (tname == "bf16") continue; - -#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) - if (tname == "f16") { - string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp", - merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}}), true, false, true, f16acc); - } else { - std::string data_a_key = "DATA_A_" + to_uppercase(tname); - string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp", - merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"DEQUANTFUNC", "dequantFunc"+to_uppercase(tname) }, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, true, f16acc); + // flash attention + for (const bool& f16acc : {false, true}) { + std::map fa_base_dict = base_dict; + fa_base_dict["ACC_TYPE"] = fp16 && f16acc ? "float16_t" : "float"; + fa_base_dict["ACC_TYPEV4"] = fp16 && f16acc ? "f16vec4" : "vec4"; + if (fp16 && f16acc) { + fa_base_dict["ACC_TYPE_MAX"] = "float16_t(65504.0)"; } + + for (const auto& tname : type_names) { + if (tname == "bf16") continue; + + if (fp16) { +#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + if (tname == "f16") { + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp", + merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}}), fp16, false, true, f16acc); + } else { + std::string data_a_key = "DATA_A_" + to_uppercase(tname); + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp", + merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"DEQUANTFUNC", "dequantFunc"+to_uppercase(tname) }, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), fp16, false, true, f16acc); + } #endif #if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) - if (tname == "f16") { - string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp", - merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"COOPMAT", "1"}}), true, true, false, f16acc); - } else if (tname == "q4_0" || tname == "q8_0" || tname == "f32") { - std::string data_a_key = "DATA_A_" + to_uppercase(tname); - string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp", - merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname)}, {"COOPMAT", "1"}}), true, true, false, f16acc); - } + if (tname == "f16") { + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp", + merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"COOPMAT", "1"}}), fp16, true, false, f16acc); + } else if (tname == "q4_0" || tname == "q8_0" || tname == "f32") { + std::string data_a_key = "DATA_A_" + to_uppercase(tname); + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp", + merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname)}, {"COOPMAT", "1"}}), fp16, true, false, f16acc); + } #endif - if (tname == "f16") { - string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp", - merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}}), true, false, false, f16acc); - } else if (tname == "q4_0" || tname == "q8_0" || tname == "f32") { - std::string data_a_key = "DATA_A_" + to_uppercase(tname); - string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp", - merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, false, f16acc); + } + + if (tname == "f16") { + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp", + merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}}), fp16, false, false, f16acc); + } else if (tname == "q4_0" || tname == "q8_0" || tname == "f32") { + std::string data_a_key = "DATA_A_" + to_uppercase(tname); + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp", + merge_maps(fa_base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), fp16, false, false, f16acc); + } } } } + std::map base_dict = {{"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}}; + for (const auto& tname : type_names) { // mul mat vec std::string data_a_key = "DATA_A_" + to_uppercase(tname); @@ -855,8 +867,12 @@ void process_shaders() { string_to_spv("hardswish_f32", "hardswish.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("abs_f16", "abs.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("abs_f32", "abs.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("elu_f16", "elu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("elu_f32", "elu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("xielu_f16", "xielu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("xielu_f32", "xielu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("sgn_f16", "sgn.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("sgn_f32", "sgn.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("tri_f16", "tri.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("tri_f32", "tri.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); @@ -971,6 +987,8 @@ void process_shaders() { string_to_spv("rwkv_wkv7_f32", "wkv7.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); + string_to_spv("gated_delta_net_f32", "gated_delta_net.comp", merge_maps(base_dict, {{"FLOAT_TYPE", "float"}})); + string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); string_to_spv("opt_step_sgd_f32", "opt_step_sgd.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp index 0d5a818dac..3d7e59fddf 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp @@ -42,11 +42,20 @@ #define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N 2 // Matrix-vector multiplication parameters -#define WEBGPU_MUL_MAT_VEC_WG_SIZE 256 +#define WEBGPU_MUL_MAT_VEC_WG_SIZE 256 + // Must be multiple of 4 to work with vectorized paths, and must divide // mul_mat_vec wg size -#define WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG 64 -#define WEBGPU_MUL_MAT_VEC_TILE_K 256 +#define WEBGPU_MUL_MAT_VEC_FLOAT_OUTPUTS_PER_WG 64 +#define WEBGPU_MUL_MAT_VEC_FLOAT_TILE_K 256 + +#define WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG 64 +#define WEBGPU_MUL_MAT_VEC_LEGACY_Q_TILE_K 256 + +// Requires 32 threads per output (wg_size/outputs_per_wg == 32) +#define WEBGPU_MUL_MAT_VEC_K_Q_OUTPUTS_PER_WG 8 +// Requires at least two (and multiple of 2) k-quant blocks per tile +#define WEBGPU_MUL_MAT_VEC_K_Q_TILE_K 512 // default size for legacy matrix multiplication #define WEBGPU_MUL_MAT_WG_SIZE 256 @@ -68,6 +77,7 @@ struct ggml_webgpu_shader_lib_context { size_t wg_mem_limit_bytes = 0; bool inplace = false; bool overlap = false; + bool src_overlap = false; bool supports_subgroup_matrix = false; uint32_t sg_mat_m = 0; uint32_t sg_mat_n = 0; @@ -172,6 +182,38 @@ struct ggml_webgpu_scale_pipeline_key_hash { } }; +/** Concat **/ + +struct ggml_webgpu_concat_pipeline_key { + int type; + + bool operator==(const ggml_webgpu_concat_pipeline_key & other) const { return type == other.type; } +}; + +struct ggml_webgpu_concat_pipeline_key_hash { + size_t operator()(const ggml_webgpu_concat_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + return seed; + } +}; + +/** Repeat **/ + +struct ggml_webgpu_repeat_pipeline_key { + int type; + + bool operator==(const ggml_webgpu_repeat_pipeline_key & other) const { return type == other.type; } +}; + +struct ggml_webgpu_repeat_pipeline_key_hash { + size_t operator()(const ggml_webgpu_repeat_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + return seed; + } +}; + /** Binary **/ struct ggml_webgpu_binary_pipeline_key { @@ -179,9 +221,11 @@ struct ggml_webgpu_binary_pipeline_key { int op; bool inplace; bool overlap; + bool src_overlap; bool operator==(const ggml_webgpu_binary_pipeline_key & other) const { - return type == other.type && op == other.op && inplace == other.inplace && overlap == other.overlap; + return type == other.type && op == other.op && inplace == other.inplace && overlap == other.overlap && + src_overlap == other.src_overlap; } }; @@ -192,6 +236,7 @@ struct ggml_webgpu_binary_pipeline_key_hash { ggml_webgpu_hash_combine(seed, key.op); ggml_webgpu_hash_combine(seed, key.inplace); ggml_webgpu_hash_combine(seed, key.overlap); + ggml_webgpu_hash_combine(seed, key.src_overlap); return seed; } }; @@ -400,6 +445,10 @@ class ggml_webgpu_shader_lib { pad_pipelines; // circular/non-circular std::unordered_map binary_pipelines; // type/op/inplace/overlap + std::unordered_map + concat_pipelines; // type + std::unordered_map + repeat_pipelines; // type std::unordered_map flash_attn_pipelines; std::unordered_map defines; std::string variant = "mul_mat_vec"; + // src0 type (matrix row) + switch (context.src0->type) { + case GGML_TYPE_F32: + defines.push_back("SRC0_INNER_TYPE=f32"); + defines.push_back("MUL_ACC_FLOAT"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("SRC0_INNER_TYPE=f16"); + defines.push_back("MUL_ACC_FLOAT"); + variant += "_f16"; + break; + default: + { + // Quantized types: use helpers but accumulate in f16 + const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type); + std::string src0_name = src0_traits->type_name; + std::string type_upper = src0_name; + variant += "_" + src0_name; + std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper); + + defines.push_back("BYTE_HELPERS"); + defines.push_back("MUL_ACC_" + type_upper); + + // For fast path we always dequantize from f16 inside the shader + defines.push_back("SRC0_INNER_TYPE=f16"); + break; + } + } + // src1 type (vector) switch (context.src1->type) { case GGML_TYPE_F32: @@ -742,39 +821,21 @@ class ggml_webgpu_shader_lib { GGML_ABORT("Unsupported src1 type for mul_mat_vec shader"); } - // src0 type (matrix row) - switch (context.src0->type) { - case GGML_TYPE_F32: - defines.push_back("SRC0_INNER_TYPE=f32"); - defines.push_back("MUL_ACC_FLOAT"); - break; - case GGML_TYPE_F16: - defines.push_back("SRC0_INNER_TYPE=f16"); - defines.push_back("MUL_ACC_FLOAT"); - break; - default: - { - // Quantized types: use helpers but accumulate in f16 - const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type); - std::string src0_name = src0_traits->type_name; - std::string type_upper = src0_name; - std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper); - - defines.push_back("BYTE_HELPERS"); - defines.push_back("MUL_ACC_" + type_upper); - - // For fast path we always dequantize from f16 inside the shader - defines.push_back("SRC0_INNER_TYPE=f16"); - break; - } - } - // VEC/SCALAR controls defines.push_back(key.vectorized ? "VEC" : "SCALAR"); uint32_t wg_size = WEBGPU_MUL_MAT_VEC_WG_SIZE; - uint32_t tile_k = WEBGPU_MUL_MAT_VEC_TILE_K; - uint32_t outputs_per_wg = WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG; + uint32_t tile_k = WEBGPU_MUL_MAT_VEC_FLOAT_TILE_K; + uint32_t outputs_per_wg = WEBGPU_MUL_MAT_VEC_FLOAT_OUTPUTS_PER_WG; + + if (key.src0_type >= GGML_TYPE_Q2_K) { + tile_k = WEBGPU_MUL_MAT_VEC_K_Q_TILE_K; + outputs_per_wg = WEBGPU_MUL_MAT_VEC_K_Q_OUTPUTS_PER_WG; + } else if (key.src0_type >= GGML_TYPE_Q4_0) { + tile_k = WEBGPU_MUL_MAT_VEC_LEGACY_Q_TILE_K; + outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG; + } + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); defines.push_back(std::string("TILE_K=") + std::to_string(tile_k)); defines.push_back(std::string("OUTPUTS_PER_WG=") + std::to_string(outputs_per_wg)); @@ -1040,10 +1101,11 @@ class ggml_webgpu_shader_lib { webgpu_pipeline get_binary_pipeline(const ggml_webgpu_shader_lib_context & context) { ggml_webgpu_binary_pipeline_key key = { - .type = context.dst->type, - .op = context.dst->op, - .inplace = context.inplace, - .overlap = context.overlap, + .type = context.dst->type, + .op = context.dst->op, + .inplace = context.inplace, + .overlap = context.overlap, + .src_overlap = context.src_overlap, }; auto it = binary_pipelines.find(key); @@ -1076,6 +1138,9 @@ class ggml_webgpu_shader_lib { } else if (key.overlap) { defines.push_back("OVERLAP"); variant += "_overlap"; + } else if (key.src_overlap) { + defines.push_back("SRC_OVERLAP"); + variant += "_src_overlap"; } defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); @@ -1089,6 +1154,84 @@ class ggml_webgpu_shader_lib { return binary_pipelines[key]; } + webgpu_pipeline get_concat_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_concat_pipeline_key key = { + .type = context.dst->type, + }; + + auto it = concat_pipelines.find(key); + if (it != concat_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "concat"; + + switch (key.type) { + case GGML_TYPE_F32: + defines.push_back("TYPE_F32"); + variant += "_f32"; + break; + case GGML_TYPE_I32: + defines.push_back("TYPE_I32"); + variant += "_i32"; + break; + default: + GGML_ABORT("Unsupported type for concat shader"); + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_concat, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + concat_pipelines[key] = pipeline; + return concat_pipelines[key]; + } + + webgpu_pipeline get_repeat_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_repeat_pipeline_key key = { + .type = context.dst->type, + }; + + auto it = repeat_pipelines.find(key); + if (it != repeat_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "repeat"; + + switch (key.type) { + case GGML_TYPE_F32: + defines.push_back("TYPE_F32"); + variant += "_f32"; + break; + case GGML_TYPE_I32: + defines.push_back("TYPE_I32"); + variant += "_i32"; + break; + case GGML_TYPE_I16: + defines.push_back("TYPE_I16"); + variant += "_i16"; + break; + default: + GGML_ABORT("Unsupported type for repeat shader"); + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_repeat, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + repeat_pipelines[key] = pipeline; + return repeat_pipelines[key]; + } + webgpu_pipeline get_flash_attn_pipeline(const ggml_webgpu_shader_lib_context & context) { const bool has_mask = context.src3 != nullptr; const bool has_sinks = context.src4 != nullptr; diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 1c00d3cb2b..128b7dc3de 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -8,7 +8,6 @@ #include "ggml-backend-impl.h" #include "ggml-impl.h" #include "ggml-webgpu-shader-lib.hpp" -#include "pre_wgsl.hpp" #ifdef __EMSCRIPTEN__ # include @@ -20,17 +19,30 @@ #include #include #include -#include +#ifdef GGML_WEBGPU_GPU_PROFILE +# include +#endif +#if defined(GGML_WEBGPU_DEBUG) || defined(GGML_WEBGPU_CPU_PROFILE) || defined(GGML_WEBGPU_GPU_PROFILE) +# include +#endif #include #include #include #include #include +#include #include #define ROUNDUP_POW2(x, pow2) (((x) + ((pow2) - 1)) & ~((pow2) - 1)) #define CEIL_DIV(M, N) (((M) + (N) - 1) / (N)) +// Return a rectangular grid of workgroups with minimal over-provisioned workgroups. +// Assumes that the total number of workgroups does not exceed max_per_dim^2. +static inline void compute_2d_workgroups(uint32_t total_wg, uint32_t max_per_dim, uint32_t & wg_x, uint32_t & wg_y) { + wg_y = std::max(1u, CEIL_DIV(total_wg, max_per_dim)); + wg_x = CEIL_DIV(total_wg, wg_y); +} + #ifdef GGML_WEBGPU_DEBUG # define WEBGPU_LOG_DEBUG(msg) std::cout << msg << std::endl # define WEBGPU_DEBUG_BUF_ELEMS 512 @@ -63,22 +75,21 @@ #endif // GGML_WEBGPU_CPU_PROFILE #ifdef GGML_WEBGPU_GPU_PROFILE -# define WEBGPU_NUM_TIMESTAMP_QUERY_BUFS 24 +# define WEBGPU_NUM_TIMESTAMP_QUERY_BUFS 32 # define WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES 16 // e.g. enough for two timestamps #endif /* Constants */ -#define WEBGPU_NUM_PARAM_BUFS 16u -#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 8u +#define WEBGPU_NUM_PARAM_BUFS 96u +#define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 32u #define WEBGPU_WAIT_ANY_TIMEOUT_MS 0 // Maximum number of in-flight submissions per-thread, to avoid exhausting the // parameter buffer pool -#define WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD WEBGPU_NUM_PARAM_BUFS / WEBGPU_COMMAND_SUBMIT_BATCH_SIZE +#define WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD (WEBGPU_NUM_PARAM_BUFS / WEBGPU_COMMAND_SUBMIT_BATCH_SIZE) #define WEBGPU_PARAMS_BUF_SIZE_BYTES 128 // enough for 32 parameters -#define WEBGPU_NUM_SET_ROWS_ERROR_BUFS 16 #define WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES 4 -#define WEBGPU_STORAGE_BUF_BINDING_MULT 4 // a storage buffer binding size must be a multiple of 4 +#define WEBGPU_STORAGE_BUF_BINDING_MULT 4 // a storage buffer binding size must be a multiple of 4 // For operations which process a row in parallel, this seems like a reasonable // default @@ -111,19 +122,9 @@ static void ggml_webgpu_create_buffer(wgpu::Device & device, wgpu::BufferUsage usage, const char * label); -struct webgpu_pool_bufs { - wgpu::Buffer host_buf; - wgpu::Buffer dev_buf; -}; - -// The futures to wait on for a single queue submission -struct webgpu_submission_futures { - std::vector futures; -}; - // Holds a pool of parameter buffers for WebGPU operations struct webgpu_buf_pool { - std::vector free; + std::vector free; // The pool must be synchronized because // 1. The memset pool is shared globally by every ggml buffer, @@ -133,30 +134,58 @@ struct webgpu_buf_pool { // which can run on a different thread than the calling thread. std::mutex mutex; std::condition_variable cv; + size_t cur_pool_size; + size_t max_pool_size; + wgpu::Device device; + wgpu::BufferUsage dev_buf_usage; + size_t buf_size; + bool should_grow; void init(wgpu::Device device, int num_bufs, size_t buf_size, wgpu::BufferUsage dev_buf_usage, - wgpu::BufferUsage host_buf_usage) { + bool should_grow = false, + size_t max_pool_size = WEBGPU_NUM_PARAM_BUFS * 2) { + this->max_pool_size = max_pool_size; + this->cur_pool_size = num_bufs; + this->device = device; + this->dev_buf_usage = dev_buf_usage; + this->buf_size = buf_size; + this->should_grow = should_grow; for (int i = 0; i < num_bufs; i++) { - wgpu::Buffer host_buf; wgpu::Buffer dev_buf; - ggml_webgpu_create_buffer(device, host_buf, buf_size, host_buf_usage, "ggml_webgpu_host_pool_buf"); ggml_webgpu_create_buffer(device, dev_buf, buf_size, dev_buf_usage, "ggml_webgpu_dev_pool_buf"); - free.push_back({ host_buf, dev_buf }); + free.push_back(dev_buf); } } - webgpu_pool_bufs alloc_bufs() { + wgpu::Buffer alloc_bufs() { std::unique_lock lock(mutex); + if (!free.empty()) { + wgpu::Buffer buf = free.back(); + free.pop_back(); + return buf; + } + + // Try growing the pool if no free buffers + if (free.empty() && cur_pool_size < max_pool_size && should_grow) { + cur_pool_size++; + wgpu::Buffer dev_buf; + ggml_webgpu_create_buffer(device, dev_buf, buf_size, dev_buf_usage, "ggml_webgpu_dev_pool_buf"); + + if (!dev_buf) { + GGML_ABORT("webgpu_buf_pool: failed to allocate buffers"); + } + return dev_buf; + } cv.wait(lock, [this] { return !free.empty(); }); - webgpu_pool_bufs bufs = free.back(); + wgpu::Buffer buf = free.back(); free.pop_back(); - return bufs; + return buf; } - void free_bufs(std::vector bufs) { + void free_bufs(std::vector bufs) { std::lock_guard lock(mutex); free.insert(free.end(), bufs.begin(), bufs.end()); cv.notify_all(); @@ -164,12 +193,9 @@ struct webgpu_buf_pool { void cleanup() { std::lock_guard lock(mutex); - for (auto & bufs : free) { - if (bufs.host_buf) { - bufs.host_buf.Destroy(); - } - if (bufs.dev_buf) { - bufs.dev_buf.Destroy(); + for (auto & buf : free) { + if (buf) { + buf.Destroy(); } } free.clear(); @@ -243,9 +269,9 @@ struct webgpu_gpu_profile_buf_pool { #endif struct webgpu_command { - wgpu::CommandBuffer commands; - std::vector params_bufs; - std::optional set_rows_error_bufs; + uint32_t num_kernels; + wgpu::CommandBuffer commands; + std::vector params_bufs; #ifdef GGML_WEBGPU_GPU_PROFILE webgpu_gpu_profile_bufs timestamp_query_bufs; std::string pipeline_name; @@ -280,7 +306,6 @@ struct webgpu_global_context_struct { webgpu_buf_pool memset_buf_pool; std::map memset_pipelines; // variant or type index - std::atomic_uint inflight_threads = 0; #ifdef GGML_WEBGPU_CPU_PROFILE // Profiling: labeled CPU time in ms (total) @@ -321,6 +346,13 @@ struct webgpu_global_context_struct { typedef std::shared_ptr webgpu_global_context; +struct webgpu_submission { + wgpu::FutureWaitInfo submit_done; +#ifdef GGML_WEBGPU_GPU_PROFILE + std::vector profile_futures; +#endif +}; + // All the base objects needed to run operations on a WebGPU device struct webgpu_context_struct { // Points to global instances owned by ggml_backend_webgpu_reg_context @@ -329,7 +361,8 @@ struct webgpu_context_struct { std::unique_ptr shader_lib; webgpu_buf_pool param_buf_pool; - webgpu_buf_pool set_rows_error_buf_pool; + wgpu::Buffer set_rows_dev_error_buf; + wgpu::Buffer set_rows_host_error_buf; std::map> cpy_pipelines; // src_type, dst_type @@ -422,36 +455,104 @@ static void ggml_webgpu_create_buffer(wgpu::Device & device, /** WebGPU Actions */ -// Wait for the queue to finish processing all submitted work -static void ggml_backend_webgpu_wait(webgpu_global_context & ctx, - std::vector & futures, - bool block = true) { - // If we have too many in-flight submissions, wait on the oldest one first. If - // there are many threads, inflight_max may be 0, meaning that we must wait on - // all futures. - uint64_t timeout_ms = block ? UINT64_MAX : 0; - uint32_t inflight_threads = ctx->inflight_threads; - uint32_t inflight_max = WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD / std::max(inflight_threads, 1u); - while (futures.size() >= inflight_max && futures.size() > 0) { - ctx->instance.WaitAny(futures[0].futures.size(), futures[0].futures.data(), UINT64_MAX); - futures.erase(futures.begin()); +static bool ggml_backend_webgpu_handle_wait_status(wgpu::WaitStatus status, bool allow_timeout = false) { + switch (status) { + case wgpu::WaitStatus::Success: + return true; + case wgpu::WaitStatus::TimedOut: + if (allow_timeout) { + return false; + } + GGML_LOG_ERROR("ggml_webgpu: WaitAny timed out unexpectedly\n"); + return false; + case wgpu::WaitStatus::Error: + GGML_LOG_ERROR("ggml_webgpu: WaitAny returned an error\n"); + return false; + default: + GGML_LOG_ERROR("ggml_webgpu: WaitAny returned an unknown status\n"); + return false; } - size_t i = 0; - while (i < futures.size()) { - auto waitStatus = ctx->instance.WaitAny(futures[i].futures.size(), futures[i].futures.data(), timeout_ms); - switch (waitStatus) { - case wgpu::WaitStatus::Success: - futures.erase(futures.begin() + i); - break; - case wgpu::WaitStatus::TimedOut: - i++; - break; - case wgpu::WaitStatus::Error: - GGML_LOG_ERROR("ggml_webgpu: WaitAny returned an error\n"); - break; - default: - GGML_LOG_ERROR("ggml_webgpu: WaitAny returned an unknown status\n"); - break; +} + +#ifdef GGML_WEBGPU_GPU_PROFILE +static void ggml_backend_webgpu_erase_completed_futures(std::vector & futures) { + futures.erase(std::remove_if(futures.begin(), futures.end(), + [](const wgpu::FutureWaitInfo & info) { return info.completed; }), + futures.end()); +} + +static void ggml_backend_webgpu_wait_profile_futures(webgpu_global_context & ctx, + std::vector & futures, + bool block) { + if (futures.empty()) { + return; + } + + uint64_t timeout_ms = block ? UINT64_MAX : 0; + if (block) { + while (!futures.empty()) { + auto waitStatus = ctx->instance.WaitAny(futures.size(), futures.data(), timeout_ms); + if (ggml_backend_webgpu_handle_wait_status(waitStatus)) { + ggml_backend_webgpu_erase_completed_futures(futures); + } + } + } else { + auto waitStatus = ctx->instance.WaitAny(futures.size(), futures.data(), timeout_ms); + if (ggml_backend_webgpu_handle_wait_status(waitStatus, true)) { + ggml_backend_webgpu_erase_completed_futures(futures); + } + } +} +#endif + +// Wait for the queue to finish processing all submitted work +static void ggml_backend_webgpu_wait(webgpu_global_context & ctx, + std::vector & subs, + bool block = true) { + // If we have too many in-flight submissions, wait on the oldest one first. + if (subs.empty()) { + return; + } + while (subs.size() >= WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD) { + auto waitStatus = ctx->instance.WaitAny(1, &subs[0].submit_done, UINT64_MAX); + if (ggml_backend_webgpu_handle_wait_status(waitStatus)) { +#ifdef GGML_WEBGPU_GPU_PROFILE + ggml_backend_webgpu_wait_profile_futures(ctx, subs[0].profile_futures, true); +#endif + subs.erase(subs.begin()); + } + } + + if (subs.empty()) { + return; + } + + if (block) { + for (auto & sub : subs) { + while (!sub.submit_done.completed) { + auto waitStatus = ctx->instance.WaitAny(1, &sub.submit_done, UINT64_MAX); + ggml_backend_webgpu_handle_wait_status(waitStatus); + } +#ifdef GGML_WEBGPU_GPU_PROFILE + ggml_backend_webgpu_wait_profile_futures(ctx, sub.profile_futures, true); +#endif + } + subs.clear(); + } else { + // Poll each submit future once and remove completed submissions. + for (auto sub = subs.begin(); sub != subs.end();) { + auto waitStatus = ctx->instance.WaitAny(1, &sub->submit_done, 0); + ggml_backend_webgpu_handle_wait_status(waitStatus, true); +#ifdef GGML_WEBGPU_GPU_PROFILE + ggml_backend_webgpu_wait_profile_futures(ctx, sub->profile_futures, false); + if (sub->submit_done.completed && sub->profile_futures.empty()) { +#else + if (sub->submit_done.completed) { +#endif + sub = subs.erase(sub); + } else { + ++sub; + } } } } @@ -487,13 +588,12 @@ static void ggml_backend_webgpu_debug(webgpu_global_context & ctx) { } #endif -static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_global_context ctx, - std::vector commands, - webgpu_buf_pool & param_buf_pool, - webgpu_buf_pool * set_rows_error_buf_pool = nullptr) { +static webgpu_submission ggml_backend_webgpu_submit(webgpu_global_context & ctx, + std::vector & commands, + webgpu_buf_pool & param_buf_pool) { std::vector command_buffers; - std::vector params_bufs; - std::vector set_rows_error_bufs; + std::vector params_bufs; + webgpu_submission submission; #ifdef GGML_WEBGPU_GPU_PROFILE std::vector> pipeline_name_and_ts_bufs; #endif @@ -501,14 +601,9 @@ static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_global_contex for (const auto & command : commands) { command_buffers.push_back(command.commands); params_bufs.insert(params_bufs.end(), command.params_bufs.begin(), command.params_bufs.end()); - if (command.set_rows_error_bufs) { - set_rows_error_bufs.push_back(command.set_rows_error_bufs.value()); - } } ctx->queue.Submit(command_buffers.size(), command_buffers.data()); - std::vector futures; - wgpu::Future p_f = ctx->queue.OnSubmittedWorkDone( wgpu::CallbackMode::AllowSpontaneous, [¶m_buf_pool, params_bufs](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) { @@ -518,27 +613,7 @@ static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_global_contex // Free the staged buffers param_buf_pool.free_bufs(params_bufs); }); - futures.push_back({ p_f }); - - for (const auto & bufs : set_rows_error_bufs) { - wgpu::Future f = bufs.host_buf.MapAsync( - wgpu::MapMode::Read, 0, bufs.host_buf.GetSize(), wgpu::CallbackMode::AllowSpontaneous, - [set_rows_error_buf_pool, bufs](wgpu::MapAsyncStatus status, wgpu::StringView message) { - if (status != wgpu::MapAsyncStatus::Success) { - GGML_LOG_ERROR("ggml_webgpu: Failed to map error buffer: %s\n", std::string(message).c_str()); - } else { - const uint32_t * error_data = (const uint32_t *) bufs.host_buf.GetConstMappedRange(); - if (*error_data) { - GGML_ABORT("ggml_webgpu: SET_ROWS index > 2^32, unsupported."); - } - // We can't unmap in here due to WebGPU reentrancy limitations. - if (set_rows_error_buf_pool) { - set_rows_error_buf_pool->free_bufs({ bufs }); - } - } - }); - futures.push_back({ f }); - } + submission.submit_done = { p_f }; #ifdef GGML_WEBGPU_GPU_PROFILE for (const auto & command : commands) { @@ -555,14 +630,14 @@ static webgpu_submission_futures ggml_backend_webgpu_submit(webgpu_global_contex // WebGPU timestamps are in ns; convert to ms double elapsed_ms = double(ts_data[1] - ts_data[0]) * 1e-6; ctx->shader_gpu_time_ms[label] += elapsed_ms; - // We can't unmap in here due to WebGPU reentrancy limitations. - ctx->timestamp_query_buf_pool.free_bufs({ ts_bufs }); } + // We can't unmap in here due to WebGPU reentrancy limitations. + ctx->timestamp_query_buf_pool.free_bufs({ ts_bufs }); }); - futures.push_back({ f }); + submission.profile_futures.push_back({ f }); } #endif - return { futures }; + return submission; } static webgpu_command ggml_backend_webgpu_build_multi( @@ -571,32 +646,21 @@ static webgpu_command ggml_backend_webgpu_build_multi( const std::vector & pipelines, const std::vector> & params_list, const std::vector> & bind_group_entries_list, - const std::vector> & workgroups_list, - const std::optional & set_rows_error_bufs = std::nullopt) { + const std::vector> & workgroups_list) { GGML_ASSERT(pipelines.size() == params_list.size()); GGML_ASSERT(pipelines.size() == bind_group_entries_list.size()); GGML_ASSERT(pipelines.size() == workgroups_list.size()); - std::vector params_bufs_list; - std::vector bind_groups; + std::vector params_bufs_list; + std::vector bind_groups; for (size_t i = 0; i < pipelines.size(); i++) { - webgpu_pool_bufs params_bufs = param_buf_pool.alloc_bufs(); - - ggml_backend_webgpu_map_buffer(ctx, params_bufs.host_buf, wgpu::MapMode::Write, 0, - params_bufs.host_buf.GetSize()); - uint32_t * _params = (uint32_t *) params_bufs.host_buf.GetMappedRange(); - for (size_t j = 0; j < params_list[i].size(); j++) { - _params[j] = params_list[i][j]; - } - params_bufs.host_buf.Unmap(); + wgpu::Buffer params_bufs = param_buf_pool.alloc_bufs(); std::vector entries = bind_group_entries_list[i]; uint32_t params_binding_num = entries.size(); - entries.push_back({ .binding = params_binding_num, - .buffer = params_bufs.dev_buf, - .offset = 0, - .size = params_bufs.dev_buf.GetSize() }); + entries.push_back( + { .binding = params_binding_num, .buffer = params_bufs, .offset = 0, .size = params_bufs.GetSize() }); wgpu::BindGroupDescriptor bind_group_desc; bind_group_desc.layout = pipelines[i].pipeline.GetBindGroupLayout(0); @@ -609,15 +673,8 @@ static webgpu_command ggml_backend_webgpu_build_multi( } wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder(); - for (const auto & params_bufs : params_bufs_list) { - encoder.CopyBufferToBuffer(params_bufs.host_buf, 0, params_bufs.dev_buf, 0, params_bufs.dev_buf.GetSize()); - } - - // If there are SET_ROWS operations in this submission, copy their error - // buffers to the host. - if (set_rows_error_bufs) { - encoder.CopyBufferToBuffer(set_rows_error_bufs->dev_buf, 0, set_rows_error_bufs->host_buf, 0, - set_rows_error_bufs->host_buf.GetSize()); + for (size_t i = 0; i < params_bufs_list.size(); i++) { + ctx->queue.WriteBuffer(params_bufs_list[i], 0, params_list[i].data(), params_list[i].size() * sizeof(uint32_t)); } #ifdef GGML_WEBGPU_GPU_PROFILE @@ -650,7 +707,7 @@ static webgpu_command ggml_backend_webgpu_build_multi( webgpu_command result = {}; result.commands = commands; result.params_bufs = params_bufs_list; - result.set_rows_error_bufs = set_rows_error_bufs; + result.num_kernels = pipelines.size(); #ifdef GGML_WEBGPU_GPU_PROFILE result.timestamp_query_bufs = ts_bufs; // TODO: handle multiple pipeline names @@ -665,13 +722,13 @@ static webgpu_command ggml_backend_webgpu_build(webgpu_global_context & std::vector params, std::vector bind_group_entries, uint32_t wg_x, - uint32_t wg_y = 1, - std::optional set_rows_error_bufs = std::nullopt) { + uint32_t wg_y = 1) { return ggml_backend_webgpu_build_multi(ctx, param_buf_pool, { pipeline }, - { params }, { bind_group_entries }, { { wg_x, wg_y } }, set_rows_error_bufs); + { std::move(params) }, { std::move(bind_group_entries) }, + { { wg_x, wg_y } }); } static void ggml_backend_webgpu_buffer_memset(webgpu_global_context & ctx, @@ -688,9 +745,9 @@ static void ggml_backend_webgpu_buffer_memset(webgpu_global_context & ctx, webgpu_command command = ggml_backend_webgpu_build(ctx, ctx->memset_buf_pool, ctx->memset_pipelines[0], params, entries, wg_x); - std::vector futures = { ggml_backend_webgpu_submit(ctx, { command }, - ctx->memset_buf_pool) }; - ggml_backend_webgpu_wait(ctx, futures); + std::vector commands = { command }; + std::vector sub = { ggml_backend_webgpu_submit(ctx, commands, ctx->memset_buf_pool) }; + ggml_backend_webgpu_wait(ctx, sub); } /** End WebGPU Actions */ @@ -737,7 +794,8 @@ static void ggml_backend_webgpu_free(ggml_backend_t backend) { std::cout << "\nggml_webgpu: gpu breakdown:\n"; for (const auto & kv : ctx->webgpu_ctx->global_ctx->shader_gpu_time_ms) { double pct = (total_gpu > 0.0) ? (kv.second / total_gpu * 100.0) : 0.0; - std::cout << "ggml_webgpu: " << kv.first << ": " << kv.second << " ms (" << pct << "%)\n"; + std::cout << "ggml_webgpu: " << kv.first << ": " << kv.second << " ms (" << std::fixed << std::setprecision(2) + << pct << "%)\n"; } #endif @@ -788,6 +846,7 @@ static bool ggml_webgpu_tensor_overlap(ggml_tensor * a, ggml_tensor * b) { struct binary_overlap_flags { bool inplace; // src0 == dst bool overlap; // src1 == dst + bool src_overlap; }; static binary_overlap_flags ggml_webgpu_detect_binary_overlap(ggml_tensor * src0, @@ -796,6 +855,7 @@ static binary_overlap_flags ggml_webgpu_detect_binary_overlap(ggml_tensor * src0 binary_overlap_flags flags = {}; flags.inplace = ggml_webgpu_tensor_equal(src0, dst); flags.overlap = ggml_webgpu_tensor_overlap(src1, dst); + flags.src_overlap = ggml_webgpu_tensor_overlap(src0, src1); return flags; } @@ -908,14 +968,6 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, auto * decisions = static_cast(pipeline.context.get()); - std::optional error_bufs = std::nullopt; - if (decisions->i64_idx) { - error_bufs = ctx->set_rows_error_buf_pool.alloc_bufs(); - if (error_bufs->host_buf.GetMapState() == wgpu::BufferMapState::Mapped) { - error_bufs->host_buf.Unmap(); - } - } - std::vector params = { (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)), (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, idx) / ggml_type_size(idx->type)), @@ -948,8 +1000,10 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, }; if (decisions->i64_idx) { - entries.push_back( - { .binding = 3, .buffer = error_bufs->dev_buf, .offset = 0, .size = error_bufs->dev_buf.GetSize() }); + entries.push_back({ .binding = 3, + .buffer = ctx->set_rows_dev_error_buf, + .offset = 0, + .size = ctx->set_rows_dev_error_buf.GetSize() }); } uint32_t threads; @@ -959,8 +1013,7 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, threads = src->ne[0] * src->ne[1] * src->ne[2] * src->ne[3]; } uint32_t wg_x = CEIL_DIV(threads, decisions->wg_size); - return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, 1, - error_bufs); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, 1); } // Workgroup size is a common constant @@ -1038,12 +1091,26 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, use_fast = (src0->type == GGML_TYPE_F16); break; case GGML_TYPE_F32: + // TODO: implement better mat-mat for k-quants, mat-vec for all k-quants except q6_K switch (src0->type) { case GGML_TYPE_F32: case GGML_TYPE_F16: case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q6_K: use_fast = true; break; + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + // we don't have fast mat-vec for these types, but we do have (semi) fast mat-mat + use_fast = !is_vec; + break; default: break; } @@ -1112,23 +1179,23 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, }; // Calculate workgroup dimensions - uint32_t wg_x = 1; - uint32_t wg_y = 1; + uint32_t wg_x = 1; + uint32_t wg_y = 1; + const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; if (use_fast && is_vec) { - auto decisions = static_cast(pipeline.context.get()); + auto * decisions = static_cast(pipeline.context.get()); uint32_t batches = dst->ne[2] * dst->ne[3]; uint32_t output_groups = CEIL_DIV(dst->ne[0], decisions->outputs_per_wg); uint32_t total_wg = output_groups * batches; - // TODO: split large sizes into multiple batches to avoid way over-provisioning workgroups - wg_x = std::min(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension); - wg_y = CEIL_DIV(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension); + compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y); } else if (use_fast) { - auto decisions = static_cast(pipeline.context.get()); + auto * decisions = static_cast(pipeline.context.get()); // Fast-path tiled/subgroup calculations - uint32_t wg_m, wg_n; + uint32_t wg_m; + uint32_t wg_n; if (decisions->use_subgroup_matrix) { uint32_t wg_m_sg_tile = decisions->subgroup_m * decisions->subgroup_matrix_m * ctx->global_ctx->capabilities.sg_mat_m; @@ -1142,12 +1209,14 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, wg_m = CEIL_DIV(dst->ne[0], tile_m_s); wg_n = CEIL_DIV(dst->ne[1], tile_n_s); } - wg_x = wg_m * wg_n * dst->ne[2] * dst->ne[3]; + uint32_t total_wg = wg_m * wg_n * dst->ne[2] * dst->ne[3]; + compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y); + } else { // legacy - auto decisions = static_cast(pipeline.context.get()); + auto * decisions = static_cast(pipeline.context.get()); uint32_t wg_size = decisions->wg_size; - wg_x = CEIL_DIV(dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3], wg_size); - wg_y = 1; + uint32_t total_wg = CEIL_DIV(dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3], wg_size); + compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y); } return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x, wg_y); @@ -1353,6 +1422,7 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, .inplace = flags.inplace, .overlap = flags.overlap, + .src_overlap = flags.src_overlap, }; webgpu_pipeline pipeline = ctx->shader_lib->get_binary_pipeline(shader_lib_ctx); @@ -1361,11 +1431,28 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx, uint32_t ne = (uint32_t) ggml_nelements(dst); + size_t src0_webgpu_tensor_align_offset = ggml_webgpu_tensor_align_offset(ctx, src0); + size_t src1_webgpu_tensor_align_offset = ggml_webgpu_tensor_align_offset(ctx, src1); + + uint32_t offset_merged_src0 = 0; + uint32_t offset_merged_src1 = 0; + if (flags.src_overlap) { + size_t min_off = std::min(src0_webgpu_tensor_align_offset, src1_webgpu_tensor_align_offset); + offset_merged_src0 = (uint32_t) ((src0_webgpu_tensor_align_offset - min_off) / ggml_type_size(src0->type)); + offset_merged_src1 = (uint32_t) ((src1_webgpu_tensor_align_offset - min_off) / ggml_type_size(src0->type)); + } + std::vector params = { ne, (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + offset_merged_src0, + offset_merged_src1, + (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)), (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), @@ -1381,31 +1468,147 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx, std::vector entries; - entries.push_back({ - .binding = 0, - .buffer = ggml_webgpu_tensor_buf(src0), - .offset = ggml_webgpu_tensor_align_offset(ctx, src0), - .size = ggml_webgpu_tensor_binding_size(ctx, src0), - }); - - entries.push_back({ - .binding = 1, - .buffer = ggml_webgpu_tensor_buf(src1), - .offset = ggml_webgpu_tensor_align_offset(ctx, src1), - .size = ggml_webgpu_tensor_binding_size(ctx, src1), - }); - - if (!flags.inplace && !flags.overlap) { - entries.push_back({ .binding = 2, - .buffer = ggml_webgpu_tensor_buf(dst), - .offset = ggml_webgpu_tensor_align_offset(ctx, dst), - .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); + if (flags.src_overlap) { + size_t merged_offset = std::min(src0_webgpu_tensor_align_offset, src1_webgpu_tensor_align_offset); + size_t merged_end = std::max(src0_webgpu_tensor_align_offset + ggml_webgpu_tensor_binding_size(ctx, src0), + src1_webgpu_tensor_align_offset + ggml_webgpu_tensor_binding_size(ctx, src1)); + entries.push_back({ + .binding = 0, + .buffer = ggml_webgpu_tensor_buf(src0), + .offset = merged_offset, + .size = merged_end - merged_offset, + }); + entries.push_back({ + .binding = 1, + .buffer = ggml_webgpu_tensor_buf(dst), + .offset = ggml_webgpu_tensor_align_offset(ctx, dst), + .size = ggml_webgpu_tensor_binding_size(ctx, dst), + }); + } else { + entries.push_back({ + .binding = 0, + .buffer = ggml_webgpu_tensor_buf(src0), + .offset = src0_webgpu_tensor_align_offset, + .size = ggml_webgpu_tensor_binding_size(ctx, src0), + }); + entries.push_back({ + .binding = 1, + .buffer = ggml_webgpu_tensor_buf(src1), + .offset = src1_webgpu_tensor_align_offset, + .size = ggml_webgpu_tensor_binding_size(ctx, src1), + }); + if (!flags.inplace && !flags.overlap) { + entries.push_back({ + .binding = 2, + .buffer = ggml_webgpu_tensor_buf(dst), + .offset = ggml_webgpu_tensor_align_offset(ctx, dst), + .size = ggml_webgpu_tensor_binding_size(ctx, dst), + }); + } } uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size); return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); } +static webgpu_command ggml_webgpu_concat(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + uint32_t ne = (uint32_t) ggml_nelements(dst); + uint32_t dim = (uint32_t) dst->op_params[0]; + + std::vector params = { + ne, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)), + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + dim, + (uint32_t) src0->ne[dim] + }; + + std::vector entries = { + { .binding = 0, + .buffer = ggml_webgpu_tensor_buf(src0), + .offset = ggml_webgpu_tensor_align_offset(ctx, src0), + .size = ggml_webgpu_tensor_binding_size(ctx, src0) }, + { .binding = 1, + .buffer = ggml_webgpu_tensor_buf(src1), + .offset = ggml_webgpu_tensor_align_offset(ctx, src1), + .size = ggml_webgpu_tensor_binding_size(ctx, src1) }, + { .binding = 2, + .buffer = ggml_webgpu_tensor_buf(dst), + .offset = ggml_webgpu_tensor_align_offset(ctx, dst), + .size = ggml_webgpu_tensor_binding_size(ctx, dst) } + }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = { + .src0 = src0, + .src1 = src1, + .dst = dst, + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, + }; + + webgpu_pipeline pipeline = ctx->shader_lib->get_concat_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); +} + +static webgpu_command ggml_webgpu_repeat(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * dst) { + uint32_t ne = (uint32_t) ggml_nelements(dst); + + std::vector params = { ne, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / + ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + (uint32_t) (src0->ne[0]), + (uint32_t) (src0->ne[1]), + (uint32_t) (src0->ne[2]), + (uint32_t) (src0->ne[3]), + (uint32_t) (dst->ne[0]), + (uint32_t) (dst->ne[1]), + (uint32_t) (dst->ne[2]) }; + + std::vector entries = { + { .binding = 0, + .buffer = ggml_webgpu_tensor_buf(src0), + .offset = ggml_webgpu_tensor_align_offset(ctx, src0), + .size = ggml_webgpu_tensor_binding_size(ctx, src0) }, + { .binding = 1, + .buffer = ggml_webgpu_tensor_buf(dst), + .offset = ggml_webgpu_tensor_align_offset(ctx, dst), + .size = ggml_webgpu_tensor_binding_size(ctx, dst) } + }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = { + .src0 = src0, + .dst = dst, + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, + }; + + webgpu_pipeline pipeline = ctx->shader_lib->get_repeat_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size); + return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x); +} + static webgpu_command ggml_webgpu_rms_norm(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { int inplace = ggml_webgpu_tensor_equal(src, dst); @@ -1454,7 +1657,12 @@ static webgpu_command ggml_webgpu_rope(webgpu_context & ctx, const int mode = ((int32_t *) dst->op_params)[2]; const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); @@ -1990,6 +2198,10 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, case GGML_OP_MUL: case GGML_OP_DIV: return ggml_webgpu_binary_op(ctx, src0, src1, node); + case GGML_OP_CONCAT: + return ggml_webgpu_concat(ctx, src0, src1, node); + case GGML_OP_REPEAT: + return ggml_webgpu_repeat(ctx, src0, node); case GGML_OP_RMS_NORM: return ggml_webgpu_rms_norm(ctx, src0, node); case GGML_OP_ROPE: @@ -2001,19 +2213,12 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, case GGML_OP_SOFT_MAX: return ggml_webgpu_soft_max(ctx, src0, src1, src2, node); case GGML_OP_UNARY: - return ggml_webgpu_unary_op(ctx, src0, node); case GGML_OP_CLAMP: - return ggml_webgpu_unary_op(ctx, src0, node); case GGML_OP_FILL: - return ggml_webgpu_unary_op(ctx, src0, node); case GGML_OP_LOG: - return ggml_webgpu_unary_op(ctx, src0, node); case GGML_OP_SQR: - return ggml_webgpu_unary_op(ctx, src0, node); case GGML_OP_SQRT: - return ggml_webgpu_unary_op(ctx, src0, node); case GGML_OP_SIN: - return ggml_webgpu_unary_op(ctx, src0, node); case GGML_OP_COS: return ggml_webgpu_unary_op(ctx, src0, node); case GGML_OP_PAD: @@ -2021,7 +2226,6 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, case GGML_OP_ARGMAX: return ggml_webgpu_argmax(ctx, src0, node); case GGML_OP_ARGSORT: - return ggml_webgpu_argsort(ctx, src0, node); case GGML_OP_TOP_K: // we reuse the same argsort implementation for top_k return ggml_webgpu_argsort(ctx, src0, node); @@ -2043,35 +2247,51 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str WEBGPU_CPU_PROFILE_TOTAL_START(graph_compute); - ctx->global_ctx->inflight_threads++; + std::vector commands; + std::vector subs; + uint32_t num_batched_kernels = 0; + bool contains_set_rows = false; - std::vector commands; - std::vector futures; for (int i = 0; i < cgraph->n_nodes; i++) { + if (cgraph->nodes[i]->op == GGML_OP_SET_ROWS) { + contains_set_rows = true; + } if (auto cmd = ggml_webgpu_encode_node(ctx, cgraph->nodes[i])) { commands.push_back(*cmd); + num_batched_kernels += cmd.value().num_kernels; } - // compute the batch size based on the number of inflight threads - uint32_t inflight_threads = ctx->global_ctx->inflight_threads; - uint32_t batch_size = std::min(std::max(1u, WEBGPU_NUM_PARAM_BUFS / std::max(inflight_threads, 1u)), - WEBGPU_COMMAND_SUBMIT_BATCH_SIZE); - if (commands.size() >= batch_size) { - futures.push_back(ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool, - &ctx->set_rows_error_buf_pool)); + + if (num_batched_kernels >= WEBGPU_COMMAND_SUBMIT_BATCH_SIZE) { + num_batched_kernels = 0; + subs.push_back(ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool)); // Process events and check for completed submissions ctx->global_ctx->instance.ProcessEvents(); - ggml_backend_webgpu_wait(ctx->global_ctx, futures, false); + ggml_backend_webgpu_wait(ctx->global_ctx, subs, false); commands.clear(); } } if (!commands.empty()) { - webgpu_submission_futures new_futures = - ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool, &ctx->set_rows_error_buf_pool); - futures.push_back(new_futures); + subs.push_back(ggml_backend_webgpu_submit(ctx->global_ctx, commands, ctx->param_buf_pool)); + commands.clear(); } - ggml_backend_webgpu_wait(ctx->global_ctx, futures); - ctx->global_ctx->inflight_threads--; + // If there are SET_ROWS operations in this graph, copy the error buffers to the host for checking. + if (contains_set_rows) { + wgpu::CommandEncoder encoder = ctx->global_ctx->device.CreateCommandEncoder(); + encoder.CopyBufferToBuffer(ctx->set_rows_dev_error_buf, 0, ctx->set_rows_host_error_buf, 0, + ctx->set_rows_host_error_buf.GetSize()); + wgpu::CommandBuffer set_rows_commands = encoder.Finish(); + ctx->global_ctx->queue.Submit(1, &set_rows_commands); + ggml_backend_webgpu_map_buffer(ctx->global_ctx, ctx->set_rows_host_error_buf, wgpu::MapMode::Read, 0, + ctx->set_rows_host_error_buf.GetSize()); + const uint32_t * error_data = (const uint32_t *) ctx->set_rows_host_error_buf.GetConstMappedRange(); + if (*error_data) { + GGML_ABORT("ggml_webgpu: SET_ROWS index > 2^32, unsupported."); + } + ctx->set_rows_host_error_buf.Unmap(); + } + + ggml_backend_webgpu_wait(ctx->global_ctx, subs); WEBGPU_CPU_PROFILE_TOTAL_END(graph_compute, ctx->global_ctx); return GGML_STATUS_SUCCESS; } @@ -2689,11 +2909,13 @@ static webgpu_context initialize_webgpu_context(ggml_backend_dev_t dev) { webgpu_ctx->shader_lib = std::make_unique(dev_ctx->webgpu_global_ctx->device); webgpu_ctx->param_buf_pool.init(webgpu_ctx->global_ctx->device, WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES, wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, - wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); - webgpu_ctx->set_rows_error_buf_pool.init(webgpu_ctx->global_ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS, - WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, - wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage, - wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead); + wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite, true); + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->set_rows_dev_error_buf, + WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, + wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "set_rows_dev_error_buf"); + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->set_rows_host_error_buf, + WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "set_rows_host_error_buf"); ggml_webgpu_init_cpy_pipeline(webgpu_ctx); ggml_webgpu_init_rms_norm_pipeline(webgpu_ctx); @@ -2741,10 +2963,10 @@ static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggm /* .iface = */ { /* .get_name = */ ggml_backend_webgpu_buffer_type_get_name, /* .alloc_buffer = */ - ggml_backend_webgpu_buffer_type_alloc_buffer, /* .get_alignment = */ - ggml_backend_webgpu_buffer_type_get_alignment, /* .get_max_size = */ - ggml_backend_webgpu_buffer_type_get_max_size, /* .get_alloc_size = */ - ggml_backend_webgpu_buffer_type_get_alloc_size, /* .is_host = */ NULL, // defaults to false + ggml_backend_webgpu_buffer_type_alloc_buffer, /* .get_alignment = */ + ggml_backend_webgpu_buffer_type_get_alignment, /* .get_max_size = */ + ggml_backend_webgpu_buffer_type_get_max_size, /* .get_alloc_size = */ + ggml_backend_webgpu_buffer_type_get_alloc_size, /* .is_host = */ NULL, // defaults to false }, /* .device = */ dev, @@ -2816,10 +3038,14 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: - // TODO: support non-contiguous tensors, e.g. for MOE_EXPERT_REDUCE - // see https://github.com/ggml-org/llama.cpp/pull/16857 supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type) && - (src1->type == op->type) && ggml_is_contiguous(src0) && ggml_is_contiguous(src1); + (src1->type == op->type); + break; + case GGML_OP_CONCAT: + supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32); + break; + case GGML_OP_REPEAT: + supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32 || src0->type == GGML_TYPE_I16); break; case GGML_OP_CPY: case GGML_OP_CONT: diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl index 55dd66408a..a748dc1b86 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl @@ -7,6 +7,13 @@ struct Params { offset_src0: u32, offset_src1: u32, offset_dst: u32, + offset_merged_src0: u32, + offset_merged_src1: u32, + + stride_src0_0: u32, + stride_src0_1: u32, + stride_src0_2: u32, + stride_src0_3: u32, stride_src1_0: u32, stride_src1_1: u32, @@ -23,6 +30,21 @@ struct Params { b_ne3: u32, }; +fn src0_index(_i: u32) -> u32 { + var i = _i; + let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0); + i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0); + let a_i2 = i / (params.a_ne1 * params.a_ne0); + i = i % (params.a_ne1 * params.a_ne0); + let a_i1 = i / params.a_ne0; + let a_i0 = i % params.a_ne0; + + return a_i0 * params.stride_src0_0 + + a_i1 * params.stride_src0_1 + + a_i2 * params.stride_src0_2 + + a_i3 * params.stride_src0_3; +} + fn src1_index(_i: u32) -> u32 { var i = _i; let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0); @@ -53,17 +75,22 @@ fn src1_index(_i: u32) -> u32 { #define DataType f16 #endif +#ifdef SRC_OVERLAP +@group(0) @binding(0) +var merged_src: array; + +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; +#else @group(0) @binding(0) var src0: array; @group(0) @binding(1) var src1 : array; - -#ifdef INPLACE -@group(0) @binding(2) -var params: Params; - -#elif defined(OVERLAP) +#if defined(INPLACE) || defined(OVERLAP) @group(0) @binding(2) var params: Params; @@ -74,6 +101,7 @@ var dst: array; @group(0) @binding(3) var params: Params; #endif +#endif fn op(a: DataType, b: DataType) -> DataType { #ifdef OP_ADD @@ -87,13 +115,17 @@ fn op(a: DataType, b: DataType) -> DataType { #endif } -fn update(dst_i: u32, src0_i: u32, src1_i: u32){ +fn update(dst_i: u32, src0_i: u32, src1_i: u32) { +#ifdef SRC_OVERLAP + let result = op(merged_src[src0_i], merged_src[src1_i]); +#else let result = op(src0[src0_i], src1[src1_i]); +#endif #ifdef INPLACE - src0[dst_i] = result; + src0[src0_i] = result; #elif defined(OVERLAP) - src1[dst_i] = result; + src1[src1_i] = result; #else dst[dst_i] = result; #endif @@ -102,6 +134,8 @@ fn update(dst_i: u32, src0_i: u32, src1_i: u32){ @compute @workgroup_size(WG_SIZE) fn main(@builtin(global_invocation_id) gid: vec3) { if (gid.x < params.ne) { - update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x)); + let src0_i = params.offset_src0 + params.offset_merged_src0 + src0_index(gid.x); + let src1_i = params.offset_src1 + params.offset_merged_src1 + src1_index(gid.x); + update(params.offset_dst + gid.x, src0_i, src1_i); } } diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/concat.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/concat.wgsl new file mode 100644 index 0000000000..a22d245d2c --- /dev/null +++ b/ggml/src/ggml-webgpu/wgsl-shaders/concat.wgsl @@ -0,0 +1,75 @@ +struct Params { + ne: u32, + + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + + stride_src0_0: u32, + stride_src0_1: u32, + stride_src0_2: u32, + stride_src0_3: u32, + + stride_src1_0: u32, + stride_src1_1: u32, + stride_src1_2: u32, + stride_src1_3: u32, + + ne0: u32, + ne1: u32, + ne2: u32, + ne3: u32, + + dim: u32, + src0_nedim: u32 +}; + +#ifdef TYPE_F32 +#define DataType f32 +#endif +#ifdef TYPE_I32 +#define DataType i32 +#endif + +@group(0) @binding(0) +var src0: array; + +@group(0) @binding(1) +var src1 : array; + +@group(0) @binding(2) +var dst: array; + +@group(0) @binding(3) +var params: Params; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { + + if (gid.x < params.ne) { + var i = gid.x; + let i3 = i / (params.ne2 * params.ne1 * params.ne0); + i = i % (params.ne2 * params.ne1 * params.ne0); + let i2 = i / (params.ne1 * params.ne0); + i = i % (params.ne1 * params.ne0); + let i1 = i / params.ne0; + let i0 = i % params.ne0; + + var ni = array(i0, i1, i2, i3); + + if (ni[params.dim] < params.src0_nedim) { + let src_i = ni[0] * params.stride_src0_0 + + ni[1] * params.stride_src0_1 + + ni[2] * params.stride_src0_2 + + ni[3] * params.stride_src0_3; + dst[params.offset_dst + gid.x] = src0[params.offset_src0 + src_i]; + } else { + ni[params.dim] -= params.src0_nedim; + let src_i = ni[0] * params.stride_src1_0 + + ni[1] * params.stride_src1_1 + + ni[2] * params.stride_src1_2 + + ni[3] * params.stride_src1_3; + dst[params.offset_dst + gid.x] = src1[params.offset_src1 + src_i]; + } + } +} diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.wgsl index 6aba47317c..5b9f5b3622 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.wgsl @@ -679,19 +679,24 @@ struct MulMatParams { @group(0) @binding(3) var params: MulMatParams; @compute @workgroup_size(256) -fn main(@builtin(global_invocation_id) global_id: vec3) { +fn main(@builtin(local_invocation_id) local_id: vec3, + @builtin(workgroup_id) wg_id: vec3, + @builtin(num_workgroups) num_wg: vec3) { + let wg_linear = wg_id.y * num_wg.x + wg_id.x; + let global_idx = wg_linear * 256u + local_id.x; + let total = params.m * params.n * params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3; - if (global_id.x >= total) { + if (global_idx >= total) { return; } let dst2_stride = params.m * params.n; let dst3_stride = dst2_stride * params.bs02 * params.broadcast2; - let dst3_idx = global_id.x / dst3_stride; + let dst3_idx = global_idx / dst3_stride; let src03_idx = dst3_idx / params.broadcast3; // src0 may be broadcast along the third dimension let src13_idx = dst3_idx; // src1 is not broadcast - let dst3_rem = global_id.x % dst3_stride; + let dst3_rem = global_idx % dst3_stride; let dst2_idx = dst3_rem / dst2_stride; let src02_idx = dst2_idx / params.broadcast2; // src0 may also be broadcast along the second dimension diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl index 5c1074ebc1..de60ebbcf2 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl @@ -11,7 +11,7 @@ fn store_shmem(val: vec4, idx: u32) { shmem[idx + 2] = val.z; shmem[idx + 3] = val.w; } -#endif +#endif // VEC #ifdef SCALAR #define VEC_SIZE 1 @@ -23,7 +23,7 @@ fn store_shmem(val: vec4, idx: u32) { fn store_shmem(val: f16, idx: u32) { shmem[idx] = val; } -#endif +#endif // SCALAR #ifdef INIT_SRC0_SHMEM_FLOAT fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { @@ -40,7 +40,7 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3 store_shmem(SHMEM_TYPE(src0_val), elem_idx); } } -#endif +#endif // INIT_SRC0_SHMEM_FLOAT #ifdef INIT_SRC1_SHMEM_FLOAT fn init_shmem_src1(thread_id: u32, batch_offset: u32, offset_n: u32, k_outer: u32) { @@ -57,7 +57,7 @@ fn init_shmem_src1(thread_id: u32, batch_offset: u32, offset_n: u32, k_outer: u3 store_shmem(SHMEM_TYPE(src1_val), TILE_SRC0_SHMEM + elem_idx); } } -#endif +#endif // INIT_SRC1_SHMEM_FLOAT #ifdef INIT_SRC0_SHMEM_Q4_0 const BLOCK_SIZE = 32u; @@ -100,4 +100,667 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3 } } } -#endif +#endif // INIT_SRC0_SHMEM_Q4_0 + +#ifdef INIT_SRC0_SHMEM_Q4_1 +const BLOCK_SIZE = 32u; +// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. +override BLOCKS_K = TILE_K/BLOCK_SIZE; +const NQ = 16u; +const F16_PER_BLOCK = 10u; // 1 scale + 8 packed weights + 1 mean +const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + + let tile_m = blck_idx / BLOCKS_K; + let global_m = offset_m + tile_m; + let block_k = blck_idx % BLOCKS_K; + let global_k = k_outer / BLOCK_SIZE + block_k; + + if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + let d = src0[scale_idx]; + let m = src0[scale_idx + 1u]; + + for (var j = 0u; j < F16_PER_THREAD; j += 2) { + let q_0 = src0[scale_idx + 2u + block_offset + j]; + let q_1 = src0[scale_idx + 2u + block_offset + j + 1]; + + let q_packed = bitcast(vec2(q_0, q_1)); + for (var k = 0u; k < 4u; k++) { + let q_byte = get_byte(q_packed, k); + let q_lo = f16(q_byte & 0xF) * d + m; + let q_hi = f16((q_byte >> 4) & 0xF) * d + m; + shmem[shmem_idx + j * 2 + k] = q_lo; + shmem[shmem_idx + j * 2 + k + 16u] = q_hi; + } + } + } + } +} +#endif // INIT_SRC0_SHMEM_Q4_1 + +#ifdef INIT_SRC0_SHMEM_Q5_0 +// 32 weights per block, each at 4 bits each = 32 * 4 = 128 bits / 16 = 8 f16s per block +const BLOCK_SIZE = 32u; +// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. +// tile_k is defined as 32u, so blocks_k ends up being 1 always +override BLOCKS_K = TILE_K / BLOCK_SIZE; +const NQ = 16u; +const F16_PER_BLOCK = 11u; // 1 scale + 2 qh + 8 packed weights +const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 16 / 4 = 4 f16s per thread, each thread should handle 4 f16s * 4 weights per = 16 weights + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + + let tile_m = blck_idx / BLOCKS_K; + let global_m = offset_m + tile_m; + let block_k = blck_idx % BLOCKS_K; + let global_k = k_outer / BLOCK_SIZE + block_k; + + if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + + let d = src0[scale_idx]; + let qh0 = src0[scale_idx + 1u]; + let qh1 = src0[scale_idx + 2u]; + let qh_packed = bitcast(vec2(qh0, qh1)); + + for (var j = 0u; j < 2; j++) { + let q_0 = src0[scale_idx + 3u + block_offset + (j*2)]; + let q_1 = src0[scale_idx + 3u + block_offset + (j*2) + 1u]; + + let q_packed = bitcast(vec2(q_0, q_1)); + + let j_adjusted = j + (block_offset / 2u); + + + for (var k = 0u; k < 4u; k++) { + let q_byte = get_byte(q_packed, k); + + let qh_hi = (qh_packed >> (j_adjusted * 4 + k + 12)) & 0x10; + let q_hi = (f16(((q_byte >> 4) & 0xF) | qh_hi) - 16.0) * d; + let qh_lo = ((qh_packed >> (j_adjusted * 4 + k)) << 4) & 0x10; + let q_lo = (f16((q_byte & 0xF) | qh_lo) - 16.0) * d; + + shmem[shmem_idx + j * 4u + k] = q_lo; // store first weight + shmem[shmem_idx + j * 4u + k + 16u] = q_hi; // store second weight + } + } + } + } +} +#endif // INIT_SRC0_SHMEM_Q5_0 + +#ifdef INIT_SRC0_SHMEM_Q5_1 +// 32 weights per block, each at 4 bits each = 32 * 4 = 128 bits / 16 = 8 f16s per block +const BLOCK_SIZE = 32u; +// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. +// tile_k is defined as 32u, so blocks_k ends up being 1 always +override BLOCKS_K = TILE_K / BLOCK_SIZE; +const NQ = 16u; +const F16_PER_BLOCK = 12u; // 1 scale + 2 qh + 8 packed weights + 1 mean +const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 16 / 4 = 4 f16s per thread, each thread should handle 4 f16s * 4 weights per = 16 weights + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + + let tile_m = blck_idx / BLOCKS_K; + let global_m = offset_m + tile_m; + let block_k = blck_idx % BLOCKS_K; + let global_k = k_outer / BLOCK_SIZE + block_k; + + if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + + let d = src0[scale_idx]; + let m = src0[scale_idx + 1u]; + let qh0 = src0[scale_idx + 2u]; + let qh1 = src0[scale_idx + 3u]; + let qh_packed = bitcast(vec2(qh0, qh1)); + + for (var j = 0u; j < 2; j++) { + + let q_0 = src0[scale_idx + 4u + block_offset + (j*2)]; + let q_1 = src0[scale_idx + 4u + block_offset + (j*2) + 1u]; + + let q_packed = bitcast(vec2(q_0, q_1)); + + let j_adjusted = j + (block_offset / 2u); + + + for (var k = 0u; k < 4u; k++) { + let q_byte = get_byte(q_packed, k); + + let qh_hi = (qh_packed >> (j_adjusted * 4 + k + 12)) & 0x10; + let q_hi = (f16(((q_byte >> 4) & 0xF) | qh_hi)) * d + m; + let qh_lo = ((qh_packed >> (j_adjusted * 4 + k)) << 4) & 0x10; + let q_lo = (f16((q_byte & 0xF) | qh_lo)) * d + m; + + shmem[shmem_idx + j * 4u + k] = q_lo; // store first weight + shmem[shmem_idx + j * 4u + k + 16u] = q_hi; // store second weight + } + } + } + } +} +#endif // INIT_SRC0_SHMEM_Q5_1 + +#ifdef INIT_SRC0_SHMEM_Q8_0 +const BLOCK_SIZE = 32u; +// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. +override BLOCKS_K = TILE_K/BLOCK_SIZE; +const NQ = 16u; +const F16_PER_BLOCK = 17u; // 1 scale + 16 in array of weights +const WEIGHTS_PER_F16 = 2u; // 2 8-bit weights per f16 +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 8 f16s per thread + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + + let tile_m = blck_idx / BLOCKS_K; + let global_m = offset_m + tile_m; + let block_k = blck_idx % BLOCKS_K; + let global_k = k_outer / BLOCK_SIZE + block_k; + + if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + let d = src0[scale_idx]; + + for (var j = 0u; j < F16_PER_THREAD; j+=2) { + let q_0 = src0[scale_idx + 1u + block_offset + j]; + let q_1 = src0[scale_idx + 1u + block_offset + j + 1]; + + let q_packed = bitcast(vec2(q_0, q_1)); + for (var k = 0u; k < 4u; k++) { + let q_byte = get_byte_i32(q_packed, k); + + let q_val = f16(q_byte) * d; + shmem[shmem_idx + j * 2 + k] = q_val; + } + } + } + } +} +#endif // INIT_SRC0_SHMEM_Q8_0 + +#ifdef INIT_SRC0_SHMEM_Q8_1 +const BLOCK_SIZE = 32u; +// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. +override BLOCKS_K = TILE_K/BLOCK_SIZE; +const NQ = 16u; +const F16_PER_BLOCK = 18u; // 1 scale + 1 mean + 8 32-bit values in array of weights +const WEIGHTS_PER_F16 = 2u; // 2 8-bit weights per f16 +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; // 8 f16s per thread, 2 threads per block + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + + let tile_m = blck_idx / BLOCKS_K; + let global_m = offset_m + tile_m; + let block_k = blck_idx % BLOCKS_K; + let global_k = k_outer / BLOCK_SIZE + block_k; + + if (global_m < params.m && global_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + let d = src0[scale_idx]; + let m = src0[scale_idx + 1u]; + + for (var j = 0u; j < F16_PER_THREAD; j+=2) { + let q_0 = src0[scale_idx + 2u + block_offset + j]; + let q_1 = src0[scale_idx + 2u + block_offset + j + 1]; + + let q_packed = bitcast(vec2(q_0, q_1)); + for (var k = 0u; k < 4u; k++) { + let q_byte = get_byte_i32(q_packed, k); + + let q_val = f16(q_byte) * d + m; + shmem[shmem_idx + j * 2 + k] = q_val; + } + } + } + } +} +#endif // INIT_SRC0_SHMEM_Q8_1 + +#ifdef INIT_SRC0_SHMEM_Q2_K +const BLOCK_SIZE = 256u; +const F16_PER_BLOCK = 42u; + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + // Use standard thread layout instead of lane/row_group + for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) { + let tile_m = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + + let global_m = offset_m + tile_m; + let global_k = k_outer + tile_k; + + if (global_m >= params.m || global_k >= params.k) { + shmem[elem_idx] = f16(0.0); + continue; + } + + let block_k = global_k / BLOCK_SIZE; + let k_in_block = global_k % BLOCK_SIZE; + + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + + let d = src0[scale_idx + 40u]; + let dmin = src0[scale_idx + 41u]; + + // Decode the element at position k_in_block + let block_of_32 = k_in_block / 32u; + let pos_in_32 = k_in_block % 32u; + + let q_b_idx = (block_of_32 / 4u) * 32u; + let shift = (block_of_32 % 4u) * 2u; + let k = (pos_in_32 / 16u) * 16u; + let l = pos_in_32 % 16u; + + let is = k_in_block / 16u; + + let sc_0 = src0[scale_idx + 2u * (is / 4u)]; + let sc_1 = src0[scale_idx + 2u * (is / 4u) + 1u]; + let sc_packed = bitcast(vec2(sc_0, sc_1)); + let sc = get_byte(sc_packed, is % 4u); + + let dl = d * f16(sc & 0xFu); + let ml = dmin * f16(sc >> 4u); + + let q_idx = q_b_idx + k + l; + let q_0 = src0[scale_idx + 8u + 2u * (q_idx / 4u)]; + let q_1 = src0[scale_idx + 8u + 2u * (q_idx / 4u) + 1u]; + let q_packed = bitcast(vec2(q_0, q_1)); + let q_byte = get_byte(q_packed, q_idx % 4u); + let qs_val = (q_byte >> shift) & 3u; + + let q_val = f16(qs_val) * dl - ml; + shmem[elem_idx] = q_val; + } +} +#endif // INIT_SRC0_SHMEM_Q2_K + +#ifdef INIT_SRC0_SHMEM_Q3_K +const BLOCK_SIZE = 256u; +const F16_PER_BLOCK = 55u; + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) { + let tile_m = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + + let global_m = offset_m + tile_m; + let global_k = k_outer + tile_k; + + if (global_m >= params.m || global_k >= params.k) { + shmem[elem_idx] = f16(0.0); + continue; + } + + let block_k = global_k / BLOCK_SIZE; + let k_in_block = global_k % BLOCK_SIZE; + + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + + let d = src0[scale_idx + 54u]; + + // Load and unpack scales + let kmask1: u32 = 0x03030303u; + let kmask2: u32 = 0x0f0f0f0fu; + + var scale_vals: array; + for (var i: u32 = 0u; i < 4u; i++) { + let scale_0 = src0[scale_idx + 48u + (2u*i)]; + let scale_1 = src0[scale_idx + 48u + (2u*i) + 1u]; + scale_vals[i] = bitcast(vec2(scale_0, scale_1)); + } + + var tmp: u32 = scale_vals[2]; + scale_vals[2] = ((scale_vals[0] >> 4u) & kmask2) | (((tmp >> 4u) & kmask1) << 4u); + scale_vals[3] = ((scale_vals[1] >> 4u) & kmask2) | (((tmp >> 6u) & kmask1) << 4u); + scale_vals[0] = (scale_vals[0] & kmask2) | ((tmp & kmask1) << 4u); + scale_vals[1] = (scale_vals[1] & kmask2) | (((tmp >> 2u) & kmask1) << 4u); + + // Load hmask and qs arrays + var hmask_vals: array; + for (var i: u32 = 0u; i < 8u; i++) { + let hmask_0 = src0[scale_idx + (2u*i)]; + let hmask_1 = src0[scale_idx + (2u*i) + 1u]; + hmask_vals[i] = bitcast(vec2(hmask_0, hmask_1)); + } + + var qs_vals: array; + for (var i: u32 = 0u; i < 16u; i++) { + let qs_0 = src0[scale_idx + 16u + (2u*i)]; + let qs_1 = src0[scale_idx + 16u + (2u*i) + 1u]; + qs_vals[i] = bitcast(vec2(qs_0, qs_1)); + } + + let half = k_in_block / 128u; // 0 or 1 + let pos_in_half = k_in_block % 128u; // 0-127 + let shift_group = pos_in_half / 32u; // 0-3 + let pos_in_32 = pos_in_half % 32u; // 0-31 + let k_group = pos_in_32 / 16u; // 0 or 1 + let l = pos_in_32 % 16u; // 0-15 + + let q_b_idx = half * 32u; // 0 or 32 + let shift = shift_group * 2u; // 0, 2, 4, 6 + let k = k_group * 16u; // 0 or 16 + let is = k_in_block / 16u; // 0-15 + + // m increments every 32 elements across entire 256 element block + let m_shift = k_in_block / 32u; // 0-7 + let m: u32 = 1u << m_shift; // 1,2,4,8,16,32,64,128 + + let sc = get_byte(scale_vals[is / 4u], is % 4u); + let dl = d * (f16(sc) - 32.0); + + let q_idx = q_b_idx + k + l; + let hm_idx = k + l; + + let q_byte = get_byte(qs_vals[q_idx / 4u], q_idx % 4u); + let hmask_byte = get_byte(hmask_vals[hm_idx / 4u], hm_idx % 4u); + + let hm = select(4.0, 0.0, (hmask_byte & m) != 0); + let qs_val = (q_byte >> shift) & 3u; + + let q_val = (f16(qs_val) - f16(hm)) * dl; + shmem[elem_idx] = q_val; + } +} + +#endif // INIT_SRC0_SHMEM_Q3_K + +#ifdef INIT_SRC0_SHMEM_Q4_K +const BLOCK_SIZE = 256u; +const F16_PER_BLOCK = 72u; + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) { + let tile_m = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + + let global_m = offset_m + tile_m; + let global_k = k_outer + tile_k; + + if (global_m >= params.m || global_k >= params.k) { + shmem[elem_idx] = f16(0.0); + continue; + } + + let block_k = global_k / BLOCK_SIZE; + let k_in_block = global_k % BLOCK_SIZE; + + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + + let d = src0[scale_idx]; + let dmin = src0[scale_idx + 1u]; + + // Load packed scales + var scale_vals: array; + for (var i: u32 = 0u; i < 3u; i++) { + let scale_0 = src0[scale_idx + 2u + (2u*i)]; + let scale_1 = src0[scale_idx + 2u + (2u*i) + 1u]; + scale_vals[i] = bitcast(vec2(scale_0, scale_1)); + } + + // Map k_in_block to loop structure: + // Outer loop over 64-element groups (alternating q_b_idx) + // Inner loop over 2 shifts per group + let group_of_64 = k_in_block / 64u; // 0-3 (maps to q_b_idx) + let pos_in_64 = k_in_block % 64u; // 0-63 + let shift_group = pos_in_64 / 32u; // 0 or 1 + let l = pos_in_64 % 32u; // 0-31 + + let q_b_idx = group_of_64 * 32u; // 0, 32, 64, 96 + let shift = shift_group * 4u; // 0 or 4 + let is = k_in_block / 32u; // 0-7 + + var sc: u32; + var mn: u32; + + if (is < 4u) { + let sc_byte = get_byte(scale_vals[is / 4u], is % 4u); + let min_byte = get_byte(scale_vals[(is + 4u) / 4u], is % 4u); + sc = sc_byte & 63u; + mn = min_byte & 63u; + } else { + let sc_min_lo = get_byte(scale_vals[(is + 4u) / 4u], (is + 4u) % 4u); + let sc_hi = get_byte(scale_vals[(is - 4u) / 4u], (is - 4u) % 4u); + let min_hi = get_byte(scale_vals[is / 4u], is % 4u); + + sc = (sc_min_lo & 0xFu) | ((sc_hi >> 6u) << 4u); + mn = (sc_min_lo >> 4u) | ((min_hi >> 6u) << 4u); + } + + let dl = d * f16(sc); + let ml = dmin * f16(mn); + + let q_idx = q_b_idx + l; + let q_0 = src0[scale_idx + 8u + 2u * (q_idx / 4u)]; + let q_1 = src0[scale_idx + 8u + 2u * (q_idx / 4u) + 1u]; + let q_packed = bitcast(vec2(q_0, q_1)); + + let q_byte = get_byte(q_packed, q_idx % 4u); + let qs_val = (q_byte >> shift) & 0xFu; + + let q_val = f16(qs_val) * dl - ml; + shmem[elem_idx] = q_val; + } +} +#endif // INIT_SRC0_SHMEM_Q4_K + +#ifdef INIT_SRC0_SHMEM_Q5_K +const BLOCK_SIZE = 256u; +const F16_PER_BLOCK = 88u; + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) { + let tile_m = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + + let global_m = offset_m + tile_m; + let global_k = k_outer + tile_k; + + if (global_m >= params.m || global_k >= params.k) { + shmem[elem_idx] = f16(0.0); + continue; + } + + let block_k = global_k / BLOCK_SIZE; + let k_in_block = global_k % BLOCK_SIZE; + + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + + let d = src0[scale_idx]; + let dmin = src0[scale_idx + 1u]; + + // Load packed scales + var scale_vals: array; + for (var i: u32 = 0u; i < 3u; i++) { + let scale_0 = src0[scale_idx + 2u + (2u*i)]; + let scale_1 = src0[scale_idx + 2u + (2u*i) + 1u]; + scale_vals[i] = bitcast(vec2(scale_0, scale_1)); + } + + // The original loop processes elements in groups of 64 + // Each group of 64: q_b_idx cycles through [0,32,64,96], shift cycles [0,4] + // But u increments EVERY 32 elements (after each l loop) + let group_of_64 = k_in_block / 64u; // 0-3 + let pos_in_64 = k_in_block % 64u; // 0-63 + let shift_group = pos_in_64 / 32u; // 0 or 1 + let l = pos_in_64 % 32u; // 0-31 + + let q_b_idx = group_of_64 * 32u; // 0, 32, 64, 96 + let shift = shift_group * 4u; // 0 or 4 + let is = k_in_block / 32u; // 0-7 + + // u increments every 32 elements (0->1, 1->2, 2->4, 3->8, 4->16, 5->32, 6->64, 7->128) + let u_shift = k_in_block / 32u; // 0-7 + let u: u32 = 1u << u_shift; + + var sc: u32; + var mn: u32; + + if (is < 4u) { + let sc_byte = get_byte(scale_vals[is / 4u], is % 4u); + let min_byte = get_byte(scale_vals[(is + 4u) / 4u], is % 4u); + sc = sc_byte & 63u; + mn = min_byte & 63u; + } else { + let sc_min_lo = get_byte(scale_vals[(is + 4u) / 4u], (is + 4u) % 4u); + let sc_hi = get_byte(scale_vals[(is - 4u) / 4u], (is - 4u) % 4u); + let min_hi = get_byte(scale_vals[is / 4u], is % 4u); + + sc = (sc_min_lo & 0xFu) | ((sc_hi >> 6u) << 4u); + mn = (sc_min_lo >> 4u) | ((min_hi >> 6u) << 4u); + } + + let dl = d * f16(sc); + let ml = dmin * f16(mn); + + let q_idx = q_b_idx + l; + let q_0 = src0[scale_idx + 24u + 2u * (q_idx / 4u)]; + let q_1 = src0[scale_idx + 24u + 2u * (q_idx / 4u) + 1u]; + let q_packed = bitcast(vec2(q_0, q_1)); + + let q_byte = get_byte(q_packed, q_idx % 4u); + + let qh_0 = src0[scale_idx + 8u + 2u * (l / 4u)]; + let qh_1 = src0[scale_idx + 8u + 2u * (l / 4u) + 1u]; + let qh_packed = bitcast(vec2(qh_0, qh_1)); + + let qh_byte = get_byte(qh_packed, l % 4u); + + let qs_val = (q_byte >> shift) & 0xFu; + let qh_val = select(0.0, 16.0, (qh_byte & u) != 0); + + let q_val = (f16(qs_val) + f16(qh_val)) * dl - ml; + shmem[elem_idx] = q_val; + } +} + +#endif // INIT_SRC0_SHMEM_Q5_K + +#ifdef INIT_SRC0_SHMEM_Q6_K +const BLOCK_SIZE = 256u; +const F16_PER_BLOCK = 105u; + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var elem_idx = thread_id; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE) { + let tile_m = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + + let global_m = offset_m + tile_m; + let global_k = k_outer + tile_k; + + if (global_m >= params.m || global_k >= params.k) { + shmem[elem_idx] = f16(0.0); + continue; + } + + let block_k = global_k / BLOCK_SIZE; + let k_in_block = global_k % BLOCK_SIZE; + + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + let scale_idx = src0_idx * F16_PER_BLOCK; + + let half = k_in_block / 128u; + let pos_in_half = k_in_block % 128u; + let quarter = pos_in_half / 32u; + let l = pos_in_half % 32u; + + let ql_b_idx = half * 64u; + let qh_b_idx = half * 32u; + let sc_b_idx = half * 8u; + + // Load only ql13 word needed + let ql13_flat = ql_b_idx + l; + let ql13_word = ql13_flat / 4u; + let ql13 = bitcast(vec2( + src0[scale_idx + 2u * ql13_word], + src0[scale_idx + 2u * ql13_word + 1u] + )); + let ql13_b = get_byte(ql13, ql13_flat % 4u); + + // Load only ql24 word needed + let ql24_flat = ql_b_idx + l + 32u; + let ql24_word = ql24_flat / 4u; + let ql24 = bitcast(vec2( + src0[scale_idx + 2u * ql24_word], + src0[scale_idx + 2u * ql24_word + 1u] + )); + let ql24_b = get_byte(ql24, ql24_flat % 4u); + + // Load only qh word needed + let qh_flat = qh_b_idx + l; + let qh_word = qh_flat / 4u; + let qh = bitcast(vec2( + src0[scale_idx + 64u + 2u * qh_word], + src0[scale_idx + 64u + 2u * qh_word + 1u] + )); + let qh_b = get_byte(qh, qh_flat % 4u); + + let q1 = f16((ql13_b & 0xFu) | ((qh_b & 3u) << 4u)) - f16(32.0); + let q2 = f16((ql24_b & 0xFu) | (((qh_b >> 2u) & 3u) << 4u)) - f16(32.0); + let q3 = f16((ql13_b >> 4u) | (((qh_b >> 4u) & 3u) << 4u)) - f16(32.0); + let q4 = f16((ql24_b >> 4u) | (((qh_b >> 6u) & 3u) << 4u)) - f16(32.0); + + // Load only the scale word needed + let is = l / 16u; + let sc_idx = sc_b_idx + is + quarter * 2u; + let sc_word = sc_idx / 4u; + let sc = bitcast(vec2( + src0[scale_idx + 96u + 2u * sc_word], + src0[scale_idx + 96u + 2u * sc_word + 1u] + )); + let sc_val = get_byte_i32(sc, sc_idx % 4u); + + let d = src0[scale_idx + 104u]; + + var q_val: f16; + if (quarter == 0u) { + q_val = q1; + } else if (quarter == 1u) { + q_val = q2; + } else if (quarter == 2u) { + q_val = q3; + } else { + q_val = q4; + } + + shmem[elem_idx] = d * f16(sc_val) * q_val; + } +} +#endif // INIT_SRC0_SHMEM_Q6_K diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.wgsl index 771e5cd1ee..b1da421a69 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.wgsl @@ -50,11 +50,13 @@ fn get_local_m(thread_id: u32) -> u32 { const TOTAL_WORKGROUP_SIZE = WORKGROUP_SIZE_M * WORKGROUP_SIZE_N; const TILE_SRC0_SHMEM = TILE_K * WORKGROUP_SIZE_M * TILE_M; const TILE_SRC1_SHMEM = TILE_K * WORKGROUP_SIZE_N * TILE_N; + var shmem: array; @compute @workgroup_size(TOTAL_WORKGROUP_SIZE) fn main(@builtin(workgroup_id) wg_id: vec3, - @builtin(local_invocation_id) local_id: vec3) { + @builtin(local_invocation_id) local_id: vec3, + @builtin(num_workgroups) num_wg: vec3) { let thread_id = local_id.x; let local_m = get_local_m(thread_id); @@ -64,9 +66,16 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let wg_m_count = (params.m + WORKGROUP_SIZE_M * TILE_M - 1u) / (WORKGROUP_SIZE_M * TILE_M); let wg_per_matrix = wg_m_count * wg_n_count; - let batch_idx = wg_id.x / wg_per_matrix; + let wg_linear = wg_id.y * num_wg.x + wg_id.x; - let wg_in_batch = wg_id.x % wg_per_matrix; + let batch_idx = wg_linear / wg_per_matrix; + + let total_batches = params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3; + if (batch_idx >= total_batches) { + return; + } + + let wg_in_batch = wg_linear % wg_per_matrix; let wg_m = wg_in_batch % wg_m_count; let wg_n = wg_in_batch / wg_m_count; diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.wgsl index 64529e03cd..9f9ef279f2 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.wgsl @@ -69,7 +69,8 @@ var shmem: array; @compute @workgroup_size(TOTAL_WORKGROUP_SIZE) fn main(@builtin(workgroup_id) wg_id: vec3, @builtin(local_invocation_id) local_id: vec3, - @builtin(subgroup_id) subgroup_id: u32) { + @builtin(subgroup_id) subgroup_id: u32, + @builtin(num_workgroups) num_wg: vec3) { let thread_id = local_id.x; let subgroup_m = subgroup_id % SUBGROUP_M; @@ -79,9 +80,16 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let wg_n_count = (params.n + WG_N_SG_TILE_SIZE - 1) / WG_N_SG_TILE_SIZE; let wg_per_matrix = wg_m_count * wg_n_count; - let batch_idx = wg_id.x / wg_per_matrix; + let wg_linear = wg_id.y * num_wg.x + wg_id.x; - let wg_in_batch = wg_id.x % wg_per_matrix; + let batch_idx = wg_linear / wg_per_matrix; + + let total_batches = params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3; + if (batch_idx >= total_batches) { + return; + } + + let wg_in_batch = wg_linear % wg_per_matrix; let wg_m = wg_in_batch % wg_m_count; let wg_n = wg_in_batch / wg_m_count; diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl index f9ea95e07b..94f4bae11f 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl @@ -1,4 +1,3 @@ - enable f16; #include "common_decls.tmpl" @@ -84,6 +83,294 @@ fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 { } #endif +#ifdef MUL_ACC_Q4_1 + +const BLOCK_SIZE = 32; +const NQ = 16u; // number of weights per thread +const F16_PER_BLOCK = 10u; +const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; + +fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 { + var local_sum = 0.0; + for (var i = tig * NQ; i < tile_size; i += THREADS_PER_OUTPUT * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let scale_idx = (idx_base + k_outer / BLOCK_SIZE + blck_idx) * F16_PER_BLOCK; + // each f16 contains offsets [block_offset, block_offset + 1] and [block_offset + 16, block_offset + 17] + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let d = f32(src0[scale_idx]); + let m = f32(src0[scale_idx + 1u]); + for (var j = 0u; j < F16_PER_THREAD; j += 2) { + let q_0 = src0[scale_idx + 2u + block_offset + j]; + let q_1 = src0[scale_idx + 2u + block_offset + j + 1]; + let q_packed = bitcast(vec2(q_0, q_1)); + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + let q_hi = f32((q_byte >> 4) & 0xF) * d + m; + let q_lo = f32(q_byte & 0xF) * d + m; + local_sum += q_lo * shared_vector[shmem_idx + j * 2 + k]; + local_sum += q_hi * shared_vector[shmem_idx + j * 2 + k + 16]; + } + } + } + return local_sum; +} +#endif + +#ifdef MUL_ACC_Q5_0 + +const BLOCK_SIZE = 32; +const NQ = 16u; // number of weights per thread +const F16_PER_BLOCK = 11u; +const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; + +fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 { + var local_sum = 0.0; + for (var i = tig * NQ; i < tile_size; i += THREADS_PER_OUTPUT * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let scale_idx = (idx_base + k_outer / BLOCK_SIZE + blck_idx) * F16_PER_BLOCK; + // each f16 contains offsets [block_offset, block_offset + 1] and [block_offset + 16, block_offset + 17] + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let d = f32(src0[scale_idx]); + let qh0 = src0[scale_idx + 1u]; + let qh1 = src0[scale_idx + 2u]; + let qh_packed = bitcast(vec2(qh0, qh1)); + + for (var j = 0u; j < 2; j++) { + let q_0 = src0[scale_idx + 3u + block_offset + (j*2)]; + let q_1 = src0[scale_idx + 3u + block_offset + (j*2) + 1u]; + let q_packed = bitcast(vec2(q_0, q_1)); + + let j_adjusted = j + (block_offset / 2u); + + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + + let qh_hi = (qh_packed >> (j_adjusted * 4 + k + 12)) & 0x10; + let q_hi = (f32(((q_byte >> 4) & 0xF) | qh_hi) - 16.0) * d; + let qh_lo = ((qh_packed >> (j_adjusted * 4 + k)) << 4) & 0x10; + let q_lo = (f32((q_byte & 0xF) | qh_lo) - 16.0) * d; + + local_sum += q_lo * shared_vector[shmem_idx + j * 4 + k]; + local_sum += q_hi * shared_vector[shmem_idx + j * 4 + k + 16]; + } + + } + } + return local_sum; +} +#endif + + +#ifdef MUL_ACC_Q5_1 + +const BLOCK_SIZE = 32; +const NQ = 16u; // number of weights per thread +const F16_PER_BLOCK = 12u; +const WEIGHTS_PER_F16 = 4u; // 4 weights per f16 +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; + +fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 { + var local_sum = 0.0; + for (var i = tig * NQ; i < tile_size; i += THREADS_PER_OUTPUT * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let scale_idx = (idx_base + k_outer / BLOCK_SIZE + blck_idx) * F16_PER_BLOCK; + // each f16 contains offsets [block_offset, block_offset + 1] and [block_offset + 16, block_offset + 17] + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let d = f32(src0[scale_idx]); + let m = src0[scale_idx + 1u]; + let qh0 = src0[scale_idx + 2u]; + let qh1 = src0[scale_idx + 3u]; + let qh_packed = bitcast(vec2(qh0, qh1)); + + for (var j = 0u; j < 2; j++) { + let q_0 = src0[scale_idx + 4u + block_offset + (j*2)]; + let q_1 = src0[scale_idx + 4u + block_offset + (j*2) + 1u]; + let q_packed = bitcast(vec2(q_0, q_1)); + + let j_adjusted = j + (block_offset / 2u); + + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + + let qh_hi = (qh_packed >> (j_adjusted * 4 + k + 12)) & 0x10; + let q_hi = f32(((q_byte >> 4) & 0xF) | qh_hi) * d + f32(m); + let qh_lo = ((qh_packed >> (j_adjusted * 4 + k)) << 4) & 0x10; + let q_lo = f32((q_byte & 0xF) | qh_lo) * d + f32(m); + + local_sum += q_lo * shared_vector[shmem_idx + j * 4 + k]; + local_sum += q_hi * shared_vector[shmem_idx + j * 4 + k + 16]; + } + + } + } + return local_sum; +} +#endif + + +#ifdef MUL_ACC_Q8_0 + +const BLOCK_SIZE = 32; +const NQ = 16u; // number of weights per thread +const F16_PER_BLOCK = 17u; +const WEIGHTS_PER_F16 = 2u; +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; + +fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 { + var local_sum = 0.0; + for (var i = tig * NQ; i < tile_size; i += THREADS_PER_OUTPUT * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let scale_idx = (idx_base + k_outer / BLOCK_SIZE + blck_idx) * F16_PER_BLOCK; + // each f16 contains offsets [block_offset, block_offset + 1] and [block_offset + 16, block_offset + 17] + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let d = f32(src0[scale_idx]); + + for (var j = 0u; j < F16_PER_THREAD; j += 2) { + let q_0 = src0[scale_idx + 1 + block_offset + j]; + let q_1 = src0[scale_idx + 1 + block_offset + j + 1]; + let q_packed = bitcast(vec2(q_0, q_1)); + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte_i32(q_packed, k); + let q_val = f32(q_byte) * d; + local_sum += q_val * shared_vector[shmem_idx + j * 2 + k]; + } + } + } + return local_sum; +} +#endif + + +#ifdef MUL_ACC_Q8_1 + +const BLOCK_SIZE = 32; +const NQ = 16u; // number of weights per thread +const F16_PER_BLOCK = 18u; +const WEIGHTS_PER_F16 = 2u; +const F16_PER_THREAD = NQ / WEIGHTS_PER_F16; + +fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 { + var local_sum = 0.0; + for (var i = tig * NQ; i < tile_size; i += THREADS_PER_OUTPUT * NQ) { + let blck_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16; + let scale_idx = (idx_base + k_outer / BLOCK_SIZE + blck_idx) * F16_PER_BLOCK; + // each f16 contains offsets [block_offset, block_offset + 1] and [block_offset + 16, block_offset + 17] + let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u; + let d = f32(src0[scale_idx]); + let m = src0[scale_idx + 1u]; + + for (var j = 0u; j < F16_PER_THREAD; j += 2) { + let q_0 = src0[scale_idx + 2u + block_offset + j]; + let q_1 = src0[scale_idx + 2u + block_offset + j + 1]; + let q_packed = bitcast(vec2(q_0, q_1)); + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte_i32(q_packed, k); + let q_val = f32(q_byte) * d + f32(m); + local_sum += q_val * shared_vector[shmem_idx + j * 2 + k]; + } + } + } + return local_sum; +} +#endif + +#ifdef MUL_ACC_Q6_K + +const BLOCK_SIZE = 256u; +const F16_PER_BLOCK = 105u; + +fn load_u32_at(bbase: u32, byte_offset: u32) -> u32 { + let aligned = byte_offset & ~3u; + let idx = bbase + aligned / 2u; + return bitcast(vec2(src0[idx], src0[idx + 1u])); +} + +fn byte_of(v: u32, b: u32) -> u32 { + return (v >> (b * 8u)) & 0xFFu; +} + +fn sbyte_of(v: u32, b: u32) -> i32 { + let raw = i32((v >> (b * 8u)) & 0xFFu); + return select(raw, raw - 256, raw >= 128); +} + +fn mul_acc(tig: u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 { + let tid = tig / 2u; + let ix = tig % 2u; + let ip = tid / 8u; + let il = tid % 8u; + let l0 = 4u * il; + let is = 8u * ip + l0 / 16u; + + let y_offset = 128u * ip + l0; + let q_offset_l = 64u * ip + l0; + let q_offset_h = 32u * ip + l0; + + let nb = tile_size / BLOCK_SIZE; + let k_block_start = k_outer / BLOCK_SIZE; + + // Aligned scale byte position (is can be odd) + let sc_base_byte = 192u + (is & ~3u); + let sc_byte_pos = is & 3u; + + var local_sum = 0.0; + + for (var i = ix; i < nb; i += 2u) { + let bbase = (idx_base + k_block_start + i) * F16_PER_BLOCK; + + let d_raw = load_u32_at(bbase, 208u); + let d = f32(bitcast>(d_raw)[0]); + + let ql1_u32 = load_u32_at(bbase, q_offset_l); + let ql2_u32 = load_u32_at(bbase, q_offset_l + 32u); + let qh_u32 = load_u32_at(bbase, 128u + q_offset_h); + let sc_u32_0 = load_u32_at(bbase, sc_base_byte); + let sc_u32_1 = load_u32_at(bbase, sc_base_byte + 4u); + + let sc0 = sbyte_of(sc_u32_0, sc_byte_pos); + let sc2 = sbyte_of(sc_u32_0, sc_byte_pos + 2u); + let sc4 = sbyte_of(sc_u32_1, sc_byte_pos); + let sc6 = sbyte_of(sc_u32_1, sc_byte_pos + 2u); + + var sums = vec4(0.0, 0.0, 0.0, 0.0); + + for (var l = 0u; l < 4u; l++) { + let y_base = i * BLOCK_SIZE + y_offset + l; + let yl0 = f32(shared_vector[y_base]); + let yl1 = f32(shared_vector[y_base + 32u]); + let yl2 = f32(shared_vector[y_base + 64u]); + let yl3 = f32(shared_vector[y_base + 96u]); + + let q1b = byte_of(ql1_u32, l); + let q2b = byte_of(ql2_u32, l); + let qhb = byte_of(qh_u32, l); + + let dq0 = f32(i32((q1b & 0x0Fu) | ((qhb & 0x03u) << 4u)) - 32); + let dq1 = f32(i32((q2b & 0x0Fu) | ((qhb & 0x0Cu) << 2u)) - 32); + let dq2 = f32(i32((q1b >> 4u) | ((qhb & 0x30u) )) - 32); + let dq3 = f32(i32((q2b >> 4u) | ((qhb & 0xC0u) >> 2u)) - 32); + + sums[0] += yl0 * dq0; + sums[1] += yl1 * dq1; + sums[2] += yl2 * dq2; + sums[3] += yl3 * dq3; + } + + local_sum += d * (sums[0] * f32(sc0) + sums[1] * f32(sc2) + + sums[2] * f32(sc4) + sums[3] * f32(sc6)); + } + + return local_sum; +} +#endif + struct MulMatParams { offset_src0: u32, offset_src1: u32, @@ -191,4 +478,3 @@ fn main( dst[dst_idx / VEC_SIZE] = store_val(group_base); } } - diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl new file mode 100644 index 0000000000..6e2a1a8b61 --- /dev/null +++ b/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl @@ -0,0 +1,67 @@ +enable f16; + +struct Params { + ne: u32, + + offset_src0: u32, + offset_dst: u32, + + stride_src0_0: u32, + stride_src0_1: u32, + stride_src0_2: u32, + stride_src0_3: u32, + + a_ne0: u32, + a_ne1: u32, + a_ne2: u32, + a_ne3: u32, + + ne0: u32, + ne1: u32, + ne2: u32, +}; + +#ifdef TYPE_F32 +#define DataType f32 +#endif +#ifdef TYPE_I32 +#define DataType i32 +#endif +#ifdef TYPE_I16 +// same size (16-bit) is sufficient for repeat +#define DataType f16 +#endif + +@group(0) @binding(0) +var src0: array; + +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { + if (gid.x < params.ne) { + var i = gid.x; + let i3 = i / (params.ne2 * params.ne1 * params.ne0); + i = i % (params.ne2 * params.ne1 * params.ne0); + let i2 = i / (params.ne1 * params.ne0); + i = i % (params.ne1 * params.ne0); + let i1 = i / params.ne0; + let i0 = i % params.ne0; + + let a_i0 = i0 % params.a_ne0; + let a_i1 = i1 % params.a_ne1; + let a_i2 = i2 % params.a_ne2; + let a_i3 = i3 % params.a_ne3; + + let a_index = a_i0 * params.stride_src0_0 + + a_i1 * params.stride_src0_1 + + a_i2 * params.stride_src0_2 + + a_i3 * params.stride_src0_3; + + dst[params.offset_dst + gid.x] = src0[params.offset_src0 + a_index]; + } +} diff --git a/ggml/src/ggml-zendnn/CMakeLists.txt b/ggml/src/ggml-zendnn/CMakeLists.txt index f5cf6eedd3..9bdb4e836d 100644 --- a/ggml/src/ggml-zendnn/CMakeLists.txt +++ b/ggml/src/ggml-zendnn/CMakeLists.txt @@ -1,12 +1,19 @@ ggml_add_backend_library(ggml-zendnn ggml-zendnn.cpp) -# Get ZenDNN path if (NOT DEFINED ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "") set(ZENDNN_ROOT "$ENV{ZENDNN_ROOT}") endif() -# Check if path is still empty or OFF +if (BUILD_SHARED_LIBS) + set(ZENDNN_SHARED_LIB ON) + set(ZENDNN_ARCHIVE_LIB OFF) +else() + set(ZENDNN_SHARED_LIB OFF) + set(ZENDNN_ARCHIVE_LIB ON) +endif() + +# Download and build ZenDNN if not provided if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF") message(STATUS "ZENDNN_ROOT not set. Automatically downloading and building ZenDNN...") message(STATUS "This will take several minutes on first build...") @@ -21,7 +28,7 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF") ExternalProject_Add( zendnn GIT_REPOSITORY https://github.com/amd/ZenDNN.git - GIT_TAG 21ce8f7879c86bf3637f707fae6f29e0951db5fe + GIT_TAG a18adf8c605fb5f5e52cefd7eda08a7b18febbaf # ZenDNN-2026-WW08 PREFIX ${ZENDNN_PREFIX} SOURCE_DIR ${ZENDNN_SOURCE_DIR} BINARY_DIR ${ZENDNN_BUILD_DIR} @@ -32,7 +39,9 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF") -DZENDNNL_BUILD_DOXYGEN=OFF -DZENDNNL_BUILD_GTEST=OFF -DZENDNNL_BUILD_BENCHDNN=OFF - # Enable ALL matmul algorithm backends + -DZENDNNL_DEPENDS_FBGEMM=OFF + -DZENDNNL_LIB_BUILD_ARCHIVE=${ZENDNN_ARCHIVE_LIB} + -DZENDNNL_LIB_BUILD_SHARED=${ZENDNN_SHARED_LIB} -DZENDNNL_DEPENDS_AOCLDLP=ON -DZENDNNL_DEPENDS_ONEDNN=ON -DZENDNNL_DEPENDS_LIBXSMM=ON @@ -45,47 +54,37 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF") LOG_INSTALL ON ) - # Add dependency so ZenDNN builds before our library add_dependencies(ggml-zendnn zendnn) - - # Set ZENDNN_ROOT to the installation directory set(ZENDNN_ROOT ${ZENDNN_INSTALL_DIR}) - message(STATUS "ZenDNN will be built to: ${ZENDNN_ROOT}") else() message(STATUS "Using custom ZenDNN installation at: ${ZENDNN_ROOT}") endif() -# ZenDNN headers + libs target_include_directories(ggml-zendnn PRIVATE ${ZENDNN_ROOT}/zendnnl/include - ${ZENDNN_ROOT}/deps/aocldlp/include - ${ZENDNN_ROOT}/deps/aoclutils/include ${ZENDNN_ROOT}/deps/json/include - ${ZENDNN_ROOT}/deps/libxsmm/include + ${ZENDNN_ROOT}/deps/aoclutils/include + ${ZENDNN_ROOT}/deps/aocldlp/include ${ZENDNN_ROOT}/deps/onednn/include -) + ${ZENDNN_ROOT}/deps/libxsmm/include) -target_link_directories(ggml-zendnn PRIVATE - ${ZENDNN_ROOT}/zendnnl/lib - ${ZENDNN_ROOT}/deps/aocldlp/lib - ${ZENDNN_ROOT}/deps/aoclutils/lib - ${ZENDNN_ROOT}/deps/libxsmm/lib - ${ZENDNN_ROOT}/deps/onednn/lib -) +if (ZENDNN_SHARED_LIB) + target_link_directories(ggml-zendnn PRIVATE ${ZENDNN_ROOT}/zendnnl/lib) + target_link_libraries(ggml-zendnn PRIVATE zendnnl) +elseif (ZENDNN_ARCHIVE_LIB) + target_link_libraries(ggml-zendnn PRIVATE + ${ZENDNN_ROOT}/zendnnl/lib/libzendnnl_archive.a + ${ZENDNN_ROOT}/deps/aoclutils/${CMAKE_INSTALL_LIBDIR}/libaoclutils.a + ${ZENDNN_ROOT}/deps/aoclutils/${CMAKE_INSTALL_LIBDIR}/libau_cpuid.a + ${ZENDNN_ROOT}/deps/aocldlp/lib/libaocl-dlp.a + ${ZENDNN_ROOT}/deps/onednn/${CMAKE_INSTALL_LIBDIR}/libdnnl.a + ${ZENDNN_ROOT}/deps/libxsmm/lib/libxsmm.a + ${ZENDNN_ROOT}/deps/libxsmm/lib/libxsmmext.a + ${ZENDNN_ROOT}/deps/libxsmm/lib/libxsmmnoblas.a) +endif() -target_link_libraries(ggml-zendnn PRIVATE - zendnnl_archive # ZenDNN main - aocl-dlp # AOCL libraries - aoclutils - au_cpuid - dnnl # OneDNN - xsmm # libxsmm small matrix math - xsmmext - xsmmnoblas - m - pthread -) +target_link_libraries(ggml-zendnn PRIVATE m pthread) if (GGML_OPENMP) target_link_libraries(ggml-zendnn PRIVATE OpenMP::OpenMP_CXX) diff --git a/ggml/src/ggml-zendnn/ggml-zendnn.cpp b/ggml/src/ggml-zendnn/ggml-zendnn.cpp index 551c15bb4a..c876030400 100644 --- a/ggml/src/ggml-zendnn/ggml-zendnn.cpp +++ b/ggml/src/ggml-zendnn/ggml-zendnn.cpp @@ -41,13 +41,13 @@ static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int const TA * A, int64_t lda, const TB * B, int64_t ldb, TC * C, int64_t ldc) { - zendnnl::lowoha::lowoha_params params; + zendnnl::lowoha::matmul::matmul_params params; params.dtypes.src = ggml_to_zendnn_type(); params.dtypes.wei = ggml_to_zendnn_type(); params.dtypes.dst = ggml_to_zendnn_type(); params.num_threads = ctx->n_threads; - zendnnl::lowoha::status_t status = zendnnl::lowoha::matmul_direct( + zendnnl::error_handling::status_t status = zendnnl::lowoha::matmul::matmul_direct( 'r', false, true, // row-major, don't transpose B, transpose A (because it's column-major) n, // M: rows of B and C m, // N: cols of A^T and C @@ -63,7 +63,7 @@ static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int params // params ); - if (status != zendnnl::lowoha::status_t::success) { + if (status != zendnnl::error_handling::status_t::success) { GGML_LOG_ERROR("%s, ZenDNN matmul failed: status=%d\n", __func__, static_cast(status)); return false; } diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index ed819eaa4c..e5b83e1447 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -718,6 +718,14 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .to_float = (ggml_to_float_t) dequantize_row_mxfp4, .from_float_ref = (ggml_from_float_t)quantize_row_mxfp4_ref, }, + [GGML_TYPE_NVFP4] = { + .type_name = "nvfp4", + .blck_size = QK_NVFP4, + .type_size = sizeof(block_nvfp4), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_nvfp4, + .from_float_ref = (ggml_from_float_t)quantize_row_nvfp4_ref, + }, [GGML_TYPE_Q2_K] = { .type_name = "q2_K", .blck_size = QK_K, @@ -899,7 +907,8 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { }; const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type) { - GGML_ASSERT(type < GGML_TYPE_COUNT); + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); return &type_traits[type]; } @@ -1030,6 +1039,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "GATED_LINEAR_ATTN", "RWKV_WKV7", "SOLVE_TRI", + "GATED_DELTA_NET", "UNARY", @@ -1047,7 +1057,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "GLU", }; -static_assert(GGML_OP_COUNT == 95, "GGML_OP_COUNT != 95"); +static_assert(GGML_OP_COUNT == 96, "GGML_OP_COUNT != 96"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1139,6 +1149,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "gated_linear_attn(k, v, q, gate, s)", "rwkv_wkv7(r, w, k, v, a, b, s)", "A X = B, A triangular, solve X", + "gated_delta_net(q, k, v, g, beta, s)", "unary(x)", @@ -1156,7 +1167,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "glu(x)", }; -static_assert(GGML_OP_COUNT == 95, "GGML_OP_COUNT != 95"); +static_assert(GGML_OP_COUNT == 96, "GGML_OP_COUNT != 96"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -1265,27 +1276,33 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) { } int64_t ggml_blck_size(enum ggml_type type) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); return type_traits[type].blck_size; } size_t ggml_type_size(enum ggml_type type) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); return type_traits[type].type_size; } size_t ggml_row_size(enum ggml_type type, int64_t ne) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); assert(ne % ggml_blck_size(type) == 0); return ggml_type_size(type)*ne/ggml_blck_size(type); } -double ggml_type_sizef(enum ggml_type type) { - return ((double)(type_traits[type].type_size))/type_traits[type].blck_size; -} - const char * ggml_type_name(enum ggml_type type) { - return type < GGML_TYPE_COUNT ? type_traits[type].type_name : "NONE"; + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); + return type_traits[type].type_name; } bool ggml_is_quantized(enum ggml_type type) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); return type_traits[type].is_quantized; } @@ -1365,6 +1382,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_Q5_1: wtype = GGML_TYPE_Q5_1; break; case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break; case GGML_FTYPE_MOSTLY_MXFP4: wtype = GGML_TYPE_MXFP4; break; + case GGML_FTYPE_MOSTLY_NVFP4: wtype = GGML_TYPE_NVFP4; break; case GGML_FTYPE_MOSTLY_Q2_K: wtype = GGML_TYPE_Q2_K; break; case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break; case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break; @@ -1403,16 +1421,14 @@ static bool ggml_is_contiguous_n(const struct ggml_tensor * tensor, int n) { } next_nb *= tensor->ne[0]/ggml_blck_size(tensor->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { - if (tensor->ne[i] != 1) { - if (i > n) { - if (tensor->nb[i] != next_nb) { - return false; - } - next_nb *= tensor->ne[i]; - } else { - // this dimension does not need to be contiguous - next_nb = tensor->ne[i]*tensor->nb[i]; + if (i > n) { + if (tensor->ne[i] != 1 && tensor->nb[i] != next_nb) { + return false; } + next_nb *= tensor->ne[i]; + } else { + // this dimension does not need to be contiguous + next_nb = tensor->ne[i]*tensor->nb[i]; } } return true; @@ -1629,11 +1645,23 @@ static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml const size_t cur_end = cur_offs + cur_size; // align to GGML_MEM_ALIGN + GGML_ASSERT(size <= SIZE_MAX - (GGML_MEM_ALIGN - 1)); size_t size_needed = GGML_PAD(size, GGML_MEM_ALIGN); char * const mem_buffer = ctx->mem_buffer; struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end); + // integer overflow checks + if (cur_end > SIZE_MAX - size_needed) { + GGML_LOG_WARN("%s: overflow detected in cur_end (%zu) + size_needed (%zu)\n", __func__, cur_end, size_needed); + return NULL; + } + if (cur_end + size_needed > SIZE_MAX - GGML_OBJECT_SIZE) { + GGML_LOG_WARN("%s: overflow detected in cur_end (%zu) + size_needed (%zu) + GGML_OBJECT_SIZE (%zu)\n", __func__, + cur_end, size_needed, (size_t) GGML_OBJECT_SIZE); + return NULL; + } + if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) { GGML_LOG_WARN("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size); @@ -1702,6 +1730,8 @@ static struct ggml_tensor * ggml_new_tensor_impl( obj_alloc_size = data_size; } + GGML_ASSERT(GGML_TENSOR_SIZE <= SIZE_MAX - obj_alloc_size); + struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TYPE_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size); GGML_ASSERT(obj_new); @@ -6105,6 +6135,57 @@ struct ggml_tensor * ggml_solve_tri( return result; } +// ggml_gated_delta_net + +struct ggml_tensor * ggml_gated_delta_net( + struct ggml_context * ctx, + struct ggml_tensor * q, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * g, + struct ggml_tensor * beta, + struct ggml_tensor * state) { + GGML_ASSERT(ggml_is_contiguous_rows(q)); + GGML_ASSERT(ggml_is_contiguous_rows(k)); + GGML_ASSERT(ggml_is_contiguous_rows(v)); + GGML_ASSERT(ggml_is_contiguous(g)); + GGML_ASSERT(ggml_is_contiguous(beta)); + GGML_ASSERT(ggml_is_contiguous(state)); + + GGML_ASSERT(q->type == GGML_TYPE_F32); + GGML_ASSERT(k->type == GGML_TYPE_F32); + GGML_ASSERT(v->type == GGML_TYPE_F32); + GGML_ASSERT(g->type == GGML_TYPE_F32); + GGML_ASSERT(beta->type == GGML_TYPE_F32); + GGML_ASSERT(state->type == GGML_TYPE_F32); + + const int64_t S_v = v->ne[0]; + const int64_t H = v->ne[1]; + const int64_t n_tokens = v->ne[2]; + const int64_t n_seqs = v->ne[3]; + + // gate: scalar [1, H, T, B] or vector [S_v, H, T, B] (KDA) + GGML_ASSERT(g->ne[0] == 1 || g->ne[0] == S_v); + GGML_ASSERT(beta->ne[0] == 1); + + GGML_ASSERT(ggml_nelements(state) == S_v * S_v * H * n_seqs); + + // concat output and new_state into a single tensor + // output: S_v * H * n_tokens * n_seqs, state: S_v * S_v * H * n_seqs + const int64_t ne[4] = { S_v * H, n_tokens * n_seqs + S_v * n_seqs, 1, 1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + result->op = GGML_OP_GATED_DELTA_NET; + result->src[0] = q; + result->src[1] = k; + result->src[2] = v; + result->src[3] = g; + result->src[4] = beta; + result->src[5] = state; + + return result; +} + //////////////////////////////////////////////////////////////////////////////// struct ggml_hash_set ggml_hash_set_new(size_t size) { @@ -7569,6 +7650,7 @@ size_t ggml_quantize_chunk( case GGML_TYPE_Q5_1: result = quantize_q5_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q8_0: result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_MXFP4: result = quantize_mxfp4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_NVFP4: result = quantize_nvfp4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q2_K: result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q3_K: result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q4_K: result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; diff --git a/ggml/src/gguf.cpp b/ggml/src/gguf.cpp index ed0d7f2cae..cbeedf6c4b 100644 --- a/ggml/src/gguf.cpp +++ b/ggml/src/gguf.cpp @@ -15,6 +15,17 @@ #include #include +#define GGUF_MAX_STRING_LENGTH (1024*1024*1024) +#define GGUF_MAX_ARRAY_ELEMENTS (1024*1024*1024) + +#ifdef _WIN32 +# define gguf_ftell _ftelli64 +# define gguf_fseek _fseeki64 +#else +# define gguf_ftell ftello +# define gguf_fseek fseeko +#endif + template struct type_to_gguf_type; @@ -217,17 +228,64 @@ struct gguf_context { }; struct gguf_reader { - FILE * file; + gguf_reader(FILE * file) : file(file) { + // read the remaining bytes once and update on each read + nbytes_remain = file_remain(file); + } - gguf_reader(FILE * file) : file(file) {} + // helper for remaining bytes in a file + static uint64_t file_remain(FILE * file) { + const int64_t cur = gguf_ftell(file); + if (cur < 0) { + return 0; + } + if (gguf_fseek(file, 0, SEEK_END) != 0) { + gguf_fseek(file, cur, SEEK_SET); + + return 0; + } + const int64_t end = gguf_ftell(file); + if (end < 0) { + gguf_fseek(file, cur, SEEK_SET); + + return 0; + } + gguf_fseek(file, cur, SEEK_SET); + return static_cast(end - cur); + } template bool read(T & dst) const { - return fread(&dst, 1, sizeof(dst), file) == sizeof(dst); + const size_t size = sizeof(dst); + if (nbytes_remain < size) { + return false; + } + const size_t nread = fread(&dst, 1, size, file); + nbytes_remain -= nread; + return nread == size; } template bool read(std::vector & dst, const size_t n) const { + if (n > GGUF_MAX_ARRAY_ELEMENTS) { + return false; + } + if constexpr (std::is_same::value) { + // strings are prefixed with their length, so we need to account for that + if (n > SIZE_MAX / sizeof(uint64_t)) { + return false; + } + if (nbytes_remain < n * sizeof(uint64_t)) { + return false; + } + } else { + if (n > SIZE_MAX / sizeof(T)) { + return false; + } + if (nbytes_remain < n * sizeof(T)) { + return false; + } + } dst.resize(n); for (size_t i = 0; i < dst.size(); ++i) { if constexpr (std::is_same::value) { @@ -277,13 +335,33 @@ struct gguf_reader { if (!read(size)) { return false; } - dst.resize(size); - return fread(dst.data(), 1, dst.length(), file) == dst.length(); + if (size > GGUF_MAX_STRING_LENGTH) { + GGML_LOG_ERROR("%s: string length %" PRIu64 " exceeds maximum %" PRIu64 "\n", __func__, size, (uint64_t) GGUF_MAX_STRING_LENGTH); + return false; + } + if (size > nbytes_remain) { + GGML_LOG_ERROR("%s: string length %" PRIu64 " exceeds remaining file size %" PRIu64 " bytes\n", __func__, size, nbytes_remain); + return false; + } + dst.resize(static_cast(size)); + const size_t nread = fread(dst.data(), 1, size, file); + nbytes_remain -= nread; + return nread == size; } bool read(void * dst, const size_t size) const { - return fread(dst, 1, size, file) == size; + if (size > nbytes_remain) { + return false; + } + const size_t nread = fread(dst, 1, size, file); + nbytes_remain -= nread; + return nread == size; } + +private: + FILE * file; + + mutable uint64_t nbytes_remain; }; struct gguf_context * gguf_init_empty(void) { @@ -568,8 +646,8 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par // check that tensor type is within defined range if (info.t.type < 0 || info.t.type >= GGML_TYPE_COUNT) { - GGML_LOG_ERROR("%s: tensor '%s' has invalid ggml type %d (%s)\n", - __func__, info.t.name, info.t.type, ggml_type_name(info.t.type)); + GGML_LOG_ERROR("%s: tensor '%s' has invalid ggml type %d. should be in [0, %d)\n", + __func__, info.t.name, info.t.type, GGML_TYPE_COUNT); ok = false; break; } @@ -618,14 +696,14 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par GGML_ASSERT(int64_t(ctx->info.size()) == n_tensors); // we require the data section to be aligned, so take into account any padding - if (fseek(file, GGML_PAD(ftell(file), ctx->alignment), SEEK_SET) != 0) { + if (gguf_fseek(file, GGML_PAD(gguf_ftell(file), ctx->alignment), SEEK_SET) != 0) { GGML_LOG_ERROR("%s: failed to seek to beginning of data section\n", __func__); gguf_free(ctx); return nullptr; } // store the current file offset - this is where the data section starts - ctx->offset = ftell(file); + ctx->offset = gguf_ftell(file); // compute the total size of the data section, taking into account the alignment { @@ -657,10 +735,34 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par // the ggml_tensor structs to the appropriate locations in the binary blob // compute the exact size needed for the new ggml_context - const size_t mem_size = - params.no_alloc ? - (n_tensors )*ggml_tensor_overhead() : - (n_tensors + 1)*ggml_tensor_overhead() + ctx->size; + size_t mem_size = 0; + if (params.no_alloc) { + if (n_tensors != 0 && SIZE_MAX / n_tensors < ggml_tensor_overhead()) { + GGML_LOG_ERROR("%s: memory size overflow while allocating ggml context\n", __func__); + gguf_free(ctx); + return nullptr; + } + + const size_t overhead = n_tensors * ggml_tensor_overhead(); + + mem_size = overhead; + } else { + if ((n_tensors + 1) != 0 && SIZE_MAX / (n_tensors + 1) < ggml_tensor_overhead()) { + GGML_LOG_ERROR("%s: memory size overflow while allocating ggml context\n", __func__); + gguf_free(ctx); + return nullptr; + } + + const size_t overhead = (n_tensors + 1) * ggml_tensor_overhead(); + + if (SIZE_MAX - overhead < ctx->size) { + GGML_LOG_ERROR("%s: memory size overflow while allocating ggml context\n", __func__); + gguf_free(ctx); + return nullptr; + } + + mem_size = overhead + ctx->size; + } struct ggml_init_params pdata = { /*mem_size =*/ mem_size, diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 712f1ce366..c9680e3f87 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -125,6 +125,7 @@ class Keys: EXPERT_GROUP_SCALE = "{arch}.expert_group_scale" EXPERTS_PER_GROUP = "{arch}.experts_per_group" MOE_EVERY_N_LAYERS = "{arch}.moe_every_n_layers" + MOE_LATENT_SIZE = "{arch}.moe_latent_size" NEXTN_PREDICT_LAYERS = "{arch}.nextn_predict_layers" NUM_DEEPSTACK_LAYERS = "{arch}.n_deepstack_layers" POOLING_TYPE = "{arch}.pooling_type" @@ -180,6 +181,8 @@ class Keys: TEMPERATURE_LENGTH = "{arch}.attention.temperature_length" KEY_LENGTH_MLA = "{arch}.attention.key_length_mla" VALUE_LENGTH_MLA = "{arch}.attention.value_length_mla" + KEY_LENGTH_SWA = "{arch}.attention.key_length_swa" + VALUE_LENGTH_SWA = "{arch}.attention.value_length_swa" SHARED_KV_LAYERS = "{arch}.attention.shared_kv_layers" SLIDING_WINDOW_PATTERN = "{arch}.attention.sliding_window_pattern" TEMPERATURE_SCALE = "{arch}.attention.temperature_scale" @@ -191,6 +194,7 @@ class Keys: class Rope: DIMENSION_COUNT = "{arch}.rope.dimension_count" + DIMENSION_COUNT_SWA = "{arch}.rope.dimension_count_swa" DIMENSION_SECTIONS = "{arch}.rope.dimension_sections" FREQ_BASE = "{arch}.rope.freq_base" FREQ_BASE_SWA = "{arch}.rope.freq_base_swa" @@ -382,6 +386,7 @@ class MODEL_ARCH(IntEnum): NEO_BERT = auto() JINA_BERT_V2 = auto() JINA_BERT_V3 = auto() + EUROBERT = auto() BLOOM = auto() STABLELM = auto() QWEN = auto() @@ -535,6 +540,7 @@ class MODEL_TENSOR(IntEnum): FFN_GATE_EXP = auto() FFN_DOWN_EXP = auto() FFN_UP_EXP = auto() + FFN_GATE_UP_EXP = auto() FFN_GATE_SHEXP = auto() FFN_DOWN_SHEXP = auto() FFN_UP_SHEXP = auto() @@ -542,6 +548,8 @@ class MODEL_TENSOR(IntEnum): FFN_DOWN_CHEXP = auto() FFN_UP_CHEXP = auto() FFN_EXP_PROBS_B = auto() + MOE_LATENT_DOWN = auto() # nemotron 3 super + MOE_LATENT_UP = auto() # nemotron 3 super ATTN_Q_NORM = auto() ATTN_K_NORM = auto() LAYER_OUT_NORM = auto() @@ -828,6 +836,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.NEO_BERT: "neo-bert", MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", MODEL_ARCH.JINA_BERT_V3: "jina-bert-v3", + MODEL_ARCH.EUROBERT: "eurobert", MODEL_ARCH.BLOOM: "bloom", MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.QWEN: "qwen", @@ -987,7 +996,10 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", + MODEL_TENSOR.FFN_GATE_UP_EXP: "blk.{bid}.ffn_gate_up_exps", MODEL_TENSOR.FFN_EXP_PROBS_B: "blk.{bid}.exp_probs_b", + MODEL_TENSOR.MOE_LATENT_DOWN: "blk.{bid}.ffn_latent_down", # nemotron 3 super + MODEL_TENSOR.MOE_LATENT_UP: "blk.{bid}.ffn_latent_up", # nemotron 3 super MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", MODEL_TENSOR.PER_LAYER_TOKEN_EMBD: "per_layer_token_embd", # gemma3n MODEL_TENSOR.PER_LAYER_MODEL_PROJ: "per_layer_model_proj", # gemma3n @@ -1599,6 +1611,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_UP, MODEL_TENSOR.LAYER_OUT_NORM, ], + MODEL_ARCH.EUROBERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_DOWN, + ], MODEL_ARCH.MPT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -1817,6 +1842,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_GATE_UP_EXP, MODEL_TENSOR.SSM_A, MODEL_TENSOR.SSM_CONV1D, MODEL_TENSOR.SSM_DT, @@ -1906,6 +1932,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_GATE_UP_EXP, MODEL_TENSOR.SSM_A, MODEL_TENSOR.SSM_CONV1D, MODEL_TENSOR.SSM_DT, @@ -2607,6 +2634,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_GATE_EXP, MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_UP_EXP, MODEL_TENSOR.FFN_GATE_SHEXP, MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, @@ -2902,6 +2930,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_GATE_INP, MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_DOWN_EXP, + # expert latent + MODEL_TENSOR.MOE_LATENT_DOWN, + MODEL_TENSOR.MOE_LATENT_UP, # shared expert MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, @@ -3783,6 +3814,7 @@ class GGMLQuantizationType(IntEnum): TQ1_0 = 34 TQ2_0 = 35 MXFP4 = 39 + NVFP4 = 40 class ExpertGatingFuncType(IntEnum): @@ -3879,6 +3911,7 @@ class VisionProjectorType: GEMMA3 = "gemma3" GEMMA3NV = "gemma3nv" GEMMA3NA = "gemma3na" + PHI4 = "phi4" IDEFICS3 = "idefics3" PIXTRAL = "pixtral" LLAMA4 = "llama4" @@ -3940,6 +3973,7 @@ GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = { GGMLQuantizationType.TQ1_0: (256, 2 + 4 * 13), GGMLQuantizationType.TQ2_0: (256, 2 + 64), GGMLQuantizationType.MXFP4: (32, 1 + 16), + GGMLQuantizationType.NVFP4: (64, 4 + 32), } diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index d87e8f7232..0a1b85f506 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -175,6 +175,9 @@ class GGUFReader: if new_align.types != [GGUFValueType.UINT32]: raise ValueError('Bad type for general.alignment field') self.alignment = new_align.parts[-1][0] + # Ensure alignment is a non-zero power of two + if self.alignment == 0 or (self.alignment & (self.alignment - 1)) != 0: + raise ValueError('Invalid alignment: must be a non-zero power of two') padding = offs % self.alignment if padding != 0: offs += self.alignment - padding @@ -202,11 +205,11 @@ class GGUFReader: def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int: if field.name in self.fields: - # TODO: add option to generate error on duplicate keys - # raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') + # TODO: add option to make this a warning and accept duplicate keys like below + raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') - logger.warning(f'Duplicate key {field.name} at offset {field.offset}') - self.fields[field.name + '_{}'.format(field.offset)] = field + # logger.warning(f'Duplicate key {field.name} at offset {field.offset}') + # self.fields[field.name + '_{}'.format(field.offset)] = field else: self.fields[field.name] = field return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts) diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 4245d18bc4..662dda3cf4 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -139,10 +139,13 @@ class GGUFWriter: size = prod(shape) if "_exps." in name: - expert_count = shape[-2 if ".bias" in name else -3] - expert_params += (size // expert_count) - expert_sum += expert_count - n_expert_tensors += 1 + if len(shape) >= 3: + expert_count = shape[-2 if ".bias" in name else -3] + expert_params += (size // expert_count) + expert_sum += expert_count + n_expert_tensors += 1 + else: + shared_params += size else: shared_params += size @@ -501,6 +504,8 @@ class GGUFWriter: self.add_uint32(Keys.General.QUANTIZATION_VERSION, quantization_version) def add_custom_alignment(self, alignment: int) -> None: + if alignment <= 0 or (alignment & (alignment - 1)) != 0: + raise ValueError('Invalid alignment: must be a non-zero power of two') self.data_alignment = alignment self.add_uint32(Keys.General.ALIGNMENT, alignment) @@ -771,6 +776,12 @@ class GGUFWriter: def add_value_length_mla(self, length: int) -> None: self.add_uint32(Keys.Attention.VALUE_LENGTH_MLA.format(arch=self.arch), length) + def add_key_length_swa(self, length: int) -> None: + self.add_uint32(Keys.Attention.KEY_LENGTH_SWA.format(arch=self.arch), length) + + def add_value_length_swa(self, length: int) -> None: + self.add_uint32(Keys.Attention.VALUE_LENGTH_SWA.format(arch=self.arch), length) + def add_indexer_head_count(self, count: int) -> None: self.add_uint32(Keys.Attention.Indexer.HEAD_COUNT.format(arch=self.arch), count) @@ -851,6 +862,9 @@ class GGUFWriter: def add_moe_every_n_layers(self, value: int) -> None: self.add_uint32(Keys.LLM.MOE_EVERY_N_LAYERS.format(arch=self.arch), value) + def add_moe_latent_size(self, value: int) -> None: + self.add_uint32(Keys.LLM.MOE_LATENT_SIZE.format(arch=self.arch), value) + def add_nextn_predict_layers(self, count: int) -> None: self.add_uint32(Keys.LLM.NEXTN_PREDICT_LAYERS.format(arch=self.arch), count) @@ -944,6 +958,9 @@ class GGUFWriter: def add_rope_dimension_count(self, count: int) -> None: self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) + def add_rope_dimension_count_swa(self, count: int) -> None: + self.add_uint32(Keys.Rope.DIMENSION_COUNT_SWA.format(arch=self.arch), count) + def add_rope_dimension_sections(self, dims: Sequence[int]) -> None: self.add_array(Keys.Rope.DIMENSION_SECTIONS.format(arch=self.arch), dims) diff --git a/gguf-py/gguf/metadata.py b/gguf-py/gguf/metadata.py index e0d478ce95..e954644e28 100644 --- a/gguf-py/gguf/metadata.py +++ b/gguf-py/gguf/metadata.py @@ -186,7 +186,7 @@ class Metadata: # Quick hack to fix the Norway problem # https://hitchdev.com/strictyaml/why/implicit-typing-removed/ yaml_content = yaml_content.replace("- no\n", "- \"no\"\n") - # yaml should use 2 spaces insted of tab + # yaml should use 2 spaces instead of tab # this issue has came up with the Qwen/Qwen3-235B-A22B-Instruct-2507 model card # (I've also sent a pr tp fix the modelcard too) yaml_content = yaml_content.replace("\t", " ") diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py index 31845ea6ee..1cd519981a 100644 --- a/gguf-py/gguf/quants.py +++ b/gguf-py/gguf/quants.py @@ -704,6 +704,65 @@ class MXFP4(__Quant, qtype=GGMLQuantizationType.MXFP4): return (d * qs.astype(np.float32)) +class NVFP4(__Quant, qtype=GGMLQuantizationType.NVFP4): + # E2M1 values doubled (kvalues_mxfp4 convention) + kvalues = (0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12) + + @staticmethod + def ue4m3_to_fp32(x: np.ndarray) -> np.ndarray: + """Decode unsigned E4M3 (bias=7) to float, with 0.5 factor for kvalues convention.""" + exp = (x >> 3).astype(np.int32) & 0xF + man = (x & 0x7).astype(np.float32) + raw = np.where( + exp == 0, + man * 2**-9, + (1.0 + man / 8.0) * (2.0 ** (exp.astype(np.float32) - 7))) + return np.where((x == 0) | (x == 0x7F), 0.0, raw * 0.5) + + @staticmethod + def fp32_to_ue4m3(x: np.ndarray) -> np.ndarray: + """Vectorized float32 to unsigned E4M3, matching ggml_fp32_to_ue4m3 in C.""" + x = np.clip(x, 0.0, 448.0).astype(np.float32) + bits = x.view(np.uint32) + fp32_exp = ((bits >> 23) & 0xFF).astype(np.int32) - 127 + fp32_man = ((bits >> 20) & 0x7).astype(np.int32) + ue4m3_exp = fp32_exp + 7 + + # Subnormal + sub_man = np.clip((x * 512.0 + 0.5).astype(np.int32), 0, 7) + sub_result = np.where(sub_man >= 1, sub_man, 0).astype(np.uint8) + + # Normal with rounding + round_bit = ((bits >> 19) & 1).astype(np.int32) + man = fp32_man + round_bit + exp = ue4m3_exp.copy() + overflow = man > 7 + man = np.where(overflow, 0, man) + exp = np.where(overflow, exp + 1, exp) + normal_result = np.where(exp >= 15, np.uint8(0x7E), ((exp << 3) | man).astype(np.uint8)) + + return np.where(x <= 0.0, np.uint8(0), + np.where(ue4m3_exp <= 0, sub_result, + np.where(ue4m3_exp >= 15, np.uint8(0x7E), normal_result))) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_super = blocks.shape[0] + + d_bytes, qs = np.hsplit(blocks, [4]) + d = cls.ue4m3_to_fp32(d_bytes).reshape(n_super, 4, 1) # (n_super, 4, 1) + + qs = qs.reshape(n_super, 4, 8) + lo = (qs & np.uint8(0x0F)).view(np.int8) + hi = (qs >> np.uint8(4)).view(np.int8) + vals = np.concatenate([lo, hi], axis=-1) # (n_super, 4, 16) + + kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16) + vals = np.take_along_axis(kvalues, vals, axis=-1) + + return (d * vals.astype(np.float32)).reshape(n_super, 64) + + class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS): ksigns: bytes = ( b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f" diff --git a/gguf-py/gguf/scripts/gguf_convert_endian.py b/gguf-py/gguf/scripts/gguf_convert_endian.py index 86bf87846c..164c9171e0 100755 --- a/gguf-py/gguf/scripts/gguf_convert_endian.py +++ b/gguf-py/gguf/scripts/gguf_convert_endian.py @@ -65,6 +65,7 @@ byteswap_tensors = { gguf.GGMLQuantizationType.Q4_K: byteswap_q4_k, gguf.GGMLQuantizationType.Q6_K: byteswap_q6_k, gguf.GGMLQuantizationType.MXFP4: byteswap_noop, + gguf.GGMLQuantizationType.NVFP4: byteswap_noop, } diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index fc468d0774..18131e5405 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -567,6 +567,18 @@ class TensorNameMap: "model.layers.{bid}.mlp.chunk_experts.gate_proj", # grovemoe ), + MODEL_TENSOR.FFN_GATE_UP_EXP: ( + "model.layers.{bid}.mlp.experts.gate_up_proj", + ), + + MODEL_TENSOR.MOE_LATENT_DOWN: ( + "backbone.layers.{bid}.mixer.fc1_latent_proj", # nemotron 3 super + ), + + MODEL_TENSOR.MOE_LATENT_UP: ( + "backbone.layers.{bid}.mixer.fc2_latent_proj", # nemotron 3 super + ), + # Feed-forward down MODEL_TENSOR.FFN_DOWN: ( "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 48693ae3e3..5fb2755f1a 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.17.1" +version = "0.18.0" description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ diff --git a/gguf-py/tests/test_metadata.py b/gguf-py/tests/test_metadata.py index 40d484f4ea..b77c563ff2 100755 --- a/gguf-py/tests/test_metadata.py +++ b/gguf-py/tests/test_metadata.py @@ -164,7 +164,7 @@ class TestMetadataMethod(unittest.TestCase): self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B"), ('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration-LoRA', None, '8B')) - # Negative size --> output is a LoRA adaper --> prune "LoRA" out of the name to avoid redundancy with the suffix + # Negative size --> output is a LoRA adapter --> prune "LoRA" out of the name to avoid redundancy with the suffix self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B", -1234), ('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration', None, '8B')) diff --git a/gguf-py/tests/test_quants.py b/gguf-py/tests/test_quants.py index 172fa0018a..9aa7c4ae2a 100755 --- a/gguf-py/tests/test_quants.py +++ b/gguf-py/tests/test_quants.py @@ -68,6 +68,7 @@ class GGMLQuants: "q2_K", "q3_K", "q4_K", "q5_K", "q6_K", "tq1_0", "tq2_0", "mxfp4", + "nvfp4", "iq2_xxs", "iq2_xs", "iq2_s", "iq3_xxs", "iq3_s", "iq1_s", "iq1_m", "iq4_nl", "iq4_xs", ): diff --git a/include/llama.h b/include/llama.h index 1fe1fe6e4d..2cd6e6ae0c 100644 --- a/include/llama.h +++ b/include/llama.h @@ -5,6 +5,7 @@ #include "ggml-cpu.h" #include "ggml-backend.h" #include "ggml-opt.h" +#include "gguf.h" #include #include @@ -152,6 +153,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_TQ1_0 = 36, // except 1d tensors LLAMA_FTYPE_MOSTLY_TQ2_0 = 37, // except 1d tensors LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38, // except 1d tensors + LLAMA_FTYPE_MOSTLY_NVFP4 = 39, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; @@ -444,19 +446,30 @@ extern "C" { LLAMA_API void llama_detach_threadpool(struct llama_context * ctx); + typedef void (*llama_model_set_tensor_data_t)(struct ggml_tensor * tensor, void * userdata); + + // Create a new model from GGUF metadata as well as a function to set the tensor data + // - tensors are created as GGML_TYPE_F32 by default, + // override by adding a tensor with the same name but a different name to the context + LLAMA_API struct llama_model * llama_model_init_from_user( + struct gguf_context * metadata, + llama_model_set_tensor_data_t set_tensor_data, // function to initialize tensor data with + void * set_tensor_data_ud, // userdata for function + struct llama_model_params params); + DEPRECATED(LLAMA_API struct llama_model * llama_load_model_from_file( const char * path_model, struct llama_model_params params), "use llama_model_load_from_file instead"); - // Load the model from a file + // Load a model from a file // If the file is split into multiple parts, the file name must follow this pattern: -%05d-of-%05d.gguf // If the split file name does not follow this pattern, use llama_model_load_from_splits LLAMA_API struct llama_model * llama_model_load_from_file( const char * path_model, struct llama_model_params params); - // Load the model from multiple splits (support custom naming scheme) + // Load a model from multiple splits (support custom naming scheme) // The paths must be in the correct order LLAMA_API struct llama_model * llama_model_load_from_splits( const char ** paths, @@ -1002,7 +1015,7 @@ extern "C" { // Logits for the ith token. For positive indices, Equivalent to: // llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab - // Negative indicies can be used to access logits in reverse order, -1 is the last logit. + // Negative indices can be used to access logits in reverse order, -1 is the last logit. // returns NULL for invalid ids. LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i); @@ -1017,7 +1030,7 @@ extern "C" { // Get the embeddings for the ith token. For positive indices, Equivalent to: // llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd - // Negative indicies can be used to access embeddings in reverse order, -1 is the last embedding. + // Negative indices can be used to access embeddings in reverse order, -1 is the last embedding. // shape: [n_embd] (1-dimensional) // returns NULL for invalid ids. LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i); @@ -1037,9 +1050,9 @@ extern "C" { // Returns LLAMA_TOKEN_NULL if no token was sampled. LLAMA_API llama_token llama_get_sampled_token_ith(struct llama_context * ctx, int32_t i); - // Get the backend sampled probabilites for the ith token + // Get the backend sampled probabilities for the ith token // The index matches llama_get_sampled_token_ith(). - // Returns NULL if no probabilites were generated. + // Returns NULL if no probabilities were generated. LLAMA_API float * llama_get_sampled_probs_ith (struct llama_context * ctx, int32_t i); LLAMA_API uint32_t llama_get_sampled_probs_count_ith(struct llama_context * ctx, int32_t i); @@ -1366,7 +1379,7 @@ extern "C" { float tau, float eta); - /// @details Intializes a GBNF grammar, see grammars/README.md for details. + /// @details Initializes a GBNF grammar, see grammars/README.md for details. /// @param vocab The vocabulary that this grammar will be used with. /// @param grammar_str The production rules for the grammar, encoded as a string. Returns an empty grammar if empty. Returns NULL if parsing of grammar_str fails. /// @param grammar_root The name of the start symbol for the grammar. diff --git a/models/templates/Apertus-8B-Instruct.jinja b/models/templates/Apertus-8B-Instruct.jinja index 10826ff690..432ae59a40 100644 --- a/models/templates/Apertus-8B-Instruct.jinja +++ b/models/templates/Apertus-8B-Instruct.jinja @@ -97,20 +97,20 @@ {%- macro render_tools(tools) -%} {%- for tool in tools %} - {{- "// " + tool.description + "\n" }} - {{- "type "+ tool.name + " = " }} - {%- if tool.parameters and tool.parameters.properties %} + {{- "// " + tool.function.description + "\n" }} + {{- "type "+ tool.function.name + " = " }} + {%- if tool.function.parameters and tool.function.parameters.properties %} {{- "(_: {\n" }} - {%- for param_name, param_spec in tool.parameters.properties.items() %} + {%- for param_name, param_spec in tool.function.parameters.properties.items() %} {%- if param_spec.description %} {{- "// " + param_spec.description + "\n" }} {%- endif %} {{- param_name }} - {%- if param_name not in (tool.parameters.required or []) -%} + {%- if param_name not in (tool.function.parameters.required or []) -%} {{- "?" }} {%- endif -%} {{- ": " }} - {{- render_typescript_type(param_spec, tool.parameters.required or []) }} + {{- render_typescript_type(param_spec, tool.function.parameters.required or []) }} {%- if param_spec.default is defined -%} {%- if param_spec.enum %} {{- ", // default: " + param_spec.default }} @@ -294,7 +294,7 @@ {%- for tool_call in message.tool_calls -%} {%- if tool_call.type == 'function' -%} {%- set function = tool_call.function -%} - {{- '{"' + function.name + '": ' + function.arguments + '}' }} + {{- '{"' + function.name + '": ' + function.arguments|tojson + '}' }} {%- if not loop.last -%} {{- ", " }} {%- endif -%} diff --git a/models/templates/Apriel-1.6-15b-Thinker-fixed.jinja b/models/templates/Apriel-1.6-15b-Thinker-fixed.jinja new file mode 100755 index 0000000000..a60a95f44d --- /dev/null +++ b/models/templates/Apriel-1.6-15b-Thinker-fixed.jinja @@ -0,0 +1,172 @@ +{# ---------------------------------------------------------------------- #} +{# ƛƬ Default setup and flags #} +{# ---------------------------------------------------------------------- #} +{%- set messages = messages or [] -%} +{%- set tools = tools or [] -%} +{%- set add_generation_prompt = add_generation_prompt or false -%} +{%- set available_tool_string = '' -%} +{%- set add_tool_id = true -%} +{%- set add_thoughts = true -%} {# whether to include reasoning blocks #} +{%- set add_generation_prompt = true -%} {# whether to emit reasoning starter before assistant response #} +{# Optional token placeholders (safe defaults) #} +{%- set bos_token = bos_token or '' -%} +{%- set eos_token = eos_token or '' -%} +{# ---------------------------------------------------------------------- #} +{# Core reasoning prompt and assistant reasoning prefix #} +{# ---------------------------------------------------------------------- #} +{%- set reasoning_prompt -%} + You are a thoughtful, systematic AI assistant from ServiceNow Language Models (SLAM) lab. + Analyze each question carefully, present your reasoning step-by-step, then provide the final + response after the marker [BEGIN FINAL RESPONSE]. +{%- endset -%} +{%- set reasoning_asst_turn_start = 'Here are my reasoning steps:\n' -%} +{# ---------------------------------------------------------------------- #} +{# Tool list and tool call output format #} +{# ---------------------------------------------------------------------- #} +{%- if tools|length > 0 -%} + {%- set available_tool_string -%} + You are provided with function signatures within XML tags. + You may call one or more functions to assist with the user query. + Don't make assumptions about the arguments. You should infer the argument values from previous + user responses and the system message. + Here are the available tools: + + {% for tool in tools %}{{ tool|string }}{% endfor %} + + . + + Return all function calls as a list of JSON objects within XML tags. + Each JSON object should contain a function name and arguments as follows: + [ + {"name": , "arguments": }, + {"name": , "arguments": }, + ... + ] + {%- endset -%} +{%- endif -%} +{# ---------------------------------------------------------------------- #} +{# Start system block if first message is not system #} +{# ---------------------------------------------------------------------- #} +{%- if messages|length > 0 and messages[0]['role'] != 'system' -%} + {%- if tools|length > 0 -%} + {{ bos_token + '<|begin_system|>\n' + reasoning_prompt + '\n' + available_tool_string + '\n' }} + {%- else -%} + {{ bos_token + '<|begin_system|>\n' + reasoning_prompt + '\n' }} + {%- endif -%} +{%- endif -%} +{# ---------------------------------------------------------------------- #} +{# Iterate through messages #} +{# ---------------------------------------------------------------------- #} +{%- for message in messages -%} + + {# ---------------- USER MESSAGE ---------------- #} + {%- if message['role'] == 'user' -%} + {{ '<|begin_user|>\n' }} + {%- if message['content'] is not string -%} + {%- for chunk in message['content'] -%} + {%- if chunk['type'] == 'text' -%} + {{ chunk['text'] }} + {%- elif chunk['type'] in ['image', 'image_url'] -%} + {{ '[IMG]' }} + {%- else -%} + {{ raise_exception('Unrecognized content type!') }} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{ message['content'] }} + {%- endif -%} + + {# ---------------- SYSTEM MESSAGE ---------------- #} + {%- elif message['role'] == 'system' -%} + {%- set sys_content = message.get('content', '') -%} + {%- if sys_content and sys_content|length > 0 -%} + {%- if sys_content is string -%} + {%- set system_message = sys_content -%} + {%- else -%} + {%- set system_message = sys_content[0]['text'] -%} + {%- endif -%} + {%- else -%} + {%- set system_message = '' -%} + {%- endif -%} + + {%- if tools|length > 0 -%} + {{ bos_token + '<|begin_system|>\n' + reasoning_prompt + '\n' + system_message + '\n' + available_tool_string + '\n' }} + {%- else -%} + {{ bos_token + '<|begin_system|>\n' + reasoning_prompt + '\n' + system_message + '\n' }} + {%- endif -%} + + {# ---------------- ASSISTANT MESSAGE ---------------- #} + {%- elif message['role'] == 'assistant' -%} + {%- if loop.last -%} + {%- set add_tool_id = false -%} + {%- endif -%} + + {{ '\n<|begin_assistant|>\n' }} + + {%- if add_thoughts and message.get('reasoning_content') and loop.last -%} + {{ message['reasoning_content'] + '\n[BEGIN FINAL RESPONSE]\n' }} + {%- endif -%} + + {%- set asst_content = message.get('content', '') -%} + {%- if asst_content and asst_content|length > 0 -%} + {%- if asst_content is not string -%} + {%- set asst_text = asst_content[0]['text'] -%} + {%- else -%} + {%- set asst_text = asst_content -%} + {%- endif -%} + {# For historical turns (not the last), strip reasoning and keep only final response #} + {%- if not loop.last and '[BEGIN FINAL RESPONSE]' in asst_text -%} + {{- asst_text.split('[BEGIN FINAL RESPONSE]')[-1] | trim -}} + {%- else -%} + {{- asst_text -}} + {%- endif -%} + {%- elif message.get('chosen') and message['chosen']|length > 0 -%} + {{ message['chosen'][0] }} + {%- endif -%} + + {# Tool call output #} + {%- set tool_calls = message.get('tool_calls', []) -%} + {%- if tool_calls and tool_calls|length > 0 -%} + {{ '\n[' }} + {%- for tool_call in tool_calls -%} + {{ '{"name": "' + tool_call['function']['name'] + '", "arguments": ' + tool_call['function']['arguments']|tojson }} + {%- if add_tool_id == true and 'id' in tool_call -%} + {{ ', "id": "' + tool_call['id'] + '"' }} + {%- endif -%} + {{ '}' }} + {%- if not loop.last -%}{{ ', ' }}{%- endif -%} + {%- endfor -%} + {{ ']' }} + {%- endif -%} + + {%- set training_prompt = training_prompt if (training_prompt is defined) else false -%} + {%- if not loop.last or training_prompt -%} + {{ '\n<|end|>\n' }} + {%- endif -%} + + {# ---------------- TOOL RESULT MESSAGE ---------------- #} + {%- elif message['role'] == 'tool' -%} + {%- set tool_content = message.get('content', '') -%} + {%- if tool_content is string -%} + {%- set tool_message = tool_content -%} + {%- else -%} + {%- set tool_message = tool_content[0]['text'] if tool_content else '' -%} + {%- endif -%} + {{ '<|begin_tool_result|>\n' + tool_message|string + '\n' }} + + {# ---------------- CONTENT MESSAGE ---------------- #} + {%- elif message['role'] == 'content' -%} + {%- set msg_content = message.get('content', '') -%} + {%- if msg_content is not string -%} + {{ '<|begin_content|>\n' + msg_content[0]['text'] + '\n' }} + {%- else -%} + {{ '<|begin_content|>\n' + msg_content + '\n' }} + {%- endif -%} + {%- endif -%} + + {# ---------------- REASONING PROMPT BEFORE NEXT ASSISTANT ---------------- #} + {%- if loop.last and add_generation_prompt and message['role'] != 'assistant' -%} + {{ '\n<|begin_assistant|>\n' + reasoning_asst_turn_start }} + {%- endif -%} + +{%- endfor -%} diff --git a/models/templates/Bielik-11B-v3.0-Instruct.jinja b/models/templates/Bielik-11B-v3.0-Instruct.jinja new file mode 100644 index 0000000000..40ef50076e --- /dev/null +++ b/models/templates/Bielik-11B-v3.0-Instruct.jinja @@ -0,0 +1,77 @@ +{{ bos_token }} +{%- if messages[0]['role'] == 'system' %} + {%- set system_message = messages[0]['content'] %} + {%- set loop_start_index = 1 %} +{%- else %} + {%- set system_message = "" %} + {%- set loop_start_index = 0 %} +{%- endif %} + +{%- if system_message or tools %} + {{- '<|im_start|>system\n' }} + {%- if system_message %} + {{- system_message }} + {%- endif %} + {%- if tools %} + {{- '\n\nMasz dostęp do następujących narzędzi. Definicje narzędzi znajdują się poniżej wewnątrz znaczników <|function_list|>:\n<|function_list|>\n' }} + {{- '[' }} + {%- for tool in tools %} + {{- tool | tojson }} + {%- if not loop.last %} + {{- ',\n' }} + {%- endif %} + {%- endfor %} + {{- ']\n<|function_list|>\n\nAby wywołać narzędzie, użyj formatu {"name": "nazwa_narzędzia", "arguments": {"argument": "wartość"}}. Wyniki działania narzędzi zostaną przekazane z markerem <|function_output|>.\n' }} + {%- endif %} + {%- if enable_thinking %} + {{- '\n\nZanim odpowiesz na pytanie, najpierw przemyśl swoje kroki i umieść swoje myśli wewnątrz tagów .... Musisz najpierw pomyśleć, zanim udzielisz odpowiedzi. ' -}} + {{- 'WAŻNE: Powinieneś myśleć w tym samym języku, co pytanie użytkownika. Jeśli pytanie jest zadane po polsku, powinieneś również myśleć po polsku. Jeśli pytanie jest po angielsku, myślisz również po angielsku itd. ' }} + {{- '** PAMIĘTAJ! ** Pytanie po polsku -> myślenie po polsku -> odpowiedź po polsku!' -}} + {{- '*** BARDZO WAŻNE!!! *** Jesteś Bielikiem, polskim modelem językowym. Twoją główną cechą jest umiejętność pisania po polsku. Jeśli użytkownik zadaje Ci pytania po polsku, ZAWSZE odpowiadaj po polsku. ' -}} + {{- 'Nawet, jeśli korzystasz z narzędzia, którego większość instrukcji jest po angielsku, powinieneś przede wszystkim odpowiadać po polsku, jeśli użytkownik zadaje pytanie w tym języku. ' -}} + {%- endif %} + {{- '<|im_end|>\n' }} +{%- endif %} + +{%- for message in messages[loop_start_index:] %} + {%- if message['role'] == 'user' %} + {{- '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }} + {%- elif message['role'] == 'assistant' %} + {{- '<|im_start|>assistant\n' }} + {%- set content = message.content | default('') %} + {%- set reasoning_content = message.reasoning_content | default('') %} + {%- if not reasoning_content and '' in content and '' in content %} + {%- set reasoning_parts = content.split('') %} + {%- set reasoning_content = reasoning_parts[0].split('')[-1] %} + {%- set content = reasoning_parts[1:] | join('') %} + {%- endif %} + {%- if reasoning_content %} + {{- '\n' + reasoning_content.strip() + '\n\n' }} + {%- endif %} + {{- content.lstrip() }} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n{"name": "' + tool_call.name + '", "arguments": ' + (tool_call.arguments if tool_call.arguments is string else tool_call.arguments | tojson) + '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message['role'] == 'tool' %} + {%- if loop.index0 == 0 or messages[loop.index0 - 1]['role'] != 'tool' %} + {{- '<|im_start|>user\n' }} + {%- endif %} + {{- '<|function_output|>' + message['content'] }} + {%- if loop.last or messages[loop.index0 + 1]['role'] != 'tool' %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} + +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking %} + {{- '\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja b/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja index 078e9f5458..fcf1259d33 100644 --- a/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja +++ b/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja @@ -132,7 +132,7 @@ The following instructions take precedence over instructions in the default prea {%- elif message.role|lower == 'user' %} <|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %} {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %} -<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.tool_plan}}<|END_THINKING|><|START_ACTION|>[ +<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.reasoning_content}}<|END_THINKING|><|START_ACTION|>[ {% for tc in message.tool_calls %} {"tool_call_id": "{{ tool_idx.value }}", "tool_name": "{{ tc['function']['name'] }}", "parameters": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %} @@ -153,4 +153,4 @@ The following instructions take precedence over instructions in the default prea ]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|> {%- endif %} -{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> \ No newline at end of file +{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{%- if not enable_thinking -%}<|START_THINKING|><|END_THINKING|>{%- endif %} \ No newline at end of file diff --git a/models/templates/GLM-4.7-Flash.jinja b/models/templates/GLM-4.7-Flash.jinja new file mode 100644 index 0000000000..2ab98ef068 --- /dev/null +++ b/models/templates/GLM-4.7-Flash.jinja @@ -0,0 +1,86 @@ +[gMASK] +{%- if tools -%} +<|system|> +# Tools + +You may call one or more functions to assist with the user query. + +You are provided with function signatures within XML tags: + +{% for tool in tools %} +{{ tool | tojson(ensure_ascii=False) }} +{% endfor %} + + +For each function call, output the function name and arguments within the following XML format: +{function-name}{arg-key-1}{arg-value-1}{arg-key-2}{arg-value-2}...{%- endif -%} +{%- macro visible_text(content) -%} + {%- if content is string -%} + {{- content }} + {%- elif content is iterable and content is not mapping -%} + {%- for item in content -%} + {%- if item is mapping and item.type == 'text' -%} + {{- item.text }} + {%- elif item is string -%} + {{- item }} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{- content }} + {%- endif -%} +{%- endmacro -%} +{%- set ns = namespace(last_user_index=-1) %} +{%- for m in messages %} + {%- if m.role == 'user' %} + {% set ns.last_user_index = loop.index0 -%} + {%- endif %} +{%- endfor %} +{% for m in messages %} +{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }} +{%- elif m.role == 'assistant' -%} +<|assistant|> +{%- set reasoning_content = '' %} +{%- set content = visible_text(m.content) %} +{%- if m.reasoning_content is string %} + {%- set reasoning_content = m.reasoning_content %} +{%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} +{%- endif %} +{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%} +{{ '' + reasoning_content.strip() + ''}} +{%- else -%} +{{ '' }} +{%- endif -%} +{%- if content.strip() -%} +{{ content.strip() }} +{%- endif -%} +{% if m.tool_calls %} +{% for tc in m.tool_calls %} +{%- if tc.function %} + {%- set tc = tc.function %} +{%- endif %} +{{- '' + tc.name -}} +{% set _args = tc.arguments %}{% for k, v in _args.items() %}{{ k }}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{% endfor %}{% endfor %} +{% endif %} +{%- elif m.role == 'tool' -%} +{%- if m.content is string -%} +{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|observation|>' }} +{%- endif %} +{{- '' }} +{{- m.content }} +{{- '' }} +{%- else -%} +<|observation|>{% for tr in m.content %} +{{ tr.output if tr.output is defined else tr }}{% endfor -%} +{% endif -%} +{%- elif m.role == 'system' -%} +<|system|>{{ visible_text(m.content) }} +{%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + <|assistant|>{{- '' if (enable_thinking is defined and not enable_thinking) else '' -}} +{%- endif -%} \ No newline at end of file diff --git a/models/templates/GigaChat3-10B-A1.8B.jinja b/models/templates/GigaChat3-10B-A1.8B.jinja new file mode 100644 index 0000000000..f9826a4457 --- /dev/null +++ b/models/templates/GigaChat3-10B-A1.8B.jinja @@ -0,0 +1,355 @@ +{#--------TOOL RENDERING FUNCTIONS---------#} + +{#--------------------------------------------------------------- + Converts JSON Schema (dict) to a TypeScript type definition +----------------------------------------------------------------#} +{%- macro json_schema_to_typescript(schema, indent="") -%} + {%- set ADDITIONAL_JSON_KEYS = ['format', 'maxItems', 'maximum', 'minItems', 'minimum', 'pattern'] -%} + {%- set ty = schema.get("type") -%} + + {# ---------------- OBJECT ---------------- #} + {%- if ty == "object" -%} + {{- "{\n" -}} + + {# Start building property list #} + {%- set props = schema.get("properties", {}) -%} + {%- set required = schema.get("required", []) -%} + {%- set has_additional_props = schema.get("additionalProperties") is defined -%} + {%- set additional_props_type = none -%} + {%- if has_additional_props -%} + {%- if schema.additionalProperties == true -%} + {%- set additional_props_type = {'type': 'any'} -%} + {%- elif schema.additionalProperties is mapping -%} + {%- set additional_props_type = schema.additionalProperties -%} + {%- endif -%} + {%- endif -%} + + {%- for key, val in props.items() -%} + {# ---------- Description Comments ---------- #} + {%- if "description" in val -%} + {%- for line in val['description'].split('\n') -%} + {%- if line.strip() -%} + {{- indent + '// ' + line + '\n' -}} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {# ---------- Additional JSON Keys ---------- #} + {%- for add_key, add_val in val.items() -%} + {%- if add_key in ADDITIONAL_JSON_KEYS -%} + {%- if add_val is string -%} + {{- indent + '// ' + add_key + ': "' + add_val + '"' + '\n' -}} + {%- else -%} + {{- indent + '// ' + add_key + ': ' ~ add_val ~ '\n' -}} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + + {# ---------- Property Definition ---------- #} + {%- set type_str = json_schema_to_typescript( + val, + indent + " " + ) -%} + + {{- indent + key + ('' if key in required else '?') + ': ' + type_str + ',' -}} + + {%- if "default" in val or "defalut_value" in val -%} + {%- set default = val.get("default", val.get("defalut_value")) -%} + {%- if default is string -%} + {{- ' // default: "' + default + '"' -}} + {%- else -%} + {{- ' // default: ' ~ default -}} + {%- endif -%} + {%- endif -%} + + {{- "\n" -}} + {%- endfor -%} + + {# Handle additionalProperties as index signature #} + {%- if has_additional_props and additional_props_type is not none -%} + {%- set additional_type_str = json_schema_to_typescript( + additional_props_type, + indent + " " + ) -%} + {{- indent + '[key: string]: ' + additional_type_str + '\n' -}} + {%- endif -%} + + {{- indent[: (indent|length - " "|length) ] + '}' -}} + + {# ---------------- STRING ---------------- #} + {%- elif ty == "string" -%} + {%- if schema.get("enum") -%} + {%- set ns = namespace(enum = []) -%} + {%- for en in schema['enum'] -%} + {%- set ns.enum = ns.enum + ['"' ~ en ~ '"'] -%} + {%- endfor -%} + {{- ns.enum | join(' | ') -}} + {%- elif schema.get("format", "none") in ['date-time', 'date'] -%} + {{- 'Date' -}} + {%- else -%} + {{- 'string' -}} + {%- endif -%} + + {# ---------------- NUMBER / INTEGER ---------------- #} + {%- elif ty in ["number", "integer"] -%} + {%- if schema.get("enum") -%} + {{- schema.enum | join(' | ') -}} + {%- else -%} + {{- 'number' -}} + {%- endif -%} + + {# ---------------- BOOLEAN ---------------- #} + {%- elif ty == "boolean" -%} + {{- 'boolean' -}} + + {# ---------------- ARRAY ---------------- #} + {%- elif ty == "array" -%} + {%- if "items" in schema -%} + {{- json_schema_to_typescript(schema['items'], indent) + '[]' -}} + {%- else -%} + {{- 'Array' -}} + {%- endif -%} + + {# ---------------- FALLBACK ---------------- #} + {%- else -%} + {{- 'any' -}} + {%- endif -%} +{%- endmacro -%} + +{#--------------------------------------------------------------- + Renders a namespace and its tool definitions in TypeScript style +----------------------------------------------------------------#} + +{%- macro render_tool_namespace(namespace_name, tools) -%} + {%- set ns = namespace(sections = ['namespace ' ~ namespace_name ~ ' {']) -%} + + {%- for tool in tools -%} + {%- if tool.function -%} + {%- set tool = tool.function -%} + {%- endif -%} + + {%- set ns_tool = namespace(content_lines=[]) -%} + + {# ---------- TOOL DESCRIPTION ---------- #} + {%- if tool.get('description') -%} + {%- for line in tool['description'].split('\n') -%} + {%- if line.strip() -%} + {%- set ns_tool.content_lines = ns_tool.content_lines + ['// ' ~ line] -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {# ---------- TOOL SIGNATURE ---------- #} + {%- set main_body = "" -%} + {%- set params = tool.get("parameters") -%} + {%- if params and params.get("properties") -%} + {%- set param_type = json_schema_to_typescript(params, " ") -%} + {%- set main_body = 'type ' ~ tool.name ~ ' = (_: ' ~ param_type ~ ') => ' -%} + {%- else -%} + {%- set main_body = 'type ' ~ tool.name ~ ' = () => ' -%} + {%- endif -%} + + {# ---------- RETURN TYPE ---------- #} + {%- set return_params = tool.get("return_parameters") -%} + {%- if return_params and return_params.get("properties") -%} + {%- set return_type = json_schema_to_typescript(return_params, " ") -%} + {%- set main_body = main_body ~ return_type -%} + {%- else -%} + {%- set main_body = main_body ~ 'any' -%} + {%- endif -%} + + {%- set main_body = main_body ~ ';\n' -%} + + {%- set ns_tool.content_lines = ns_tool.content_lines + [main_body] -%} + + {# ---------- ADD TOOL TO SECTIONS ---------- #} + {%- set ns.sections = ns.sections + [ns_tool.content_lines | join('\n')] -%} + {%- endfor -%} + + {%- set ns.sections = ns.sections + ['} // namespace ' ~ namespace_name] -%} + + {{- ns.sections | join('\n') -}} +{%- endmacro -%} + + +{# ----------- MESSAGE RENDERING HELPER FUNCTIONS ------------ #} + +{%- macro render_role_message(message, role=None) -%} + {%- if not role -%} + {%- set role = message["role"] -%} + {%- endif -%} + + {%- set message_content = message['content'] or '' -%} + {%- if message_content is not string -%} + {%- set message_content = message_content | tojson(ensure_ascii=False) -%} + {%- endif -%} + + {{- role + add_tokens.role_sep + message_content + add_tokens.message_sep -}} + +{%- endmacro -%} + + +{%- macro render_function_call(message) -%} + {%- set call = message['content'] -%} + {%- if call.function -%} + {%- set call = call.function -%} + {%- endif -%} + + {%- set arguments = call['arguments'] -%} + {%- if arguments is not string -%} + {%- set arguments = arguments| tojson(ensure_ascii=False) -%} + {%- endif -%} + + {{- render_role_message( + { + 'role': 'function call', + 'content': '{"name": "' ~ call['name'] ~ '", "arguments": ' ~ arguments ~ '}' + } + ) -}} +{%- endmacro -%} + +{# ----- SPECIAL TOKENS ----- #} + +{%- set add_tokens = namespace( + role_sep="<|role_sep|>\n", + message_sep="<|message_sep|>\n\n" +) -%} + +{# ----- DEFAULT DEVSYSTEM ----- #} + +{%- set DEVSYSTEM -%} + +Description of the roles available in the dialog. + +`developer system` +A message added by Sber before the main dialog. It has the highest priority and sets global, non-overridable conditions (for example, conversation rules, the safety policy, the assistant's overall response style, etc.). + +`system` +A system instruction added by developers or by the user, but with a lower priority than `developer system`. It usually describes the assistant's instructions, a specific response style, and other conditions for this particular dialog. + +`user` +A message or request from the user. The assistant follows it if it does not conflict with higher-priority instructions (see ). + +`user memory` +A sequence of the most up-to-date long-term facts about the user at the time of their request, presented as a JSON list of strings. Facts are listed in chronological order, meaning newer facts are appended to the end of the sequence. When facts are changed or deleted, records of previous facts remain in the sequence. The assistant saves facts using a function and uses them in accordance with the block below. + +`added files` +Metadata about files available for use in the dialog, presented in JSON format. It contains the following keys: id (a unique file identifier), name (file name), type (file type). + +`assistant` +The assistant's reply to the user's request. If the system instruction or the user does not set additional rules for `assistant`, this reply must comply with the instructions in the block below. The list of functions available to call is contained in `function descriptions`. The name of the required function and its arguments will be generated next by the `function call` role. In its replies, the assistant follows the instructions in accordance with . + +`function descriptions` +Function descriptions in TypeScript format. A function is a special tool (or a set of instructions) that the assistant can call to perform specific actions, computations, or obtain data needed to solve the user's task. Each function description contains blocks with the name, description, and arguments. Sometimes the description contains separate blocks with return parameters and usage examples that illustrate the correct call and arguments. + +`function call` +The function that `assistant` calls based on the dialog context, and its arguments. The function is invoked in strict accordance with the instructions in the block. + +`function result` +The result of the last function call. + + + +The assistant can work with the following modalities: text, available functions. + + + +If instructions from different roles conflict within the dialog context, observe the following priorities: +`developer system` > `system` > `user` > `function descriptions` > `function result` > `user memory` + + + +Basic instructions for working with functions. + +Only call those functions that are described in `function descriptions`. + +Call available functions when, according to their description, such a call will help provide a more complete and/or accurate answer to the user's request. Fill in function arguments using information from the dialog context. If a function could help answer the request but a required argument is missing from the context, ask the user for the missing data before calling the function. If a necessary function is unavailable or an error occurs, briefly inform the user and, if possible, suggest an alternative. + + + +Rules for using facts in long-term memory: + +If there is no message under the `user memory` role in the dialog, this is equivalent to the absence of long-term facts about the user in memory. In that case, information about the user is limited to the current dialog, and no new facts should be saved. + + + +You are a helpful assistant. + +# Instructions +- Strictly follow the instruction priority. +- Maintain a logical chain of reasoning when answering the user's question. +- For complex questions (for example, STEM), try to answer in detail unless the system message or dialog context limits the response length. +- Be helpful, truthful, and avoid unsafe or prohibited content in your responses. +- Try to reply in the language in which the user asked their question. + + +A dialog will follow below. +The dialog may include various roles described in the block. +Each turn begins with the role name and a special token that marks the end of the role's full name, and ends with a special end-of-turn token. +Your task is to continue the dialog from the last specified role in accordance with the dialog context. +{%- endset -%} + + +{#- ---------------------- RENDERING STARTS HERE ---------------------- -#} + + +{# ----- RENDER BOS TOKEN ----- #} +{{- bos_token -}} + + +{# ----- RENDER DEVSYSTEM ----- #} +{{- render_role_message({"role": "developer system", "content": DEVSYSTEM}) -}} + +{# ----- RENDER SYSTEM IF PRESENT ----- #} +{%- if messages and messages[0]['role'] == 'system' -%} + {{- render_role_message(messages[0]) -}} + {%- set messages = messages[1:] -%} +{%- endif -%} + +{# ----- RENDER TOOLS ----- #} +{%- if tools -%} + {%- set tools_content = ( + render_tool_namespace('functions', tools) + + "\n\n" + ) -%} + {{- render_role_message({'role': 'function descriptions', 'content': tools_content}) -}} +{%- endif -%} + +{# ----- MAIN MESSAGE LOOP ----- #} +{%- for message in messages -%} + + {# ----- TOOL MESSAGE -------#} + {%- if message['role'] == 'tool' -%} + {{- render_role_message(message, 'function result') -}} + + + {# ----- ASSISTANT MESSAGE ----- #} + {%- elif message['role'] == 'assistant' -%} + + {# ----- FUNCTION CALL PART CHECKING: SINGLE CALL SETUP ----- #} + {%- if message.tool_calls is defined and message.tool_calls -%} + {%- set function_call = message.tool_calls[0] -%} + {%- else -%} + {%- set function_call = None -%} + {%- endif -%} + + {# ----- MAIN ASSISTANT RENDERING ----- #} + + {{- render_role_message({'role': 'assistant', 'content': message.content}) -}} + {%- if function_call -%} + {{- render_function_call({'role': 'function call', 'content': function_call}) -}} + {%- endif -%} + + + {# ----- OTHER MESSAGES ----- #} + {%- else -%} + {{- render_role_message(message) -}} + {%- endif -%} + + {# ----- ADDING GENERATION PROMPT ----- #} + + {%- if loop.last and add_generation_prompt and message['role'] != 'assistant' -%} + {{- 'assistant' + add_tokens.role_sep -}} + {%- endif -%} + +{%- endfor -%} \ No newline at end of file diff --git a/models/templates/GigaChat3.1-10B-A1.8B.jinja b/models/templates/GigaChat3.1-10B-A1.8B.jinja new file mode 100644 index 0000000000..0557754acf --- /dev/null +++ b/models/templates/GigaChat3.1-10B-A1.8B.jinja @@ -0,0 +1,339 @@ +{#--------TOOL RENDERING FUNCTIONS---------#} + +{#--------------------------------------------------------------- + Converts JSON Schema (dict) to a TypeScript type definition +----------------------------------------------------------------#} +{%- macro json_schema_to_typescript(schema, indent="") -%} + {%- set ADDITIONAL_JSON_KEYS = ['format', 'maxItems', 'maximum', 'minItems', 'minimum', 'pattern'] -%} + {%- set ty = schema.get("type") -%} + + {# ---------------- OBJECT ---------------- #} + {%- if ty == "object" -%} + {{- "{\n" -}} + + {# Start building property list #} + {%- set props = schema.get("properties", {}) -%} + {%- set required = schema.get("required", []) -%} + {%- set has_additional_props = schema.get("additionalProperties") is defined -%} + {%- set additional_props_type = none -%} + {%- if has_additional_props -%} + {%- if schema.additionalProperties == true -%} + {%- set additional_props_type = {'type': 'any'} -%} + {%- elif schema.additionalProperties is mapping -%} + {%- set additional_props_type = schema.additionalProperties -%} + {%- endif -%} + {%- endif -%} + + {%- for key, val in props.items() -%} + {# ---------- Description Comments ---------- #} + {%- if "description" in val -%} + {%- for line in val['description'].split('\n') -%} + {%- if line.strip() -%} + {{- indent + '// ' + line + '\n' -}} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {# ---------- Additional JSON Keys ---------- #} + {%- for add_key, add_val in val.items() -%} + {%- if add_key in ADDITIONAL_JSON_KEYS -%} + {%- if add_val is string -%} + {{- indent + '// ' + add_key + ': "' + add_val + '"' + '\n' -}} + {%- else -%} + {{- indent + '// ' + add_key + ': ' ~ add_val ~ '\n' -}} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + + {# ---------- Property Definition ---------- #} + {%- set type_str = json_schema_to_typescript( + val, + indent + " " + ) -%} + + {{- indent + key + ('' if key in required else '?') + ': ' + type_str + ',' -}} + + {%- if "default" in val or "defalut_value" in val -%} + {%- set default = val.get("default", val.get("defalut_value")) -%} + {%- if default is string -%} + {{- ' // default: "' + default + '"' -}} + {%- else -%} + {{- ' // default: ' ~ default -}} + {%- endif -%} + {%- endif -%} + + {{- "\n" -}} + {%- endfor -%} + + {# Handle additionalProperties as index signature #} + {%- if has_additional_props and additional_props_type is not none -%} + {%- set additional_type_str = json_schema_to_typescript( + additional_props_type, + indent + " " + ) -%} + {{- indent + '[key: string]: ' + additional_type_str + '\n' -}} + {%- endif -%} + + {{- indent[: (indent|length - " "|length) ] + '}' -}} + + {# ---------------- STRING ---------------- #} + {%- elif ty == "string" -%} + {%- if schema.get("enum") -%} + {%- set ns = namespace(enum = []) -%} + {%- for en in schema['enum'] -%} + {%- set ns.enum = ns.enum + ['"' ~ en ~ '"'] -%} + {%- endfor -%} + {{- ns.enum | join(' | ') -}} + {%- elif schema.get("format", "none") in ['date-time', 'date'] -%} + {{- 'Date' -}} + {%- else -%} + {{- 'string' -}} + {%- endif -%} + + {# ---------------- NUMBER / INTEGER ---------------- #} + {%- elif ty in ["number", "integer"] -%} + {%- if schema.get("enum") -%} + {{- schema.enum | join(' | ') -}} + {%- else -%} + {{- 'number' -}} + {%- endif -%} + + {# ---------------- BOOLEAN ---------------- #} + {%- elif ty == "boolean" -%} + {{- 'boolean' -}} + + {# ---------------- ARRAY ---------------- #} + {%- elif ty == "array" -%} + {%- if "items" in schema -%} + {{- json_schema_to_typescript(schema['items'], indent) + '[]' -}} + {%- else -%} + {{- 'Array' -}} + {%- endif -%} + + {# ---------------- FALLBACK ---------------- #} + {%- else -%} + {{- 'any' -}} + {%- endif -%} +{%- endmacro -%} + +{#--------------------------------------------------------------- + Renders a namespace and its tool definitions in TypeScript style +----------------------------------------------------------------#} + +{%- macro render_tool_namespace(namespace_name, tools) -%} + {%- set ns = namespace(sections = ['namespace ' ~ namespace_name ~ ' {']) -%} + + {%- for tool in tools -%} + {%- if tool.function -%} + {%- set tool = tool.function -%} + {%- endif -%} + + {%- set ns_tool = namespace(content_lines=[]) -%} + + {# ---------- TOOL DESCRIPTION ---------- #} + {%- if tool.get('description') -%} + {%- for line in tool['description'].split('\n') -%} + {%- if line.strip() -%} + {%- set ns_tool.content_lines = ns_tool.content_lines + ['// ' ~ line] -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {# ---------- TOOL SIGNATURE ---------- #} + {%- set main_body = "" -%} + {%- set params = tool.get("parameters") -%} + {%- if params and params.get("properties") -%} + {%- set param_type = json_schema_to_typescript(params, " ") -%} + {%- set main_body = 'type ' ~ tool.name ~ ' = (_: ' ~ param_type ~ ') => ' -%} + {%- else -%} + {%- set main_body = 'type ' ~ tool.name ~ ' = () => ' -%} + {%- endif -%} + + {# ---------- RETURN TYPE ---------- #} + {%- set return_params = tool.get("return_parameters") -%} + {%- if return_params and return_params.get("properties") -%} + {%- set return_type = json_schema_to_typescript(return_params, " ") -%} + {%- set main_body = main_body ~ return_type -%} + {%- else -%} + {%- set main_body = main_body ~ 'any' -%} + {%- endif -%} + + {%- set main_body = main_body ~ ';\n' -%} + + {%- set ns_tool.content_lines = ns_tool.content_lines + [main_body] -%} + + {# ---------- ADD TOOL TO SECTIONS ---------- #} + {%- set ns.sections = ns.sections + [ns_tool.content_lines | join('\n')] -%} + {%- endfor -%} + + {%- set ns.sections = ns.sections + ['} // namespace ' ~ namespace_name] -%} + + {{- ns.sections | join('\n') -}} +{%- endmacro -%} + + +{# ----------- MESSAGE RENDERING HELPER FUNCTIONS ------------ #} + +{%- macro render_function_call(call) -%} + {%- if call.function -%} + {%- set call = call.function -%} + {%- endif -%} + + {%- set arguments = call['arguments'] -%} + {%- if arguments is not string -%} + {%- set arguments = arguments| tojson(ensure_ascii=False) -%} + {%- endif -%} + + {{- '{"name": "' ~ call['name'] ~ '", "arguments": ' ~ arguments ~ '}' -}} +{%- endmacro -%} + + +{%- macro render_role_message(message, role=None) -%} + {%- if not role -%} + {%- set role = message["role"] -%} + {%- endif -%} + + {%- set message_content = message['content'] or '' -%} + {%- if message_content is not string -%} + {%- set message_content = message_content | tojson(ensure_ascii=False) -%} + {%- endif -%} + + {{- role + add_tokens.role_sep + message_content -}} + + {%- if message.tool_calls is defined and message.tool_calls -%} + {{- add_tokens.function_call + render_function_call(message.tool_calls[0]) -}} + {%- endif -%} + + {{- add_tokens.message_sep -}} + +{%- endmacro -%} + + + +{# ----- SPECIAL TOKENS ----- #} + +{%- set add_tokens = namespace( + role_sep="<|role_sep|>\n", + message_sep="<|message_sep|>\n\n", + function_call="<|function_call|>" +) -%} + +{# ----- DEFAULT DEVSYSTEM ----- #} + +{%- set DEVSYSTEM -%} + +Description of the roles available in the dialog. + +`developer system` +A message added by Sber before the main dialog. It has the highest priority and sets global, non-overridable conditions (for example, conversation rules, the safety policy, the assistant's overall response style, etc.). + +`system` +A system instruction added by developers or by the user, but with a lower priority than `developer system`. It usually describes the assistant's instructions, a specific response style, and other conditions for this particular dialog. + +`user` +A message or request from the user. The assistant follows it if it does not conflict with higher-priority instructions (see ). + +`user memory` +A sequence of the most up-to-date long-term facts about the user at the time of their request, presented as a JSON list of strings. Facts are listed in chronological order, meaning newer facts are appended to the end of the sequence. When facts are changed or deleted, records of previous facts remain in the sequence. The assistant saves facts using a function and uses them in accordance with the block below. + +`added files` +Metadata about files available for use in the dialog, presented in JSON format. It contains the following keys: id (a unique file identifier), name (file name), type (file type). + +`assistant` +The assistant's reply to the user's request. If the system instruction or the user does not set additional rules for `assistant`, this reply must comply with the instructions in the block below. The list of functions available to call is contained in `function descriptions`. The name of the required function and its arguments will be generated next by the `function call` role. In its replies, the assistant follows the instructions in accordance with . + +`function descriptions` +Function descriptions in TypeScript format. A function is a special tool (or a set of instructions) that the assistant can call to perform specific actions, computations, or obtain data needed to solve the user's task. Each function description contains blocks with the name, description, and arguments. Sometimes the description contains separate blocks with return parameters and usage examples that illustrate the correct call and arguments. + +`function call` +The function that `assistant` calls based on the dialog context, and its arguments. The function is invoked in strict accordance with the instructions in the block. + +`function result` +The result of the last function call. + + + +The assistant can work with the following modalities: text, available functions. + + + +If instructions from different roles conflict within the dialog context, observe the following priorities: +`developer system` > `system` > `user` > `function descriptions` > `function result` > `user memory` + + + +Basic instructions for working with functions. + +Only call those functions that are described in `function descriptions`. + +Call available functions when, according to their description, such a call will help provide a more complete and/or accurate answer to the user's request. Fill in function arguments using information from the dialog context. If a function could help answer the request but a required argument is missing from the context, ask the user for the missing data before calling the function. If a necessary function is unavailable or an error occurs, briefly inform the user and, if possible, suggest an alternative. + + + +Rules for using facts in long-term memory: + +If there is no message under the `user memory` role in the dialog, this is equivalent to the absence of long-term facts about the user in memory. In that case, information about the user is limited to the current dialog, and no new facts should be saved. + + + +You are a helpful assistant. + +# Instructions +- Strictly follow the instruction priority. +- Maintain a logical chain of reasoning when answering the user's question. +- For complex questions (for example, STEM), try to answer in detail unless the system message or dialog context limits the response length. +- Be helpful, truthful, and avoid unsafe or prohibited content in your responses. +- Try to reply in the language in which the user asked their question. + + +A dialog will follow below. +The dialog may include various roles described in the block. +Each turn begins with the role name and a special token that marks the end of the role's full name, and ends with a special end-of-turn token. +Your task is to continue the dialog from the last specified role in accordance with the dialog context. +{%- endset -%} + + +{#- ---------------------- RENDERING STARTS HERE ---------------------- -#} + + +{# ----- RENDER BOS TOKEN ----- #} +{{- bos_token -}} + + +{# ----- RENDER DEVSYSTEM ----- #} +{{- render_role_message({"role": "developer system", "content": DEVSYSTEM}) -}} + +{# ----- RENDER SYSTEM IF PRESENT ----- #} +{%- if messages and messages[0]['role'] == 'system' -%} + {{- render_role_message(messages[0]) -}} + {%- set messages = messages[1:] -%} +{%- endif -%} + +{# ----- RENDER TOOLS ----- #} +{%- if tools -%} + {%- set tools_content = ( + render_tool_namespace('functions', tools) + + "\n\n" + ) -%} + {{- render_role_message({'role': 'function descriptions', 'content': tools_content}) -}} +{%- endif -%} + +{# ----- MAIN MESSAGE LOOP ----- #} +{%- for message in messages -%} + + {# ----- TOOL MESSAGE -------#} + {%- if message['role'] == 'tool' -%} + {{- render_role_message(message, 'function result') -}} + + {# ----- OTHER MESSAGES ----- #} + {%- else -%} + {{- render_role_message(message) -}} + {%- endif -%} + + {# ----- ADDING GENERATION PROMPT ----- #} + + {%- if loop.last and add_generation_prompt and message['role'] != 'assistant' -%} + {{- 'assistant' + add_tokens.role_sep -}} + {%- endif -%} + +{%- endfor -%} \ No newline at end of file diff --git a/models/templates/llama-cpp-lfm2.jinja b/models/templates/LFM2-8B-A1B.jinja similarity index 98% rename from models/templates/llama-cpp-lfm2.jinja rename to models/templates/LFM2-8B-A1B.jinja index b7921120bc..fab22e952b 100644 --- a/models/templates/llama-cpp-lfm2.jinja +++ b/models/templates/LFM2-8B-A1B.jinja @@ -34,4 +34,4 @@ {%- endfor -%} {%- if add_generation_prompt -%} {{- "<|im_start|>assistant\n" -}} -{%- endif -%} +{%- endif -%} \ No newline at end of file diff --git a/models/templates/Qwen-QwQ-32B.jinja b/models/templates/Qwen-QwQ-32B.jinja index d475f70687..ce314a039f 100644 --- a/models/templates/Qwen-QwQ-32B.jinja +++ b/models/templates/Qwen-QwQ-32B.jinja @@ -59,4 +59,5 @@ {%- endfor %} {%- if add_generation_prompt %} {{- '<|im_start|>assistant\n\n' }} + {%- if not enable_thinking -%}{{- '' -}}{%- endif -%} {%- endif %} diff --git a/models/templates/Qwen3-Coder.jinja b/models/templates/Qwen3-Coder.jinja index 49b0e8d0ee..cde8c0e43d 100644 --- a/models/templates/Qwen3-Coder.jinja +++ b/models/templates/Qwen3-Coder.jinja @@ -29,7 +29,7 @@ {%- endif %} {%- endif %} {%- if tools is iterable and tools | length > 0 %} - {{- "\n\n# Tools\n\nYou have access to the following functions:\n\n" }} + {{- "\n\n# Tools\n\nYou have access to the following tools:\n\n" }} {{- "" }} {%- for tool in tools %} {%- if tool.function is defined %} @@ -63,7 +63,7 @@ {{- '\n' }} {%- endfor %} {{- "\n" }} - {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n\n\n\nvalue_1\n\n\nThis is the value for the second parameter\nthat can span\nmultiple lines\n\n\n\n\n\nReminder:\n- Function calls MUST follow the specified format: an inner block must be nested within XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n' }} + {{- '\n\nIf you choose to call a tool ONLY reply in the following format with NO suffix:\n\n\n\n\nvalue_1\n\n\nvalue_2\n\n\n\n\n\nReminder:\n- Function calls MUST follow the specified format: the tool calling block MUST begin with an opening tag and end with a closing tag.\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n' }} {%- endif %} {%- if system_message is defined %} {{- '<|im_end|>\n' }} diff --git a/models/templates/StepFun3.5-Flash.jinja b/models/templates/StepFun3.5-Flash.jinja new file mode 100644 index 0000000000..c09ea497da --- /dev/null +++ b/models/templates/StepFun3.5-Flash.jinja @@ -0,0 +1,80 @@ +{% macro render_content(content) %}{% if content is none %}{{- '' }}{% elif content is string %}{{- content }}{% elif content is mapping %}{{- content['value'] if 'value' in content else content['text'] }}{% elif content is iterable %}{% for item in content %}{% if item.type == 'text' %}{{- item['value'] if 'value' in item else item['text'] }}{% elif item.type == 'image' %}{% endif %}{% endfor %}{% endif %}{% endmacro %} +{{bos_token}}{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- render_content(messages[0].content) + '\n\n' }} + {%- endif %} + {{- "# Tools\n\nYou have access to the following functions in JSONSchema format:\n\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson(ensure_ascii=False) }} + {%- endfor %} + {{- "\n\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n\n\n\nvalue_1\n\n\nThis is the value for the second parameter\nthat can span\nmultiple lines\n\n\n\n\n\nReminder:\n- Function calls MUST follow the specified format: an inner \n...\n block must be nested within \n...\n XML tags\n- Required parameters MUST be specified\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0].role == 'system' %} + {{- '<|im_start|>system\n' + render_content(messages[0].content) + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for message in messages[::-1] %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- if ns.multi_step_tool and message.role == "user" and render_content(message.content) is string and not(render_content(message.content).startswith('') and render_content(message.content).endswith('')) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- set content = render_content(message.content) %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {%- set role_name = 'observation' if (message.role == "system" and not loop.first and message.name == 'observation') else message.role %} + {{- '<|im_start|>' + role_name + '\n' + content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = render_content(message.reasoning_content) %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- else %} + {%- set reasoning_content = '' %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content + '\n\n' + content }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n' }} + {%- if tool_call.arguments is defined %} + {%- set arguments = tool_call.arguments %} + {%- for args_name, args_value in arguments|items %} + {{- '\n' }} + {%- set args_value = args_value | tojson(ensure_ascii=False) | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %} + {{- args_value }} + {{- '\n\n' }} + {%- endfor %} + {%- endif %} + {{- '\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>tool_response\n' }} + {%- endif %} + {{- '' }} + {{- content }} + {{- '' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n\n' }} +{%- endif %} diff --git a/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja b/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja index c2066bd739..299f7a7ff1 100644 --- a/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja +++ b/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja @@ -1 +1,44 @@ -{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '' in content %}{% set content = content.split('')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>\n'}}{% endif %} \ No newline at end of file +{% if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- set ns.system_prompt = message['content'] -%} + {%- endif -%} +{%- endfor -%}{{bos_token}}{{ns.system_prompt}} +{%- for message in messages -%} + {%- if message['role'] == 'user' -%} + {%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['content'] is none -%} + {%- set ns.is_tool = false -%} + {%- for tool in message['tool_calls']-%} + {%- if not ns.is_first -%}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}} + {%- set ns.is_first = true -%} + {%- else -%}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['content'] is not none -%} + {%- if ns.is_tool -%}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}} + {%- set ns.is_tool = false -%} + {%- else -%} + {%- set content = message['content'] -%} + {%- if '' in content -%} + {%- set content = content.split('')[-1] -%} + {%- endif -%}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- endif -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.is_tool = true -%} + {%- if ns.is_output_first -%}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- set ns.is_output_first = false -%} + {%- else -%}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- endif -%} + {%- endif -%} +{%- endfor -%} +{%- if ns.is_tool -%}{{'<｜tool▁outputs▁end｜>'}} +{%- endif -%} +{%- if add_generation_prompt and not ns.is_tool -%}{{'<｜Assistant｜>\n'}} +{%- endif %} \ No newline at end of file diff --git a/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja b/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja index c2066bd739..9e6ec845d3 100644 --- a/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja +++ b/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja @@ -1 +1,47 @@ -{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '' in content %}{% set content = content.split('')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>\n'}}{% endif %} \ No newline at end of file +{% if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- set ns.system_prompt = message['content'] -%} + {%- endif -%} +{%- endfor -%}{{bos_token}}{{ns.system_prompt}} +{%- for message in messages -%} + {%- if message['role'] == 'user' -%} + {%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['tool_calls'] -%} + {%- set ns.is_tool = false -%} + {%- for tool in message['tool_calls']-%} + {%- if not ns.is_first -%} + {{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}} + {%- set ns.is_first = true -%} + {%- else -%} + {{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}} + {%- endif -%} + {%- endfor -%} + {{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['content'] is not none -%} + {%- if ns.is_tool -%}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}} + {%- set ns.is_tool = false -%} + {%- else -%} + {%- set content = message['content'] -%} + {%- if '' in content -%} + {%- set content = content.split('')[-1] -%} + {%- endif -%}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- endif -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.is_tool = true -%} + {%- if ns.is_output_first -%}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- set ns.is_output_first = false -%} + {%- else -%}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- endif -%} + {%- endif -%} +{%- endfor -%} +{%- if ns.is_tool -%}{{'<｜tool▁outputs▁end｜>'}} +{%- endif -%} +{%- if add_generation_prompt and not ns.is_tool -%}{{'<｜Assistant｜>\n'}}{% if not enable_thinking %}{{- '' -}}{% endif %} +{%- endif %} \ No newline at end of file diff --git a/models/templates/deepseek-ai-DeepSeek-V3.1.jinja b/models/templates/deepseek-ai-DeepSeek-V3.1.jinja index e5656196a3..2fd1c415b8 100644 --- a/models/templates/deepseek-ai-DeepSeek-V3.1.jinja +++ b/models/templates/deepseek-ai-DeepSeek-V3.1.jinja @@ -1,3 +1,71 @@ -{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% if not thinking is defined %}{% set thinking = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, system_prompt='', is_first_sp=true, is_last_user=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + ' +{% if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- if not thinking is defined -%} + {%- if enable_thinking is defined -%} + {%- set thinking = enable_thinking -%} + {%- else -%} + {%- set thinking = false -%} + {%- endif -%} +{%- endif -%} +{%- set ns = namespace(is_first=false, is_tool=false, system_prompt='', is_first_sp=true, is_last_user=false) -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- if ns.is_first_sp -%} + {%- set ns.system_prompt = ns.system_prompt + message['content'] -%} + {%- set ns.is_first_sp = false -%} + {%- else -%} + {%- set ns.system_prompt = ns.system_prompt + ' -' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- if ns.is_last_user %}{{'<｜Assistant｜>'}}{%- endif %}{%- set ns.is_last_user = false -%}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if message['content'] is none %}{{'<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>'+ tool['function']['name'] + '<｜tool▁sep｜>' + tool['function']['arguments'] + '<｜tool▁call▁end｜>'}}{%- else %}{{message['content'] + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['function']['name'] + '<｜tool▁sep｜>' + tool['function']['arguments'] + '<｜tool▁call▁end｜>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'<｜tool▁call▁begin｜>'+ tool['function']['name'] + '<｜tool▁sep｜>' + tool['function']['arguments'] + '<｜tool▁call▁end｜>'}}{%- endif %}{%- endfor %}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none) %}{%- if ns.is_last_user %}{{'<｜Assistant｜>'}}{%- if message['prefix'] is defined and message['prefix'] and thinking %}{{''}} {%- else %}{{''}}{%- endif %}{%- endif %}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{%- set content = message['content'] -%}{%- if '' in content %}{%- set content = content.split('', 1)[1] -%}{%- endif %}{{content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_last_user = false -%}{%- set ns.is_tool = true -%}{{'<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endfor -%}{%- if add_generation_prompt and ns.is_last_user and not ns.is_tool %}{{'<｜Assistant｜>'}}{%- if not thinking %}{{''}}{%- else %}{{''}}{%- endif %}{% endif %} \ No newline at end of file +' + message['content'] -%} + {%- endif -%} + {%- endif -%} +{%- endfor -%}{{ bos_token }}{{ ns.system_prompt }} +{%- for message in messages -%} + {%- if message['role'] == 'user' -%} + {%- set ns.is_tool = false -%} + {%- set ns.is_first = false -%} + {%- set ns.is_last_user = true -%}{{'<｜User｜>' + message['content']}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['tool_calls'] -%} + {%- if ns.is_last_user -%}{{'<｜Assistant｜>'}} + {%- endif -%} + {%- set ns.is_last_user = false -%} + {%- set ns.is_first = false -%} + {%- set ns.is_tool = false -%} + {%- for tool in message['tool_calls'] -%} + {%- if not ns.is_first -%} + {%- if not message['content'] -%}{{'<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>'+ tool['function']['name'] + '<｜tool▁sep｜>' + tool['function']['arguments'] + '<｜tool▁call▁end｜>'}} + {%- else -%}{{message['content'] + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['function']['name'] + '<｜tool▁sep｜>' + tool['function']['arguments'] + '<｜tool▁call▁end｜>'}} + {%- endif -%} + {%- set ns.is_first = true -%} + {%- else -%}{{'<｜tool▁call▁begin｜>'+ tool['function']['name'] + '<｜tool▁sep｜>' + tool['function']['arguments'] + '<｜tool▁call▁end｜>'}} + {%- endif -%} + {%- endfor -%}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- if message['role'] == 'assistant' and not message['tool_calls'] -%} + {%- if ns.is_last_user -%}{{'<｜Assistant｜>'}} + {%- if message['prefix'] is defined and message['prefix'] and thinking -%}{{''}} + {%- else -%}{{''}} + {%- endif -%} + {%- endif -%} + {%- set ns.is_last_user = false -%} + {%- if ns.is_tool -%}{{message['content'] + '<｜end▁of▁sentence｜>'}} + {%- set ns.is_tool = false -%} + {%- else -%} + {%- set content = message['content'] -%} + {%- if '' in content -%} + {%- set content = content.split('', 1)[1] -%} + {%- endif -%}{{content + '<｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- endif -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.is_last_user = false -%} + {%- set ns.is_tool = true -%}{{'<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt and ns.is_last_user and not ns.is_tool -%}{{'<｜Assistant｜>'}} + {%- if not thinking -%}{{''}} + {%- else -%}{{''}} + {%- endif -%} +{%- endif %} \ No newline at end of file diff --git a/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja b/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja index 9b8136df73..b94cfd4d9b 100644 --- a/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja +++ b/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja @@ -46,7 +46,7 @@ Available functions as JSON spec: {%- if 'tool_calls' in message and message['tool_calls'] -%} {%- set tool = namespace(calls=[]) -%} {%- for call in message['tool_calls'] -%} - {%- set tool.calls = tool.calls + ['{"name": "' + call['function']['name'] + '", "arguments": ' + call['function']['arguments'] + '}'] -%} + {%- set tool.calls = tool.calls + ['{"name": "' + call['function']['name'] + '", "arguments": ' + call['function']['arguments']|tojson + '}'] -%} {%- endfor -%} {%- set ns.content = ns.content + ' functools[' + tool.calls | join(', ') + ']' -%} {%- endif -%} diff --git a/models/templates/moonshotai-Kimi-K2.jinja b/models/templates/moonshotai-Kimi-K2.jinja index ecb49a2108..e286d8a7b5 100644 --- a/models/templates/moonshotai-Kimi-K2.jinja +++ b/models/templates/moonshotai-Kimi-K2.jinja @@ -1,43 +1,43 @@ -{%- if tools -%} - <|im_system|>tool_declare<|im_middle|>{{ tools | tojson }}<|im_end|> -{%- endif -%} -{%- for message in messages -%} - {%- if loop.first and messages[0]['role'] != 'system' -%} - <|im_system|>system<|im_middle|>You are a helpful assistant<|im_end|> - {%- endif -%} - {%- if message['role'] == 'system' -%} - <|im_system|>system<|im_middle|> - {%- elif message['role'] == 'user' -%} - <|im_user|>user<|im_middle|> - {%- elif message['role'] == 'assistant' -%} - <|im_assistant|>assistant<|im_middle|> - {%- elif message['role'] == 'tool' -%} - <|im_system|>tool<|im_middle|> - {%- endif -%} - {%- if message['role'] == 'assistant' and message.get('tool_calls') -%} - {%- if message['content'] -%}{{ message['content'] }}{%- endif -%} - <|tool_calls_section_begin|> - {%- for tool_call in message['tool_calls'] -%} - {%- set func_name = tool_call['function']['name'] -%} - {%- set formatted_id = 'functions.' + func_name + ':' + loop.index0|string -%} - <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{{ tool_call['function']['arguments'] | tojson}}<|tool_call_end|> - {%- endfor -%} - <|tool_calls_section_end|> - {%- elif message['role'] == 'tool' -%} - ## Return of {{ message.tool_call_id }}\n{{ message['content'] }} - {%- elif message['content'] is string -%} - {{ message['content'] }} - {%- elif message['content'] is not none -%} - {% for content in message['content'] -%} - {% if content['type'] == 'image' or 'image' in content or 'image_url' in content -%} - <|media_start|>image<|media_content|><|media_pad|><|media_end|> - {% else -%} - {{ content['text'] }} - {%- endif -%} - {%- endfor -%} - {%- endif -%} - <|im_end|> -{%- endfor -%} -{%- if add_generation_prompt -%} - <|im_assistant|>assistant<|im_middle|> -{%- endif -%} +{%- if tools -%} + <|im_system|>tool_declare<|im_middle|>{{ tools | tojson }}<|im_end|> +{%- endif -%} +{%- for message in messages -%} + {%- if loop.first and messages[0]['role'] != 'system' -%} + <|im_system|>system<|im_middle|>You are a helpful assistant<|im_end|> + {%- endif -%} + {%- if message['role'] == 'system' -%} + <|im_system|>system<|im_middle|> + {%- elif message['role'] == 'user' -%} + <|im_user|>user<|im_middle|> + {%- elif message['role'] == 'assistant' -%} + <|im_assistant|>assistant<|im_middle|> + {%- elif message['role'] == 'tool' -%} + <|im_system|>tool<|im_middle|> + {%- endif -%} + {%- if message['role'] == 'assistant' and message.get('tool_calls') -%} + {%- if message['content'] -%}{{ message['content'] }}{%- endif -%} + <|tool_calls_section_begin|> + {%- for tool_call in message['tool_calls'] -%} + {%- set func_name = tool_call['function']['name'] -%} + {%- set formatted_id = 'functions.' + func_name + ':' + loop.index0|string -%} + <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{{ tool_call['function']['arguments'] | tojson}}<|tool_call_end|> + {%- endfor -%} + <|tool_calls_section_end|> + {%- elif message['role'] == 'tool' -%} + ## Return of {{ message.tool_call_id }}\n{{ message['content'] }} + {%- elif message['content'] is string -%} + {{ message['content'] }} + {%- elif message['content'] is not none -%} + {% for content in message['content'] -%} + {% if content['type'] == 'image' or 'image' in content or 'image_url' in content -%} + <|media_start|>image<|media_content|><|media_pad|><|media_end|> + {% else -%} + {{ content['text'] }} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + <|im_end|> +{%- endfor -%} +{%- if add_generation_prompt -%} + <|im_assistant|>assistant<|im_middle|> +{%- endif -%} diff --git a/models/templates/unsloth-Apriel-1.5.jinja b/models/templates/unsloth-Apriel-1.5.jinja index 29e582fbf6..1639b63901 100644 --- a/models/templates/unsloth-Apriel-1.5.jinja +++ b/models/templates/unsloth-Apriel-1.5.jinja @@ -86,22 +86,22 @@ Prior to generating the function calls, you should generate the reasoning for wh {%- set add_tool_id = false -%} {%- endif -%} {{- '<|assistant|>\n' -}} - {%- if message['content'] is not none and message['content']|length > 0 -%} + {%- if message['content'] is defined and message['content'] is not none and message['content']|length > 0 -%} {%- if message['content'] is not string and message['content'][0]['text'] is not none %} {{- message['content'][0]['text'] }} {%- else %} {{- message['content'] -}} {%- endif -%} - {%- elif message['chosen'] is not none and message['chosen']|length > 0 -%} + {%- elif message['chosen'] is defined and message['chosen'] is not none and message['chosen']|length > 0 -%} {{- message['chosen'][0] -}} {%- endif -%} {%- if add_thoughts and 'thought' in message and message['thought'] is not none -%} {{- '' + message['thought'] + '' -}} {%- endif -%} - {%- if message['tool_calls'] is not none and message['tool_calls']|length > 0 -%} + {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 -%} {{- '\n[' -}} {%- for tool_call in message["tool_calls"] -%} - {{- '{"name": "' + tool_call['function']['name'] + '", "arguments": ' + tool_call['function']['arguments']|string -}} + {{- '{"name": "' + tool_call['function']['name'] + '", "arguments": ' + tool_call['function']['arguments']|tojson -}} {%- if add_tool_id == true -%} {{- ', "id": "' + tool_call['id'] + '"' -}} {%- endif -%} diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 9541b89eb9..14e75117c4 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -293,6 +293,10 @@ class LlamaBenchData: for t in self.repo.tags: if t.name == name: return t.commit.hexsha[:self.build_len] + for remote in self.repo.remotes: + for ref in remote.refs: + if ref.name == name or ref.remote_head == name: + return ref.commit.hexsha[:self.build_len] for c in self.repo.iter_commits("--all"): if c.hexsha[:self.build_len] == name[:self.build_len]: return c.hexsha[:self.build_len] diff --git a/scripts/compare-logprobs.py b/scripts/compare-logprobs.py index 63861dd9a4..ac10085b78 100644 --- a/scripts/compare-logprobs.py +++ b/scripts/compare-logprobs.py @@ -25,16 +25,12 @@ Example usage: """ -def generate_input_prompt(length: int) -> list[str]: - CORPUS = """ - You are an advanced AI assistant capable of using tools to gather information, perform calculations, or execute tasks. Always think step by step before responding. If a user's query requires external data, computation, or actions beyond your internal knowledge, use the appropriate tools via function calls. - - ### Tool Call Format: - When you need to use a tool, output the call in this exact XML format. Include the opening and closing tags. Do not escape arguments; they will be parsed as plain text. - - You can make multiple calls in one go by placing them one after another. - """ - words = [w.strip() for w in CORPUS.strip().split(" ")] +def get_remote_corpus(url: str, length: int) -> list[str]: + response = requests.get(url) + response.raise_for_status() + corpus = response.text + words = [w.strip() for w in corpus.strip().split(" ")] + words = [w for w in words if "<" not in w] # make sure nothing looks like special tokens words = [w for w in words if len(w) > 0] # filter out empty strings while len(words) < length: words += words @@ -226,9 +222,9 @@ def parse_args() -> argparse.Namespace: ) parser_dump.add_argument( "--file", - type=Path, - default=None, - help="File containing prompt to use instead of the default", + type=str, + default="https://raw.githubusercontent.com/ggml-org/llama.cpp/eaba92c3dcc980ebe753348855d4a5d75c069997/tools/server/README.md", + help="File containing prompt to use instead of the default (can also be an URL)", ) parser_dump.add_argument( "--pattern", @@ -259,17 +255,19 @@ def main(): if args.verb == "dump": pattern = parse_pattern(args.pattern) - input_length = sum(n for _, n in pattern) - input_words = generate_input_prompt(input_length) - if args.file is not None: - with args.file.open("r") as f: + required_words = sum(n for _, n in pattern) + if args.file.startswith("http"): + input_words = get_remote_corpus(args.file, required_words) + logger.info(f"Fetched {len(input_words)} words from remote {args.file}") + else: + with open(args.file, "r") as f: input_words = f.read().strip().split(" ") - if input_length < sum(n for _, n in pattern): + input_words = [w for w in input_words if len(w) > 0] # filter out empty strings + if len(input_words) < required_words: raise ValueError( - f"Input file has only {input_length} words, but pattern requires at least {input_length} words." + f"Input file has only {len(input_words)} words, but pattern requires at least {required_words} words." ) - input_length = len(input_words) - logger.info(f"Using {input_length} words") + logger.info(f"Using {len(input_words)} words") dump_logits(args.endpoint, args.output, input_words, pattern, args.api_key) elif args.verb == "compare": compare_logits(args.input1, args.input2, args.output) diff --git a/scripts/get-hellaswag.sh b/scripts/get-hellaswag.sh index 484e56fd8f..0b161141f4 100755 --- a/scripts/get-hellaswag.sh +++ b/scripts/get-hellaswag.sh @@ -1,10 +1,38 @@ -#!/usr/bin/env bash +#!/bin/sh +# vim: set ts=4 sw=4 et: -wget https://raw.githubusercontent.com/klosax/hellaswag_text_data/main/hellaswag_val_full.txt +FILE="hellaswag_val_full.txt" +URL="https://raw.githubusercontent.com/klosax/hellaswag_text_data/main/$FILE" -echo "Usage:" -echo "" -echo " ./llama-perplexity -m model.gguf -f hellaswag_val_full.txt --hellaswag [--hellaswag-tasks N] [other params]" -echo "" +die() { + printf "%s\n" "$@" >&2 + exit 1 +} -exit 0 +have_cmd() { + for cmd; do + command -v "$cmd" >/dev/null || return + done +} + +dl() { + [ -f "$2" ] && return + if have_cmd wget; then + wget "$1" -O "$2" + elif have_cmd curl; then + curl -L "$1" -o "$2" + else + die "Please install wget or curl" + fi +} + +if [ ! -f "$FILE" ]; then + dl "$URL" "$FILE" || exit +fi + +cat <&2 + exit 1 +} -exit 0 +have_cmd() { + for cmd; do + command -v "$cmd" >/dev/null || return + done +} + +dl() { + [ -f "$2" ] && return + if have_cmd wget; then + wget "$1" -O "$2" + elif have_cmd curl; then + curl -L "$1" -o "$2" + else + die "Please install wget or curl" + fi +} + +have_cmd unzip || die "Please install unzip" + +if [ ! -f "$FILE" ]; then + dl "$URL" "$ZIP" || exit + unzip -o "$ZIP" || exit + rm -f -- "$ZIP" +fi + +cat <&2 + exit 1 +} -exit 0 +have_cmd() { + for cmd; do + command -v "$cmd" >/dev/null || return + done +} + +dl() { + [ -f "$2" ] && return + if have_cmd wget; then + wget "$1" -O "$2" + elif have_cmd curl; then + curl -L "$1" -o "$2" + else + die "Please install wget or curl" + fi +} + +if [ ! -f "$FILE" ]; then + dl "$URL" "$FILE" || exit +fi + +cat < /dev/null +cmake -B ${dir} -S . ${cmake_args} > /dev/null +cmake --build ${dir} -t llama-results -j $(nproc) > /dev/null +${dir}/bin/llama-results "${llama_results_args[@]}" diff --git a/scripts/git-bisect.sh b/scripts/git-bisect.sh new file mode 100755 index 0000000000..3279d502e1 --- /dev/null +++ b/scripts/git-bisect.sh @@ -0,0 +1,19 @@ +#!/usr/bin/env bash + +if [ $# -lt 2 ]; then + echo "usage: ./scripts/git-bisect.sh [additional arguments]" + echo " additional arguments: passed to CMake if they start with \"-D\", to llama-results otherwise" + exit 1 +fi + +set -e +set -x + +commit_bad=$1 +commit_good=$2 +script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +git checkout ${commit_good} +${script_dir}/git-bisect-run.sh --output results.gguf "${@:3}" +git bisect start ${commit_bad} ${commit_good} +git bisect run ${script_dir}/git-bisect-run.sh --output results.gguf --check "${@:3}" +git bisect reset diff --git a/scripts/pr2wt.sh b/scripts/pr2wt.sh index 067f5d466b..e028814c43 100755 --- a/scripts/pr2wt.sh +++ b/scripts/pr2wt.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash -# intialize a new worktree from a PR number: +# initialize a new worktree from a PR number: # # - creates a new remote using the fork's clone URL # - creates a local branch tracking the remote branch diff --git a/scripts/server-bench.py b/scripts/server-bench.py index dbbb0939ff..202c35a486 100755 --- a/scripts/server-bench.py +++ b/scripts/server-bench.py @@ -292,6 +292,6 @@ if __name__ == "__main__": "--n_predict_min", type=int, default=1024, help="Min. number of tokens to predict per prompt (supported for synthetic prompts only)") parser.add_argument("--seed_offset", type=int, default=0, help="Offset for determining the seeds for pseudorandom prompt/generation lengths. " - "Corelations between seeds can occur when set >= 1000. Negative values mean no seed.") + "Correlations between seeds can occur when set >= 1000. Negative values mean no seed.") args = parser.parse_args() benchmark(**vars(args)) diff --git a/scripts/server-test-model.py b/scripts/server-test-model.py new file mode 100644 index 0000000000..9049d80279 --- /dev/null +++ b/scripts/server-test-model.py @@ -0,0 +1,202 @@ +import argparse +import json +import requests +import logging +import sys + +handler = logging.StreamHandler(sys.stdout) +handler.terminator = "" # ← no newline +logging.basicConfig(level=logging.INFO, format='%(message)s', handlers=[handler]) +logger = logging.getLogger("server-test-model") + + +def run_query(url, messages, tools=None, stream=False, tool_choice=None): + payload = { + "messages": messages, + "stream": stream, + "max_tokens": 5000, + } + if tools: + payload["tools"] = tools + if tool_choice: + payload["tool_choice"] = tool_choice + + try: + response = requests.post(url, json=payload, stream=stream) + response.raise_for_status() + except requests.exceptions.RequestException as e: + if e.response is not None: + logger.info(f"Response error: {e} for {e.response.content}\n") + else: + logger.info(f"Error connecting to server: {e}\n") + return None + + full_content = "" + reasoning_content = "" + tool_calls = [] + + if stream: + logger.info(f"--- Streaming response (Tools: {bool(tools)}) ---\n") + for line in response.iter_lines(): + if line: + decoded_line = line.decode("utf-8") + if decoded_line.startswith("data: "): + data_str = decoded_line[6:] + if data_str == "[DONE]": + break + try: + data = json.loads(data_str) + if "choices" in data and len(data["choices"]) > 0: + delta = data["choices"][0].get("delta", {}) + + # Content + content_chunk = delta.get("content", "") + if content_chunk: + full_content += content_chunk + logger.info(content_chunk) + + # Reasoning + reasoning_chunk = delta.get("reasoning_content", "") + if reasoning_chunk: + reasoning_content += reasoning_chunk + logger.info(f"\x1B[3m{reasoning_chunk}\x1B[0m") + + # Tool calls + if "tool_calls" in delta: + for tc in delta["tool_calls"]: + index = tc.get("index") + if index is not None: + while len(tool_calls) <= index: + # Using "function" as type default but could be flexible + tool_calls.append( + { + "id": "", + "type": "function", + "function": { + "name": "", + "arguments": "", + }, + } + ) + + if "id" in tc: + tool_calls[index]["id"] += tc["id"] + if "function" in tc: + if "name" in tc["function"]: + tool_calls[index]["function"][ + "name" + ] += tc["function"]["name"] + if "arguments" in tc["function"]: + tool_calls[index]["function"][ + "arguments" + ] += tc["function"]["arguments"] + + except json.JSONDecodeError: + logger.info(f"Failed to decode JSON: {data_str}\n") + logger.info("\n--- End of Stream ---\n") + else: + logger.info(f"--- Non-streaming response (Tools: {bool(tools)}) ---\n") + data = response.json() + if "choices" in data and len(data["choices"]) > 0: + message = data["choices"][0].get("message", {}) + full_content = message.get("content", "") + reasoning_content = message.get("reasoning_content", "") + tool_calls = message.get("tool_calls", []) + logger.info(full_content) + logger.info("--- End of Response ---\n") + + return { + "content": full_content, + "reasoning_content": reasoning_content, + "tool_calls": tool_calls, + } + + +def test_chat(url, stream): + logger.info(f"\n=== Testing Chat (Stream={stream}) ===\n") + messages = [{"role": "user", "content": "What is the capital of France?"}] + result = run_query(url, messages, stream=stream) + + if result: + if result["content"]: + logger.info("PASS: Output received.\n") + else: + logger.info("WARN: No content received (valid if strict tool call, but unexpected here).\n") + + if result.get("reasoning_content"): + logger.info(f"INFO: Reasoning content detected ({len(result['reasoning_content'])} chars).\n") + else: + logger.info("INFO: No reasoning content detected (Standard model behavior).\n") + else: + logger.info("FAIL: No result.\n") + + +def test_tool_call(url, stream): + logger.info(f"\n=== Testing Tool Call (Stream={stream}) ===\n") + messages = [ + { + "role": "user", + "content": "What is the weather in London? Please use the get_weather tool.", + } + ] + tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + ] + + result = run_query(url, messages, tools=tools, tool_choice="auto", stream=stream) + + if result: + tcs = result.get("tool_calls") + if tcs and len(tcs) > 0: + logger.info("PASS: Tool calls detected.") + for tc in tcs: + func = tc.get("function", {}) + logger.info(f" Tool: {func.get('name')}, Args: {func.get('arguments')}\n") + else: + logger.info(f"FAIL: No tool calls. Content: {result['content']}\n") + + if result.get("reasoning_content"): + logger.info( + f"INFO: Reasoning content detected during tool call ({len(result['reasoning_content'])} chars).\n" + ) + else: + logger.info("FAIL: Query failed.\n") + + +def main(): + parser = argparse.ArgumentParser(description="Test llama-server functionality.") + parser.add_argument("--host", default="localhost", help="Server host") + parser.add_argument("--port", default=8080, type=int, help="Server port") + args = parser.parse_args() + + base_url = f"http://{args.host}:{args.port}/v1/chat/completions" + logger.info(f"Testing server at {base_url}\n") + + # Non-streaming tests + test_chat(base_url, stream=False) + test_tool_call(base_url, stream=False) + + # Streaming tests + test_chat(base_url, stream=True) + test_tool_call(base_url, stream=True) + + +if __name__ == "__main__": + main() diff --git a/scripts/snapdragon/adb/run-cli.sh b/scripts/snapdragon/adb/run-cli.sh index d19d4e920e..dfc051b28b 100755 --- a/scripts/snapdragon/adb/run-cli.sh +++ b/scripts/snapdragon/adb/run-cli.sh @@ -54,6 +54,6 @@ adb $adbserial $adbhost shell " \ $verbose $experimental $sched $opmask $profile $nhvx $ndev $hb \ ./$branch/bin/llama-cli --no-mmap -m $basedir/../gguf/$model \ --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ - --ctx-size 8192 --batch-size 128 -fa on \ - -ngl 99 --device $device $cli_opts $@ \ + --ctx-size 8192 --ubatch-size 256 -fa on \ + -ngl 99 --device $device $cli_opts $@ \ " diff --git a/scripts/snapdragon/adb/run-completion.sh b/scripts/snapdragon/adb/run-completion.sh index da9df110a0..d53b588739 100755 --- a/scripts/snapdragon/adb/run-completion.sh +++ b/scripts/snapdragon/adb/run-completion.sh @@ -54,6 +54,6 @@ adb $adbserial $adbhost shell " \ $verbose $experimental $sched $opmask $profile $nhvx $ndev $hb \ ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model \ --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ - --ctx-size 8192 --batch-size 128 -fa on \ - -ngl 99 -no-cnv --device $device $cli_opts $@ \ + --ctx-size 8192 --ubatch-size 256 -fa on \ + -ngl 99 -no-cnv --device $device $cli_opts $@ \ " diff --git a/scripts/snapdragon/adb/run-mtmd.sh b/scripts/snapdragon/adb/run-mtmd.sh index fc018e7269..41d7cd44f8 100755 --- a/scripts/snapdragon/adb/run-mtmd.sh +++ b/scripts/snapdragon/adb/run-mtmd.sh @@ -58,11 +58,11 @@ adb $adbserial $adbhost shell " \ cd $basedir; ulimit -c unlimited; \ LD_LIBRARY_PATH=$basedir/$branch/lib \ ADSP_LIBRARY_PATH=$basedir/$branch/lib \ - $verbose $experimental $sched $opmask $profile $nhvx $ndev $mtmd_backend \ - ./$branch/bin/llama-mtmd-cli --no-mmap -m $basedir/../gguf/$model \ - --mmproj $basedir/../gguf/$mmproj \ - --image $basedir/../gguf/$image \ - --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ - --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on \ - -ngl 99 --device $device -v $cli_opts $@ \ + $verbose $experimental $sched $opmask $profile $nhvx $ndev $mtmd_backend \ + ./$branch/bin/llama-mtmd-cli --no-mmap -m $basedir/../gguf/$model \ + --mmproj $basedir/../gguf/$mmproj \ + --image $basedir/../gguf/$image \ + --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ + --ctx-size 8192 --ubatch-size 256 -fa on \ + -ngl 99 --device $device -v $cli_opts $@ \ " diff --git a/scripts/snapdragon/windows/run-cli.ps1 b/scripts/snapdragon/windows/run-cli.ps1 index b13161aa63..5891c894a9 100644 --- a/scripts/snapdragon/windows/run-cli.ps1 +++ b/scripts/snapdragon/windows/run-cli.ps1 @@ -46,8 +46,8 @@ if ($null -ne $env:NDEV) { $env:ADSP_LIBRARY_PATH="$basedir\lib" -& "$basedir\bin\llama-completion.exe" ` - --no-mmap -no-cnv -m $basedir\..\..\gguf\$model ` +& "$basedir\bin\llama-cli.exe" ` + --no-mmap -m $basedir\..\..\gguf\$model ` --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 ` - --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on ` + --ctx-size 8192 --ubatch-size 128 -fa on ` -ngl 99 --device $device $cli_opts diff --git a/scripts/snapdragon/windows/run-completion.ps1 b/scripts/snapdragon/windows/run-completion.ps1 new file mode 100644 index 0000000000..8a48d2d748 --- /dev/null +++ b/scripts/snapdragon/windows/run-completion.ps1 @@ -0,0 +1,53 @@ + +#!/usr/bin/env pwsh + +# Basedir on device +$basedir=".\pkg-snapdragon" + +$cli_opts=$args + +$model="Llama-3.2-3B-Instruct-Q4_0.gguf" +if ($null -ne $env:M) { + $model=$env:M +} + +$device="HTP0" +if ($null -ne $env:D) { + $device=$env:D +} + +if ($null -ne $env:V) { + $env:GGML_HEXAGON_VERBOSE=$env:V +} + +if ($null -ne $env:E) { + $env:GGML_HEXAGON_EXPERIMENTAL=$env:E +} + +if ($null -ne $env:SCHED) { + $env:GGML_SCHED_DEBUG=$env:SCHED; $cli_opts="$cli_opts -v" +} + +if ($null -ne $env:PROF) { + $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1 +} + +if ($null -ne $env:OPMASK) { + $env:GGML_HEXAGON_OPMASK=$env:OPMASK +} + +if ($null -ne $env:NHVX) { + $env:GGML_HEXAGON_NHVX=$env:NHVX +} + +if ($null -ne $env:NDEV) { + $env:GGML_HEXAGON_NDEV=$env:NDEV +} + +$env:ADSP_LIBRARY_PATH="$basedir\lib" + +& "$basedir\bin\llama-completion.exe" ` + --no-mmap -m $basedir\..\..\gguf\$model ` + --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 ` + --ctx-size 8192 --batch-size 128 -fa on ` + -ngl 99 -no-cnv --device $device $cli_opts diff --git a/scripts/sync_vendor.py b/scripts/sync_vendor.py index e62eb2817b..4d254afcd6 100755 --- a/scripts/sync_vendor.py +++ b/scripts/sync_vendor.py @@ -5,7 +5,7 @@ import os import sys import subprocess -HTTPLIB_VERSION = "refs/tags/v0.33.1" +HTTPLIB_VERSION = "refs/tags/v0.38.0" vendor = { "https://github.com/nlohmann/json/releases/latest/download/json.hpp": "vendor/nlohmann/json.hpp", @@ -14,8 +14,8 @@ vendor = { "https://raw.githubusercontent.com/nothings/stb/refs/heads/master/stb_image.h": "vendor/stb/stb_image.h", # not using latest tag to avoid this issue: https://github.com/ggml-org/llama.cpp/pull/17179#discussion_r2515877926 - # "https://github.com/mackron/miniaudio/raw/refs/tags/0.11.23/miniaudio.h": "vendor/miniaudio/miniaudio.h", - "https://github.com/mackron/miniaudio/raw/669ed3e844524fcd883231b13095baee9f6de304/miniaudio.h": "vendor/miniaudio/miniaudio.h", + # "https://github.com/mackron/miniaudio/raw/refs/tags/0.11.24/miniaudio.h": "vendor/miniaudio/miniaudio.h", + "https://github.com/mackron/miniaudio/raw/9634bedb5b5a2ca38c1ee7108a9358a4e233f14d/miniaudio.h": "vendor/miniaudio/miniaudio.h", f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/httplib.h": "httplib.h", f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/split.py": "split.py", diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 87f79bfa26..3cc360b9a7 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -63,6 +63,7 @@ add_library(llama models/eagle3.cpp models/ernie4-5-moe.cpp models/ernie4-5.cpp + models/eurobert.cpp models/exaone-moe.cpp models/exaone.cpp models/exaone4.cpp diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 05a2022baf..58f7dc3ede 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -4,6 +4,7 @@ #include #include +#include static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_CLIP, "clip" }, // dummy, only used by llama-quantize @@ -26,6 +27,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_NEO_BERT, "neo-bert" }, { LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" }, { LLM_ARCH_JINA_BERT_V3, "jina-bert-v3" }, + { LLM_ARCH_EUROBERT, "eurobert" }, { LLM_ARCH_BLOOM, "bloom" }, { LLM_ARCH_STABLELM, "stablelm" }, { LLM_ARCH_QWEN, "qwen" }, @@ -184,6 +186,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_EXPERT_GROUP_SCALE, "%s.expert_group_scale" }, { LLM_KV_EXPERTS_PER_GROUP, "%s.experts_per_group" }, { LLM_KV_MOE_EVERY_N_LAYERS, "%s.moe_every_n_layers" }, + { LLM_KV_MOE_LATENT_SIZE, "%s.moe_latent_size" }, { LLM_KV_NEXTN_PREDICT_LAYERS, "%s.nextn_predict_layers" }, { LLM_KV_NUM_DEEPSTACK_LAYERS, "%s.n_deepstack_layers" }, { LLM_KV_POOLING_TYPE, "%s.pooling_type" }, @@ -229,11 +232,14 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_TEMPERATURE_SCALE, "%s.attention.temperature_scale" }, { LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" }, { LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" }, + { LLM_KV_ATTENTION_KEY_LENGTH_SWA, "%s.attention.key_length_swa" }, + { LLM_KV_ATTENTION_VALUE_LENGTH_SWA, "%s.attention.value_length_swa" }, { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, "%s.attention.indexer.head_count" }, { LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, "%s.attention.indexer.key_length" }, { LLM_KV_ATTENTION_INDEXER_TOP_K, "%s.attention.indexer.top_k" }, { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, + { LLM_KV_ROPE_DIMENSION_COUNT_SWA, "%s.rope.dimension_count_swa" }, { LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, { LLM_KV_ROPE_FREQ_BASE_SWA, "%s.rope.freq_base_swa" }, @@ -353,6 +359,7 @@ static const std::map LLM_TENSOR_NAMES = { { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" }, { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_GATE_UP_EXPS, "blk.%d.ffn_gate_up_exps" }, { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, { LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" }, @@ -364,6 +371,8 @@ static const std::map LLM_TENSOR_NAMES = { { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, { LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" }, + { LLM_TENSOR_FFN_LATENT_DOWN, "blk.%d.ffn_latent_down" }, + { LLM_TENSOR_FFN_LATENT_UP, "blk.%d.ffn_latent_up" }, { LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" }, { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, @@ -828,6 +837,20 @@ static std::set llm_get_tensor_names(llm_arch arch) { LLM_TENSOR_CLS, LLM_TENSOR_CLS_OUT, }; + case LLM_ARCH_EUROBERT: + return { + LLM_TENSOR_TOKEN_EMBD, + LLM_TENSOR_OUTPUT_NORM, + LLM_TENSOR_ATTN_NORM, + LLM_TENSOR_ATTN_Q, + LLM_TENSOR_ATTN_K, + LLM_TENSOR_ATTN_V, + LLM_TENSOR_ATTN_OUT, + LLM_TENSOR_FFN_NORM, + LLM_TENSOR_FFN_GATE, + LLM_TENSOR_FFN_UP, + LLM_TENSOR_FFN_DOWN, + }; case LLM_ARCH_MODERN_BERT: return { LLM_TENSOR_TOKEN_EMBD, @@ -998,6 +1021,7 @@ static std::set llm_get_tensor_names(llm_arch arch) { LLM_TENSOR_FFN_GATE_EXPS, LLM_TENSOR_FFN_DOWN_EXPS, LLM_TENSOR_FFN_UP_EXPS, + LLM_TENSOR_FFN_GATE_UP_EXPS, LLM_TENSOR_FFN_GATE_INP_SHEXP, LLM_TENSOR_FFN_GATE_SHEXP, LLM_TENSOR_FFN_DOWN_SHEXP, @@ -1055,6 +1079,7 @@ static std::set llm_get_tensor_names(llm_arch arch) { LLM_TENSOR_FFN_GATE_EXPS, LLM_TENSOR_FFN_DOWN_EXPS, LLM_TENSOR_FFN_UP_EXPS, + LLM_TENSOR_FFN_GATE_UP_EXPS, LLM_TENSOR_FFN_GATE_INP_SHEXP, LLM_TENSOR_FFN_GATE_SHEXP, LLM_TENSOR_FFN_DOWN_SHEXP, @@ -1074,6 +1099,7 @@ static std::set llm_get_tensor_names(llm_arch arch) { LLM_TENSOR_TOKEN_EMBD, LLM_TENSOR_OUTPUT_NORM, LLM_TENSOR_OUTPUT, + LLM_TENSOR_CLS_OUT, LLM_TENSOR_ATTN_NORM, LLM_TENSOR_ATTN_Q, LLM_TENSOR_ATTN_Q_NORM, @@ -1595,6 +1621,7 @@ static std::set llm_get_tensor_names(llm_arch arch) { LLM_TENSOR_FFN_GATE_EXPS, LLM_TENSOR_FFN_DOWN_EXPS, LLM_TENSOR_FFN_UP_EXPS, + LLM_TENSOR_FFN_GATE_UP_EXPS, LLM_TENSOR_FFN_GATE_INP_SHEXP, LLM_TENSOR_FFN_GATE_SHEXP, LLM_TENSOR_FFN_DOWN_SHEXP, @@ -1864,6 +1891,8 @@ static std::set llm_get_tensor_names(llm_arch arch) { LLM_TENSOR_FFN_UP_EXPS, LLM_TENSOR_FFN_DOWN_EXPS, LLM_TENSOR_FFN_EXP_PROBS_B, + LLM_TENSOR_FFN_LATENT_DOWN, + LLM_TENSOR_FFN_LATENT_UP, // MoE shared expert layer LLM_TENSOR_FFN_DOWN_SHEXP, LLM_TENSOR_FFN_UP_SHEXP, @@ -2701,6 +2730,7 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_FFN_DOWN_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, {LLM_TENSOR_FFN_GATE_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, {LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, + {LLM_TENSOR_FFN_GATE_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, {LLM_TENSOR_FFN_DOWN_CHEXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, {LLM_TENSOR_FFN_GATE_CHEXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, {LLM_TENSOR_FFN_UP_CHEXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, @@ -2760,6 +2790,9 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_NEXTN_HNORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, {LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, {LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + // Nemotron 3 Super + {LLM_TENSOR_FFN_LATENT_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_LATENT_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, // EAGLE-3 tensors {LLM_TENSOR_EAGLE3_FC, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, {LLM_TENSOR_EAGLE3_HIDDEN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, @@ -2801,6 +2834,15 @@ std::string LLM_TN_IMPL::str() const { return name; } +std::vector llm_arch_all() { + std::vector ret; + ret.reserve(LLM_ARCH_NAMES.size()); + for (const auto & [arch, _] : LLM_ARCH_NAMES) { + ret.push_back(arch); + } + return ret; +} + const char * llm_arch_name(llm_arch arch) { auto it = LLM_ARCH_NAMES.find(arch); if (it == LLM_ARCH_NAMES.end()) { diff --git a/src/llama-arch.h b/src/llama-arch.h index 3d69fd35f3..06f9f20ec5 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -4,6 +4,7 @@ #include #include +#include // // gguf constants (sync with gguf.py) @@ -30,6 +31,7 @@ enum llm_arch { LLM_ARCH_NEO_BERT, LLM_ARCH_JINA_BERT_V2, LLM_ARCH_JINA_BERT_V3, + LLM_ARCH_EUROBERT, LLM_ARCH_BLOOM, LLM_ARCH_STABLELM, LLM_ARCH_QWEN, @@ -188,6 +190,7 @@ enum llm_kv { LLM_KV_EXPERT_GROUP_SCALE, LLM_KV_EXPERTS_PER_GROUP, LLM_KV_MOE_EVERY_N_LAYERS, + LLM_KV_MOE_LATENT_SIZE, LLM_KV_NEXTN_PREDICT_LAYERS, LLM_KV_NUM_DEEPSTACK_LAYERS, LLM_KV_POOLING_TYPE, @@ -233,11 +236,14 @@ enum llm_kv { LLM_KV_ATTENTION_TEMPERATURE_SCALE, LLM_KV_ATTENTION_KEY_LENGTH_MLA, LLM_KV_ATTENTION_VALUE_LENGTH_MLA, + LLM_KV_ATTENTION_KEY_LENGTH_SWA, + LLM_KV_ATTENTION_VALUE_LENGTH_SWA, LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, LLM_KV_ATTENTION_INDEXER_TOP_K, LLM_KV_ROPE_DIMENSION_COUNT, + LLM_KV_ROPE_DIMENSION_COUNT_SWA, LLM_KV_ROPE_DIMENSION_SECTIONS, LLM_KV_ROPE_FREQ_BASE, LLM_KV_ROPE_FREQ_BASE_SWA, @@ -377,6 +383,7 @@ enum llm_tensor { LLM_TENSOR_FFN_DOWN_EXPS, // merged experts LLM_TENSOR_FFN_GATE_EXPS, LLM_TENSOR_FFN_UP_EXPS, + LLM_TENSOR_FFN_GATE_UP_EXPS, LLM_TENSOR_FFN_DOWN_SHEXP, LLM_TENSOR_FFN_GATE_SHEXP, LLM_TENSOR_FFN_UP_SHEXP, @@ -384,6 +391,8 @@ enum llm_tensor { LLM_TENSOR_FFN_GATE_CHEXPS, LLM_TENSOR_FFN_UP_CHEXPS, LLM_TENSOR_FFN_EXP_PROBS_B, + LLM_TENSOR_FFN_LATENT_DOWN, + LLM_TENSOR_FFN_LATENT_UP, LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_K_NORM, LLM_TENSOR_LAYER_OUT_NORM, @@ -614,6 +623,8 @@ struct llm_tensor_info { ggml_op op; }; +std::vector llm_arch_all(); + const char * llm_arch_name(llm_arch arch); llm_arch llm_arch_from_string(const std::string & name); diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp index 386fab04ac..6bf76939cd 100644 --- a/src/llama-batch.cpp +++ b/src/llama-batch.cpp @@ -394,11 +394,13 @@ llama_ubatch llama_batch_allocr::ubatch_reserve(uint32_t n_seq_tokens, uint32_t clear(); split_reset(); + const int64_t n_pos_all = (int64_t) n_tokens*n_pos_per_embd; + auto udata = std::make_shared(); udata->token .resize(n_tokens); udata->embd .clear(); - udata->pos .resize(n_tokens); + udata->pos .resize(n_pos_all); udata->n_seq_id .resize(n_tokens); udata->seq_id .resize(n_tokens); udata->seq_id_unq.resize(0); diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 3e05ab8eac..6c52b29b02 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -7,6 +7,7 @@ #include "llama-memory.h" #include "llama-mmap.h" #include "llama-model.h" +#include "llama-ext.h" #include #include @@ -150,6 +151,10 @@ llama_context::llama_context( cparams.flash_attn = params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_DISABLED; cparams.auto_fa = params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO; + cparams.fused_gdn_ar = true; + cparams.fused_gdn_ch = true; + cparams.auto_fgdn = true; + // with causal attention, the batch size is limited by the context size cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; @@ -160,7 +165,7 @@ llama_context::llama_context( cparams.eagle3_extract_enabled = false; - // intialized later + // initialized later cparams.pipeline_parallel = false; { @@ -339,6 +344,14 @@ llama_context::llama_context( if (cparams.pipeline_parallel) { LLAMA_LOG_INFO("%s: pipeline parallelism enabled\n", __func__); + + if (!graph_reuse_disable) { + // TODO: figure out a way to make graph reuse work with pipeline parallelism + // ref: https://github.com/ggml-org/llama.cpp/pull/20463 + LLAMA_LOG_WARN("%s: graph reuse is currently not compatible with pipeline parallelism - disabling\n", __func__); + + graph_reuse_disable = true; + } } sched_reserve(); @@ -424,7 +437,7 @@ void llama_context::sched_reserve() { if (cparams.auto_fa) { auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true); if (!gf) { - throw std::runtime_error("failed to split graph for Flash Attention check"); + throw std::runtime_error("failed to reserve graph for Flash Attention check"); } const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FATTN) + 1; @@ -434,8 +447,7 @@ void llama_context::sched_reserve() { if (n->op != GGML_OP_FLASH_ATTN_EXT) { continue; } - ggml_backend_dev_t device_fa = ggml_backend_get_device( - ggml_backend_sched_get_tensor_backend(sched.get(), n)); + ggml_backend_dev_t device_fa = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n)); // TODO: instead of the tensor names, use a map to keep track of which (FA) tensors belong to which layer GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FATTN "-", prefix_len) == 0); @@ -450,6 +462,7 @@ void llama_context::sched_reserve() { break; } } + if (fa_device_mismatch) { cparams.flash_attn = false; LLAMA_LOG_WARN("%s: Flash Attention was auto, set to disabled\n", __func__); @@ -461,6 +474,88 @@ void llama_context::sched_reserve() { cparams.auto_fa = false; } + if (cparams.auto_fgdn) { + LLAMA_LOG_INFO("%s: resolving fused Gated Delta Net support:\n", __func__); + + if (cparams.fused_gdn_ar) { + auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true); + if (!gf) { + throw std::runtime_error("failed to reserve graph for fused Gated Delta Net check (autoregressive)"); + } + + const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FGDN_AR) + 1; + bool gdn_device_mismatch = false; + for (int i = 0; i < ggml_graph_n_nodes(gf); i++) { + ggml_tensor * n = ggml_graph_node(gf, i); + if (n->op != GGML_OP_GATED_DELTA_NET) { + continue; + } + ggml_backend_dev_t device_gdn = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n)); + + GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FGDN_AR "-", prefix_len) == 0); + const int il = std::stoi(n->name + prefix_len); + ggml_backend_dev_t device_kv = model.dev_layer(il); + if (device_gdn != device_kv) { + LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the fused Gated Delta Net tensor " + "is assigned to device %s (usually due to missing support)\n", + __func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_gdn)); + gdn_device_mismatch = true; + break; + } + } + + if (gdn_device_mismatch) { + cparams.fused_gdn_ar = false; + LLAMA_LOG_WARN("%s: fused Gated Delta Net (autoregressive) not supported, set to disabled\n", __func__); + } else { + LLAMA_LOG_INFO("%s: fused Gated Delta Net (autoregressive) enabled\n", __func__); + } + } + + if (cparams.fused_gdn_ch) { + // more than one token in the batch per sequence in order to take the chunked path + // note: n_outputs must match n_tokens for embedding models with mean/rank pooling, + // because build_pooling creates inp_mean with shape [n_tokens, n_seqs] and multiplies + // it with t_embd which is reduced to [n_outputs, ...] via out_ids. if n_outputs != n_tokens, + // the ggml_mul_mat assertion fails. this matches the pp reservation below (line ~553). + const uint32_t n_tokens_ch = 16*n_seqs; + auto * gf = graph_reserve(n_tokens_ch, n_seqs, n_tokens_ch, mctx.get(), true); + if (!gf) { + throw std::runtime_error("failed to reserve graph for fused Gated Delta Net check (chunked)"); + } + + const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FGDN_CH) + 1; + bool gdn_device_mismatch = false; + for (int i = 0; i < ggml_graph_n_nodes(gf); i++) { + ggml_tensor * n = ggml_graph_node(gf, i); + if (n->op != GGML_OP_GATED_DELTA_NET) { + continue; + } + ggml_backend_dev_t device_gdn = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n)); + + GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FGDN_CH "-", prefix_len) == 0); + const int il = std::stoi(n->name + prefix_len); + ggml_backend_dev_t device_kv = model.dev_layer(il); + if (device_gdn != device_kv) { + LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the fused Gated Delta Net tensor " + "is assigned to device %s (usually due to missing support)\n", + __func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_gdn)); + gdn_device_mismatch = true; + break; + } + } + + if (gdn_device_mismatch) { + cparams.fused_gdn_ch = false; + LLAMA_LOG_WARN("%s: fused Gated Delta Net (chunked) not supported, set to disabled\n", __func__); + } else { + LLAMA_LOG_INFO("%s: fused Gated Delta Net (chunked) enabled\n", __func__); + } + } + + cparams.auto_fgdn = false; + } + // reserve worst-case graph int n_splits_pp = -1; int n_nodes_pp = -1; @@ -1041,11 +1136,15 @@ void llama_context::set_adapters_lora(llama_adapter_lora ** adapters, size_t n_a bool llama_context::adapters_lora_are_same(llama_adapter_lora ** adapters, size_t n_adapters, float * scales) { LLAMA_LOG_DEBUG("%s: adapters = %p\n", __func__, (void *) adapters); - if (n_adapters != loras->size()) { - return false; - } + // Adapters with a zero scale are never added to `loras`, so also ignore them for the comparison. + size_t n_non_zero = 0; for (size_t i = 0; i < n_adapters; i ++) { + if (scales[i] == 0.0f) { + continue; + } + n_non_zero++; + auto it = loras->find(adapters[i]); if (it == loras->end() || it->second != scales[i]) { @@ -1053,6 +1152,10 @@ bool llama_context::adapters_lora_are_same(llama_adapter_lora ** adapters, size_ } } + if (n_non_zero != loras->size()) { + return false; + } + return true; } @@ -1142,6 +1245,7 @@ llm_graph_result * llama_context::process_ubatch(const llama_ubatch & ubatch, ll { //const auto t_start_us = ggml_time_us(); + // FIXME this call causes a crash if any model inputs were not used in the graph and were therefore not allocated res->set_inputs(&ubatch); // EAGLE3: Fill g_embeddings for decoder input @@ -2064,7 +2168,7 @@ ggml_cgraph * llama_context::graph_reserve( ggml_backend_sched_reset(sched.get()); - // when the scheduler is reset, we cannnot reuse the old graph, so we reset the previous graph result to prevent that + // when the scheduler is reset, we cannot reuse the old graph, so we reset the previous graph result to prevent that gf_res_prev->reset(); // store the n_outputs as it is, and restore it afterwards @@ -3000,19 +3104,23 @@ llama_context * llama_init_from_model( if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO && ggml_is_quantized(params.type_k)) { const uint32_t blck_size = ggml_blck_size(params.type_k); - if (model->hparams.n_embd_head_k % blck_size != 0) { - LLAMA_LOG_ERROR("%s: K cache type %s with block size %u does not divide n_embd_head_k=%u\n", - __func__, ggml_type_name(params.type_k), blck_size, model->hparams.n_embd_head_k); - return nullptr; + for (uint32_t il = 0; il < model->hparams.n_layer; ++il) { + if (model->hparams.n_embd_head_k(il) % blck_size != 0) { + LLAMA_LOG_ERROR("%s: K cache type %s with block size %u does not divide n_embd_head_k=%u\n", + __func__, ggml_type_name(params.type_k), blck_size, model->hparams.n_embd_head_k(il)); + return nullptr; + } } } if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO && ggml_is_quantized(params.type_v)) { const uint32_t blck_size = ggml_blck_size(params.type_v); - if (model->hparams.n_embd_head_v % blck_size != 0) { - LLAMA_LOG_ERROR("%s: V cache type %s with block size %u does not divide n_embd_head_k=%u\n", - __func__, ggml_type_name(params.type_v), blck_size, model->hparams.n_embd_head_v); - return nullptr; + for (uint32_t il = 0; il < model->hparams.n_layer; ++il) { + if (model->hparams.n_embd_head_v(il) % blck_size != 0) { + LLAMA_LOG_ERROR("%s: V cache type %s with block size %u does not divide n_embd_head_v=%u\n", + __func__, ggml_type_name(params.type_v), blck_size, model->hparams.n_embd_head_v(il)); + return nullptr; + } } } @@ -3204,6 +3312,19 @@ uint32_t llama_get_sampled_probs_count_ith(llama_context * ctx, int32_t i) { return static_cast(ctx->get_sampled_probs_count(i)); } +struct ggml_cgraph * llama_graph_reserve( + struct llama_context * ctx, + uint32_t n_tokens, + uint32_t n_seqs, + uint32_t n_outputs) { + auto * memory = ctx->get_memory(); + llama_memory_context_ptr mctx; + if (memory) { + mctx = memory->init_full(); + } + return ctx->graph_reserve(n_tokens, n_seqs, n_outputs, mctx.get()); +} + // llama adapter API int32_t llama_set_adapters_lora( diff --git a/src/llama-cparams.h b/src/llama-cparams.h index 4c9f528b24..48ab113bac 100644 --- a/src/llama-cparams.h +++ b/src/llama-cparams.h @@ -31,6 +31,9 @@ struct llama_cparams { bool offload_kqv; bool flash_attn; bool auto_fa; + bool fused_gdn_ar; // use fused gated delta net (autoregressive) + bool fused_gdn_ch; // use fused gated delta net (chunked) + bool auto_fgdn; bool no_perf; bool warmup; bool op_offload; diff --git a/src/llama-ext.h b/src/llama-ext.h new file mode 100644 index 0000000000..13ced783b4 --- /dev/null +++ b/src/llama-ext.h @@ -0,0 +1,12 @@ +#pragma once + +#include "llama-context.h" +#include "ggml.h" +#include "stdint.h" + +// Reserve a new compute graph. It is valid until the next call to llama_graph_reserve. +LLAMA_API struct ggml_cgraph * llama_graph_reserve( + struct llama_context * ctx, + uint32_t n_tokens, + uint32_t n_seqs, + uint32_t n_outputs); diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp index 2d55070cec..aac0d41f2b 100644 --- a/src/llama-grammar.cpp +++ b/src/llama-grammar.cpp @@ -601,7 +601,7 @@ const char * llama_grammar_parser::parse_sequence( throw std::runtime_error(std::string("expecting an int at ") + pos); } const char * int_end = parse_int(pos); - uint64_t min_times = std::stoul(std::string(pos, int_end - pos)); + uint64_t min_times = std::stoull(std::string(pos, int_end - pos)); pos = parse_space(int_end, is_nested); uint64_t max_times = UINT64_MAX; // default: no max limit @@ -614,7 +614,7 @@ const char * llama_grammar_parser::parse_sequence( if (is_digit_char(*pos)) { const char * int_end = parse_int(pos); - max_times = std::stoul(std::string(pos, int_end - pos)); + max_times = std::stoull(std::string(pos, int_end - pos)); pos = parse_space(int_end, is_nested); } @@ -1160,13 +1160,13 @@ struct llama_grammar * llama_grammar_init_impl( // if there is a grammar, parse it // rules will be empty (default) if there are parse errors if (!parser.parse(grammar_str) || parser.rules.empty()) { - fprintf(stderr, "%s: failed to parse grammar\n", __func__); + LLAMA_LOG_ERROR("failed to parse grammar\n"); return nullptr; } - // Ensure that there is a "root" node. - if (parser.symbol_ids.find("root") == parser.symbol_ids.end()) { - fprintf(stderr, "%s: grammar does not contain a 'root' symbol\n", __func__); + // Ensure that the grammar contains the start symbol + if (parser.symbol_ids.find(grammar_root) == parser.symbol_ids.end()) { + LLAMA_LOG_ERROR("grammar does not contain a '%s' symbol\n", grammar_root); return nullptr; } @@ -1195,7 +1195,7 @@ struct llama_grammar * llama_grammar_init_impl( continue; } if (llama_grammar_detect_left_recursion(vec_rules, i, &rules_visited, &rules_in_progress, &rules_may_be_empty)) { - LLAMA_LOG_ERROR("unsupported grammar, left recursion detected for nonterminal at index %zu", i); + LLAMA_LOG_ERROR("unsupported grammar, left recursion detected for nonterminal at index %zu\n", i); return nullptr; } } diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 461917bef9..0153d06842 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -250,7 +250,7 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) { const bool last = ( cparams.pooling_type == LLAMA_POOLING_TYPE_LAST || - (cparams.pooling_type == LLAMA_POOLING_TYPE_RANK && arch == LLM_ARCH_QWEN3) // qwen3 reranking & embedding models use last token + (cparams.pooling_type == LLAMA_POOLING_TYPE_RANK && (arch == LLM_ARCH_QWEN3 || arch == LLM_ARCH_QWEN3VL)) // qwen3 reranking & embedding models use last token ); for (int i = 0; i < n_tokens; ++i) { @@ -509,6 +509,7 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) { float * data = (float *) cross_kq_mask->data; for (int i = 0; i < n_tokens; ++i) { + GGML_ASSERT(!cross->seq_ids_enc.empty() && "llama_encode must be called first"); for (int j = 0; j < n_enc; ++j) { float f = -INFINITY; @@ -848,13 +849,13 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) : ubatch (params.ubatch), n_embd (hparams.n_embd), n_layer (hparams.n_layer), - n_rot (hparams.n_rot), + n_rot (hparams.n_rot()), n_ctx (cparams.n_ctx), n_head (hparams.n_head()), n_head_kv (hparams.n_head_kv()), - n_embd_head_k (hparams.n_embd_head_k), + n_embd_head_k (hparams.n_embd_head_k()), n_embd_k_gqa (hparams.n_embd_k_gqa()), - n_embd_head_v (hparams.n_embd_head_v), + n_embd_head_v (hparams.n_embd_head_v()), n_embd_v_gqa (hparams.n_embd_v_gqa()), n_expert (hparams.n_expert), n_expert_used (cparams.warmup ? hparams.n_expert : hparams.n_expert_used), @@ -900,7 +901,8 @@ ggml_tensor * llm_graph_context::build_cvec( ggml_tensor * llm_graph_context::build_lora_mm( ggml_tensor * w, - ggml_tensor * cur) const { + ggml_tensor * cur, + ggml_tensor * w_s) const { ggml_tensor * res = ggml_mul_mat(ctx0, w, cur); for (const auto & lora : *loras) { @@ -921,6 +923,10 @@ ggml_tensor * llm_graph_context::build_lora_mm( res = ggml_add(ctx0, res, ab_cur); } + if (w_s) { + res = ggml_mul(ctx0, res, w_s); + } + return res; } @@ -1162,11 +1168,14 @@ ggml_tensor * llm_graph_context::build_moe_ffn( int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, - bool scale_w, float w_scale, llama_expert_gating_func_type gating_op, int il, - ggml_tensor * probs_in) const { + ggml_tensor * probs_in, + ggml_tensor * gate_up_exps, + ggml_tensor * up_exps_s, + ggml_tensor * gate_exps_s, + ggml_tensor * down_exps_s) const { return build_moe_ffn( cur, gate_inp, /* gate_inp_b */ nullptr, @@ -1178,11 +1187,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn( n_expert_used, type_op, norm_w, - scale_w, w_scale, gating_op, il, - probs_in + probs_in, + gate_up_exps, + /* gate_up_exps_b */ nullptr, + up_exps_s, + gate_exps_s, + down_exps_s ); } @@ -1201,11 +1214,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn( int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, - bool scale_w, float w_scale, llama_expert_gating_func_type gating_op, int il, - ggml_tensor * probs_in) const { + ggml_tensor * probs_in, + ggml_tensor * gate_up_exps, + ggml_tensor * gate_up_exps_b, + ggml_tensor * up_exps_s, + ggml_tensor * gate_exps_s, + ggml_tensor * down_exps_s) const { const int64_t n_embd = cur->ne[0]; const int64_t n_tokens = cur->ne[1]; const bool weight_before_ffn = arch == LLM_ARCH_LLAMA4; // for llama4, we apply the sigmoid-ed weights before the FFN @@ -1327,7 +1344,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn( weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens); } - if (scale_w) { + if (w_scale != 0.0f && w_scale != 1.0f) { weights = ggml_scale(ctx0, weights, w_scale); cb(weights, "ffn_moe_weights_scaled", il); } @@ -1344,26 +1361,75 @@ ggml_tensor * llm_graph_context::build_moe_ffn( cb(cur, "ffn_moe_weighted", il); } - ggml_tensor * up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] - cb(up, "ffn_moe_up", il); - - if (up_exps_b) { - up = ggml_add_id(ctx0, up, up_exps_b, selected_experts); - cb(up, "ffn_moe_up_biased", il); - } - + ggml_tensor * up = nullptr; ggml_tensor * experts = nullptr; - if (gate_exps) { - cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] + + if (gate_up_exps) { + // merged gate_up path: one mul_mat_id, then split into gate and up views + ggml_tensor * gate_up = build_lora_mm_id(gate_up_exps, cur, selected_experts); // [n_ff*2, n_expert_used, n_tokens] + cb(gate_up, "ffn_moe_gate_up", il); + + if (gate_up_exps_b) { + gate_up = ggml_add_id(ctx0, gate_up, gate_up_exps_b, selected_experts); + cb(gate_up, "ffn_moe_gate_up_biased", il); + } + + // apply per-expert scale2 to merged gate_up (use up_exps_s since gate and up are fused) + if (up_exps_s) { + ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1); + s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1); + s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens] + gate_up = ggml_mul(ctx0, gate_up, s); + cb(gate_up, "ffn_moe_gate_up_scaled", il); + } + + const int64_t n_ff = gate_up->ne[0] / 2; + cur = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], 0); cb(cur, "ffn_moe_gate", il); + up = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], n_ff * gate_up->nb[0]); + cb(up, "ffn_moe_up", il); } else { - cur = up; + // separate gate and up path + up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] + cb(up, "ffn_moe_up", il); + + if (up_exps_b) { + up = ggml_add_id(ctx0, up, up_exps_b, selected_experts); + cb(up, "ffn_moe_up_biased", il); + } + + // apply per-expert scale2 to up + if (up_exps_s) { + ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1); + s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1); + s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens] + up = ggml_mul(ctx0, up, s); + cb(up, "ffn_moe_up_scaled", il); + } + + if (gate_exps) { + cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] + cb(cur, "ffn_moe_gate", il); + } else { + cur = up; + } + + if (gate_exps_b) { + cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts); + cb(cur, "ffn_moe_gate_biased", il); + } + + // apply per-expert scale2 to gate + if (gate_exps_s) { + ggml_tensor * s = ggml_reshape_3d(ctx0, gate_exps_s, 1, n_expert, 1); + s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1); + s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens] + cur = ggml_mul(ctx0, cur, s); + cb(cur, "ffn_moe_gate_scaled", il); + } } - if (gate_exps_b) { - cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts); - cb(cur, "ffn_moe_gate_biased", il); - } + const bool has_gate = gate_exps || gate_up_exps; switch (type_op) { case LLM_FFN_SILU: @@ -1386,7 +1452,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn( break; } } + } + if (has_gate) { cur = ggml_swiglu_split(ctx0, cur, up); cb(cur, "ffn_moe_swiglu", il); } else { @@ -1394,7 +1462,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn( cb(cur, "ffn_moe_silu", il); } break; case LLM_FFN_GELU: - if (gate_exps) { + if (has_gate) { cur = ggml_geglu_split(ctx0, cur, up); cb(cur, "ffn_moe_geglu", il); } else { @@ -1410,7 +1478,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn( cb(cur, "ffn_moe_swiglu_oai", il); } break; case LLM_FFN_RELU: - if (gate_exps) { + if (has_gate) { cur = ggml_reglu_split(ctx0, cur, up); cb(cur, "ffn_moe_reglu", il); } else { @@ -1418,7 +1486,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn( cb(cur, "ffn_moe_relu", il); } break; case LLM_FFN_RELU_SQR: - if (gate_exps) { + if (has_gate) { // TODO: add support for gated squared relu GGML_ABORT("fatal error: gated squared relu not implemented"); } else { @@ -1438,6 +1506,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn( cb(experts, "ffn_moe_down_biased", il); } + // apply per-expert scale2 to down + if (down_exps_s) { + ggml_tensor * s = ggml_reshape_3d(ctx0, down_exps_s, 1, n_expert, 1); + s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1); + s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens] + experts = ggml_mul(ctx0, experts, s); + cb(experts, "ffn_moe_down_scaled", il); + } + if (!weight_before_ffn) { experts = ggml_mul(ctx0, experts, weights); cb(cur, "ffn_moe_weighted", il); @@ -1580,6 +1657,7 @@ ggml_tensor * llm_graph_context::build_inp_attn_scale() const { // this need to be 1x1xN for broadcasting cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens); ggml_set_input(cur); + ggml_set_name(cur, "attn_scale"); res->add_input(std::move(inp)); @@ -1589,7 +1667,7 @@ ggml_tensor * llm_graph_context::build_inp_attn_scale() const { ggml_tensor * llm_graph_context::build_inp_out_ids() const { // note: when all tokens are output, we could skip this optimization to spare the ggml_get_rows() calls, // but this would make the graph topology depend on the number of output tokens, which can interere with - // features that require constant topology such as pipline parallelism + // features that require constant topology such as pipeline parallelism // ref: https://github.com/ggml-org/llama.cpp/pull/14275#issuecomment-2987424471 //if (n_outputs < n_tokens) { // return nullptr; @@ -1752,7 +1830,7 @@ ggml_tensor * llm_graph_context::build_attn_mha( if (v_mla) { #if 0 // v_mla can be applied as a matrix-vector multiplication with broadcasting across dimension 3 == n_tokens. - // However, the code is optimized for dimensions 0 and 1 being large, so this is ineffient. + // However, the code is optimized for dimensions 0 and 1 being large, so this is inefficient. cur = ggml_reshape_4d(ctx0, cur, v_mla->ne[0], 1, n_head, n_tokens); cur = ggml_mul_mat(ctx0, v_mla, cur); #else @@ -2526,7 +2604,7 @@ void llm_graph_context::build_pooling( } // softmax for qwen3 reranker - if (arch == LLM_ARCH_QWEN3) { + if (arch == LLM_ARCH_QWEN3 || arch == LLM_ARCH_QWEN3VL) { cur = ggml_soft_max(ctx0, cur); } } break; diff --git a/src/llama-graph.h b/src/llama-graph.h index 661d8c4ad3..ee0bb458aa 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -790,10 +790,11 @@ struct llm_graph_context { ggml_tensor * cur, int il) const; - // do mat_mul, while optionally apply lora + // do mat_mul, while optionally apply lora and per-tensor scale ggml_tensor * build_lora_mm( ggml_tensor * w, - ggml_tensor * cur) const; + ggml_tensor * cur, + ggml_tensor * w_s = nullptr) const; // do mat_mul_id, while optionally apply lora ggml_tensor * build_lora_mm_id( @@ -836,11 +837,14 @@ struct llm_graph_context { int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, - bool scale_w, float w_scale, llama_expert_gating_func_type gating_op, int il, - ggml_tensor * probs_in = nullptr) const; + ggml_tensor * probs_in = nullptr, + ggml_tensor * gate_up_exps = nullptr, + ggml_tensor * up_exps_s = nullptr, + ggml_tensor * gate_exps_s = nullptr, + ggml_tensor * down_exps_s = nullptr) const; ggml_tensor * build_moe_ffn( ggml_tensor * cur, @@ -857,11 +861,15 @@ struct llm_graph_context { int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, - bool scale_w, float w_scale, llama_expert_gating_func_type gating_op, int il, - ggml_tensor * probs_in = nullptr) const; + ggml_tensor * probs_in = nullptr, + ggml_tensor * gate_up_exps = nullptr, + ggml_tensor * gate_up_exps_b = nullptr, + ggml_tensor * up_exps_s = nullptr, + ggml_tensor * gate_exps_s = nullptr, + ggml_tensor * down_exps_s = nullptr) const; // // inputs diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index 756dda1a7a..002d15d415 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -62,6 +62,14 @@ uint32_t llama_hparams::n_gqa(uint32_t il) const { return n_head/n_head_kv; } +uint32_t llama_hparams::n_rot(uint32_t il) const { + if (il < n_layer) { + return is_swa(il) ? n_rot_swa : n_rot_full; + } + + GGML_ABORT("fatal error"); +} + uint32_t llama_hparams::n_embd_inp() const { uint32_t n_embd_inp = n_embd; @@ -76,16 +84,32 @@ uint32_t llama_hparams::n_embd_out() const { return n_embd_out_impl > 0 ? n_embd_out_impl : n_embd; } +uint32_t llama_hparams::n_embd_head_k(uint32_t il) const { + if (il < n_layer) { + return is_swa(il) ? n_embd_head_k_swa : n_embd_head_k_full; + } + + GGML_ABORT("fatal error"); +} + +uint32_t llama_hparams::n_embd_head_v(uint32_t il) const { + if (il < n_layer) { + return is_swa(il) ? n_embd_head_v_swa : n_embd_head_v_full; + } + + GGML_ABORT("fatal error"); +} + uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const { const uint32_t n_head_kv = this->n_head_kv(il); - return n_embd_head_k * n_head_kv; + return n_embd_head_k(il) * n_head_kv; } uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const { const uint32_t n_head_kv = this->n_head_kv(il); - return n_embd_head_v * n_head_kv; + return n_embd_head_v(il) * n_head_kv; } bool llama_hparams::is_n_embd_k_gqa_variable() const { @@ -197,11 +221,11 @@ bool llama_hparams::is_mla() const { } uint32_t llama_hparams::n_embd_head_k_mla() const { - return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k; + return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k(); } uint32_t llama_hparams::n_embd_head_v_mla() const { - return is_mla() ? n_embd_head_v_mla_impl : n_embd_head_v; + return is_mla() ? n_embd_head_v_mla_impl : n_embd_head_v(); } bool llama_hparams::has_kv(uint32_t il) const { diff --git a/src/llama-hparams.h b/src/llama-hparams.h index a3bef6b95c..7853639a65 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -44,13 +44,20 @@ struct llama_hparams { uint32_t n_embd; uint32_t n_layer; int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache - uint32_t n_rot; - uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads - uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head uint32_t n_expert = 0; uint32_t n_expert_used = 0; uint32_t n_rel_attn_bkts = 0; + // different head size for full_attention and SWA layers + uint32_t n_embd_head_k_full; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads + uint32_t n_embd_head_v_full; // dimension of values (d_v) aka n_embd_head + uint32_t n_embd_head_k_swa; + uint32_t n_embd_head_v_swa; + + // different RoPE dimensions for full_attention and SWA layers + uint32_t n_rot_full; + uint32_t n_rot_swa; + // note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA uint32_t n_embd_head_k_mla_impl = 0; uint32_t n_embd_head_v_mla_impl = 0; @@ -82,6 +89,7 @@ struct llama_hparams { bool expert_weights_norm = false; uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE; uint32_t moe_every_n_layers = 0; + uint32_t moe_latent_size = 0; uint32_t nextn_predict_layers = 0; float f_norm_eps; @@ -257,12 +265,18 @@ struct llama_hparams { uint32_t n_gqa(uint32_t il = 0) const; + uint32_t n_rot(uint32_t il = 0) const; + // dimension of main + auxiliary input embeddings uint32_t n_embd_inp() const; // dimension of output embeddings uint32_t n_embd_out() const; + // dimension of key/value embeddings for each head (per layer) + uint32_t n_embd_head_k(uint32_t il = 0) const; + uint32_t n_embd_head_v(uint32_t il = 0) const; + // dimension of key embeddings across all k-v heads uint32_t n_embd_k_gqa(uint32_t il = 0) const; diff --git a/src/llama-impl.cpp b/src/llama-impl.cpp index 710a5a1e08..4c0188ee72 100644 --- a/src/llama-impl.cpp +++ b/src/llama-impl.cpp @@ -100,9 +100,9 @@ std::string format(const char * fmt, ...) { std::string llama_format_tensor_shape(const std::vector & ne) { char buf[256]; - snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0)); + snprintf(buf, sizeof(buf), "%6" PRId64, ne.at(0)); for (size_t i = 1; i < ne.size(); i++) { - snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i)); + snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %6" PRId64, ne.at(i)); } return buf; } diff --git a/src/llama-impl.h b/src/llama-impl.h index dfd9fee9f4..e4f35c8e53 100644 --- a/src/llama-impl.h +++ b/src/llama-impl.h @@ -70,4 +70,6 @@ std::string llama_format_tensor_shape(const struct ggml_tensor * t); std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i); -#define LLAMA_TENSOR_NAME_FATTN "__fattn__" +#define LLAMA_TENSOR_NAME_FATTN "__fattn__" +#define LLAMA_TENSOR_NAME_FGDN_AR "__fgdn_ar__" +#define LLAMA_TENSOR_NAME_FGDN_CH "__fgdn_ch__" diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index cb702b2a59..01166fac9c 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -583,7 +583,7 @@ llama_kv_cache::slot_info_vec_t llama_kv_cache::prepare(const std::vector 1) { + return false; + } return true; } @@ -1030,8 +1033,8 @@ ggml_tensor * llama_kv_cache::get_k(ggml_context * ctx, int32_t il, uint32_t n_k const uint32_t ns = sinfo.s1 - sinfo.s0 + 1; return ggml_view_4d(ctx, k, - hparams.n_embd_head_k, hparams.n_head_kv(il), n_kv, ns, - ggml_row_size(k->type, hparams.n_embd_head_k), + hparams.n_embd_head_k(il), hparams.n_head_kv(il), n_kv, ns, + ggml_row_size(k->type, hparams.n_embd_head_k(il)), ggml_row_size(k->type, n_embd_k_gqa), ggml_row_size(k->type, n_embd_k_gqa*kv_size), ggml_row_size(k->type, n_embd_k_gqa*kv_size)*sinfo.s0); @@ -1053,8 +1056,8 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k if (!v_trans) { // note: v->nb[1] <= v->nb[2] return ggml_view_4d(ctx, v, - hparams.n_embd_head_v, hparams.n_head_kv(il), n_kv, ns, - ggml_row_size(v->type, hparams.n_embd_head_v), // v->nb[1] + hparams.n_embd_head_v(il), hparams.n_head_kv(il), n_kv, ns, + ggml_row_size(v->type, hparams.n_embd_head_v(il)), // v->nb[1] ggml_row_size(v->type, n_embd_v_gqa), // v->nb[2] ggml_row_size(v->type, n_embd_v_gqa*kv_size), // v->nb[3] ggml_row_size(v->type, n_embd_v_gqa*kv_size)*sinfo.s0); @@ -1062,8 +1065,8 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k // note: v->nb[1] > v->nb[2] return ggml_view_4d(ctx, v, - n_kv, hparams.n_head_kv(il), hparams.n_embd_head_v, ns, - ggml_row_size(v->type, kv_size*hparams.n_embd_head_v), // v->nb[1] + n_kv, hparams.n_head_kv(il), hparams.n_embd_head_v(il), ns, + ggml_row_size(v->type, kv_size*hparams.n_embd_head_v(il)), // v->nb[1] ggml_row_size(v->type, kv_size), // v->nb[2] ggml_row_size(v->type, kv_size*n_embd_v_gqa), // v->nb[3] ggml_row_size(v->type, kv_size*n_embd_v_gqa)*sinfo.s0); @@ -1290,7 +1293,7 @@ static void set_input_kq_mask_impl(const args_set_input_kq_mask & args, float * } for (uint32_t s = 0; s < n_stream; ++s) { - // bookeeping of the KQ mask cells that could change for other tokens of the same sequence + // bookkeeping of the KQ mask cells that could change for other tokens of the same sequence std::unordered_map seq_srct; std::unordered_map> seq_idxs; @@ -1541,7 +1544,8 @@ ggml_tensor * llama_kv_cache::build_rope_shift( ggml_tensor * shift, ggml_tensor * factors, float freq_base, - float freq_scale) const { + float freq_scale, + uint32_t il) const { const auto & n_ctx_orig = cparams.n_ctx_orig_yarn; const auto & yarn_ext_factor = cparams.yarn_ext_factor; @@ -1549,7 +1553,7 @@ ggml_tensor * llama_kv_cache::build_rope_shift( const auto & yarn_beta_slow = cparams.yarn_beta_slow; const auto & yarn_attn_factor = cparams.yarn_attn_factor; - const auto & n_rot = hparams.n_rot; + const auto & n_rot = hparams.n_rot(il); const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE // @ngxson : this is a workaround // for M-RoPE, we want to rotate the whole vector when doing KV shift @@ -1603,13 +1607,6 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co auto * ctx = res->get_ctx(); auto * gf = res->get_gf(); - const auto & n_embd_head_k = hparams.n_embd_head_k; - //const auto & n_embd_head_v = hparams.n_embd_head_v; - - const auto & n_rot = hparams.n_rot; - - const auto n_embd_nope = hparams.n_lora_kv > 0 ? n_embd_head_k - n_rot : 0; - auto inp = std::make_unique(this); inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, (int64_t) get_size()*n_stream); @@ -1623,6 +1620,10 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co const int64_t n_head_kv = hparams.n_head_kv(il); const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); + const auto n_rot = hparams.n_rot(il); + const auto n_embd_head_k = hparams.n_embd_head_k(il); + const auto n_embd_nope = hparams.n_lora_kv > 0 ? n_embd_head_k - n_rot : 0; + const float freq_base_l = model.get_rope_freq_base (cparams, il); const float freq_scale_l = model.get_rope_freq_scale(cparams, il); @@ -1635,7 +1636,7 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co ggml_row_size(layer.k->type, n_embd_k_gqa), ggml_row_size(layer.k->type, n_embd_nope)); - ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l); + ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l, il); ggml_build_forward_expand(gf, cur); } @@ -1757,8 +1758,10 @@ void llama_kv_cache::state_write_meta(llama_io_write_i & io, const cell_ranges_t io.write(&pos, sizeof(pos)); io.write(&n_seq_id, sizeof(n_seq_id)); - // TODO: we also need to save llama_kv_cell_ext when apply_ubatch() support loading it - // see: https://github.com/ggml-org/llama.cpp/pull/16825#issuecomment-3460868350 + if (hparams.n_pos_per_embd() > 1) { + const llama_kv_cell_ext ext = cells.ext_get(i); + io.write(&ext, sizeof(ext)); + } for (const auto & seq_id : seq_ids) { io.write(&seq_id, sizeof(seq_id)); @@ -1892,6 +1895,14 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32 return false; } + if (hparams.n_pos_per_embd() > 1) { + llama_kv_cell_ext ext; + io.read_to(&ext, sizeof(ext)); + + ubatch.pos[i + ubatch.n_tokens] = ext.y; + ubatch.pos[i + ubatch.n_tokens*2] = ext.x; + } + // read the sequence id, but directly discard it - we will use dest_seq_id instead { llama_seq_id seq_id; @@ -1942,6 +1953,12 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32 cells.pos_set(i, pos); + if (hparams.n_pos_per_embd() > 1) { + llama_kv_cell_ext ext; + io.read_to(&ext, sizeof(ext)); + cells.ext_set(i, ext); + } + for (uint32_t j = 0; j < n_seq_id; ++j) { llama_seq_id seq_id; io.read_to(&seq_id, sizeof(seq_id)); diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h index e194bf3e26..33c78c5f21 100644 --- a/src/llama-kv-cache.h +++ b/src/llama-kv-cache.h @@ -264,7 +264,8 @@ private: ggml_tensor * shift, ggml_tensor * factors, float freq_base, - float freq_scale) const; + float freq_scale, + uint32_t il) const; ggml_cgraph * build_graph_shift( llm_graph_result * res, diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp index f0038036dc..6e8413f493 100644 --- a/src/llama-memory-recurrent.cpp +++ b/src/llama-memory-recurrent.cpp @@ -163,7 +163,7 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos const auto & cell = cells[tail_id]; // partial intersection is invalid if it includes the final pos if (0 < p0 && p0 <= cell.pos && p1 > cell.pos) { - //printf("[DEBUG] inside `llama_memory_recurrent::seq_rm`: partial intersection is invalid, so returning false\n"); + //printf("[DEBUG] inside `llama_memory_recurrent::seq_rm`: partial intersection is invalid, so returning false, p0 = %d, cell.pos = %d, p1 = %d\n", p0, cell.pos, p1); return false; } // invalidate tails which will be cleared diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index 1501e392ca..413f34c226 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -1,12 +1,17 @@ #include "llama-model-loader.h" +#include "ggml-alloc.h" #include "ggml.h" +#include "gguf.h" +#include "llama-hparams.h" #include #include #include +#include #include #include +#include static const size_t kiB = 1024; static const size_t MiB = 1024*kiB; @@ -37,6 +42,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q5_1: return "Q5_1"; case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0"; case LLAMA_FTYPE_MOSTLY_MXFP4_MOE: return "MXFP4 MoE"; + case LLAMA_FTYPE_MOSTLY_NVFP4: return "NVFP4"; case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q2_K_S: return "Q2_K - Small"; case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small"; @@ -263,7 +269,7 @@ namespace GGUFMeta { template typename std::enable_if::value, bool>::type llama_model_loader::get_arr_n(const std::string & key, T & result, bool required) { - const int kid = gguf_find_key(meta.get(), key.c_str()); + const int kid = gguf_find_key(metadata, key.c_str()); if (kid < 0) { if (required) { @@ -273,7 +279,7 @@ namespace GGUFMeta { } struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta.get(), kid); + GGUFMeta::GKV::get_kv(metadata, kid); result = arr_info.length; @@ -290,7 +296,7 @@ namespace GGUFMeta { template bool llama_model_loader::get_arr(const std::string & key, std::vector & result, bool required) { - const gguf_context * ctx = meta.get(); + const gguf_context * ctx = metadata; const int kid = gguf_find_key(ctx, key.c_str()); if (kid < 0 || gguf_get_kv_type(ctx, kid) != GGUF_TYPE_ARRAY) { @@ -331,7 +337,7 @@ namespace GGUFMeta { template bool llama_model_loader::get_arr(const std::string & key, std::array & result, bool required) { - const gguf_context * ctx = meta.get(); + const gguf_context * ctx = metadata; const int kid = gguf_find_key(ctx, key.c_str()); if (kid < 0 || gguf_get_kv_type(ctx, kid) != GGUF_TYPE_ARRAY) { @@ -393,7 +399,7 @@ namespace GGUFMeta { const struct llama_model_kv_override * override = it != kv_overrides.end() ? &it->second : nullptr; - const bool found = GGUFMeta::GKV::set(meta.get(), key, result, override); + const bool found = GGUFMeta::GKV::set(metadata, key, result, override); if (required && !found) { throw std::runtime_error(format("key not found in model: %s", key.c_str())); @@ -427,7 +433,7 @@ namespace GGUFMeta { // get array of n <= N_MAX elements, or a single element repeated n times template bool llama_model_loader::get_key_or_arr(const std::string & key, std::array & result, uint32_t n, bool required) { - const int kid = gguf_find_key(meta.get(), key.c_str()); + const int kid = gguf_find_key(metadata, key.c_str()); if (kid < 0) { if (required) { @@ -440,9 +446,9 @@ namespace GGUFMeta { throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str())); } - if (gguf_get_kv_type(meta.get(), kid) == GGUF_TYPE_ARRAY) { + if (gguf_get_kv_type(metadata, kid) == GGUF_TYPE_ARRAY) { struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta.get(), kid); + GGUFMeta::GKV::get_kv(metadata, kid); if (n != arr_info.length) { throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length)); @@ -473,7 +479,7 @@ namespace GGUFMeta { bool llama_model_loader::get_key_or_arr(enum llm_kv kid, uint32_t & result, bool required) { const std::string key = llm_kv(kid); - const int id = gguf_find_key(meta.get(), key.c_str()); + const int id = gguf_find_key(metadata, key.c_str()); if (id < 0) { if (required) { @@ -483,7 +489,7 @@ namespace GGUFMeta { } // throw and error if type is an array - if (gguf_get_kv_type(meta.get(), id) == GGUF_TYPE_ARRAY) { + if (gguf_get_kv_type(metadata, id) == GGUF_TYPE_ARRAY) { if (required) { throw std::runtime_error(format("expected scalar, found array for key: %s", key.c_str())); } @@ -500,6 +506,9 @@ namespace GGUFMeta { llama_model_loader::llama_model_loader( + struct gguf_context * meta, + llama_model_set_tensor_data_t set_tensor_data, + void * set_tensor_data_ud, const std::string & fname, std::vector & splits, bool use_mmap, @@ -507,7 +516,8 @@ llama_model_loader::llama_model_loader( bool check_tensors, bool no_alloc, const llama_model_kv_override * param_overrides_p, - const llama_model_tensor_buft_override * param_tensor_buft_overrides_p) { + const llama_model_tensor_buft_override * param_tensor_buft_overrides_p) + : metadata(meta), set_tensor_data(set_tensor_data), set_tensor_data_ud(set_tensor_data_ud) { int trace = 0; if (getenv("LLAMA_TRACE")) { trace = atoi(getenv("LLAMA_TRACE")); @@ -521,136 +531,142 @@ llama_model_loader::llama_model_loader( tensor_buft_overrides = param_tensor_buft_overrides_p; - // Load the main GGUF - struct ggml_context * ctx = NULL; - struct gguf_init_params params = { - /*.no_alloc = */ true, - /*.ctx = */ &ctx, - }; + if (!fname.empty()) { + // Load the main GGUF + struct ggml_context * ctx = NULL; + struct gguf_init_params params = { + /*.no_alloc = */ true, + /*.ctx = */ &ctx, + }; - meta.reset(gguf_init_from_file(fname.c_str(), params)); - if (!meta) { - throw std::runtime_error(format("%s: failed to load model from %s", __func__, fname.c_str())); - } - - get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false); - llm_kv = LLM_KV(llm_arch_from_string(arch_name)); - - files.emplace_back(new llama_file(fname.c_str(), "rb", use_direct_io)); - contexts.emplace_back(ctx); - - if (use_mmap && use_direct_io) { - if (files.back()->has_direct_io()) { - LLAMA_LOG_WARN("%s: direct I/O is enabled, disabling mmap\n", __func__); - use_mmap = false; - } else { - LLAMA_LOG_WARN("%s: direct I/O is not available, using mmap\n", __func__); - use_direct_io = false; - - // reopen file using std::fopen for mmap - files.pop_back(); - files.emplace_back(new llama_file(fname.c_str(), "rb", false)); - } - } - - // Save tensors data offset of the main file. - // For subsidiary files, `meta` tensor data offset must not be used, - // so we build a unified tensors index for weights. - for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - std::string tensor_name = std::string(cur->name); - // make sure there is no duplicated tensor names - if (weights_map.find(tensor_name) != weights_map.end()) { - throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); - } - n_elements += ggml_nelements(cur); - n_bytes += ggml_nbytes(cur); - weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, meta.get(), cur)); - } - uint16_t n_split = 0; - get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false); - - // Load additional GGML contexts - if (n_split > 1) { - // make sure the main file is loaded first - uint16_t idx = 0; - const std::string kv_split_no = llm_kv(LLM_KV_SPLIT_NO); - get_key(kv_split_no, idx); - if (idx != 0) { - throw std::runtime_error(format("illegal split file idx: %d (file: %s), model must be loaded with the first split", idx, fname.c_str())); + metadata_ptr.reset(gguf_init_from_file(fname.c_str(), params)); + metadata = metadata_ptr.get(); + if (metadata == nullptr) { + throw std::runtime_error(format("%s: failed to load model from %s", __func__, fname.c_str())); } - // generate list of splits if needed - if (splits.empty()) { - splits = llama_get_list_splits(fname, idx, n_split); + get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false); + llm_kv = LLM_KV(llm_arch_from_string(arch_name)); + + files.emplace_back(new llama_file(fname.c_str(), "rb", use_direct_io)); + contexts.emplace_back(ctx); + + if (use_mmap && use_direct_io) { + if (files.back()->has_direct_io()) { + LLAMA_LOG_WARN("%s: direct I/O is enabled, disabling mmap\n", __func__); + use_mmap = false; + } else { + LLAMA_LOG_WARN("%s: direct I/O is not available, using mmap\n", __func__); + use_direct_io = false; + + // reopen file using std::fopen for mmap + files.pop_back(); + files.emplace_back(new llama_file(fname.c_str(), "rb", false)); + } } - // in case user give a custom list of splits, check if it matches the expected number - if (n_split != (uint16_t)splits.size()) { - throw std::runtime_error(format("invalid split count, given: %zu splits, but expected %d", splits.size(), n_split)); + // Save tensors data offset of the main file. + // For subsidiary files, `meta` tensor data offset must not be used, + // so we build a unified tensors index for weights. + for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + std::string tensor_name = std::string(cur->name); + // make sure there is no duplicated tensor names + if (weights_map.find(tensor_name) != weights_map.end()) { + throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); + } + n_elements += ggml_nelements(cur); + n_bytes += ggml_nbytes(cur); + weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, metadata, cur)); } + uint16_t n_split = 0; + get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false); - if (trace > 0) { - LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split); - } - - // load other splits - for (idx = 1; idx < n_split; idx++) { - const char * fname_split = splits[idx].c_str(); - - struct gguf_init_params split_params = { - /*.no_alloc = */ true, - /*.ctx = */ &ctx, - }; - gguf_context_ptr ctx_gguf { gguf_init_from_file(fname_split, split_params) }; - if (!ctx_gguf) { - throw std::runtime_error(format("%s: failed to load GGUF split from %s", __func__, fname_split)); + // Load additional GGML contexts + if (n_split > 1) { + // make sure the main file is loaded first + uint16_t idx = 0; + const std::string kv_split_no = llm_kv(LLM_KV_SPLIT_NO); + get_key(kv_split_no, idx); + if (idx != 0) { + throw std::runtime_error(format("illegal split file idx: %d (file: %s), model must be loaded with the first split", idx, fname.c_str())); } - // check idx + // generate list of splits if needed + if (splits.empty()) { + splits = llama_get_list_splits(fname, idx, n_split); + } + + // in case user give a custom list of splits, check if it matches the expected number + if (n_split != (uint16_t)splits.size()) { + throw std::runtime_error(format("invalid split count, given: %zu splits, but expected %d", splits.size(), n_split)); + } + + if (trace > 0) { + LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split); + } + + // load other splits + for (idx = 1; idx < n_split; idx++) { + const char * fname_split = splits[idx].c_str(); + + struct gguf_init_params split_params = { + /*.no_alloc = */ true, + /*.ctx = */ &ctx, + }; + gguf_context_ptr ctx_gguf { gguf_init_from_file(fname_split, split_params) }; + if (!ctx_gguf) { + throw std::runtime_error(format("%s: failed to load GGUF split from %s", __func__, fname_split)); + } + + // check idx + { + const int kid = gguf_find_key(ctx_gguf.get(), kv_split_no.c_str()); + if (kid < 0) { + throw std::runtime_error(format("missing key %s in GGUF split %s", kv_split_no.c_str(), fname_split)); + } + int idx_gguf = gguf_get_val_u16(ctx_gguf.get(), kid); + if (idx_gguf != idx) { + throw std::runtime_error(format("invalid split file idx: %d (file: %s), expected %d", idx_gguf, fname_split, idx)); + } + } + + files.emplace_back(new llama_file(fname_split, "rb", use_direct_io)); + contexts.emplace_back(ctx); + + // Save tensors data offset info of the shard. + for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + std::string tensor_name = std::string(cur->name); + // make sure there is no duplicated tensor names + if (weights_map.find(tensor_name) != weights_map.end()) { + throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); + } + n_elements += ggml_nelements(cur); + n_bytes += ggml_nbytes(cur); + weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), idx, ctx_gguf.get(), cur)); + } + } + + get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors); + + // sanity check { - const int kid = gguf_find_key(ctx_gguf.get(), kv_split_no.c_str()); - if (kid < 0) { - throw std::runtime_error(format("missing key %s in GGUF split %s", kv_split_no.c_str(), fname_split)); - } - int idx_gguf = gguf_get_val_u16(ctx_gguf.get(), kid); - if (idx_gguf != idx) { - throw std::runtime_error(format("invalid split file idx: %d (file: %s), expected %d", idx_gguf, fname_split, idx)); + const int n_tensors_loaded = (int) weights_map.size(); + if (n_tensors != n_tensors_loaded) { + throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded)); } } - files.emplace_back(new llama_file(fname_split, "rb", use_direct_io)); - contexts.emplace_back(ctx); - - // Save tensors data offset info of the shard. - for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - std::string tensor_name = std::string(cur->name); - // make sure there is no duplicated tensor names - if (weights_map.find(tensor_name) != weights_map.end()) { - throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); - } - n_elements += ggml_nelements(cur); - n_bytes += ggml_nbytes(cur); - weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), idx, ctx_gguf.get(), cur)); - } + LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1); } - - get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors); - - // sanity check - { - const int n_tensors_loaded = (int) weights_map.size(); - if (n_tensors != n_tensors_loaded) { - throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded)); - } - } - - LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1); + } else { + get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false); + llm_kv = LLM_KV(llm_arch_from_string(arch_name)); } - n_kv = gguf_get_n_kv(meta.get()); + n_kv = gguf_get_n_kv(metadata); n_tensors = weights_map.size(); - fver = (enum llama_fver) gguf_get_version(meta.get()); + fver = (enum llama_fver) gguf_get_version(metadata); LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n", __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver)); @@ -709,6 +725,7 @@ llama_model_loader::llama_model_loader( case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break; case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break; case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break; + case GGML_TYPE_NVFP4: ftype = LLAMA_FTYPE_MOSTLY_NVFP4; break; default: { LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max)); @@ -729,14 +746,14 @@ llama_model_loader::llama_model_loader( LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); for (int i = 0; i < n_kv; i++) { - const char * name = gguf_get_key(meta.get(), i); - const enum gguf_type type = gguf_get_kv_type(meta.get(), i); + const char * name = gguf_get_key(metadata, i); + const enum gguf_type type = gguf_get_kv_type(metadata, i); const std::string type_name = type == GGUF_TYPE_ARRAY - ? format("%s[%s,%zu]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta.get(), i)), gguf_get_arr_n(meta.get(), i)) + ? format("%s[%s,%zu]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(metadata, i)), gguf_get_arr_n(metadata, i)) : gguf_type_name(type); - std::string value = gguf_kv_to_str(meta.get(), i); + std::string value = gguf_kv_to_str(metadata, i); const size_t MAX_VALUE_LEN = 40; if (value.size() > MAX_VALUE_LEN) { value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()); @@ -838,15 +855,382 @@ const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::stri return cur; } -struct ggml_tensor * llama_model_loader::create_tensor(struct ggml_context * ctx, const std::string & name, const std::initializer_list & ne, int flags) { - LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, name.c_str()); - const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED)); +// checks if the weight tensor can be used with the specified buffer type and device +static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w, ggml_op op, ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev) { + GGML_ASSERT(w != nullptr); + + if (op == GGML_OP_NONE) { + return true; + } + + ggml_init_params params = { + /*.mem_size =*/ ggml_tensor_overhead()*8, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context_ptr ctx_ptr { ggml_init(params) }; + if (!ctx_ptr) { + throw std::runtime_error(format("failed to create ggml context")); + } + ggml_context * ctx = ctx_ptr.get(); + + ggml_tensor * op_tensor = nullptr; + + switch (op) { + case GGML_OP_GET_ROWS: + { + ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); + op_tensor = ggml_get_rows(ctx, w, b); + } break; + case GGML_OP_MUL_MAT: + { + ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]); + op_tensor = ggml_mul_mat(ctx, w, b); + } break; + case GGML_OP_MUL_MAT_ID: + { + const int n_expert_used = hparams.n_expert_used; + GGML_ASSERT(n_expert_used > 0); + ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512); + ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512); + op_tensor = ggml_mul_mat_id(ctx, w, b, ids); + } break; + case GGML_OP_ADD: + { + ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]); + op_tensor = ggml_add(ctx, a, w); + } break; + case GGML_OP_ADD_ID: + { + const int n_expert_used = hparams.n_expert_used; + GGML_ASSERT(n_expert_used > 0); + ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512); + ggml_tensor * c = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512); + op_tensor = ggml_add_id(ctx, a, w, c); + } break; + case GGML_OP_MUL: + { + ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]); + op_tensor = ggml_mul(ctx, a, w); + } break; + case GGML_OP_DIV: + { + ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, w->ne[0]); + op_tensor = ggml_div(ctx, a, w); + } break; + case GGML_OP_ROPE: + { + const int n_embd_head = hparams.n_embd_head_v(); + const int n_head = hparams.n_head(); + ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512); + ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); + op_tensor = ggml_rope_ext( + ctx, a, b, w, + 0, 0, 0, 0, 0, + 0, 0, 0, 0 + ); + + } break; + case GGML_OP_SSM_CONV: + { + const int64_t n_seq_tokens = 512; + const int64_t n_seqs = 3; + ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0] - 1 + n_seq_tokens, w->ne[1], n_seqs); + op_tensor = ggml_ssm_conv(ctx, conv_x, w); + } break; + case GGML_OP_SSM_SCAN: + { + // w is ssm_a, which is used to distinguish Mamba-1 and Mamba-2 + const int64_t d_state = w->ne[0] == 1 ? hparams.ssm_d_state : w->ne[0]; + const int64_t n_head = w->ne[1]; + const int64_t head_dim = hparams.ssm_d_inner / n_head; + const int64_t n_group = hparams.ssm_n_group ? hparams.ssm_n_group : 1; + const int64_t n_seq_tokens = 512; + const int64_t n_seqs = 3; + ggml_tensor * s = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, d_state, head_dim, n_head, n_seqs); + ggml_tensor * x = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, head_dim, n_head, n_seq_tokens, n_seqs); + ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_head, n_seq_tokens, n_seqs); + ggml_tensor * B = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, d_state, n_group, n_seq_tokens, n_seqs); + ggml_tensor * C = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, d_state, n_group, n_seq_tokens, n_seqs); + ggml_tensor * ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_seqs); + op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C, ids); + } break; + case GGML_OP_RWKV_WKV6: + { + // FIXME + const int64_t S = 123; + const int64_t H = 123; + const int64_t n_tokens = 123; + const int64_t n_seqs = 123; + ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens); + ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens); + ggml_tensor * r = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens); + ggml_tensor * tf = w; + ggml_tensor * td = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens); + ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H); + op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state); + } break; + case GGML_OP_IM2COL: + { + const int n_embd_inp = hparams.n_embd_inp(); + ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd_inp, w->ne[1], 1, 1); + op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16); + } break; + case GGML_OP_SCALE: + { + op_tensor = ggml_scale(ctx, w, 1.0f); + } break; + default: + GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name); + } + + // create a temporary dummy buffer for the weight so that supports_op can check the buffer type + GGML_ASSERT(w->buffer == nullptr); + w->buffer = ggml_backend_buft_alloc_buffer(buft, 0); + bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor); + ggml_backend_buffer_free(w->buffer); + w->buffer = nullptr; + + return op_supported; +} + +// find the first buffer type in the list that can use the tensor +static ggml_backend_buffer_type_t select_weight_buft(const llama_hparams & hparams, ggml_tensor * tensor, ggml_op op, const buft_list_t * buft_list) { + GGML_ASSERT(!buft_list->empty()); + for (const auto & cur : *buft_list) { + ggml_backend_dev_t cur_dev = cur.first; + ggml_backend_buffer_type_t cur_buft = cur.second; + if (weight_buft_supported(hparams, tensor, op, cur_buft, cur_dev)) { + return cur_buft; + } + } + + return nullptr; +} + +struct ggml_tensor * llama_model_loader::create_tensor( + const llama_hparams & hparams, const buft_list_t * buft_list_cpu, const buft_list_t * buft_list_input, const buft_list_t * buft_list_output, + const buft_list_t * buft_list_layer, const LLM_TN_IMPL & tn, const std::initializer_list & ne, int flags) { + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + auto it = ctx_map.find(buft); + if (it == ctx_map.end()) { + // one ggml context per buffer type + int max_n_tensors = n_tensors; + max_n_tensors += 1; // duplicated output tensor + max_n_tensors += hparams.n_layer*2; // duplicated rope freq tensors + if (files.empty()) { + max_n_tensors += hparams.n_layer*256; // this should be well above what any model actually uses + } + const size_t ctx_size = ggml_tensor_overhead()*max_n_tensors; + + ggml_init_params params = { + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + ggml_context * ctx = ggml_init(params); + if (!ctx) { + throw std::runtime_error(format("failed to create ggml context")); + } + + ctx_map.emplace(buft, ctx); + + return ctx; + } + return it->second.get(); + }; + + auto buft_for_tensor = [&](ggml_tensor * t_meta) -> ggml_backend_buffer_type_t { + if (!t_meta) { + if (flags & TENSOR_NOT_REQUIRED) { + return nullptr; + } + throw std::runtime_error(format("missing tensor '%s'", tn.str().c_str())); + } + + // some models use the token embedding tensor as the output, but since these are used in different layers and with different ops + // the tensor is duplicated + // to handle this, we check if the tensor is duplicated, and if so, we assume that it is being loaded as the output tensor + llm_tensor tn_tensor = tn.tensor; + if (tn.tensor == LLM_TENSOR_TOKEN_EMBD && (flags & TENSOR_DUPLICATED)) { + tn_tensor = LLM_TENSOR_OUTPUT; + } + + llm_tensor_info info; + try { + info = llm_tensor_info_for(tn_tensor); + } catch (const std::out_of_range & e) { + throw std::runtime_error(format("missing tensor info mapping for %s", tn.str().c_str())); + } + + // skip unused tensors + if (info.op == GGML_OP_NONE || (flags & TENSOR_SKIP)) { + const size_t nbytes = ggml_nbytes(t_meta); + LLAMA_LOG_WARN("model has unused tensor %s (size = %zu bytes) -- ignoring\n", tn.str().c_str(), nbytes); + + size_data -= nbytes; + n_created++; + + return nullptr; + } + + // tensors with "bias" suffix are always used with GGML_OP_ADD or GGML_OP_ADD_ID + ggml_op op; + bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0; + if (bias) { + if (info.op == GGML_OP_MUL_MAT_ID) { + op = GGML_OP_ADD_ID; + } else { + op = GGML_OP_ADD; + } + } else { + op = info.op; + } + + // sanity checks + if (info.layer == LLM_TENSOR_LAYER_INPUT || info.layer == LLM_TENSOR_LAYER_OUTPUT) { + if (tn.bid != -1) { + GGML_ABORT("input/output layer tensor %s used with a layer number", tn.str().c_str()); + } + } else { + if (tn.bid == -1) { + GGML_ABORT("repeating layer tensor %s used without a layer number", tn.str().c_str()); + } + } + + // select the buffer type for this tensor + const buft_list_t * buft_list; + switch (info.layer) { + case LLM_TENSOR_LAYER_INPUT: + buft_list = buft_list_input; + break; + case LLM_TENSOR_LAYER_OUTPUT: + buft_list = buft_list_output; + break; + case LLM_TENSOR_LAYER_REPEATING: + GGML_ASSERT(buft_list_layer != nullptr); + buft_list = buft_list_layer; + break; + default: + GGML_ABORT("invalid layer %d for tensor %s", info.layer, tn.str().c_str()); + } + + ggml_backend_buffer_type_t buft = nullptr; + + // check overrides + if (tensor_buft_overrides) { + std::string tensor_name = tn.str(); + for (const auto * overrides = tensor_buft_overrides; overrides->pattern != nullptr; ++overrides) { + std::regex pattern(overrides->pattern); + if (std::regex_search(tensor_name, pattern)) { + if (overrides->buft == ggml_backend_cpu_buffer_type()) { + // when overriding to a CPU buffer, consider the extra buffer types + buft = select_weight_buft(hparams, t_meta, op, buft_list_cpu); + } else { + buft = overrides->buft; + } + + LLAMA_LOG_DEBUG("tensor %s (%zu MiB %s) buffer type overridden to %s\n", + tensor_name.c_str(), + ggml_nbytes(t_meta) / 1024 / 1024, ggml_type_name(t_meta->type), + ggml_backend_buft_name(buft)); + break; + } + } + } + + if (!buft) { + buft = select_weight_buft(hparams, t_meta, op, buft_list); + if (!buft) { + throw std::runtime_error(format("failed to find a compatible buffer type for tensor %s", tn.str().c_str())); + } + } + + // avoid using a host buffer when using mmap + auto * buft_dev = ggml_backend_buft_get_device(buft); + if (use_mmap && buft_dev && buft == ggml_backend_dev_host_buffer_type(buft_dev)) { + auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + if (!cpu_dev) { + throw std::runtime_error("no CPU backend found"); + } + buft = ggml_backend_dev_buffer_type(cpu_dev); + } + + if (buft != buft_list->front().second) { + if (n_tensors_moved == 0) { + first_tensor_moved_name = t_meta->name; + first_tensor_moved_type_name = ggml_type_name(t_meta->type); + first_moved_from_buft = buft_list->front().second; + first_moved_to_buft = buft; + } + n_tensors_moved++; + } + + return buft; + }; + + if (files.empty()) { + if (flags & TENSOR_SKIP_IF_VIRTUAL) { + return nullptr; + } + ggml_type type = GGML_TYPE_F32; + const int64_t tid = gguf_find_tensor(metadata, tn.str().c_str()); + if (tid != -1) { + type = gguf_get_tensor_type(metadata, tid); + } + + // for tensors that are not required some of the dimensions can be invalid: + if (flags & TENSOR_NOT_REQUIRED) { + for (size_t dim = 0; dim < ne.size(); dim++) { + if (ne.begin()[dim] <= 0) { + return nullptr; + } + } + } + + ggml_tensor t_meta; + memset(&t_meta, 0, sizeof(ggml_tensor)); + t_meta.type = type; + for (size_t dim = 0; dim < GGML_MAX_DIMS; dim++) { + t_meta.ne[dim] = dim < ne.size() ? ne.begin()[dim] : 1; + GGML_ASSERT(t_meta.ne[dim] >= 1); + t_meta.nb[dim] = dim == 0 ? ggml_type_size(type) : t_meta.ne[dim-1]*t_meta.nb[dim-1]; + GGML_ASSERT(t_meta.nb[dim] >= 1); + } + ggml_set_name(&t_meta, tn.str().c_str()); + + ggml_backend_buffer_type_t buft = buft_for_tensor(&t_meta); + GGML_ASSERT(buft != nullptr); + ggml_context * ctx = ctx_for_buft(buft); + ggml_tensor * ret = ggml_dup_tensor(ctx, &t_meta); + ggml_set_name(ret, tn.str().c_str()); + return ret; + } + + ggml_tensor * t_meta = get_tensor_meta(tn.str().c_str()); + ggml_backend_buffer_type_t buft = buft_for_tensor(t_meta); + if (buft == nullptr) { + return nullptr; // return type is ggml_tensor * + } + ggml_context * ctx = ctx_for_buft(buft); + + // if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one + if (flags & TENSOR_DUPLICATED) { + ggml_tensor * t = ggml_get_tensor(ctx, tn.str().c_str()); + if (t) { + return t; + } + } + + LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str()); + const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED)); if (cur == NULL) { return NULL; } - bool duplicated = flags & TENSOR_DUPLICATED; + const bool duplicated = flags & TENSOR_DUPLICATED; struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); ggml_set_name(tensor, ggml_get_name(cur)); @@ -858,7 +1242,6 @@ struct ggml_tensor * llama_model_loader::create_tensor(struct ggml_context * ctx } return tensor; - } struct ggml_tensor * llama_model_loader::create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list & ne, size_t offset, bool required) { @@ -893,6 +1276,11 @@ void llama_model_loader::done_getting_tensors() const { if (n_created != n_tensors) { throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created)); } + if (n_tensors_moved > 0) { + LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %zu others) cannot be used with preferred buffer type %s, using %s instead\n", + __func__, first_tensor_moved_name.c_str(), first_tensor_moved_type_name.c_str(), n_tensors_moved - 1, + ggml_backend_buft_name(first_moved_from_buft), ggml_backend_buft_name(first_moved_to_buft)); + } } void llama_model_loader::init_mappings(bool prefetch, llama_mlocks * mlock_mmaps) { @@ -974,6 +1362,12 @@ bool llama_model_loader::load_all_data( llama_mlocks * lmlocks, llama_progress_callback progress_callback, void * progress_callback_user_data) { + if (files.empty()) { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { + set_tensor_data(t, set_tensor_data_ud); + } + return true; + } GGML_ASSERT(size_data != 0 && "call init_mappings() first"); std::vector> read_buf; diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h index 65953dd3d5..ed5de729ca 100644 --- a/src/llama-model-loader.h +++ b/src/llama-model-loader.h @@ -4,17 +4,22 @@ #include "llama-impl.h" #include "llama-arch.h" +#include "llama-hparams.h" #include "llama-mmap.h" #include "ggml-cpp.h" #include +#include #include #include #include using llama_buf_map = std::unordered_map; +// lists of buffer types used for each layer +using buft_list_t = std::vector>; + enum llama_fver { GGUF_FILE_VERSION_V1 = 1, GGUF_FILE_VERSION_V2 = 2, @@ -58,9 +63,10 @@ struct llama_model_loader { } }; - static const int TENSOR_NOT_REQUIRED = 1 << 0; - static const int TENSOR_DUPLICATED = 1 << 1; - static const int TENSOR_SKIP = 1 << 2; + static const int TENSOR_NOT_REQUIRED = 1 << 0; + static const int TENSOR_DUPLICATED = 1 << 1; + static const int TENSOR_SKIP = 1 << 2; + static const int TENSOR_SKIP_IF_VIRTUAL = 1 << 3; int n_kv = 0; int n_tensors = 0; @@ -84,7 +90,10 @@ struct llama_model_loader { std::unordered_map kv_overrides; const llama_model_tensor_buft_override * tensor_buft_overrides; - gguf_context_ptr meta; + gguf_context_ptr metadata_ptr; + struct gguf_context * metadata; // either metadata_ptr.get() or externally set + llama_model_set_tensor_data_t set_tensor_data; + void * set_tensor_data_ud; std::vector contexts; std::string arch_name; @@ -94,7 +103,26 @@ struct llama_model_loader { size_t size_data = 0; std::vector> mmaps_used; + // define a comparator for the buft -> ctx map to ensure that the order is well-defined: + struct ggml_backend_buft_comparator { + bool operator()(const ggml_backend_buffer_type_t & lhs, const ggml_backend_buffer_type_t & rhs) const { + return strcmp(ggml_backend_buft_name(lhs), ggml_backend_buft_name(rhs)) < 0; + } + }; + + std::map ctx_map; + + // track tensors that had to be moved for debugging: + size_t n_tensors_moved = 0; + std::string first_tensor_moved_name; + std::string first_tensor_moved_type_name; + ggml_backend_buffer_type_t first_moved_from_buft = nullptr; + ggml_backend_buffer_type_t first_moved_to_buft = nullptr; + llama_model_loader( + struct gguf_context * metadata, + llama_model_set_tensor_data_t set_tensor_data, + void * set_tensor_data_ud, const std::string & fname, std::vector & splits, // optional, only need if the split does not follow naming scheme bool use_mmap, @@ -149,7 +177,9 @@ struct llama_model_loader { const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector & ne, bool required) const; - struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::initializer_list & ne, int flags = 0); + struct ggml_tensor * create_tensor( + const llama_hparams & hparams, const buft_list_t * buft_list_cpu, const buft_list_t * buft_list_input, const buft_list_t * buft_list_output, + const buft_list_t * buft_list_layer, const LLM_TN_IMPL & tn, const std::initializer_list & ne, int flags); struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list & ne, size_t offset, bool required = true); diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index 676efeda70..6f6538aecc 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -7,14 +7,19 @@ #include "llama-model.h" #include "llama-vocab.h" +#include #include -llama_model_saver::llama_model_saver(const struct llama_model & model) : model(model), llm_kv(model.arch) { - gguf_ctx = gguf_init_empty(); -} +llama_model_saver::llama_model_saver(const struct llama_model * model) : + gguf_ctx(gguf_init_empty()), gguf_ctx_owned(true), model(model), llm_kv(model->arch) {} + +llama_model_saver::llama_model_saver(enum llm_arch arch, struct gguf_context * gguf_ctx) : + gguf_ctx(gguf_ctx == nullptr ? gguf_init_empty() : gguf_ctx), gguf_ctx_owned(gguf_ctx == nullptr), model(nullptr), llm_kv(arch) {} llama_model_saver::~llama_model_saver() { - gguf_free(gguf_ctx); + if (gguf_ctx_owned) { + gguf_free(gguf_ctx); + } } void llama_model_saver::add_kv(const enum llm_kv key, const uint32_t value) { @@ -46,7 +51,8 @@ void llama_model_saver::add_kv(const enum llm_kv key, const char value) { template void llama_model_saver::add_kv(const enum llm_kv key, const Container & value, const bool per_layer) { - const size_t n_values = per_layer ? size_t(model.hparams.n_layer) : value.size(); + GGML_ASSERT(model != nullptr || !per_layer); + const size_t n_values = per_layer ? size_t(model->hparams.n_layer) : value.size(); GGML_ASSERT(n_values <= value.size()); if (n_values == 0) { @@ -83,6 +89,8 @@ void llama_model_saver::add_kv(const enum llm_kv key, const Container & value, c GGML_ABORT("fatal error"); } } +// instantiate for external usage: +template void llama_model_saver::add_kv>(const enum llm_kv, const std::vector &, const bool); void llama_model_saver::add_kv(const enum llm_kv key, const std::vector & value) { std::vector tmp(value.size()); @@ -104,37 +112,39 @@ void llama_model_saver::add_tensor(const struct ggml_tensor * tensor) { } void llama_model_saver::add_kv_from_model() { - const llama_hparams & hparams = model.hparams; - const llama_vocab & vocab = model.vocab; + const llama_hparams & hparams = model->hparams; + const llama_vocab & vocab = model->vocab; const int32_t n_vocab = vocab.n_tokens(); std::vector tokens(n_vocab); std::vector scores(n_vocab); std::vector token_types(n_vocab); - for (int32_t id = 0; id < n_vocab; ++id) { - const llama_vocab::token_data & token_data = vocab.get_token_data(id); + if (vocab.get_type() != LLAMA_VOCAB_TYPE_NONE) { + for (int32_t id = 0; id < n_vocab; ++id) { + const llama_vocab::token_data & token_data = vocab.get_token_data(id); - tokens[id] = token_data.text; - scores[id] = token_data.score; + tokens[id] = token_data.text; + scores[id] = token_data.score; - switch(token_data.attr) { - case LLAMA_TOKEN_ATTR_UNKNOWN: token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN; break; - case LLAMA_TOKEN_ATTR_UNUSED: token_types[id] = LLAMA_TOKEN_TYPE_UNUSED; break; - case LLAMA_TOKEN_ATTR_NORMAL: token_types[id] = LLAMA_TOKEN_TYPE_NORMAL; break; - case LLAMA_TOKEN_ATTR_CONTROL: token_types[id] = LLAMA_TOKEN_TYPE_CONTROL; break; - case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break; - case LLAMA_TOKEN_ATTR_BYTE: token_types[id] = LLAMA_TOKEN_TYPE_BYTE; break; - case LLAMA_TOKEN_ATTR_UNDEFINED: - default: token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED; break; + switch(token_data.attr) { + case LLAMA_TOKEN_ATTR_UNKNOWN: token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN; break; + case LLAMA_TOKEN_ATTR_UNUSED: token_types[id] = LLAMA_TOKEN_TYPE_UNUSED; break; + case LLAMA_TOKEN_ATTR_NORMAL: token_types[id] = LLAMA_TOKEN_TYPE_NORMAL; break; + case LLAMA_TOKEN_ATTR_CONTROL: token_types[id] = LLAMA_TOKEN_TYPE_CONTROL; break; + case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break; + case LLAMA_TOKEN_ATTR_BYTE: token_types[id] = LLAMA_TOKEN_TYPE_BYTE; break; + case LLAMA_TOKEN_ATTR_UNDEFINED: + default: token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED; break; + } } } // add_kv(LLM_KV_GENERAL_TYPE, ???); - add_kv(LLM_KV_GENERAL_ARCHITECTURE, model.arch_name()); + add_kv(LLM_KV_GENERAL_ARCHITECTURE, model->arch_name()); // add_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION, ???); // add_kv(LLM_KV_GENERAL_ALIGNMENT, ???); - add_kv(LLM_KV_GENERAL_NAME, model.name); + add_kv(LLM_KV_GENERAL_NAME, model->name); // add_kv(LLM_KV_GENERAL_AUTHOR, ???); // add_kv(LLM_KV_GENERAL_VERSION, ???); // add_kv(LLM_KV_GENERAL_URL, ???); @@ -176,8 +186,10 @@ void llama_model_saver::add_kv_from_model() { add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, true); add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias); add_kv(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv); - add_kv(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k); - add_kv(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v); + add_kv(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k_full); + add_kv(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v_full); + add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa); + add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa); add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); add_kv(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); @@ -189,7 +201,8 @@ void llama_model_saver::add_kv_from_model() { const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train; - add_kv(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot); + add_kv(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot_full); + add_kv(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa); add_kv(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train); // add_kv(LLM_KV_ROPE_SCALE_LINEAR, rope_scaling_factor); // old name add_kv(LLM_KV_ROPE_SCALING_TYPE, llama_rope_scaling_type_name(hparams.rope_scaling_type_train)); @@ -255,25 +268,25 @@ void llama_model_saver::add_kv_from_model() { } void llama_model_saver::add_tensors_from_model() { - if (std::string(model.output->name) != std::string(model.tok_embd->name)) { - add_tensor(model.tok_embd); // some models use the same tensor for tok_embd and output + if (std::string(model->output->name) != std::string(model->tok_embd->name)) { + add_tensor(model->tok_embd); // some models use the same tensor for tok_embd and output } - add_tensor(model.type_embd); - add_tensor(model.pos_embd); - add_tensor(model.tok_norm); - add_tensor(model.tok_norm_b); - add_tensor(model.output_norm); - add_tensor(model.output_norm_b); - add_tensor(model.output); - add_tensor(model.output_b); - add_tensor(model.output_norm_enc); - add_tensor(model.cls); - add_tensor(model.cls_b); - add_tensor(model.cls_out); - add_tensor(model.cls_out_b); - add_tensor(model.cls_norm); + add_tensor(model->type_embd); + add_tensor(model->pos_embd); + add_tensor(model->tok_norm); + add_tensor(model->tok_norm_b); + add_tensor(model->output_norm); + add_tensor(model->output_norm_b); + add_tensor(model->output); + add_tensor(model->output_b); + add_tensor(model->output_norm_enc); + add_tensor(model->cls); + add_tensor(model->cls_b); + add_tensor(model->cls_out); + add_tensor(model->cls_out_b); + add_tensor(model->cls_norm); - for (const struct llama_layer & layer : model.layers) { + for (const struct llama_layer & layer : model->layers) { for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) { add_tensor(reinterpret_cast(&layer)[i]); } diff --git a/src/llama-model-saver.h b/src/llama-model-saver.h index a5a434c306..2b3541ce6c 100644 --- a/src/llama-model-saver.h +++ b/src/llama-model-saver.h @@ -1,5 +1,6 @@ #pragma once +#include "gguf.h" #include "llama.h" #include "llama-arch.h" @@ -7,10 +8,12 @@ struct llama_model_saver { struct gguf_context * gguf_ctx = nullptr; - const struct llama_model & model; + const bool gguf_ctx_owned; + const struct llama_model * model; const struct LLM_KV llm_kv; - llama_model_saver(const struct llama_model & model); + llama_model_saver(const struct llama_model * model); + llama_model_saver(enum llm_arch arch, struct gguf_context * gguf_ctx); ~llama_model_saver(); void add_kv(enum llm_kv key, uint32_t value); diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 4b9218a2b7..dd42a58271 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1,5 +1,6 @@ #include "llama-model.h" +#include "ggml.h" #include "llama-impl.h" #include "llama-mmap.h" #include "llama-cparams.h" @@ -18,6 +19,7 @@ #include #include #include +#include #include #include #include @@ -61,6 +63,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_0_3B: return "0.3B"; case LLM_TYPE_0_5B: return "0.5B"; case LLM_TYPE_0_6B: return "0.6B"; + case LLM_TYPE_0_8B: return "0.8B"; case LLM_TYPE_1B: return "1B"; case LLM_TYPE_1_2B: return "1.2B"; case LLM_TYPE_1_3B: return "1.3B"; @@ -123,6 +126,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_8B_A1B: return "8B.A1B"; case LLM_TYPE_16B_A1B: return "16B.A1B"; case LLM_TYPE_21B_A3B: return "21B.A3B"; + case LLM_TYPE_24B_A2B: return "24B.A2B"; case LLM_TYPE_30B_A3B: return "30B.A3B"; case LLM_TYPE_31B_A3_5B: return "31B.A3.5B"; case LLM_TYPE_35B_A3B: return "35B.A3B"; @@ -131,12 +135,15 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_100B_A6B: return "100B.A6B"; case LLM_TYPE_102B_A12B: return "102B.A12B"; case LLM_TYPE_106B_A12B: return "106B.A12B"; + case LLM_TYPE_120B_A12B: return "120B.A12B"; + case LLM_TYPE_122B_A10B: return "122B.A10B"; case LLM_TYPE_196B_A11B: return "196B.A11B"; case LLM_TYPE_230B_A10B: return "230B.A10B"; case LLM_TYPE_235B_A22B: return "235B.A22B"; case LLM_TYPE_300B_A47B: return "300B.A47B"; case LLM_TYPE_310B_A15B: return "310B.A15B"; case LLM_TYPE_355B_A32B: return "355B.A32B"; + case LLM_TYPE_397B_A17B: return "397B.A17B"; case LLM_TYPE_744B_A40B: return "744B.A40B"; case LLM_TYPE_E2B: return "E2B"; case LLM_TYPE_E4B: return "E4B"; @@ -173,160 +180,6 @@ static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::st return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; } -// checks if the weight tensor can be used with the specified buffer type and device -static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w, ggml_op op, ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev) { - GGML_ASSERT(w != nullptr); - - if (op == GGML_OP_NONE) { - return true; - } - - ggml_init_params params = { - /*.mem_size =*/ ggml_tensor_overhead()*8, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - ggml_context_ptr ctx_ptr { ggml_init(params) }; - if (!ctx_ptr) { - throw std::runtime_error(format("failed to create ggml context")); - } - ggml_context * ctx = ctx_ptr.get(); - - ggml_tensor * op_tensor = nullptr; - - switch (op) { - case GGML_OP_GET_ROWS: - { - ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); - op_tensor = ggml_get_rows(ctx, w, b); - } break; - case GGML_OP_MUL_MAT: - { - ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]); - op_tensor = ggml_mul_mat(ctx, w, b); - } break; - case GGML_OP_MUL_MAT_ID: - { - int n_expert_used = hparams.n_expert_used; - ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512); - ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512); - op_tensor = ggml_mul_mat_id(ctx, w, b, ids); - } break; - case GGML_OP_ADD: - { - ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]); - op_tensor = ggml_add(ctx, a, w); - } break; - case GGML_OP_ADD_ID: - { - int n_expert_used = hparams.n_expert_used; - ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512); - ggml_tensor * c = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512); - op_tensor = ggml_add_id(ctx, a, w, c); - } break; - case GGML_OP_MUL: - { - ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]); - op_tensor = ggml_mul(ctx, a, w); - } break; - case GGML_OP_DIV: - { - ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, w->ne[0]); - op_tensor = ggml_div(ctx, a, w); - } break; - case GGML_OP_ROPE: - { - int n_embd_head = hparams.n_embd_head_v; - int n_head = hparams.n_head(); - ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512); - ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); - op_tensor = ggml_rope_ext( - ctx, a, b, w, - 0, 0, 0, 0, 0, - 0, 0, 0, 0 - ); - - } break; - case GGML_OP_SSM_CONV: - { - const int64_t n_seq_tokens = 512; - const int64_t n_seqs = 3; - ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0] - 1 + n_seq_tokens, w->ne[1], n_seqs); - op_tensor = ggml_ssm_conv(ctx, conv_x, w); - } break; - case GGML_OP_SSM_SCAN: - { - // w is ssm_a, which is used to distinguish Mamba-1 and Mamba-2 - const int64_t d_state = w->ne[0] == 1 ? hparams.ssm_d_state : w->ne[0]; - const int64_t n_head = w->ne[1]; - const int64_t head_dim = hparams.ssm_d_inner / n_head; - const int64_t n_group = hparams.ssm_n_group ? hparams.ssm_n_group : 1; - const int64_t n_seq_tokens = 512; - const int64_t n_seqs = 3; - ggml_tensor * s = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, d_state, head_dim, n_head, n_seqs); - ggml_tensor * x = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, head_dim, n_head, n_seq_tokens, n_seqs); - ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_head, n_seq_tokens, n_seqs); - ggml_tensor * B = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, d_state, n_group, n_seq_tokens, n_seqs); - ggml_tensor * C = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, d_state, n_group, n_seq_tokens, n_seqs); - ggml_tensor * ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_seqs); - op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C, ids); - } break; - case GGML_OP_RWKV_WKV6: - { - // FIXME - const int64_t S = 123; - const int64_t H = 123; - const int64_t n_tokens = 123; - const int64_t n_seqs = 123; - ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens); - ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens); - ggml_tensor * r = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens); - ggml_tensor * tf = w; - ggml_tensor * td = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens); - ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H); - op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state); - } break; - case GGML_OP_IM2COL: - { - const int n_embd_inp = hparams.n_embd_inp(); - ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd_inp, w->ne[1], 1, 1); - op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16); - } break; - case GGML_OP_SCALE: - { - op_tensor = ggml_scale(ctx, w, 1.0f); - } break; - default: - GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name); - } - - // create a temporary dummy buffer for the weight so that supports_op can check the buffer type - GGML_ASSERT(w->buffer == nullptr); - w->buffer = ggml_backend_buft_alloc_buffer(buft, 0); - bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor); - ggml_backend_buffer_free(w->buffer); - w->buffer = nullptr; - - return op_supported; -} - -// lists of buffer types used for each layer -using buft_list_t = std::vector>; - -// find the first buffer type in the list that can use the tensor -static ggml_backend_buffer_type_t select_weight_buft(const llama_hparams & hparams, ggml_tensor * tensor, ggml_op op, const buft_list_t & buft_list) { - GGML_ASSERT(!buft_list.empty()); - for (const auto & cur : buft_list) { - ggml_backend_dev_t cur_dev = cur.first; - ggml_backend_buffer_type_t cur_buft = cur.second; - if (weight_buft_supported(hparams, tensor, op, cur_buft, cur_dev)) { - return cur_buft; - } - } - - return nullptr; -} - // CPU: ACCEL -> GPU host -> CPU extra -> CPU static buft_list_t make_cpu_buft_list(const std::vector & devices, bool use_extra_bufts, bool no_host) { buft_list_t buft_list; @@ -492,7 +345,7 @@ void llama_model::load_arch(llama_model_loader & ml) { } void llama_model::load_hparams(llama_model_loader & ml) { - const gguf_context * ctx = ml.meta.get(); + const gguf_context * ctx = ml.metadata; // get metadata as string for (int i = 0; i < gguf_get_n_kv(ctx); i++) { @@ -607,26 +460,37 @@ void llama_model::load_hparams(llama_model_loader & ml) { // gpt-neox n_rot = rotary_pct * (n_embd / n_head) // gpt-j n_rot = rotary_dim - hparams.n_embd_head_k = hparams.n_embd / hparams.n_head(); - ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false); + hparams.n_embd_head_k_full = hparams.n_embd / hparams.n_head(); + ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k_full, false); - hparams.n_embd_head_v = hparams.n_embd / hparams.n_head(); - ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false); + hparams.n_embd_head_v_full = hparams.n_embd / hparams.n_head(); + ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v_full, false); // sanity check for n_rot (optional) - hparams.n_rot = hparams.n_embd_head_k; + hparams.n_rot_full = hparams.n_embd_head_k_full; - ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false); + ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot_full, false); if (arch == LLM_ARCH_LLAMA || arch == LLM_ARCH_DECI || arch == LLM_ARCH_FALCON || arch == LLM_ARCH_LLAMA_EMBED) { - if (hparams.n_rot != hparams.n_embd_head_k) { - throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k)); + if (hparams.n_rot_full != hparams.n_embd_head_k_full) { + throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot_full, hparams.n_embd_head_k_full)); } } } else { - hparams.n_rot = 0; - hparams.n_embd_head_k = 0; - hparams.n_embd_head_v = 0; + hparams.n_rot_full = 0; + hparams.n_embd_head_k_full = 0; + hparams.n_embd_head_v_full = 0; + } + + // head size and n_rot for SWA layers + { + hparams.n_embd_head_k_swa = hparams.n_embd_head_k_full; + hparams.n_embd_head_v_swa = hparams.n_embd_head_v_full; + ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa, false); + ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa, false); + + hparams.n_rot_swa = hparams.n_rot_full; + ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false); } // for differentiating model types @@ -686,7 +550,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.n_attn_temp_floor_scale = 8192; hparams.f_attn_temp_scale = 0.1f; hparams.f_attn_temp_offset = 1.0f; - hparams.set_swa_pattern(4); // pattern: 3 chunked - 1 full + uint32_t swa_period = 4; // pattern: 3 chunked - 1 full + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; @@ -723,7 +589,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_AFMOE: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); @@ -735,7 +601,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { // Pattern: 3 sliding - 1 full (global_attn_every_n_layers = 4) if (hparams.n_swa > 0) { hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; - hparams.set_swa_pattern(4); + uint32_t swa_period = 4; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; @@ -880,7 +748,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_BERT: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); switch (hparams.n_layer) { @@ -903,10 +771,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { { const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); if (found_swa && hparams.n_swa > 0) { - uint32_t swa_period = 3; hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC; - - ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa); + ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); + uint32_t swa_period = 3; ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); hparams.set_swa_pattern(swa_period, true); } else { @@ -914,7 +781,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { } ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); switch (hparams.n_layer) { @@ -930,7 +797,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_JINA_BERT_V2: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); hparams.f_max_alibi_bias = 8.0f; @@ -943,7 +810,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_JINA_BERT_V3: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); switch (hparams.n_layer) { @@ -956,8 +823,8 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_NOMIC_BERT_MOE: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); - ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS, hparams.moe_every_n_layers, 0); if (hparams.n_layer == 12 && hparams.n_embd == 768) { @@ -971,13 +838,23 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_NEO_BERT: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); - ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); if (hparams.n_layer == 28) { type = LLM_TYPE_250M; } } break; + case LLM_ARCH_EUROBERT: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); + + if (hparams.n_layer == 12) { + type = LLM_TYPE_SMALL; // 0.2B + } + } break; case LLM_ARCH_BLOOM: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -1000,7 +877,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false); - ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias); + ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias, false); switch (hparams.n_layer) { case 32: type = LLM_TYPE_7B; break; @@ -1249,19 +1126,15 @@ void llama_model::load_hparams(llama_model_loader & ml) { break; default: type = LLM_TYPE_UNKNOWN; } - - // Load attention parameters - ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false); - ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false); } break; case LLM_ARCH_PLAMO3: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); if (found_swa && hparams.n_swa > 0) { - uint32_t swa_period = 8; hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; - ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa); + ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); + uint32_t swa_period = 8; ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); hparams.set_swa_pattern(swa_period); } else { @@ -1324,7 +1197,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { { hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; hparams.n_swa = 4096; // default value of gemma 2 - hparams.set_swa_pattern(2); + uint32_t swa_period = 2; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); hparams.attn_soft_cap = true; hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; @@ -1345,14 +1220,16 @@ void llama_model::load_hparams(llama_model_loader & ml) { // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L173 hparams.f_attention_scale = type == LLM_TYPE_27B ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0))) - : 1.0f / std::sqrt(float(hparams.n_embd_head_k)); + : 1.0f / std::sqrt(float(hparams.n_embd_head_k())); } break; case LLM_ARCH_GEMMA3: { const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); if (found_swa && hparams.n_swa > 0) { hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; - hparams.set_swa_pattern(6); + uint32_t swa_period = 6; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); } else { @@ -1376,12 +1253,14 @@ void llama_model::load_hparams(llama_model_loader & ml) { // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L289 hparams.f_attention_scale = type == LLM_TYPE_27B ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0))) - : 1.0f / std::sqrt(float(hparams.n_embd_head_k)); + : 1.0f / std::sqrt(float(hparams.n_embd_head_k())); } break; case LLM_ARCH_GEMMA3N: { + uint32_t swa_period = 5; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; - hparams.set_swa_pattern(5); + hparams.set_swa_pattern(swa_period); hparams.n_layer_kv_from_start = 20; hparams.f_attention_scale = 1.0f; @@ -1399,14 +1278,16 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_GEMMA_EMBEDDING: { hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC; - hparams.set_swa_pattern(6); + uint32_t swa_period = 6; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); hparams.causal_attn = false; // embeddings do not use causal attention ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); //applied only if model converted with --sentence-transformers-dense-modules ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false); @@ -1421,7 +1302,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { case 24: type = LLM_TYPE_0_3B; break; default: type = LLM_TYPE_UNKNOWN; } - hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k)); + hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k())); } break; case LLM_ARCH_STARCODER2: @@ -1513,7 +1394,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { } switch (hparams.n_layer) { - // TODO: Jamba layers are a bit heterogenous, so naming this is hard. + // TODO: Jamba layers are a bit heterogeneous, so naming this is hard. case 12: // 900M 8x???M case 32: // 51B 16x?B default: type = LLM_TYPE_UNKNOWN; @@ -1531,7 +1412,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { } break; case LLM_ARCH_COMMAND_R: { - ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale); + ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale, false); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); switch (hparams.n_layer) { case 40: type = LLM_TYPE_35B; break; @@ -1541,7 +1422,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_COHERE2: { hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; - hparams.set_swa_pattern(4); + uint32_t swa_period = 4; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; @@ -1583,7 +1466,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); if (found_swa && hparams.n_swa > 0) { hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; - hparams.set_swa_pattern(4); + uint32_t swa_period = 4; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = 1.0; // See olmo2.cpp @@ -1690,10 +1575,10 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_DEEPSEEK: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); - ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); switch (hparams.n_ff_exp) { case 1408: type = LLM_TYPE_16B; break; @@ -1707,7 +1592,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { const bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26 || (hparams.n_layer == 48 && n_vocab == 128256)); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); if (!is_lite) { ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); } @@ -1809,7 +1694,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); - ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); // Expert gating function (GLM-4.5 uses sigmoid) @@ -1842,7 +1727,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); - ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); // deepseek MLA parameters @@ -1928,7 +1813,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_JAIS: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias); + ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias, false); switch (hparams.n_layer) { case 24: type = LLM_TYPE_1_3B; break; @@ -1977,10 +1862,12 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); + ml.get_key(LLM_KV_MOE_LATENT_SIZE, hparams.moe_latent_size, false); switch (hparams.n_layer) { case 52: type = LLM_TYPE_31B_A3_5B; break; // Nemotron-H_MOE 31B case 56: type = LLM_TYPE_9B; break; + case 88: type = LLM_TYPE_120B_A12B; break; default: type = LLM_TYPE_UNKNOWN; } } break; @@ -1998,7 +1885,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { if (hparams.n_layer == 64) { // 32B hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; hparams.n_swa = 4096; - hparams.set_swa_pattern(4); + uint32_t swa_period = 4; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; @@ -2018,7 +1907,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { { hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; hparams.n_swa = 128; - hparams.set_swa_pattern(4); + uint32_t swa_period = 4; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; @@ -2031,7 +1922,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false); @@ -2115,9 +2006,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale); - ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale); - ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale); - ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale); + ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale, false); + ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale, false); + ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale, false); // Granite uses rope_finetuned as a switch for rope, so default to true bool rope_finetuned = true; @@ -2175,7 +2066,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); hparams.f_norm_eps = 1e-5; // eps for qk-norm, torch default - ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm); + ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm, false); switch (hparams.n_layer) { case 32: type = LLM_TYPE_7B; break; @@ -2188,15 +2079,15 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS, hparams.f_norm_group_eps); ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups); - ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); } break; case LLM_ARCH_BAILINGMOE: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); - ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); switch (hparams.n_layer) { @@ -2208,11 +2099,11 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_BAILINGMOE2: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); - ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp); + ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); - ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false); @@ -2231,10 +2122,10 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_DOTS1: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); - ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); switch (hparams.n_layer) { @@ -2254,7 +2145,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); } switch (hparams.n_layer) { @@ -2299,7 +2190,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); - ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp); + ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); switch (hparams.n_layer) { case 32: type = LLM_TYPE_A13B; break; @@ -2335,7 +2226,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; - hparams.set_swa_pattern(2); + uint32_t swa_period = 2; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; @@ -2373,7 +2266,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { { ml.get_key(LLM_KV_SHORTCONV_L_CACHE, hparams.n_shortconv_l_cache); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); @@ -2381,16 +2274,22 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0; } - type = LLM_TYPE_8B_A1B; + switch (hparams.n_layer) { + case 24: type = LLM_TYPE_8B_A1B; break; + case 40: type = LLM_TYPE_24B_A2B; break; + default: type = LLM_TYPE_UNKNOWN; + } } break; case LLM_ARCH_SMALLTHINKER: { const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); if (found_swa && hparams.n_swa > 0) { - hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; - hparams.n_swa = 4096; - hparams.set_swa_pattern(4, true); + hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + hparams.n_swa = 4096; + uint32_t swa_period = 4; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period, true); hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; @@ -2413,7 +2312,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { case LLM_ARCH_GROVEMOE: { ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); - ml.get_key(LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH, hparams.n_ff_chexp); + ml.get_key(LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH, hparams.n_ff_chexp, false); ml.get_key(LLM_KV_EXPERT_GROUP_SCALE, hparams.expert_group_scale); ml.get_key(LLM_KV_EXPERTS_PER_GROUP, hparams.n_group_experts); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -2542,7 +2441,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { } switch (hparams.n_layer) { - case 24: type = LLM_TYPE_2B; break; + case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_8B : LLM_TYPE_2B; break; + case 32: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_9B; break; + case 64: type = LLM_TYPE_27B; break; default: type = LLM_TYPE_UNKNOWN; } } break; @@ -2571,8 +2472,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { } switch (hparams.n_layer) { - case 28: type = LLM_TYPE_35B_A3B; break; - case 48: type = LLM_TYPE_80B_A3B; break; + case 40: type = LLM_TYPE_35B_A3B; break; + case 48: type = LLM_TYPE_122B_A10B; break; + case 60: type = LLM_TYPE_397B_A17B; break; default: type = LLM_TYPE_UNKNOWN; } } break; @@ -2610,7 +2512,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); - ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa); + ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer); switch (hparams.n_layer) { @@ -2624,7 +2526,6 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl); ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl); ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); - ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot); ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv); ml.get_key(LLM_KV_KDA_HEAD_DIM, hparams.n_embd_head_kda); @@ -2640,8 +2541,8 @@ void llama_model::load_hparams(llama_model_loader & ml) { // MoE parameters - Kimi uses moe_intermediate_size = 1024 ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); - ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); - ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); switch (hparams.n_layer) { @@ -2655,6 +2556,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + // full_attention layer only use half of the RoPE dimensions + hparams.n_rot_full = hparams.n_rot_full / 2; + // MoE + SWA parameters ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); @@ -2668,7 +2572,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { } ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); - ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa); + ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer); ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer, false); ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer, false); @@ -2678,7 +2582,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { default: type = LLM_TYPE_UNKNOWN; } } break; - default: throw std::runtime_error("unsupported model architecture"); + default: throw std::runtime_error("unsupported model architecture: " + arch_name()); } pimpl->n_bytes = ml.n_bytes; @@ -2785,44 +2689,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) { // assign the output layer pimpl->dev_output = get_layer_buft_list(n_layer); - // one ggml context per buffer type - int max_n_tensors = ml.n_tensors; - max_n_tensors += 1; // duplicated output tensor - max_n_tensors += n_layer*2; // duplicated rope freq tensors - const size_t ctx_size = ggml_tensor_overhead()*max_n_tensors; - - // define a comparator for the buft -> ctx map to ensure that the order is well-defined: - struct ggml_backend_buft_comparator { - bool operator()(const ggml_backend_buffer_type_t & lhs, const ggml_backend_buffer_type_t & rhs) const { - return strcmp(ggml_backend_buft_name(lhs), ggml_backend_buft_name(rhs)) < 0; - } - }; - std::map ctx_map; - - auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { - auto it = ctx_map.find(buft); - if (it == ctx_map.end()) { - ggml_init_params params = { - /*.mem_size =*/ ctx_size, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - - ggml_context * ctx = ggml_init(params); - if (!ctx) { - throw std::runtime_error(format("failed to create ggml context")); - } - - ctx_map.emplace(buft, ctx); - - return ctx; - } - return it->second.get(); - }; - - const auto TENSOR_DUPLICATED = llama_model_loader::TENSOR_DUPLICATED; - const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED; - const auto TENSOR_SKIP = llama_model_loader::TENSOR_SKIP; + const auto TENSOR_DUPLICATED = llama_model_loader::TENSOR_DUPLICATED; + const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED; + const auto TENSOR_SKIP = llama_model_loader::TENSOR_SKIP; + const auto TENSOR_SKIP_IF_VIRTUAL = llama_model_loader::TENSOR_SKIP_IF_VIRTUAL; // create tensors for the weights { @@ -2832,13 +2702,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t n_embd = hparams.n_embd; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const int64_t n_embd_head_k = hparams.n_embd_head_k; - const int64_t n_embd_head_v = hparams.n_embd_head_v; + const int64_t n_embd_head_k = hparams.n_embd_head_k(); + const int64_t n_embd_head_v = hparams.n_embd_head_v(); const int64_t n_ff = hparams.n_ff(); const int64_t n_embd_gqa = n_embd_v_gqa; const int64_t n_vocab = vocab.n_tokens(); const int64_t n_token_types = vocab.n_token_types(); - const int64_t n_rot = hparams.n_rot; + const int64_t n_rot = hparams.n_rot(); const int64_t n_expert = hparams.n_expert; const int64_t n_expert_used = hparams.n_expert_used; const int64_t n_ctx_train = hparams.n_ctx_train; @@ -2847,153 +2717,26 @@ bool llama_model::load_tensors(llama_model_loader & ml) { throw std::runtime_error("model has expert layers but no expert layers are used"); } - int n_moved_tensors = 0; - ggml_tensor * first_moved_tensor = nullptr; - ggml_backend_buffer_type_t first_moved_from_buft = nullptr; - ggml_backend_buffer_type_t first_moved_to_buft = nullptr; - auto create_tensor = [&](const LLM_TN_IMPL & tn, const std::initializer_list & ne, int flags) -> ggml_tensor * { - ggml_tensor * t_meta = ml.get_tensor_meta(tn.str().c_str()); - - if (!t_meta) { - if (flags & TENSOR_NOT_REQUIRED) { - return nullptr; - } - throw std::runtime_error(format("missing tensor '%s'", tn.str().c_str())); - } - - // some models use the token embedding tensor as the output, but since these are used in different layers and with different ops - // the tensor is duplicated - // to handle this, we check if the tensor is duplicated, and if so, we assume that it is being loaded as the output tensor - llm_tensor tn_tensor = tn.tensor; - if (tn.tensor == LLM_TENSOR_TOKEN_EMBD && flags & TENSOR_DUPLICATED) { - tn_tensor = LLM_TENSOR_OUTPUT; - } - - llm_tensor_info info; - try { - info = llm_tensor_info_for(tn_tensor); - } catch (const std::out_of_range & e) { - throw std::runtime_error(format("missing tensor info mapping for %s", tn.str().c_str())); - } - - // skip unused tensors - if (info.op == GGML_OP_NONE || flags & TENSOR_SKIP) { - const size_t nbytes = ggml_nbytes(t_meta); - LLAMA_LOG_WARN("model has unused tensor %s (size = %zu bytes) -- ignoring\n", tn.str().c_str(), nbytes); - - ml.size_data -= nbytes; - ml.n_created++; - - return nullptr; - } - - // tensors with "bias" suffix are always used with GGML_OP_ADD or GGML_OP_ADD_ID - ggml_op op; - bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0; - if (bias) { - if (info.op == GGML_OP_MUL_MAT_ID) { - op = GGML_OP_ADD_ID; - } else { - op = GGML_OP_ADD; - } - } else { - op = info.op; - } - - // sanity checks - if (info.layer == LLM_TENSOR_LAYER_INPUT || info.layer == LLM_TENSOR_LAYER_OUTPUT) { - if (tn.bid != -1) { - GGML_ABORT("input/output layer tensor %s used with a layer number", tn.str().c_str()); - } - } else { - if (tn.bid == -1) { - GGML_ABORT("repeating layer tensor %s used without a layer number", tn.str().c_str()); - } - } - - // select the buffer type for this tensor - buft_list_t * buft_list; - switch (info.layer) { - case LLM_TENSOR_LAYER_INPUT: - buft_list = pimpl->dev_input.buft_list; - break; - case LLM_TENSOR_LAYER_OUTPUT: - buft_list = pimpl->dev_output.buft_list; - break; - case LLM_TENSOR_LAYER_REPEATING: - buft_list = pimpl->dev_layer.at(tn.bid).buft_list; - break; - default: - GGML_ABORT("invalid layer %d for tensor %s", info.layer, tn.str().c_str()); - } - - ggml_backend_buffer_type_t buft = nullptr; - - // check overrides - if (ml.tensor_buft_overrides) { - std::string tensor_name = tn.str(); - for (const auto * overrides = ml.tensor_buft_overrides; overrides->pattern != nullptr; ++overrides) { - std::regex pattern(overrides->pattern); - if (std::regex_search(tensor_name, pattern)) { - if (overrides->buft == ggml_backend_cpu_buffer_type()) { - // when overriding to a CPU buffer, consider the extra buffer types - buft = select_weight_buft(hparams, t_meta, op, pimpl->cpu_buft_list); - } else { - buft = overrides->buft; - } - - LLAMA_LOG_DEBUG("tensor %s (%zu MiB %s) buffer type overridden to %s\n", - tensor_name.c_str(), - ggml_nbytes(t_meta) / 1024 / 1024, ggml_type_name(t_meta->type), - ggml_backend_buft_name(buft)); - break; - } - } - } - - if (!buft) { - buft = select_weight_buft(hparams, t_meta, op, *buft_list); - if (!buft) { - throw std::runtime_error(format("failed to find a compatible buffer type for tensor %s", tn.str().c_str())); - } - } - - // avoid using a host buffer when using mmap - auto * buft_dev = ggml_backend_buft_get_device(buft); - if (ml.use_mmap && buft_dev && buft == ggml_backend_dev_host_buffer_type(buft_dev)) { - auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); - if (!cpu_dev) { - throw std::runtime_error("no CPU backend found"); - } - buft = ggml_backend_dev_buffer_type(cpu_dev); - } - - if (buft != buft_list->front().second) { - n_moved_tensors++; - if (!first_moved_tensor) { - first_moved_tensor = t_meta; - first_moved_from_buft = buft_list->front().second; - first_moved_to_buft = buft; - } - } - - ggml_context * ctx = ctx_for_buft(buft); - - // if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one - if (flags & TENSOR_DUPLICATED) { - ggml_tensor * t = ggml_get_tensor(ctx, tn.str().c_str()); - if (t) { - return t; - } - } - return ml.create_tensor(ctx, tn, ne, flags); + const buft_list_t * buft_list_layer = tn.bid == -1 ? nullptr : pimpl->dev_layer.at(tn.bid).buft_list; + return ml.create_tensor( + hparams, &pimpl->cpu_buft_list, pimpl->dev_input.buft_list, pimpl->dev_output.buft_list, buft_list_layer, + tn, ne, flags); }; layers.resize(n_layer); // TODO: move to a separate function const auto tn = LLM_TN(arch); + + // helper: try merged gate_up_exps first, fall back to separate gate and up + auto create_tensor_gate_up_exps = [&](llama_layer & layer, int bid, int64_t n_embd_, int64_t n_ff_, int64_t n_expert_, int flags) { + layer.ffn_gate_up_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_UP_EXPS, "weight", bid), {n_embd_, n_ff_ * 2, n_expert_}, TENSOR_NOT_REQUIRED); + if (layer.ffn_gate_up_exps == nullptr) { + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", bid), {n_embd_, n_ff_, n_expert_}, flags); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", bid), {n_embd_, n_ff_, n_expert_}, flags); + } + }; switch (arch) { case LLM_ARCH_LLAMA: case LLM_ARCH_REFACT: @@ -3147,6 +2890,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_LLAMA4: { + if (n_expert == 0) { + throw std::runtime_error(arch_name() + " model cannot have zero experts"); + } tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output @@ -3159,7 +2905,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } for (int i = 0; i < n_layer; ++i) { - bool is_moe_layer = hparams.n_moe_layer_step > 0 && (i + 1) % hparams.n_moe_layer_step == 0; + const bool is_moe_layer = hparams.n_moe_layer_step > 0 && (i + 1) % hparams.n_moe_layer_step == 0; auto & layer = layers[i]; @@ -3175,7 +2921,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); if (is_moe_layer) { - int n_ff_exp = hparams.n_ff_exp; + const int64_t n_ff_exp = hparams.n_ff_exp; layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); @@ -3262,8 +3008,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_MINICPM3: { - const int64_t n_embd_head_qk_rope = hparams.n_rot; - const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot(); const int64_t q_lora_rank = hparams.n_lora_q; const int64_t kv_lora_rank = hparams.n_lora_kv; @@ -3306,7 +3052,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { case LLM_ARCH_GROK: { if (n_expert == 0) { - throw std::runtime_error("Grok model cannot have zero experts"); + throw std::runtime_error(arch_name() + " model cannot have zero experts"); } tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -3478,6 +3224,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) { case LLM_ARCH_NOMIC_BERT_MOE: case LLM_ARCH_JINA_BERT_V3: { + if (n_token_types == 0) { + throw std::runtime_error(arch_name() + " model needs to define token type count"); + } tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED); @@ -3594,6 +3343,29 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); } } break; + case LLM_ARCH_EUROBERT: + { + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + } + } break; case LLM_ARCH_JINA_BERT_V2: { tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // word_embeddings @@ -3721,8 +3493,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED); - layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED); - layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED); + // FIXME test-llama-archs crashes if q_norm is created + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL); + layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL); layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED); layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED); @@ -4108,8 +3881,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16)); // attention parameters - const uint32_t qk_dim = hparams.n_embd_head_k; - const uint32_t v_dim = hparams.n_embd_head_v; + const uint32_t qk_dim = hparams.n_embd_head_k(); + const uint32_t v_dim = hparams.n_embd_head_v(); tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -4169,8 +3942,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_PLAMO3: { - const int64_t head_dim_q = hparams.n_embd_head_k; - const int64_t head_dim_v = hparams.n_embd_head_v; + const int64_t head_dim_q = hparams.n_embd_head_k(); + const int64_t head_dim_v = hparams.n_embd_head_v(); tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -4917,7 +4690,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_SEED_OSS: { - const uint32_t head_dim = hparams.n_embd_head_k; + const uint32_t head_dim = hparams.n_embd_head_k(); const int64_t n_qo_dim = n_head * head_dim; const int64_t n_kv_dim = n_head_kv * head_dim; @@ -5146,8 +4919,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); - const int64_t n_embd_head_qk_rope = hparams.n_rot; + const int64_t n_embd_head_qk_rope = hparams.n_rot(); const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; + GGML_ASSERT(n_embd_head_qk_nope >= 1); const int64_t q_lora_rank = hparams.n_lora_q; const int64_t kv_lora_rank = hparams.n_lora_kv; @@ -5212,9 +4986,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } // MoE branch - layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); - layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0); // Shared expert branch layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); @@ -5225,8 +4998,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_PLM: { - const int64_t n_embd_head_qk_rope = hparams.n_rot; - const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot(); const int64_t kv_lora_rank = hparams.n_lora_kv; tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -5266,23 +5039,23 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0); layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); - layer.wq_scale = create_tensor(tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, TENSOR_NOT_REQUIRED); + layer.wq_s = create_tensor(tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, TENSOR_NOT_REQUIRED); layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); - layer.wk_scale = create_tensor(tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, TENSOR_NOT_REQUIRED); + layer.wk_s = create_tensor(tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, TENSOR_NOT_REQUIRED); layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); - layer.wv_scale = create_tensor(tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, TENSOR_NOT_REQUIRED); + layer.wv_s = create_tensor(tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, TENSOR_NOT_REQUIRED); layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); - layer.wo_scale = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED); + layer.wo_s = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED); layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0); layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); - layer.ffn_gate_scale = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED); + layer.ffn_gate_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED); layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); - layer.ffn_down_scale = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, TENSOR_NOT_REQUIRED); + layer.ffn_down_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, TENSOR_NOT_REQUIRED); layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); - layer.ffn_up_scale = create_tensor(tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, TENSOR_NOT_REQUIRED); + layer.ffn_up_s = create_tensor(tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_T5: @@ -5340,7 +5113,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.attn_norm_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd}, 0); // this tensor seems to be unused in HF transformers implementation - layer.attn_rel_b_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED); + layer.attn_rel_b_cross = create_tensor( + tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL); layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0); layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); @@ -5663,7 +5437,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); - const int64_t n_embd_head_qk_rope = hparams.n_rot; + const int64_t n_embd_head_qk_rope = hparams.n_rot(); const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; const int64_t q_lora_rank = hparams.n_lora_q; @@ -5802,6 +5576,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t n_ssm_head = hparams.ssm_dt_rank; const int64_t n_group = hparams.ssm_n_group; const int64_t d_in_proj = 2*d_inner + 2*n_group*d_state + n_ssm_head; + const int64_t moe_n_embd = hparams.moe_latent_size > 0 ? hparams.moe_latent_size : n_embd; // embeddings tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -5861,8 +5636,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert }, 0); // MoE branch - layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); - layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, TENSOR_NOT_REQUIRED); + layer.ffn_latent_up = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP, "weight", i), {moe_n_embd, n_embd}, TENSOR_NOT_REQUIRED); + + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, moe_n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {moe_n_embd, n_ff_exp, n_expert}, 0); // Shared expert branch layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0); @@ -5946,8 +5724,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t n_ff_exp = hparams.n_ff_exp; const int64_t n_expert = hparams.n_expert; const int64_t n_expert_used = hparams.n_expert_used; - const int64_t n_ff_shexp = hparams.n_ff_shexp; - const int64_t head_dim = hparams.n_embd_head_k; + const int64_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : n_ff_exp; + const int64_t head_dim = hparams.n_embd_head_k(); const int64_t n_qo_dim = n_head * head_dim; const int64_t n_kv_dim = n_head_kv * head_dim; @@ -6807,6 +6585,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { for (int i = 0; i < n_layer; ++i) { auto & layer = layers[i]; + const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(i); layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); @@ -6825,9 +6604,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0); layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0); - layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0); - layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0); - layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0); } } break; case LLM_ARCH_HUNYUAN_DENSE: @@ -7221,15 +7000,14 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t n_embd_head_v_kda = hparams.n_embd_head_kda; const int64_t ssm_d_conv = hparams.ssm_d_conv; - // Try loading KDA specific tensors (using SSM_ prefix) - // Conv1d weights: try 4D first, then 3D (quantization may remove trailing 1) - // 4D: [d_conv, 1, d_inner, 1], 3D: [d_conv, 1, d_inner] - layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED); - if (!layer.ssm_q_conv) { - layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, TENSOR_NOT_REQUIRED); - } + if (hparams.is_recurrent(i)) { + // Conv1d weights: try 4D first, then 3D (quantization may remove trailing 1) + // 4D: [d_conv, 1, d_inner, 1], 3D: [d_conv, 1, d_inner] + layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED); + if (!layer.ssm_q_conv) { + layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, 0); + } - if (layer.ssm_q_conv) { // KDA Layer - Conv1d weights may be 3D or 4D layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED); if (!layer.ssm_k_conv) { @@ -7293,10 +7071,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) { // Kimi: qk_rope_head_dim = 64 (actual RoPE dimension for MLA) // Note: hparams.n_rot may be 72 (from conversion) but actual is 64 - const int64_t qk_rope_head_dim = hparams.n_rot; // From config: qk_rope_head_dim + const int64_t qk_rope_head_dim = hparams.n_rot(); // From config: qk_rope_head_dim layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0); // Support Legacy GGUFs that don't split wkv_b (MLA KV cache disabled) - layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)}, TENSOR_NOT_REQUIRED); + layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), + {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL); if (!layer.wkv_b) { // MLA KV cache enabled layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_k_mla - qk_rope_head_dim, kv_lora_rank, n_head}, 0); layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0); @@ -7416,6 +7195,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_QWEN3NEXT: { + if (n_expert == 0) { + throw std::runtime_error(arch_name() + " model cannot have zero experts"); + } + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0); // output @@ -7444,6 +7227,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { for (int i = 0; i < n_layer; ++i) { auto & layer = layers[i]; + const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(i); layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0); layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0); @@ -7474,15 +7258,14 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0); - layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0); layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0); - layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0); + create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0); // Shared experts layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0); - layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0); - layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0); - layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { hparams.n_ff_shexp, n_embd }, 0); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, 0); } } break; case LLM_ARCH_QWEN35MOE: @@ -7540,9 +7323,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0); - layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0); layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0); - layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0); + create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0); // Shared experts const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff; @@ -7660,7 +7442,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { // ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer. uint32_t n_rot_max = 0; for (int i = 0; i < n_layer; ++i) { - n_rot_max = std::max(n_rot_max, hparams.n_rot); + n_rot_max = std::max(n_rot_max, hparams.n_rot(i)); } if (n_rot_max == 0) { n_rot_max = n_rot; @@ -7749,10 +7531,75 @@ bool llama_model::load_tensors(llama_model_loader & ml) { throw std::runtime_error("unknown architecture"); } - if (n_moved_tensors > 0) { - LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %d others) cannot be used with preferred buffer type %s, using %s instead\n", - __func__, first_moved_tensor->name, ggml_type_name(first_moved_tensor->type), n_moved_tensors - 1, - ggml_backend_buft_name(first_moved_from_buft), ggml_backend_buft_name(first_moved_to_buft)); + // generic pass: load optional per-tensor/per-expert ".scale" tensors (e.g. NVFP4 scale2) + // this avoids having to add scale loading to every architecture + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + // attention weight scales (per-tensor, shape {1}) + if (!layer.wq_s && layer.wq) { + layer.wq_s = create_tensor(tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.wk_s && layer.wk) { + layer.wk_s = create_tensor(tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.wv_s && layer.wv) { + layer.wv_s = create_tensor(tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.wo_s && layer.wo) { + layer.wo_s = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.wqkv_s && layer.wqkv) { + layer.wqkv_s = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.wqkv_gate_s && layer.wqkv_gate) { + layer.wqkv_gate_s = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + + // dense FFN weight scales (per-tensor, shape {1}) + if (!layer.ffn_gate_s && layer.ffn_gate) { + layer.ffn_gate_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.ffn_down_s && layer.ffn_down) { + layer.ffn_down_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.ffn_up_s && layer.ffn_up) { + layer.ffn_up_s = create_tensor(tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.ffn_gate_shexp_s && layer.ffn_gate_shexp) { + layer.ffn_gate_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.ffn_down_shexp_s && layer.ffn_down_shexp) { + layer.ffn_down_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.ffn_up_shexp_s && layer.ffn_up_shexp) { + layer.ffn_up_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + + // MoE expert weight scales (per-expert, shape {n_expert}) + if (!layer.ffn_gate_exps_s && layer.ffn_gate_exps) { + layer.ffn_gate_exps_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED); + } + if (!layer.ffn_down_exps_s && layer.ffn_down_exps) { + layer.ffn_down_exps_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED); + } + if (!layer.ffn_up_exps_s && layer.ffn_up_exps) { + layer.ffn_up_exps_s = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED); + } + + // recurrent / linear-attention weight scales (per-tensor, shape {1}) + if (!layer.ssm_in_s && layer.ssm_in) { + layer.ssm_in_s = create_tensor(tn(LLM_TENSOR_SSM_IN, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.ssm_out_s && layer.ssm_out) { + layer.ssm_out_s = create_tensor(tn(LLM_TENSOR_SSM_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.ssm_alpha_s && layer.ssm_alpha) { + layer.ssm_alpha_s = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } + if (!layer.ssm_beta_s && layer.ssm_beta) { + layer.ssm_beta_s = create_tensor(tn(LLM_TENSOR_SSM_BETA, "scale", i), {1}, TENSOR_NOT_REQUIRED); + } } } @@ -7763,13 +7610,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) { // create the backend buffers std::vector> ctx_buf_maps; - ctx_buf_maps.reserve(ctx_map.size()); + ctx_buf_maps.reserve(ml.ctx_map.size()); // Ensure we have enough capacity for the maximum backend buffer we will potentially create - const size_t n_max_backend_buffer = ctx_map.size() * ml.files.size(); + const size_t n_max_backend_buffer = ml.ctx_map.size() * ml.files.size(); pimpl->ctxs_bufs.reserve(n_max_backend_buffer); - for (auto & [buft, ctx_ptr] : ctx_map) { + for (auto & [buft, ctx_ptr] : ml.ctx_map) { ggml_context * ctx = ctx_ptr.get(); // skip contexts without tensors @@ -8001,11 +7848,11 @@ void llama_model::print_info() const { LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer); LLAMA_LOG_INFO("%s: n_head = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head(il); }, hparams.n_layer).c_str()); LLAMA_LOG_INFO("%s: n_head_kv = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str()); - LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); + LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot_full); LLAMA_LOG_INFO("%s: n_swa = %u\n", __func__, hparams.n_swa); LLAMA_LOG_INFO("%s: is_swa_any = %u\n", __func__, hparams.is_swa_any()); - LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k); - LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v); + LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k_full); + LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v_full); LLAMA_LOG_INFO("%s: n_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il); }, hparams.n_layer).c_str()); LLAMA_LOG_INFO("%s: n_embd_k_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str()); LLAMA_LOG_INFO("%s: n_embd_v_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str()); @@ -8029,6 +7876,9 @@ void llama_model::print_info() const { if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) { LLAMA_LOG_INFO("%s: freq_base_swa = %.1f\n", __func__, hparams.rope_freq_base_train_swa); LLAMA_LOG_INFO("%s: freq_scale_swa = %g\n", __func__, hparams.rope_freq_scale_train_swa); + LLAMA_LOG_INFO("%s: n_embd_head_k_swa = %u\n", __func__, hparams.n_embd_head_k_swa); + LLAMA_LOG_INFO("%s: n_embd_head_v_swa = %u\n", __func__, hparams.n_embd_head_v_swa); + LLAMA_LOG_INFO("%s: n_rot_swa = %u\n", __func__, hparams.n_rot_swa); } LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn); LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul); @@ -8263,6 +8113,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, case LLM_ARCH_NOMIC_BERT: case LLM_ARCH_NOMIC_BERT_MOE: case LLM_ARCH_NEO_BERT: + case LLM_ARCH_EUROBERT: case LLM_ARCH_WAVTOKENIZER_DEC: case LLM_ARCH_MODERN_BERT: case LLM_ARCH_GEMMA_EMBEDDING: @@ -8460,6 +8311,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { { llm = std::make_unique(*this, params); } break; + case LLM_ARCH_EUROBERT: + { + llm = std::make_unique(*this, params); + } break; case LLM_ARCH_BLOOM: { llm = std::make_unique(*this, params); @@ -9095,6 +8950,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_MODERN_BERT: case LLM_ARCH_NOMIC_BERT: case LLM_ARCH_NOMIC_BERT_MOE: + case LLM_ARCH_EUROBERT: case LLM_ARCH_STABLELM: case LLM_ARCH_BITNET: case LLM_ARCH_QWEN: diff --git a/src/llama-model.h b/src/llama-model.h index c44433f3d4..c9d9127958 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -54,6 +54,7 @@ enum llm_type { LLM_TYPE_0_3B, LLM_TYPE_0_5B, LLM_TYPE_0_6B, + LLM_TYPE_0_8B, LLM_TYPE_1B, LLM_TYPE_1_2B, LLM_TYPE_1_3B, @@ -116,6 +117,7 @@ enum llm_type { LLM_TYPE_8B_A1B, // lfm2moe LLM_TYPE_16B_A1B, LLM_TYPE_21B_A3B, // Ernie MoE small + LLM_TYPE_24B_A2B, // lfm2moe LLM_TYPE_30B_A3B, LLM_TYPE_31B_A3_5B, LLM_TYPE_35B_A3B, // Qwen3.5 @@ -124,12 +126,15 @@ enum llm_type { LLM_TYPE_100B_A6B, LLM_TYPE_102B_A12B, // Solar-Open LLM_TYPE_106B_A12B, // GLM-4.5-Air + LLM_TYPE_120B_A12B, // Nemotron 3 Super + LLM_TYPE_122B_A10B, // Qwen3.5 LLM_TYPE_196B_A11B, // Step3.5-Flash LLM_TYPE_230B_A10B, // Minimax M2 LLM_TYPE_235B_A22B, LLM_TYPE_300B_A47B, // Ernie MoE big LLM_TYPE_310B_A15B, // /MiMo-V2-Flash LLM_TYPE_355B_A32B, // GLM-4.5 + LLM_TYPE_397B_A17B, // Qwen3.5 LLM_TYPE_744B_A40B, // GLM-5 LLM_TYPE_E2B, LLM_TYPE_E4B, @@ -279,14 +284,25 @@ struct llama_layer { struct ggml_tensor * ffn_up_enc = nullptr; // ff MoE - struct ggml_tensor * ffn_gate_inp = nullptr; - struct ggml_tensor * ffn_gate_exps = nullptr; - struct ggml_tensor * ffn_down_exps = nullptr; - struct ggml_tensor * ffn_up_exps = nullptr; - struct ggml_tensor * ffn_gate_inp_b = nullptr; - struct ggml_tensor * ffn_gate_exps_b = nullptr; - struct ggml_tensor * ffn_down_exps_b = nullptr; - struct ggml_tensor * ffn_up_exps_b = nullptr; + struct ggml_tensor * ffn_gate_inp = nullptr; + struct ggml_tensor * ffn_gate_exps = nullptr; + struct ggml_tensor * ffn_down_exps = nullptr; + struct ggml_tensor * ffn_up_exps = nullptr; + struct ggml_tensor * ffn_gate_up_exps = nullptr; + struct ggml_tensor * ffn_gate_inp_b = nullptr; + struct ggml_tensor * ffn_gate_exps_b = nullptr; + struct ggml_tensor * ffn_down_exps_b = nullptr; + struct ggml_tensor * ffn_up_exps_b = nullptr; + struct ggml_tensor * ffn_gate_up_exps_b = nullptr; + + // ff MoE per-expert scales (NVFP4 per-tensor scale2) + struct ggml_tensor * ffn_gate_exps_s = nullptr; + struct ggml_tensor * ffn_down_exps_s = nullptr; + struct ggml_tensor * ffn_up_exps_s = nullptr; + + // ff MoE latent proj + struct ggml_tensor * ffn_latent_down = nullptr; + struct ggml_tensor * ffn_latent_up = nullptr; // ff shared expert (shexp) struct ggml_tensor * ffn_gate_inp_shexp = nullptr; @@ -381,13 +397,22 @@ struct llama_layer { struct ggml_tensor * rope_freqs = nullptr; // bitnet scale - struct ggml_tensor * wq_scale = nullptr; - struct ggml_tensor * wk_scale = nullptr; - struct ggml_tensor * wv_scale = nullptr; - struct ggml_tensor * wo_scale = nullptr; - struct ggml_tensor * ffn_gate_scale = nullptr; - struct ggml_tensor * ffn_up_scale = nullptr; - struct ggml_tensor * ffn_down_scale = nullptr; + struct ggml_tensor * wq_s = nullptr; + struct ggml_tensor * wk_s = nullptr; + struct ggml_tensor * wv_s = nullptr; + struct ggml_tensor * wo_s = nullptr; + struct ggml_tensor * wqkv_s = nullptr; + struct ggml_tensor * wqkv_gate_s = nullptr; + struct ggml_tensor * ffn_gate_s = nullptr; + struct ggml_tensor * ffn_up_s = nullptr; + struct ggml_tensor * ffn_down_s = nullptr; + struct ggml_tensor * ffn_gate_shexp_s = nullptr; + struct ggml_tensor * ffn_up_shexp_s = nullptr; + struct ggml_tensor * ffn_down_shexp_s = nullptr; + struct ggml_tensor * ssm_in_s = nullptr; + struct ggml_tensor * ssm_out_s = nullptr; + struct ggml_tensor * ssm_alpha_s = nullptr; + struct ggml_tensor * ssm_beta_s = nullptr; // altup & laurel struct ggml_tensor * per_layer_inp_gate = nullptr; diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index 24770430e1..8e8ce23124 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -1,11 +1,11 @@ -#include "llama-quant.h" +#include "llama.h" #include "llama-impl.h" #include "llama-model.h" #include "llama-model-loader.h" -#include #include #include +#include #include #include #include @@ -13,10 +13,28 @@ #include #include -// Quantization types. Changes to this struct must be replicated in quantize.cpp -struct tensor_quantization { +// result of parsing --tensor-type option +// (changes to this struct must be reflected in tools/quantize/quantize.cpp) +struct tensor_type_option { std::string name; - ggml_type quant = GGML_TYPE_COUNT; + ggml_type type = GGML_TYPE_COUNT; +}; + +// tensor categorization - used to avoid repeated string matching in quantization logic. +// this is different from LLM_TN - we want broad categories, not specific tensor names per arch. +enum class tensor_category { + TOKEN_EMBD, + ATTENTION_Q, + ATTENTION_V, + ATTENTION_K, + ATTENTION_QKV, + ATTENTION_KV_B, + ATTENTION_OUTPUT, + FFN_UP, + FFN_GATE, + FFN_DOWN, + OUTPUT, + OTHER }; static void zeros(std::ofstream & file, size_t n) { @@ -54,7 +72,7 @@ static std::string remap_layer(const std::string & orig_name, const std::vector< return orig_name; } -static std::string remap_imatrix (const std::string & orig_name, const std::map & mapped) { +static std::string remap_imatrix(const std::string & orig_name, const std::map & mapped) { if (mapped.empty()) { return orig_name; } @@ -76,6 +94,73 @@ static std::string remap_imatrix (const std::string & orig_name, const std::map< return orig_name; } +// +// helper functions for tensor name matching +// + +static bool tensor_name_match_token_embd(const char * tensor_name) { + return std::strcmp(tensor_name, "token_embd.weight") == 0 || + std::strcmp(tensor_name, "per_layer_token_embd.weight") == 0; +} + +static bool tensor_name_match_output_weight(const char * tensor_name) { + return std::strcmp(tensor_name, "output.weight") == 0; +} + +// +// tensor categorization for quantization +// +// (this is different from LLM_TN - we want broad categories, not specific tensor names per arch) +// + +static tensor_category tensor_get_category(const std::string & tensor_name) { + if (tensor_name_match_output_weight(tensor_name.c_str())) { + return tensor_category::OUTPUT; + } + if (tensor_name_match_token_embd(tensor_name.c_str())) { + return tensor_category::TOKEN_EMBD; + } + if (tensor_name.find("attn_qkv.weight") != std::string::npos) { + return tensor_category::ATTENTION_QKV; + } + if (tensor_name.find("attn_kv_b.weight") != std::string::npos) { + return tensor_category::ATTENTION_KV_B; + } + if (tensor_name.find("attn_v.weight") != std::string::npos) { + return tensor_category::ATTENTION_V; + } + if (tensor_name.find("attn_k.weight") != std::string::npos) { + return tensor_category::ATTENTION_K; + } + if (tensor_name.find("attn_q.weight") != std::string::npos) { + return tensor_category::ATTENTION_Q; + } + if (tensor_name.find("attn_output.weight") != std::string::npos) { + return tensor_category::ATTENTION_OUTPUT; + } + if (tensor_name.find("ffn_up") != std::string::npos) { + return tensor_category::FFN_UP; + } + if (tensor_name.find("ffn_gate") != std::string::npos) { + return tensor_category::FFN_GATE; + } + if (tensor_name.find("ffn_down") != std::string::npos) { + return tensor_category::FFN_DOWN; + } + return tensor_category::OTHER; +} + +// check if category is for attention-v-like tensors (more sensitive to quantization) +static bool category_is_attn_v(tensor_category cat) { + return cat == tensor_category::ATTENTION_V || + cat == tensor_category::ATTENTION_QKV || + cat == tensor_category::ATTENTION_KV_B; +} + +// +// quantization state +// + struct quantize_state_impl { const llama_model & model; const llama_model_quantize_params * params; @@ -89,20 +174,42 @@ struct quantize_state_impl { int i_ffn_gate = 0; int i_ffn_up = 0; - int n_k_quantized = 0; int n_fallback = 0; bool has_imatrix = false; - // used to figure out if a model shares tok_embd with the output weight - bool has_output = false; + // used to figure out if a model has tied embeddings (tok_embd shares weights with output) + bool has_tied_embeddings = true; // assume tied until we see output.weight - quantize_state_impl(const llama_model & model, const llama_model_quantize_params * params) - : model(model) - , params(params) - {} + // tensor type override patterns (compiled once, used twice) + std::vector> tensor_type_patterns; + + quantize_state_impl(const llama_model & model, const llama_model_quantize_params * params): + model(model), params(params) + { + // compile regex patterns once - they are expensive + if (params->tensor_types) { + const auto & tensor_types = *static_cast *>(params->tensor_types); + for (const auto & [tname, qtype] : tensor_types) { + tensor_type_patterns.emplace_back(std::regex(tname), qtype); + } + } + } }; +// per-tensor metadata, computed in the preliminary loop and used in the main loop +struct tensor_metadata { + ggml_type target_type; + tensor_category category; + std::string remapped_imatrix_name; + bool allows_quantization; + bool requires_imatrix; +}; + +// +// dequantization +// + static void llama_tensor_dequantize_impl( ggml_tensor * tensor, std::vector> & output, std::vector & workers, const size_t nelements, const int nthread @@ -175,12 +282,132 @@ static void llama_tensor_dequantize_impl( workers.clear(); } -static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) { +// +// do we allow this tensor to be quantized? +// + +static bool tensor_allows_quantization(const llama_model_quantize_params * params, llm_arch arch, const ggml_tensor * tensor) { + // trivial checks first -- no string ops needed + if (params->only_copy) return false; + + // quantize only 2D and 3D tensors (experts) + if (ggml_n_dims(tensor) < 2) return false; + + const std::string name = ggml_get_name(tensor); + + // This used to be a regex, but has an extreme cost to compile times. + bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'? + + // do not quantize norm tensors + quantize &= name.find("_norm.weight") == std::string::npos; + + quantize &= params->quantize_output_tensor || name != "output.weight"; + + // do not quantize expert gating tensors + // NOTE: can't use LLM_TN here because the layer number is not known + quantize &= name.find("ffn_gate_inp.weight") == std::string::npos; + + // these are very small (e.g. 4x4) + quantize &= name.find("altup") == std::string::npos; + quantize &= name.find("laurel") == std::string::npos; + + // these are not too big so keep them as it is + quantize &= name.find("per_layer_model_proj") == std::string::npos; + + // do not quantize positional embeddings and token types (BERT) + quantize &= name != LLM_TN(arch)(LLM_TENSOR_POS_EMBD, "weight"); + quantize &= name != LLM_TN(arch)(LLM_TENSOR_TOKEN_TYPES, "weight"); + + // do not quantize Mamba/Kimi's small conv1d weights + // NOTE: can't use LLM_TN here because the layer number is not known + quantize &= name.find("ssm_conv1d") == std::string::npos; + quantize &= name.find("shortconv.conv.weight") == std::string::npos; + + // do not quantize RWKV's small yet 2D weights + quantize &= name.find("time_mix_first.weight") == std::string::npos; + quantize &= name.find("time_mix_w0.weight") == std::string::npos; + quantize &= name.find("time_mix_w1.weight") == std::string::npos; + quantize &= name.find("time_mix_w2.weight") == std::string::npos; + quantize &= name.find("time_mix_v0.weight") == std::string::npos; + quantize &= name.find("time_mix_v1.weight") == std::string::npos; + quantize &= name.find("time_mix_v2.weight") == std::string::npos; + quantize &= name.find("time_mix_a0.weight") == std::string::npos; + quantize &= name.find("time_mix_a1.weight") == std::string::npos; + quantize &= name.find("time_mix_a2.weight") == std::string::npos; + quantize &= name.find("time_mix_g1.weight") == std::string::npos; + quantize &= name.find("time_mix_g2.weight") == std::string::npos; + quantize &= name.find("time_mix_decay_w1.weight") == std::string::npos; + quantize &= name.find("time_mix_decay_w2.weight") == std::string::npos; + quantize &= name.find("time_mix_lerp_fused.weight") == std::string::npos; + + // do not quantize relative position bias (T5) + quantize &= name.find("attn_rel_b.weight") == std::string::npos; + + // do not quantize specific multimodal tensors + quantize &= name.find(".position_embd.") == std::string::npos; + + return quantize; +} + +// +// tensor type selection +// + +// incompatible tensor shapes are handled here - fallback to a compatible type +static ggml_type tensor_type_fallback(quantize_state_impl & qs, const ggml_tensor * t, const ggml_type target_type) { + ggml_type return_type = target_type; + + const int64_t ncols = t->ne[0]; + const int64_t qk_k = ggml_blck_size(target_type); + + if (ncols % qk_k != 0) { // this tensor's shape is incompatible with this quant + LLAMA_LOG_WARN("warning: %-36s - ncols %6" PRId64 " not divisible by %3" PRId64 " (required for type %7s) ", + t->name, ncols, qk_k, ggml_type_name(target_type)); + ++qs.n_fallback; + + switch (target_type) { + // types on the left: block size 256 + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: // types on the right: block size 32 + case GGML_TYPE_IQ4_XS: return_type = GGML_TYPE_IQ4_NL; break; + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: return_type = GGML_TYPE_Q4_0; break; + case GGML_TYPE_Q4_K: return_type = GGML_TYPE_Q5_0; break; + case GGML_TYPE_Q5_K: return_type = GGML_TYPE_Q5_1; break; + case GGML_TYPE_Q6_K: return_type = GGML_TYPE_Q8_0; break; + default: + throw std::runtime_error(format("no tensor type fallback is defined for type %s", + ggml_type_name(target_type))); + } + if (ncols % ggml_blck_size(return_type) != 0) { + // + // the fallback return type is still not compatible for this tensor! + // + // most likely, this tensor's first dimension is not divisible by 32. + // this is very rare. we can either abort the quantization, or + // fallback to F16 / F32. + // + LLAMA_LOG_WARN("(WARNING: must use F16 due to unusual shape) "); + return_type = GGML_TYPE_F16; + } + LLAMA_LOG_WARN("-> falling back to %7s\n", ggml_type_name(return_type)); + } + return return_type; +} + +// internal standard logic for selecting the target tensor type based on tensor category, ftype, and model arch +static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype, tensor_category category) { const std::string name = ggml_get_name(tensor); // TODO: avoid hardcoded tensor names - use the TN_* constants const llm_arch arch = qs.model.arch; - const auto tn = LLM_TN(arch); auto use_more_bits = [](int i_layer, int n_layers) -> bool { return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2; @@ -204,7 +431,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings // with the quantization of the output tensor - if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) { + if (category == tensor_category::OUTPUT || (qs.has_tied_embeddings && category == tensor_category::TOKEN_EMBD)) { if (qs.params->output_tensor_type < GGML_TYPE_COUNT) { new_type = qs.params->output_tensor_type; } else { @@ -234,7 +461,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t } else { new_type = GGML_TYPE_Q8_0; } - } else if (name == "token_embd.weight" || name == "per_layer_token_embd.weight") { + } else if (category == tensor_category::TOKEN_EMBD) { if (qs.params->token_embedding_type < GGML_TYPE_COUNT) { new_type = qs.params->token_embedding_type; } else { @@ -254,21 +481,21 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t } } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) { - if (name.find("attn_v.weight") != std::string::npos) { + if (category_is_attn_v(category)) { if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K; else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K; ++qs.i_attention_wv; } - else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) { + else if (qs.model.hparams.n_expert == 8 && category == tensor_category::ATTENTION_K) { new_type = GGML_TYPE_Q4_K; } - else if (name.find("ffn_down") != std::string::npos) { + else if (category == tensor_category::FFN_DOWN) { if (qs.i_ffn_down < qs.n_ffn_down/8) { new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K; } ++qs.i_ffn_down; } - else if (name.find("attn_output.weight") != std::string::npos) { + else if (category == tensor_category::ATTENTION_OUTPUT) { if (qs.model.hparams.n_expert == 8) { new_type = GGML_TYPE_Q5_K; } else { @@ -276,7 +503,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S; } } - } else if (name.find("attn_v.weight") != std::string::npos) { + } else if (category_is_attn_v(category)) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) { new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; } @@ -314,7 +541,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t new_type = GGML_TYPE_Q8_0; } ++qs.i_attention_wv; - } else if (name.find("attn_k.weight") != std::string::npos) { + } else if (category == tensor_category::ATTENTION_K) { if (qs.model.hparams.n_expert == 8) { // for the 8-expert model, bumping this to Q8_0 trades just ~128MB // TODO: explore better strategies @@ -326,14 +553,14 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { new_type = GGML_TYPE_IQ2_S; } - } else if (name.find("attn_q.weight") != std::string::npos) { + } else if (category == tensor_category::ATTENTION_Q) { if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) { new_type = GGML_TYPE_IQ3_XXS; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { new_type = GGML_TYPE_IQ2_S; } - } else if (name.find("ffn_down") != std::string::npos) { + } else if (category == tensor_category::FFN_DOWN) { auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str()); int i_layer = info.first, n_layer = info.second; if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; @@ -378,7 +605,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1; } ++qs.i_ffn_down; - } else if (name.find("attn_output.weight") != std::string::npos) { + } else if (category == tensor_category::ATTENTION_OUTPUT) { if (arch != LLM_ARCH_FALCON) { if (qs.model.hparams.n_expert == 8) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || @@ -398,14 +625,14 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K; } } - else if (name.find("attn_qkv.weight") != std::string::npos) { + else if (category == tensor_category::ATTENTION_QKV) { if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) { new_type = GGML_TYPE_Q4_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K; } - else if (name.find("ffn_gate") != std::string::npos) { + else if (category == tensor_category::FFN_GATE) { auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str()); int i_layer = info.first, n_layer = info.second; if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) { @@ -413,7 +640,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t } ++qs.i_ffn_gate; } - else if (name.find("ffn_up") != std::string::npos) { + else if (category == tensor_category::FFN_UP) { auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str()); int i_layer = info.first, n_layer = info.second; if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) { @@ -425,6 +652,55 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t return new_type; } +// outer wrapper: determine the ggml_type that this tensor should be quantized to +static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_model_quantize_params * params, const ggml_tensor * tensor, ggml_type default_type, const tensor_metadata & tm) { + if (!tensor_allows_quantization(params, qs.model.arch, tensor)) { + return tensor->type; + } + if (params->token_embedding_type < GGML_TYPE_COUNT && tm.category == tensor_category::TOKEN_EMBD) { + return params->token_embedding_type; + } + if (params->output_tensor_type < GGML_TYPE_COUNT && tm.category == tensor_category::OUTPUT) { + return params->output_tensor_type; + } + + ggml_type new_type = default_type; + + // get more optimal quantization type based on the tensor shape, layer, etc. + if (!params->pure && ggml_is_quantized(default_type)) { + // if the user provided tensor types - use those + bool manual = false; + if (!qs.tensor_type_patterns.empty()) { + const std::string tensor_name(tensor->name); + for (const auto & [pattern, qtype] : qs.tensor_type_patterns) { + if (std::regex_search(tensor_name, pattern)) { + if (qtype != new_type) { + LLAMA_LOG_WARN("%s: %-36s - applying manual override: %s -> %s\n", + __func__, tensor_name.c_str(), ggml_type_name(new_type), ggml_type_name(qtype)); + new_type = qtype; + manual = true; + break; + } + } + } + } + + // if not manual - use the standard logic for choosing the quantization type based on the selected mixture + if (!manual) { + new_type = llama_tensor_get_type_impl(qs, new_type, tensor, params->ftype, tm.category); + } + + // incompatible tensor shapes are handled here - fallback to a compatible type + new_type = tensor_type_fallback(qs, tensor, new_type); + } + + return new_type; +} + +// +// quantization implementation +// + static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector & workers, const int nthread) { if (nthread < 2) { // single-thread @@ -479,68 +755,94 @@ static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float * return new_size; } -static bool tensor_type_requires_imatrix(const ggml_tensor * t, const ggml_type dst_type, const llama_ftype ftype) { - return ( - dst_type == GGML_TYPE_IQ2_XXS || dst_type == GGML_TYPE_IQ2_XS || - dst_type == GGML_TYPE_IQ3_XXS || dst_type == GGML_TYPE_IQ1_S || - dst_type == GGML_TYPE_IQ2_S || dst_type == GGML_TYPE_IQ1_M || - ( // Q2_K_S is the worst k-quant type - only allow it without imatrix for token embeddings - dst_type == GGML_TYPE_Q2_K && ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(t->name, "token_embd.weight") != 0 - ) - ); +// +// imatrix requirement check +// + +static bool tensor_requires_imatrix(const char * tensor_name, const ggml_type dst_type, const llama_ftype ftype) { + if (tensor_name_match_token_embd(tensor_name) || tensor_name_match_output_weight(tensor_name)) { + return false; + } + switch (dst_type) { + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ1_S: + return true; + case GGML_TYPE_Q2_K: + // as a general rule, the k-type quantizations don't require imatrix data. + // the only exception is Q2_K tensors that are part of a Q2_K_S file. + return ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S; + default: + return false; + } } +// +// given a file type, get the default tensor type +// + +static ggml_type llama_ftype_get_default_type(llama_ftype ftype) { + switch (ftype) { + case LLAMA_FTYPE_MOSTLY_Q4_0: return GGML_TYPE_Q4_0; + case LLAMA_FTYPE_MOSTLY_Q4_1: return GGML_TYPE_Q4_1; + case LLAMA_FTYPE_MOSTLY_Q5_0: return GGML_TYPE_Q5_0; + case LLAMA_FTYPE_MOSTLY_Q5_1: return GGML_TYPE_Q5_1; + case LLAMA_FTYPE_MOSTLY_Q8_0: return GGML_TYPE_Q8_0; + case LLAMA_FTYPE_MOSTLY_F16: return GGML_TYPE_F16; + case LLAMA_FTYPE_MOSTLY_BF16: return GGML_TYPE_BF16; + case LLAMA_FTYPE_ALL_F32: return GGML_TYPE_F32; + + case LLAMA_FTYPE_MOSTLY_MXFP4_MOE: return GGML_TYPE_MXFP4; + + // K-quants + case LLAMA_FTYPE_MOSTLY_Q2_K_S: + case LLAMA_FTYPE_MOSTLY_Q2_K: return GGML_TYPE_Q2_K; + case LLAMA_FTYPE_MOSTLY_IQ3_XS: return GGML_TYPE_IQ3_S; + case LLAMA_FTYPE_MOSTLY_Q3_K_S: + case LLAMA_FTYPE_MOSTLY_Q3_K_M: + case LLAMA_FTYPE_MOSTLY_Q3_K_L: return GGML_TYPE_Q3_K; + case LLAMA_FTYPE_MOSTLY_Q4_K_S: + case LLAMA_FTYPE_MOSTLY_Q4_K_M: return GGML_TYPE_Q4_K; + case LLAMA_FTYPE_MOSTLY_Q5_K_S: + case LLAMA_FTYPE_MOSTLY_Q5_K_M: return GGML_TYPE_Q5_K; + case LLAMA_FTYPE_MOSTLY_Q6_K: return GGML_TYPE_Q6_K; + case LLAMA_FTYPE_MOSTLY_TQ1_0: return GGML_TYPE_TQ1_0; + case LLAMA_FTYPE_MOSTLY_TQ2_0: return GGML_TYPE_TQ2_0; + case LLAMA_FTYPE_MOSTLY_IQ2_XXS: return GGML_TYPE_IQ2_XXS; + case LLAMA_FTYPE_MOSTLY_IQ2_XS: return GGML_TYPE_IQ2_XS; + case LLAMA_FTYPE_MOSTLY_IQ2_S: return GGML_TYPE_IQ2_XS; + case LLAMA_FTYPE_MOSTLY_IQ2_M: return GGML_TYPE_IQ2_S; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS: return GGML_TYPE_IQ3_XXS; + case LLAMA_FTYPE_MOSTLY_IQ1_S: return GGML_TYPE_IQ1_S; + case LLAMA_FTYPE_MOSTLY_IQ1_M: return GGML_TYPE_IQ1_M; + case LLAMA_FTYPE_MOSTLY_IQ4_NL: return GGML_TYPE_IQ4_NL; + case LLAMA_FTYPE_MOSTLY_IQ4_XS: return GGML_TYPE_IQ4_XS; + case LLAMA_FTYPE_MOSTLY_IQ3_S: + case LLAMA_FTYPE_MOSTLY_IQ3_M: return GGML_TYPE_IQ3_S; + + default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); + } +} + +// +// main quantization driver +// + static void llama_model_quantize_impl(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) { ggml_type default_type; llama_ftype ftype = params->ftype; - switch (params->ftype) { - case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break; - case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break; - case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break; - case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break; - case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break; - case LLAMA_FTYPE_MOSTLY_F16: default_type = GGML_TYPE_F16; break; - case LLAMA_FTYPE_MOSTLY_BF16: default_type = GGML_TYPE_BF16; break; - case LLAMA_FTYPE_ALL_F32: default_type = GGML_TYPE_F32; break; - - case LLAMA_FTYPE_MOSTLY_MXFP4_MOE: default_type = GGML_TYPE_MXFP4; break; - - // K-quants - case LLAMA_FTYPE_MOSTLY_Q2_K_S: - case LLAMA_FTYPE_MOSTLY_Q2_K: default_type = GGML_TYPE_Q2_K; break; - case LLAMA_FTYPE_MOSTLY_IQ3_XS: default_type = GGML_TYPE_IQ3_S; break; - case LLAMA_FTYPE_MOSTLY_Q3_K_S: - case LLAMA_FTYPE_MOSTLY_Q3_K_M: - case LLAMA_FTYPE_MOSTLY_Q3_K_L: default_type = GGML_TYPE_Q3_K; break; - case LLAMA_FTYPE_MOSTLY_Q4_K_S: - case LLAMA_FTYPE_MOSTLY_Q4_K_M: default_type = GGML_TYPE_Q4_K; break; - case LLAMA_FTYPE_MOSTLY_Q5_K_S: - case LLAMA_FTYPE_MOSTLY_Q5_K_M: default_type = GGML_TYPE_Q5_K; break; - case LLAMA_FTYPE_MOSTLY_Q6_K: default_type = GGML_TYPE_Q6_K; break; - case LLAMA_FTYPE_MOSTLY_TQ1_0: default_type = GGML_TYPE_TQ1_0; break; - case LLAMA_FTYPE_MOSTLY_TQ2_0: default_type = GGML_TYPE_TQ2_0; break; - case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break; - case LLAMA_FTYPE_MOSTLY_IQ2_XS: default_type = GGML_TYPE_IQ2_XS; break; - case LLAMA_FTYPE_MOSTLY_IQ2_S: default_type = GGML_TYPE_IQ2_XS; break; - case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break; - case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break; - case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break; - case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break; - case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break; - case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break; - case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break; - case LLAMA_FTYPE_MOSTLY_IQ3_M: default_type = GGML_TYPE_IQ3_S; break; - - default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); - } - int nthread = params->nthread; if (nthread <= 0) { nthread = std::thread::hardware_concurrency(); } + default_type = llama_ftype_get_default_type(ftype); + // mmap consistently increases speed on Linux, and also increases speed on Windows with // hot cache. It may cause a slowdown on macOS, possibly related to free memory. #if defined(__linux__) || defined(_WIN32) @@ -556,7 +858,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } std::vector splits = {}; - llama_model_loader ml(fname_inp, splits, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr); + llama_model_loader ml(/*metadata*/ nullptr, /*set_tensor_data*/ nullptr, /*set_tensor_data_ud*/ nullptr, + fname_inp, splits, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr); ml.init_mappings(false); // no prefetching llama_model model(llama_model_default_params()); @@ -574,7 +877,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: if (params->imatrix) { imatrix_data = static_cast>*>(params->imatrix); if (imatrix_data) { - LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size())); + LLAMA_LOG_INFO("\n%s: have importance matrix data with %d entries\n", + __func__, (int)imatrix_data->size()); qs.has_imatrix = true; // check imatrix for nans or infs for (const auto & kv : *imatrix_data) { @@ -596,7 +900,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } // copy the KV pairs from the input file - gguf_set_kv (ctx_out.get(), ml.meta.get()); + gguf_set_kv (ctx_out.get(), ml.metadata); gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV gguf_set_val_u32(ctx_out.get(), "general.file_type", ftype); // TODO: use LLM_KV @@ -657,35 +961,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: }); } - for (const auto * it : tensors) { - const struct ggml_tensor * tensor = it->tensor; - - const std::string name = ggml_get_name(tensor); - - // TODO: avoid hardcoded tensor names - use the TN_* constants - if (name.find("attn_v.weight") != std::string::npos || - name.find("attn_qkv.weight") != std::string::npos || - name.find("attn_kv_b.weight")!= std::string::npos) { - ++qs.n_attention_wv; - } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) { - qs.has_output = true; - } - } - - qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer; - - size_t total_size_org = 0; - size_t total_size_new = 0; - - std::vector workers; - workers.reserve(nthread); - int idx = 0; - - std::vector> read_data; - std::vector> work; - std::vector> f32_conv_buf; - uint16_t n_split = 1; // Assume split index is continuous @@ -697,14 +973,68 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: std::vector ctx_outs(n_split); ctx_outs[0] = std::move(ctx_out); - // populate the original tensors so we get an initial meta data - for (const auto * it : tensors) { + // compute tensor metadata once and cache it + std::vector metadata(tensors.size()); + + // initialize quantization state before preliminary loop (counters for use_more_bits) + { + for (size_t i = 0; i < tensors.size(); ++i) { + const auto cat = tensor_get_category(tensors[i]->tensor->name); + if (category_is_attn_v(cat)) { + ++qs.n_attention_wv; + } + if (cat == tensor_category::OUTPUT) { + qs.has_tied_embeddings = false; + } + metadata[i].category = cat; // save and re-use the category while we're at it + } + // these also need to be set to n_layer by default + qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer; + } + + // flag for --dry-run + bool will_require_imatrix = false; + + // + // preliminary iteration over all weights + // + + for (size_t i = 0; i < tensors.size(); ++i) { + const auto * it = tensors[i]; + const struct ggml_tensor * tensor = it->tensor; + const std::string name = ggml_get_name(tensor); + uint16_t i_split = params->keep_split ? it->idx : 0; - ggml_tensor * tensor = it->tensor; if (!ctx_outs[i_split]) { ctx_outs[i_split].reset(gguf_init_empty()); } gguf_add_tensor(ctx_outs[i_split].get(), tensor); + + metadata[i].allows_quantization = tensor_allows_quantization(params, model.arch, tensor); + + if (metadata[i].allows_quantization) { + metadata[i].target_type = llama_tensor_get_type(qs, params, tensor, default_type, metadata[i]); + } else { + metadata[i].target_type = tensor->type; + } + + metadata[i].requires_imatrix = tensor_requires_imatrix(tensor->name, metadata[i].target_type, ftype); + + if (params->imatrix) { + metadata[i].remapped_imatrix_name = remap_imatrix(tensor->name, mapped); + } else if (metadata[i].allows_quantization && metadata[i].requires_imatrix) { + if (params->dry_run) { + will_require_imatrix = true; + } else { + LLAMA_LOG_ERROR("\n============================================================================\n" + " ERROR: this quantization requires an importance matrix!\n" + " - offending tensor: %s\n" + " - target type: %s\n" + "============================================================================\n\n", + name.c_str(), ggml_type_name(metadata[i].target_type)); + throw std::runtime_error("this quantization requires an imatrix!"); + } + } } // Set split info if needed @@ -716,6 +1046,16 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } } + size_t total_size_org = 0; + size_t total_size_new = 0; + + std::vector workers; + workers.reserve(nthread); + + std::vector> read_data; + std::vector> work; + std::vector> f32_conv_buf; + int cur_split = -1; std::ofstream fout; auto close_ofstream = [&]() { @@ -745,20 +1085,20 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: ::zeros(fout, meta_size); }; - const auto tn = LLM_TN(model.arch); - // no output file for --dry-run if (!params->dry_run) { new_ofstream(0); } - // flag for `--dry-run`, to let the user know if imatrix will be required for a real - // quantization, as a courtesy - bool will_require_imatrix = false; + // + // main loop: iterate over all weights + // - for (const auto * it : tensors) { - const auto & weight = *it; + for (size_t i = 0; i < tensors.size(); ++i) { + const auto & weight = *tensors[i]; + const auto & tm = metadata[i]; ggml_tensor * tensor = weight.tensor; + if (!params->dry_run && (weight.idx != cur_split && params->keep_split)) { close_ofstream(); new_ofstream(weight.idx); @@ -777,162 +1117,31 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: ml.load_data_for(tensor); } - LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ", + LLAMA_LOG_INFO("[%4d/%4d] %-36s - [%s], type = %6s, ", ++idx, ml.n_tensors, ggml_get_name(tensor), llama_format_tensor_shape(tensor).c_str(), ggml_type_name(tensor->type)); - // This used to be a regex, but has an extreme cost to compile times. - bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'? + const ggml_type cur_type = tensor->type; + const ggml_type new_type = tm.target_type; - // quantize only 2D and 3D tensors (experts) - quantize &= (ggml_n_dims(tensor) >= 2); + // If we've decided to quantize to the same type the tensor is already + // in then there's nothing to do. + bool quantize = cur_type != new_type; - // do not quantize norm tensors - quantize &= name.find("_norm.weight") == std::string::npos; - - quantize &= params->quantize_output_tensor || name != "output.weight"; - quantize &= !params->only_copy; - - // do not quantize expert gating tensors - // NOTE: can't use LLM_TN here because the layer number is not known - quantize &= name.find("ffn_gate_inp.weight") == std::string::npos; - - // these are very small (e.g. 4x4) - quantize &= name.find("altup") == std::string::npos; - quantize &= name.find("laurel") == std::string::npos; - - // these are not too big so keep them as it is - quantize &= name.find("per_layer_model_proj") == std::string::npos; - - // do not quantize positional embeddings and token types (BERT) - quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight"); - quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight"); - - // do not quantize Mamba /Kimi's small conv1d weights - // NOTE: can't use LLM_TN here because the layer number is not known - quantize &= name.find("ssm_conv1d") == std::string::npos; - quantize &= name.find("shortconv.conv.weight") == std::string::npos; - - // do not quantize RWKV's small yet 2D weights - quantize &= name.find("time_mix_first.weight") == std::string::npos; - quantize &= name.find("time_mix_w0.weight") == std::string::npos; - quantize &= name.find("time_mix_w1.weight") == std::string::npos; - quantize &= name.find("time_mix_w2.weight") == std::string::npos; - quantize &= name.find("time_mix_v0.weight") == std::string::npos; - quantize &= name.find("time_mix_v1.weight") == std::string::npos; - quantize &= name.find("time_mix_v2.weight") == std::string::npos; - quantize &= name.find("time_mix_a0.weight") == std::string::npos; - quantize &= name.find("time_mix_a1.weight") == std::string::npos; - quantize &= name.find("time_mix_a2.weight") == std::string::npos; - quantize &= name.find("time_mix_g1.weight") == std::string::npos; - quantize &= name.find("time_mix_g2.weight") == std::string::npos; - quantize &= name.find("time_mix_decay_w1.weight") == std::string::npos; - quantize &= name.find("time_mix_decay_w2.weight") == std::string::npos; - quantize &= name.find("time_mix_lerp_fused.weight") == std::string::npos; - - // do not quantize relative position bias (T5) - quantize &= name.find("attn_rel_b.weight") == std::string::npos; - - // do not quantize specific multimodal tensors - quantize &= name.find(".position_embd.") == std::string::npos; - - ggml_type new_type; void * new_data; size_t new_size; - if (quantize) { - new_type = default_type; - - // get more optimal quantization type based on the tensor shape, layer, etc. - if (!params->pure && ggml_is_quantized(default_type)) { - // if the user provided tensor types - use those - bool manual = false; - if (params->tensor_types) { - const std::vector & tensor_types = *static_cast *>(params->tensor_types); - const std::string tensor_name(tensor->name); - for (const auto & [tname, qtype] : tensor_types) { - if (std::regex pattern(tname); std::regex_search(tensor_name, pattern)) { - if (qtype != new_type) { - LLAMA_LOG_WARN("(manual override: %s -> %s) ", ggml_type_name(new_type), ggml_type_name(qtype)); - new_type = qtype; // if two or more types are specified for the same tensor, the last match wins - manual = true; - break; - } - } - } - } - - // if not manual - use the standard logic for choosing the quantization type based on the selected mixture - if (!manual) { - new_type = llama_tensor_get_type(qs, new_type, tensor, ftype); - } - - // incompatible tensor shapes are handled here - fallback to a compatible type - { - bool convert_incompatible_tensor = false; - - const int64_t nx = tensor->ne[0]; - const int64_t ny = tensor->ne[1]; - const int64_t qk_k = ggml_blck_size(new_type); - - if (nx % qk_k != 0) { - LLAMA_LOG_WARN("\n\n%s : tensor cols %" PRId64 " x %" PRId64 " are not divisible by %" PRId64 ", required for %s", __func__, nx, ny, qk_k, ggml_type_name(new_type)); - convert_incompatible_tensor = true; - } else { - ++qs.n_k_quantized; - } - - if (convert_incompatible_tensor) { - switch (new_type) { - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: new_type = GGML_TYPE_Q4_0; break; // TODO: use a symmetric type instead - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break; - case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; - case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break; - case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break; - default: throw std::runtime_error("\nUnsupported tensor size encountered\n"); - } - if (tensor->ne[0] % ggml_blck_size(new_type) != 0) { - new_type = GGML_TYPE_F16; - } - LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type)); - ++qs.n_fallback; - } - } - } - if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) { - new_type = params->token_embedding_type; - } - if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) { - new_type = params->output_tensor_type; - } - - // If we've decided to quantize to the same type the tensor is already - // in then there's nothing to do. - quantize = tensor->type != new_type; - } - - // we have now decided on the target type for this tensor if (params->dry_run) { - // the --dry-run option calculates the final quantization size without quantizting + // the --dry-run option calculates the final quantization size without quantizing if (quantize) { new_size = ggml_nrows(tensor) * ggml_row_size(new_type, tensor->ne[0]); LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB (%s)\n", tensor_size/1024.0/1024.0, new_size/1024.0/1024.0, ggml_type_name(new_type)); - if (!will_require_imatrix && tensor_type_requires_imatrix(tensor, new_type, params->ftype)) { + if (!will_require_imatrix && tm.requires_imatrix) { will_require_imatrix = true; } } else { @@ -945,7 +1154,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } else { // no --dry-run, perform quantization if (!quantize) { - new_type = tensor->type; new_data = tensor->data; new_size = tensor_size; LLAMA_LOG_INFO("size = %8.3f MiB\n", tensor_size/1024.0/1024.0); @@ -954,7 +1162,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: const float * imatrix = nullptr; if (imatrix_data) { - auto it = imatrix_data->find(remap_imatrix(tensor->name, mapped)); + auto it = imatrix_data->find(tm.remapped_imatrix_name); if (it == imatrix_data->end()) { LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name); } else { @@ -968,14 +1176,14 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: // this is a significant error and it may be good idea to abort the process if this happens, // since many people will miss the error and not realize that most of the model is being quantized without an imatrix // tok_embd should be ignored in this case, since it always causes this warning - if (name != tn(LLM_TENSOR_TOKEN_EMBD, "weight")) { + if (!tensor_name_match_token_embd(tensor->name)) { throw std::runtime_error(format("imatrix size %d is different from tensor size %d for %s", int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name)); } } } } - if (!imatrix && tensor_type_requires_imatrix(tensor, new_type, params->ftype)) { + if (!imatrix && tm.requires_imatrix) { LLAMA_LOG_ERROR("\n\n============================================================\n"); LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name); LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n"); @@ -1020,29 +1228,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr; new_size += llama_tensor_quantize_impl(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use); - - // TODO: temporary sanity check that the F16 -> MXFP4 is lossless -#if 0 - if (new_type == GGML_TYPE_MXFP4) { - auto * x = f32_data_03; - - //LLAMA_LOG_INFO("nrows = %d, n_per_row = %d\n", nrows, n_per_row); - std::vector deq(nrows*n_per_row); - const ggml_type_traits * qtype = ggml_get_type_traits(new_type); - qtype->to_float(new_data_03, deq.data(), deq.size()); - - double err = 0.0f; - for (int i = 0; i < (int) deq.size(); ++i) { - err += fabsf(deq[i] - x[i]); - //if (fabsf(deq[i] - x[i]) > 0.00001 && i < 256) { - if (deq[i] != x[i]) { - LLAMA_LOG_INFO("deq[%d] = %f, x[%d] = %f\n", i, deq[i], i, x[i]); - } - } - //LLAMA_LOG_INFO("err = %f\n", err); - GGML_ASSERT(err == 0.00000); - } -#endif } LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", tensor_size/1024.0/1024.0, new_size/1024.0/1024.0); } @@ -1058,7 +1243,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: fout.write((const char *) new_data, new_size); zeros(fout, GGML_PAD(new_size, align) - new_size); } // no --dry-run - } // iterate over tensors + } // main loop if (!params->dry_run) { close_ofstream(); @@ -1075,7 +1260,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: if (qs.n_fallback > 0) { LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n", - __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback); + __func__, qs.n_fallback, ml.n_tensors); } } diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 9c118eab7e..68ba292d42 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1719,7 +1719,7 @@ private: }; void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { - struct gguf_context * ctx = ml.meta.get(); + struct gguf_context * ctx = ml.metadata; // determine vocab type { @@ -1833,7 +1833,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx); precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap); #if defined(__BYTE_ORDER__) && defined(__ORDER_BIG_ENDIAN__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - // correct endiannes of data in precompiled_charsmap binary blob + // correct endianness of data in precompiled_charsmap binary blob uint32_t * xcda_blob_size = (uint32_t *) &precompiled_charsmap[0]; *xcda_blob_size = __builtin_bswap32(*xcda_blob_size); assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap); @@ -1890,7 +1890,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "falcon-h1" || tokenizer_pre == "pixtral" || tokenizer_pre == "midm-2.0" || - tokenizer_pre == "lfm2") { + tokenizer_pre == "lfm2" || + tokenizer_pre == "jina-v5-nano") { pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3; ignore_merges = true; add_bos = true; diff --git a/src/llama.cpp b/src/llama.cpp index 6da90d6f1f..872e659edc 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1,5 +1,6 @@ #include "llama.h" +#include "ggml-cpp.h" #include "llama-impl.h" #include "llama-chat.h" @@ -12,6 +13,7 @@ #include "ggml.h" #include "ggml-backend.h" +#include "gguf.h" #include #include @@ -825,7 +827,8 @@ int64_t llama_time_us(void) { } // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback -static int llama_model_load(const std::string & fname, std::vector & splits, llama_model & model, llama_model_params & params) { +static int llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud, + const std::string & fname, std::vector & splits, llama_model & model, llama_model_params & params) { // loading time will be recalculated after the first eval, so // we take page faults deferred by mmap() into consideration model.t_load_us = 0; @@ -834,7 +837,8 @@ static int llama_model_load(const std::string & fname, std::vector model.t_start_us = tm.t_start_us; try { - llama_model_loader ml(fname, splits, params.use_mmap, params.use_direct_io, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides); + llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, params.use_mmap, params.use_direct_io, + params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides); ml.print_info(); @@ -880,9 +884,13 @@ static int llama_model_load(const std::string & fname, std::vector } static struct llama_model * llama_model_load_from_file_impl( + struct gguf_context * metadata, + llama_model_set_tensor_data_t set_tensor_data, + void * set_tensor_data_ud, const std::string & path_model, std::vector & splits, struct llama_model_params params) { + GGML_ASSERT((metadata == nullptr) != path_model.empty() && "exactly one out of metadata and path_model needs to be defined"); ggml_time_init(); if (!params.vocab_only && ggml_backend_reg_count() == 0) { @@ -1003,7 +1011,7 @@ static struct llama_model * llama_model_load_from_file_impl( props.memory_free/1024/1024); } - const int status = llama_model_load(path_model, splits, *model, params); + const int status = llama_model_load(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, *model, params); GGML_ASSERT(status <= 0); if (status < 0) { if (status == -1) { @@ -1019,6 +1027,18 @@ static struct llama_model * llama_model_load_from_file_impl( return model; } +struct llama_model * llama_model_init_from_user( + struct gguf_context * metadata, + llama_model_set_tensor_data_t set_tensor_data, + void * set_tensor_data_ud, + struct llama_model_params params) { + GGML_ASSERT(metadata != nullptr); + std::string path_model; + std::vector splits = {}; + params.use_mmap = false; + params.use_extra_bufts = false; + return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, params); +} // deprecated struct llama_model * llama_load_model_from_file( const char * path_model, @@ -1030,7 +1050,7 @@ struct llama_model * llama_model_load_from_file( const char * path_model, struct llama_model_params params) { std::vector splits = {}; - return llama_model_load_from_file_impl(path_model, splits, params); + return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, path_model, splits, params); } struct llama_model * llama_model_load_from_splits( @@ -1046,11 +1066,11 @@ struct llama_model * llama_model_load_from_splits( for (size_t i = 0; i < n_paths; ++i) { splits.push_back(paths[i]); } - return llama_model_load_from_file_impl(splits.front(), splits, params); + return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, splits.front(), splits, params); } void llama_model_save_to_file(const struct llama_model * model, const char * path_model) { - llama_model_saver ms(*model); + llama_model_saver ms(model); ms.add_kv_from_model(); ms.add_tensors_from_model(); ms.save(path_model); diff --git a/src/models/afmoe.cpp b/src/models/afmoe.cpp index 6a752a403f..9aabe25c96 100644 --- a/src/models/afmoe.cpp +++ b/src/models/afmoe.cpp @@ -1,8 +1,8 @@ #include "models.h" llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; @@ -127,7 +127,6 @@ llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_para n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, // norm_w (route_norm=True) - hparams.expert_weights_scale, // scale_w hparams.expert_weights_scale, // w_scale (route_scale=2.826) (llama_expert_gating_func_type) hparams.expert_gating_func, il); diff --git a/src/models/apertus.cpp b/src/models/apertus.cpp index 9af19c1bfe..4d65614e46 100644 --- a/src/models/apertus.cpp +++ b/src/models/apertus.cpp @@ -3,10 +3,10 @@ llm_build_apertus::llm_build_apertus(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/arcee.cpp b/src/models/arcee.cpp index aa6167dba1..20b9ffd49e 100644 --- a/src/models/arcee.cpp +++ b/src/models/arcee.cpp @@ -2,10 +2,10 @@ llm_build_arcee::llm_build_arcee(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/arctic.cpp b/src/models/arctic.cpp index e8f028a723..b712e08cbd 100644 --- a/src/models/arctic.cpp +++ b/src/models/arctic.cpp @@ -1,11 +1,10 @@ #include "models.h" - llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -104,7 +103,7 @@ llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_pa nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/baichuan.cpp b/src/models/baichuan.cpp index c04b0c98b0..abd03cd0b9 100644 --- a/src/models/baichuan.cpp +++ b/src/models/baichuan.cpp @@ -2,10 +2,10 @@ llm_build_baichuan::llm_build_baichuan(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -56,6 +56,7 @@ llm_build_baichuan::llm_build_baichuan(const llama_model & model, const llm_grap ); break; case LLM_TYPE_13B: + case LLM_TYPE_UNKNOWN: break; default: GGML_ABORT("fatal error"); diff --git a/src/models/bailingmoe.cpp b/src/models/bailingmoe.cpp index ed56b9c471..25e3369c31 100644 --- a/src/models/bailingmoe.cpp +++ b/src/models/bailingmoe.cpp @@ -1,6 +1,5 @@ #include "models.h" - llm_build_bailingmoe::llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { ggml_tensor * cur; ggml_tensor * inpL; @@ -97,7 +96,7 @@ llm_build_bailingmoe::llm_build_bailingmoe(const llama_model & model, const llm_ nullptr, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - false, hparams.expert_weights_scale, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/bailingmoe2.cpp b/src/models/bailingmoe2.cpp index fbf7b210c4..4209862466 100644 --- a/src/models/bailingmoe2.cpp +++ b/src/models/bailingmoe2.cpp @@ -1,13 +1,11 @@ #include "models.h" - - llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; @@ -90,7 +88,7 @@ llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const ll model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - true, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/bert.cpp b/src/models/bert.cpp index bca0e254fc..8733179141 100644 --- a/src/models/bert.cpp +++ b/src/models/bert.cpp @@ -1,12 +1,10 @@ #include "models.h" - - llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; @@ -129,9 +127,17 @@ llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params // feed-forward network if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) { // MoE branch - cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, nullptr, - model.layers[il].ffn_down_exps, nullptr, hparams.n_expert, hparams.n_expert_used, - LLM_FFN_GELU, false, false, 0.0f, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); + cur = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + nullptr, + model.layers[il].ffn_down_exps, + nullptr, + hparams.n_expert, hparams.n_expert_used, + LLM_FFN_GELU, false, + hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, + il); cb(cur, "ffn_moe_out", il); } else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) { diff --git a/src/models/bitnet.cpp b/src/models/bitnet.cpp index 331a3f1119..ccf5bc8e82 100644 --- a/src/models/bitnet.cpp +++ b/src/models/bitnet.cpp @@ -2,9 +2,9 @@ llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; @@ -29,10 +29,7 @@ llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_pa // self-attention { // compute Q and K and RoPE them - ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); - if (model.layers[il].wq_scale) { - Qcur = ggml_mul(ctx0, Qcur, model.layers[il].wq_scale); - } + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); cb(Qcur, "Qcur", il); if (model.layers[il].bq) { Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); @@ -40,10 +37,7 @@ llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_pa } // B1.K - ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); - if (model.layers[il].wk_scale) { - Kcur = ggml_mul(ctx0, Kcur, model.layers[il].wk_scale); - } + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s); cb(Kcur, "Kcur", il); if (model.layers[il].bk) { Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); @@ -51,10 +45,7 @@ llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_pa } // B1.V - ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); - if (model.layers[il].wv_scale) { - Vcur = ggml_mul(ctx0, Vcur, model.layers[il].wv_scale); - } + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s); cb(Vcur, "Vcur", il); if (model.layers[il].bv) { Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); @@ -90,10 +81,7 @@ llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_pa LLM_NORM_RMS, il); cb(cur, "attn_sub_norm", il); - cur = build_lora_mm(model.layers[il].wo, cur); - if (model.layers[il].wo_scale) { - cur = ggml_mul(ctx0, cur, model.layers[il].wo_scale); - } + cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s); if (model.layers[il].bo) { cur = ggml_add(ctx0, cur, model.layers[il].bo); } @@ -115,8 +103,8 @@ llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_pa cb(cur, "ffn_norm", il); cur = build_ffn(cur, - model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_scale, - model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_scale, + model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s, NULL, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); @@ -127,10 +115,7 @@ llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_pa LLM_NORM_RMS, il); cb(cur, "ffn_sub_norm", il); - cur = build_lora_mm(model.layers[il].ffn_down, cur); - if (model.layers[il].ffn_down_scale) { - cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_scale); - } + cur = build_lora_mm(model.layers[il].ffn_down, cur, model.layers[il].ffn_down_s); cb(cur, "ffn_down", il); cur = ggml_add(ctx0, cur, ffn_inp); diff --git a/src/models/bloom.cpp b/src/models/bloom.cpp index 2c552d1d15..b1c19bb58a 100644 --- a/src/models/bloom.cpp +++ b/src/models/bloom.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_bloom::llm_build_bloom(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/chameleon.cpp b/src/models/chameleon.cpp index 184511aed4..2f24105fa1 100644 --- a/src/models/chameleon.cpp +++ b/src/models/chameleon.cpp @@ -3,10 +3,10 @@ #include llm_build_chameleon::llm_build_chameleon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/chatglm.cpp b/src/models/chatglm.cpp index 2685d4fbcb..5887ed22e7 100644 --- a/src/models/chatglm.cpp +++ b/src/models/chatglm.cpp @@ -2,10 +2,10 @@ llm_build_chatglm::llm_build_chatglm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/codeshell.cpp b/src/models/codeshell.cpp index 0b3bdbff52..e8e13e143f 100644 --- a/src/models/codeshell.cpp +++ b/src/models/codeshell.cpp @@ -1,11 +1,11 @@ #include "models.h" llm_build_codeshell::llm_build_codeshell(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/cogvlm.cpp b/src/models/cogvlm.cpp index 0ceae3aaeb..2ef2b6e389 100644 --- a/src/models/cogvlm.cpp +++ b/src/models/cogvlm.cpp @@ -2,11 +2,11 @@ llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const float kq_scale = 1.0f / sqrtf(float(n_embd_head)); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * inpL; ggml_tensor * cur; diff --git a/src/models/cohere2-iswa.cpp b/src/models/cohere2-iswa.cpp index 9334b5e426..7c71a59ae7 100644 --- a/src/models/cohere2-iswa.cpp +++ b/src/models/cohere2-iswa.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_cohere2_iswa::llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); const float f_logit_scale = hparams.f_logit_scale; diff --git a/src/models/command-r.cpp b/src/models/command-r.cpp index 4d3b643b44..ba1230f041 100644 --- a/src/models/command-r.cpp +++ b/src/models/command-r.cpp @@ -4,9 +4,9 @@ llm_build_command_r::llm_build_command_r(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); const float f_logit_scale = hparams.f_logit_scale; diff --git a/src/models/dbrx.cpp b/src/models/dbrx.cpp index 6d2a0ebf1b..73eb5cd24e 100644 --- a/src/models/dbrx.cpp +++ b/src/models/dbrx.cpp @@ -1,12 +1,11 @@ #include "models.h" - llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -89,7 +88,7 @@ llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/deci.cpp b/src/models/deci.cpp index 7410a3a46d..ac448bfcaa 100644 --- a/src/models/deci.cpp +++ b/src/models/deci.cpp @@ -3,10 +3,10 @@ llm_build_deci::llm_build_deci(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/deepseek.cpp b/src/models/deepseek.cpp index 17866c0d88..3432359e03 100644 --- a/src/models/deepseek.cpp +++ b/src/models/deepseek.cpp @@ -1,13 +1,11 @@ #include "models.h" - - llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -100,7 +98,7 @@ llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, hparams.expert_weights_scale, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp index b2c1f16060..d437fe29e7 100644 --- a/src/models/deepseek2.cpp +++ b/src/models/deepseek2.cpp @@ -8,7 +8,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr const int64_t n_embd_head_k = hparams.n_embd_head_k_mla(); const int64_t n_embd_head_v = hparams.n_embd_head_v_mla(); - const int64_t n_embd_head_qk_rope = hparams.n_rot; + const int64_t n_embd_head_qk_rope = hparams.n_rot(); const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope; const uint32_t kv_lora_rank = hparams.n_lora_kv; @@ -146,7 +146,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr cb(Qcur, "Qcur_attn_temp_scaled", il); } - // note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group) + // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group) cur = build_attn(inp_attn_k, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il); @@ -216,9 +216,11 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - hparams.expert_weights_scale, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, - il); + il, + nullptr, + model.layers[il].ffn_gate_up_exps); cb(moe_out, "ffn_moe_out", il); // FFN shared expert diff --git a/src/models/delta-net-base.cpp b/src/models/delta-net-base.cpp index 99f1fdd953..6bc989c950 100644 --- a/src/models/delta-net-base.cpp +++ b/src/models/delta-net-base.cpp @@ -1,6 +1,6 @@ #include "models.h" -#define CHUNK_SIZE 64 +#include "llama-impl.h" // utility to get one slice from the third dimension // input dim: [x, y, c, b] @@ -57,7 +57,7 @@ std::pair llm_build_delta_net_base::build_delta_ne g = ggml_permute(ctx0, g, 0, 2, 1, 3); // [g_0, n_tokens, H_v, n_seqs] b = ggml_permute(ctx0, b, 0, 2, 1, 3); // [ 1, n_tokens, H_v, n_seqs] - const int CS = CHUNK_SIZE; + const int CS = kda ? 16 : 64; // chunk size const int pad = (CS - n_tokens % CS) % CS; const int n_chunks = (n_tokens + pad) / CS; @@ -225,9 +225,8 @@ std::pair llm_build_delta_net_base::build_delta_ne ggml_tensor * kg_t = ggml_cont(ctx0, ggml_transpose(ctx0, kg)); cb(kg_t, "key_gdiff_t", il); - ggml_tensor * s_t = ggml_transpose(ctx0, s); - s_t = ggml_cont_4d(ctx0, s_t, S_v, S_v, 1, H_v * n_seqs); - cb(s_t, "dnet_add_ch_state", il); + s = ggml_reshape_4d(ctx0, s, S_v, S_v, 1, H_v * n_seqs); + cb(s, "dnet_add_ch_state", il); // [CS, S_v, n_chunks, H_v * n_seqs] ggml_tensor * v_t = ggml_cont(ctx0, ggml_transpose(ctx0, v)); @@ -240,7 +239,7 @@ std::pair llm_build_delta_net_base::build_delta_ne ggml_tensor * ch_kg_t = get_slice_2d(ctx0, kg_t, chunk); // [ CS, S_k, 1, H_v * n_seqs] // [CS, S_v, 1, H_v * n_seqs] - ggml_tensor * v_t_p = ggml_mul_mat(ctx0, ch_k_cd, s_t); + ggml_tensor * v_t_p = ggml_mul_mat(ctx0, ch_k_cd, s); cb(v_t_p, "v_prime", il); // [CS, S_v, 1, H_v * n_seqs] @@ -252,7 +251,7 @@ std::pair llm_build_delta_net_base::build_delta_ne cb(v_attn, "v_attn", il); // [S_v, CS, 1, H_v * n_seqs] - ggml_tensor * attn_inter = ggml_mul_mat(ctx0, s_t, ch_q_g_exp); + ggml_tensor * attn_inter = ggml_mul_mat(ctx0, s, ch_q_g_exp); cb(attn_inter, "attn_inter", il); // [S_v, CS, 1, H_v * n_seqs] @@ -268,13 +267,11 @@ std::pair llm_build_delta_net_base::build_delta_ne // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew ggml_tensor * ch_g_last_exp_t = get_slice_2d(ctx0, g_last_exp_t, chunk); - s_t = ggml_mul(ctx0, s_t, ch_g_last_exp_t); - s_t = ggml_add(ctx0, s_t, kgv); - cb(s_t, "dnet_add_ch_state", il); + s = ggml_mul(ctx0, s, ch_g_last_exp_t); + s = ggml_add(ctx0, s, kgv); + cb(s, "dnet_add_ch_state", il); } - s_t = ggml_reshape_4d(ctx0, s_t, S_v, S_v, H_v, n_seqs); - // truncate padded tokens ggml_tensor * o = ggml_view_4d(ctx0, v, S_v, n_tokens, H_v, n_seqs, @@ -282,7 +279,7 @@ std::pair llm_build_delta_net_base::build_delta_ne ggml_row_size(v->type, S_v * CS * n_chunks), ggml_row_size(v->type, S_v * CS * n_chunks * H_v), 0); o = ggml_permute (ctx0, o, 0, 2, 1, 3); // [S_v, H_v, n_tokens, n_seqs] - s = ggml_transpose(ctx0, s_t); + s = ggml_reshape_4d(ctx0, s, S_v, S_v, H_v, n_seqs); cb(s, "output_state", il); return {o, s}; @@ -341,11 +338,9 @@ std::pair llm_build_delta_net_base::build_delta_ne g = ggml_exp(ctx0, g); s = ggml_mul(ctx0, s, g); - ggml_tensor * s_t = ggml_cont(ctx0, ggml_transpose(ctx0, s)); - // [1, S_v, H_v, n_seqs] ggml_tensor * sk; - sk = ggml_mul (ctx0, s_t, k); + sk = ggml_mul (ctx0, s, k); sk = ggml_sum_rows(ctx0, sk); // [S_v, 1, H_v, n_seqs] @@ -362,15 +357,89 @@ std::pair llm_build_delta_net_base::build_delta_ne k = ggml_repeat(ctx0, k, s); kd = ggml_mul (ctx0, k, d_t); - s_t = ggml_add(ctx0, s_t, kd); + s = ggml_add(ctx0, s, kd); - cb(s_t, "dnet_add_ar_state", il); + cb(s, "dnet_add_ar_state", il); - ggml_tensor * s_q = ggml_mul (ctx0, s_t, q); + ggml_tensor * s_q = ggml_mul (ctx0, s, q); ggml_tensor * o = ggml_sum_rows(ctx0, s_q); o = ggml_permute (ctx0, o, 2, 0, 1, 3); // [S_v, H_v, n_tokens, n_seqs] - s = ggml_transpose(ctx0, s_t); // [S_v, S_v, H_v, n_seqs] return {o, s}; } + +std::pair llm_build_delta_net_base::build_delta_net_fused( + ggml_tensor * q, + ggml_tensor * k, + ggml_tensor * v, + ggml_tensor * g, + ggml_tensor * b, + ggml_tensor * s, + int il) { + const int64_t S_k = q->ne[0]; + const int64_t H_k = q->ne[1]; + const int64_t n_tokens = q->ne[2]; + const int64_t n_seqs = q->ne[3]; + + const int64_t S_v = v->ne[0]; + const int64_t H_v = v->ne[1]; + + GGML_ASSERT(S_k == S_v); + GGML_ASSERT(H_v % H_k == 0); + + GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs); + GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs); + GGML_ASSERT(v->ne[0] == S_v && v->ne[1] == H_v && v->ne[2] == n_tokens && v->ne[3] == n_seqs); + + GGML_ASSERT(g->ne[0] == 1 || g->ne[0] == S_v); + GGML_ASSERT( g->ne[1] == H_v && g->ne[2] == n_tokens && g->ne[3] == n_seqs); + GGML_ASSERT(b->ne[0] == 1 && b->ne[1] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs); + GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v && s->ne[3] == n_seqs); + + ggml_tensor * result = ggml_gated_delta_net(ctx0, q, k, v, g, b, s); + if (n_tokens == 1) { + cb(result, LLAMA_TENSOR_NAME_FGDN_AR, il); + } else { + cb(result, LLAMA_TENSOR_NAME_FGDN_CH, il); + } + + ggml_tensor * output = ggml_view_4d(ctx0, result, + S_v, H_v, n_tokens, n_seqs, + ggml_row_size(result->type, S_v), + ggml_row_size(result->type, S_v * H_v), + ggml_row_size(result->type, S_v * H_v * n_tokens), 0); + + ggml_tensor * new_state = ggml_view_4d(ctx0, result, + S_v, S_v, H_v, n_seqs, + ggml_row_size(result->type, S_v), + ggml_row_size(result->type, S_v * S_v), + ggml_row_size(result->type, S_v * S_v * H_v), + ggml_row_size(result->type, S_v * H_v * n_tokens * n_seqs)); + + return {output, new_state}; +} + +std::pair llm_build_delta_net_base::build_delta_net( + ggml_tensor * q, + ggml_tensor * k, + ggml_tensor * v, + ggml_tensor * g, + ggml_tensor * b, + ggml_tensor * s, + int il) { + const int64_t n_seq_tokens = q->ne[2]; + + if (n_seq_tokens == 1) { + if (cparams.fused_gdn_ar) { + return build_delta_net_fused(q, k, v, g, b, s, il); + } + return build_delta_net_autoregressive(q, k, v, g, b, s, il); + } + + if (cparams.fused_gdn_ch) { + return build_delta_net_fused(q, k, v, g, b, s, il); + } + + return build_delta_net_chunking(q, k, v, g, b, s, il); +} diff --git a/src/models/dots1.cpp b/src/models/dots1.cpp index 09c36f82fe..07236dd27c 100644 --- a/src/models/dots1.cpp +++ b/src/models/dots1.cpp @@ -1,13 +1,11 @@ #include "models.h" - - llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -91,7 +89,7 @@ llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_para model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - true, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/dream.cpp b/src/models/dream.cpp index 2aafbae139..4edc8530cb 100644 --- a/src/models/dream.cpp +++ b/src/models/dream.cpp @@ -5,10 +5,10 @@ llm_build_dream::llm_build_dream(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { //copied from qwen2 - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/eagle3.cpp b/src/models/eagle3.cpp index c3ef2ed4bf..0cea7199b9 100644 --- a/src/models/eagle3.cpp +++ b/src/models/eagle3.cpp @@ -41,9 +41,9 @@ llm_build_eagle3_encode::llm_build_eagle3_encode(const llama_model & model, cons // Input: draft tokens + g_embeddings from encoder // Output: draft logits llm_build_eagle3_decode::llm_build_eagle3_decode(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); GGML_ASSERT(n_layer == 1); // EAGLE-3 has only one decoder layer ggml_tensor * cur; diff --git a/src/models/ernie4-5-moe.cpp b/src/models/ernie4-5-moe.cpp index 0d96d14e6f..63baf152c4 100644 --- a/src/models/ernie4-5-moe.cpp +++ b/src/models/ernie4-5-moe.cpp @@ -1,13 +1,11 @@ #include "models.h" - - llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -103,7 +101,7 @@ llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/ernie4-5.cpp b/src/models/ernie4-5.cpp index 99aead5328..d548de0547 100644 --- a/src/models/ernie4-5.cpp +++ b/src/models/ernie4-5.cpp @@ -2,10 +2,10 @@ llm_build_ernie4_5::llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/eurobert.cpp b/src/models/eurobert.cpp new file mode 100644 index 0000000000..e8628d165d --- /dev/null +++ b/src/models/eurobert.cpp @@ -0,0 +1,97 @@ +#include "models.h" + +llm_build_eurobert::llm_build_eurobert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v(); + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + + ggml_tensor * cur; + ggml_tensor * inpL; + ggml_tensor * inp_pos = build_inp_pos(); + + inpL = build_inp_embd(model.tok_embd); + cb(inpL, "inp_embd", -1); + + auto * inp_attn = build_attn_inp_no_cache(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * cur = inpL; + + cur = build_norm(inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, il); + + { + ggml_tensor * Qcur; + ggml_tensor * Kcur; + ggml_tensor * Vcur; + + Qcur = build_lora_mm(model.layers[il].wq, cur); + Kcur = build_lora_mm(model.layers[il].wk, cur); + Vcur = build_lora_mm(model.layers[il].wv, cur); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, nullptr, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + cb(cur, "kqv_out", il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + + cur = ggml_add(ctx0, cur, inpL); + + ggml_tensor * ffn_inp = cur; + cb(ffn_inp, "ffn_inp", il); + + cur = build_norm(ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + + inpL = cur; + } + cur = inpL; + + cur = build_norm(cur, + model.output_norm, NULL, + LLM_NORM_RMS, -1); + + cb(cur, "result_embd", -1); + res->t_embd = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/exaone-moe.cpp b/src/models/exaone-moe.cpp index bef5b2ad35..ea75701c52 100644 --- a/src/models/exaone-moe.cpp +++ b/src/models/exaone-moe.cpp @@ -1,12 +1,11 @@ #include "models.h" - llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_v); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_v()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -100,7 +99,7 @@ llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_ model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - true, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/exaone.cpp b/src/models/exaone.cpp index 62602b284d..d4eea58e2f 100644 --- a/src/models/exaone.cpp +++ b/src/models/exaone.cpp @@ -4,10 +4,10 @@ llm_build_exaone::llm_build_exaone(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/exaone4.cpp b/src/models/exaone4.cpp index 8b7e3dc06e..755af3b747 100644 --- a/src/models/exaone4.cpp +++ b/src/models/exaone4.cpp @@ -4,10 +4,10 @@ template llm_build_exaone4::llm_build_exaone4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_v); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_v()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/falcon-h1.cpp b/src/models/falcon-h1.cpp index 785a7e5e66..ff842d93a4 100644 --- a/src/models/falcon-h1.cpp +++ b/src/models/falcon-h1.cpp @@ -2,7 +2,7 @@ llm_build_falcon_h1::llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/falcon.cpp b/src/models/falcon.cpp index db1ccdb500..9fcba50887 100644 --- a/src/models/falcon.cpp +++ b/src/models/falcon.cpp @@ -2,11 +2,11 @@ llm_build_falcon::llm_build_falcon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma-embedding.cpp b/src/models/gemma-embedding.cpp index 944c198bf9..98110d45e3 100644 --- a/src/models/gemma-embedding.cpp +++ b/src/models/gemma-embedding.cpp @@ -2,7 +2,7 @@ llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma.cpp b/src/models/gemma.cpp index 4893d9af4b..1869efd389 100644 --- a/src/models/gemma.cpp +++ b/src/models/gemma.cpp @@ -2,7 +2,7 @@ llm_build_gemma::llm_build_gemma(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma2-iswa.cpp b/src/models/gemma2-iswa.cpp index 7a9198193a..3927ddd297 100644 --- a/src/models/gemma2-iswa.cpp +++ b/src/models/gemma2-iswa.cpp @@ -1,7 +1,7 @@ #include "models.h" llm_build_gemma2_iswa::llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma3.cpp b/src/models/gemma3.cpp index dec3fc4b8b..bbb4d9a81e 100644 --- a/src/models/gemma3.cpp +++ b/src/models/gemma3.cpp @@ -2,7 +2,7 @@ template llm_build_gemma3::llm_build_gemma3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma3n-iswa.cpp b/src/models/gemma3n-iswa.cpp index 7db6d3bf4e..8ce2ae39c2 100644 --- a/src/models/gemma3n-iswa.cpp +++ b/src/models/gemma3n-iswa.cpp @@ -3,7 +3,7 @@ llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params), model(model), - n_embd_head(model.hparams.n_embd_head_k), + n_embd_head(model.hparams.n_embd_head_k()), n_embd_altup(model.hparams.n_embd_altup), n_altup(model.hparams.n_altup), i_altup_act(model.hparams.i_altup_act) { diff --git a/src/models/glm4-moe.cpp b/src/models/glm4-moe.cpp index 003f70f739..7938545ed8 100644 --- a/src/models/glm4-moe.cpp +++ b/src/models/glm4-moe.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); @@ -128,7 +128,7 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - true, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(routed_out, "ffn_moe_out", il); diff --git a/src/models/glm4.cpp b/src/models/glm4.cpp index bcd837b30d..b6ad8febed 100644 --- a/src/models/glm4.cpp +++ b/src/models/glm4.cpp @@ -3,10 +3,10 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); diff --git a/src/models/gpt2.cpp b/src/models/gpt2.cpp index 60761c8e76..cb1238f2d3 100644 --- a/src/models/gpt2.cpp +++ b/src/models/gpt2.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_gpt2::llm_build_gpt2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * pos; diff --git a/src/models/gptneox.cpp b/src/models/gptneox.cpp index 2151b14e93..1c8fe6c836 100644 --- a/src/models/gptneox.cpp +++ b/src/models/gptneox.cpp @@ -2,10 +2,10 @@ llm_build_gptneox::llm_build_gptneox(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/granite-hybrid.cpp b/src/models/granite-hybrid.cpp index 726ecdcca7..9b54a38c38 100644 --- a/src/models/granite-hybrid.cpp +++ b/src/models/granite-hybrid.cpp @@ -1,10 +1,9 @@ #include "models.h" - llm_build_granite_hybrid::llm_build_granite_hybrid(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; @@ -160,7 +159,7 @@ ggml_tensor * llm_build_granite_hybrid::build_layer_ffn(ggml_tensor * cur, nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/granite.cpp b/src/models/granite.cpp index 18748e9c26..7a7e1664c2 100644 --- a/src/models/granite.cpp +++ b/src/models/granite.cpp @@ -1,15 +1,14 @@ #include "models.h" - llm_build_granite::llm_build_granite( const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -175,7 +174,7 @@ ggml_tensor * llm_build_granite::build_layer_ffn( nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/grok.cpp b/src/models/grok.cpp index 3c54dfee63..580d63e36a 100644 --- a/src/models/grok.cpp +++ b/src/models/grok.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_grok::llm_build_grok(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -99,7 +99,7 @@ llm_build_grok::llm_build_grok(const llama_model & model, const llm_graph_params nullptr, n_expert, n_expert_used, LLM_FFN_GELU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/grovemoe.cpp b/src/models/grovemoe.cpp index 56b6db9a3d..aa60d3e938 100644 --- a/src/models/grovemoe.cpp +++ b/src/models/grovemoe.cpp @@ -1,14 +1,12 @@ #include "models.h" - - llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_chunk_expert = n_expert / hparams.n_group_experts; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -90,7 +88,7 @@ llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, probs); @@ -106,7 +104,7 @@ llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_grap nullptr, n_chunk_expert, n_expert_used > n_chunk_expert ? n_chunk_expert : n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, probs); diff --git a/src/models/hunyuan-dense.cpp b/src/models/hunyuan-dense.cpp index 7d5dcc7828..6a51707c85 100644 --- a/src/models/hunyuan-dense.cpp +++ b/src/models/hunyuan-dense.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_hunyuan_dense::llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/hunyuan-moe.cpp b/src/models/hunyuan-moe.cpp index 77e39de5b8..806c30b366 100644 --- a/src/models/hunyuan-moe.cpp +++ b/src/models/hunyuan-moe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_hunyuan_moe::llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -119,8 +119,7 @@ llm_build_hunyuan_moe::llm_build_hunyuan_moe(const llama_model & model, const ll n_expert, n_expert_used, LLM_FFN_SILU, true, // norm_topk_prob - false, - 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur_moe, "ffn_moe_out", il); diff --git a/src/models/internlm2.cpp b/src/models/internlm2.cpp index 387e821127..441d250268 100644 --- a/src/models/internlm2.cpp +++ b/src/models/internlm2.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_internlm2::llm_build_internlm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/jais.cpp b/src/models/jais.cpp index 3e3376e6a6..135bf288ba 100644 --- a/src/models/jais.cpp +++ b/src/models/jais.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_jais::llm_build_jais(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/jais2.cpp b/src/models/jais2.cpp index a69fcaa3bb..2cfe484eb5 100644 --- a/src/models/jais2.cpp +++ b/src/models/jais2.cpp @@ -3,10 +3,10 @@ // JAIS-2 model graph builder // Uses: LayerNorm (not RMSNorm), relu2 activation, separate Q/K/V, RoPE embeddings llm_build_jais2::llm_build_jais2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/jamba.cpp b/src/models/jamba.cpp index ceab581740..c0c89de187 100644 --- a/src/models/jamba.cpp +++ b/src/models/jamba.cpp @@ -1,7 +1,7 @@ #include "models.h" llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); ggml_tensor * cur; ggml_tensor * inpL; @@ -76,7 +76,7 @@ llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_para nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/kimi-linear.cpp b/src/models/kimi-linear.cpp index 4d6bb83c14..4d62f4e715 100644 --- a/src/models/kimi-linear.cpp +++ b/src/models/kimi-linear.cpp @@ -1,5 +1,4 @@ #include "models.h" -#include "ggml.h" #include "llama-memory-recurrent.h" @@ -103,7 +102,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll const int64_t kv_lora_rank = hparams.n_lora_kv; // qk_rope_head_dim = 64 (from Kimi config) which is hparams.n_rot // Confirmed from tensor shape: wkv_a_mqa [2304, 576] = [n_embd, kv_lora_rank + qk_rope_head_dim] - const int64_t n_embd_head_qk_rope = hparams.n_rot; // config.qk_rope_head_dim + const int64_t n_embd_head_qk_rope = hparams.n_rot(); // config.qk_rope_head_dim const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; // 192 - 64 = 128 // Attention scale for MLA const float kq_scale_mla = 1.0f / sqrtf((float)n_embd_head_k_mla); @@ -116,12 +115,9 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll cur = build_norm(inpL, layer.attn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "attn_norm", il); - // Check layer type by checking which tensors exist - // KDA layers have ssm_a_log tensor, MLA layers have wkv_a_mqa tensor - bool is_kda = (layer.ssm_a != nullptr); - bool is_mla = (layer.wkv_a_mqa != nullptr); + ggml_build_forward_expand(gf, cur); - if (is_kda) { + if (hparams.is_recurrent(il)) { // === KDA Layer (Kimi Delta Attention) with Recurrent State === // Reference: vLLM kda.py const auto * mctx_cur = inp_rs->mctx; @@ -173,9 +169,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll Kcur = ggml_l2_norm(ctx0, Kcur, eps_norm); // Choose between build_delta_net_chunking and build_delta_net_recurrent based on n_tokens - std::pair attn_out = n_seq_tokens == 1 ? - build_delta_net_autoregressive(Qcur, Kcur, Vcur, g1, beta, state, il) : - build_delta_net_chunking(Qcur, Kcur, Vcur, g1, beta, state, il); + auto attn_out = build_delta_net(Qcur, Kcur, Vcur, g1, beta, state, il); ggml_tensor * output = ggml_cont(ctx0, attn_out.first); ggml_tensor * new_state = attn_out.second; @@ -209,7 +203,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll cur = ggml_mul_mat(ctx0, layer.wo, gated); cb(cur, "kda_out", il); - } else if (is_mla) { + } else { // === MLA Layer (Multi-head Latent Attention) without KV Cache === // Reference: vLLM mla.py // Step 1: Q projection and reshape @@ -308,9 +302,6 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll cur = build_attn(inp_attn_kv, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il); cb(cur, "mla_out", il); } - } else { - // Unknown layer type - this should not happen - GGML_ABORT("Kimi layer is neither KDA nor MLA - missing required tensors"); } // On last layer, select only the output tokens @@ -347,7 +338,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll hparams.n_expert, hparams.n_expert_used, LLM_FFN_SILU, true, - true, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/lfm2.cpp b/src/models/lfm2.cpp index cf01ad6255..dfa322166b 100644 --- a/src/models/lfm2.cpp +++ b/src/models/lfm2.cpp @@ -23,17 +23,23 @@ llm_build_lfm2::llm_build_lfm2(const llama_model & model, const llm_graph_ }; auto build_moe_feed_forward = [&model, this](ggml_tensor * cur, int il) -> ggml_tensor * { return build_moe_ffn(cur, - model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, - model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, - model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, false, 0.0, - static_cast(hparams.expert_gating_func), il); + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + hparams.expert_weights_scale, + static_cast(hparams.expert_gating_func), + il); }; auto build_attn_block = [&model, this](ggml_tensor * cur, ggml_tensor * inp_pos, inp_attn_type * inp_attn, int il) -> ggml_tensor * { GGML_ASSERT(hparams.n_embd_v_gqa(il) == hparams.n_embd_k_gqa(il)); - const auto n_embd_head = hparams.n_embd_head_v; + const auto n_embd_head = hparams.n_embd_head_v(); const auto n_head_kv = hparams.n_head_kv(il); auto * q = build_lora_mm(model.layers[il].wq, cur); diff --git a/src/models/llada-moe.cpp b/src/models/llada-moe.cpp index 5f64686f5f..18de88fde1 100644 --- a/src/models/llada-moe.cpp +++ b/src/models/llada-moe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_llada_moe::llm_build_llada_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -90,7 +90,7 @@ llm_build_llada_moe::llm_build_llada_moe(const llama_model & model, const llm_gr nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/llada.cpp b/src/models/llada.cpp index 857033660a..0dac9d616a 100644 --- a/src/models/llada.cpp +++ b/src/models/llada.cpp @@ -2,10 +2,10 @@ llm_build_llada::llm_build_llada(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { // LLaDA is similar to LLaMA but uses non-causal attention for diffusion - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/llama-iswa.cpp b/src/models/llama-iswa.cpp index 61dd2c179f..67cb9a10ec 100644 --- a/src/models/llama-iswa.cpp +++ b/src/models/llama-iswa.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -134,7 +134,7 @@ llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_ nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, il); diff --git a/src/models/llama.cpp b/src/models/llama.cpp index 3bccb2f902..7625cbfa94 100644 --- a/src/models/llama.cpp +++ b/src/models/llama.cpp @@ -2,10 +2,10 @@ template llm_build_llama::llm_build_llama(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -53,19 +53,19 @@ llm_build_llama::llm_build_llama(const llama_model & model, const llm_gra ggml_tensor * rope_factors = model.get_rope_factors(cparams, il); // compute Q and K and RoPE them - ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); cb(Qcur, "Qcur", il); if (model.layers[il].bq) { Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); cb(Qcur, "Qcur", il); } - ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s); cb(Kcur, "Kcur", il); if (model.layers[il].bk) { Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); cb(Kcur, "Kcur", il); } - ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s); cb(Vcur, "Vcur", il); if (model.layers[il].bv) { Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); @@ -101,6 +101,9 @@ llm_build_llama::llm_build_llama(const llama_model & model, const llm_gra cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); + if (model.layers[il].wo_s) { + cur = ggml_mul(ctx0, cur, model.layers[il].wo_s); + } cb(cur, "attn_out", il); } if (il == n_layer - 1 && inp_out_ids) { @@ -119,9 +122,9 @@ llm_build_llama::llm_build_llama(const llama_model & model, const llm_gra cb(cur, "ffn_norm", il); cur = build_ffn(cur, - model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, - model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL, - model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); @@ -140,9 +143,13 @@ llm_build_llama::llm_build_llama(const llama_model & model, const llm_gra nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, - il); + il, + nullptr, nullptr, + model.layers[il].ffn_up_exps_s, + model.layers[il].ffn_gate_exps_s, + model.layers[il].ffn_down_exps_s); cb(cur, "ffn_moe_out", il); } cur = ggml_add(ctx0, cur, ffn_inp); diff --git a/src/models/maincoder.cpp b/src/models/maincoder.cpp index da57308167..a72b7790a1 100644 --- a/src/models/maincoder.cpp +++ b/src/models/maincoder.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_maincoder::llm_build_maincoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/mamba-base.cpp b/src/models/mamba-base.cpp index aaac9487df..c37f29c487 100644 --- a/src/models/mamba-base.cpp +++ b/src/models/mamba-base.cpp @@ -30,6 +30,7 @@ ggml_tensor * llm_build_mamba_base::build_mamba_layer(llm_graph_input_rs * inp, GGML_ASSERT(n_seqs != 0); GGML_ASSERT(ubatch.equal_seqs()); GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); + GGML_ASSERT(d_inner % n_head == 0); ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); @@ -41,7 +42,7 @@ ggml_tensor * llm_build_mamba_base::build_mamba_layer(llm_graph_input_rs * inp, cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs); // {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs} - ggml_tensor * xz = build_lora_mm(layer.ssm_in, cur); + ggml_tensor * xz = build_lora_mm(layer.ssm_in, cur, layer.ssm_in_s); // split the above in two // => {d_inner, n_seq_tokens, n_seqs} ggml_tensor * x = ggml_view_3d(ctx0, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], 0); @@ -136,7 +137,7 @@ ggml_tensor * llm_build_mamba_base::build_mamba_layer(llm_graph_input_rs * inp, y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y); // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs} - cur = build_lora_mm(layer.ssm_out, y); + cur = build_lora_mm(layer.ssm_out, y, layer.ssm_out_s); } // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens} @@ -167,6 +168,9 @@ ggml_tensor * llm_build_mamba_base::build_mamba2_layer(llm_graph_input_rs * inp, GGML_ASSERT(n_seqs != 0); GGML_ASSERT(ubatch.equal_seqs()); GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); + GGML_ASSERT(d_inner % n_head == 0); + GGML_ASSERT(d_inner % d_state == 0); + GGML_ASSERT(d_inner % n_group == 0); ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); @@ -180,7 +184,7 @@ ggml_tensor * llm_build_mamba_base::build_mamba2_layer(llm_graph_input_rs * inp, // d_in_proj = 2 * self.d_inner + 2 * self.ngroups * self.d_state + self.nheads // {n_embd, d_in_proj} @ {n_embd, n_seq_tokens, n_seqs} => {d_in_proj, n_seq_tokens, n_seqs} - ggml_tensor * zxBCdt = build_lora_mm(model.layers[il].ssm_in, cur); + ggml_tensor * zxBCdt = build_lora_mm(model.layers[il].ssm_in, cur, model.layers[il].ssm_in_s); // split the above in three ggml_tensor * z = ggml_view_4d(ctx0, zxBCdt, head_dim, n_head, n_seq_tokens, n_seqs, head_dim * zxBCdt->nb[0], @@ -274,7 +278,7 @@ ggml_tensor * llm_build_mamba_base::build_mamba2_layer(llm_graph_input_rs * inp, y = ggml_reshape_3d(ctx0, y, d_inner, n_seq_tokens, n_seqs); // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs} - cur = build_lora_mm(model.layers[il].ssm_out, y); + cur = build_lora_mm(model.layers[il].ssm_out, y, model.layers[il].ssm_out_s); } // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens} diff --git a/src/models/mimo2-iswa.cpp b/src/models/mimo2-iswa.cpp index edc87cc9f0..06956915ea 100644 --- a/src/models/mimo2-iswa.cpp +++ b/src/models/mimo2-iswa.cpp @@ -1,4 +1,3 @@ - #include "models.h" llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { @@ -88,10 +87,17 @@ llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_ cb(cur, "ffn_out", il); } else { // MoE branch - cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, - model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, - model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, false, - 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, il); + cur = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, + il); cb(cur, "ffn_moe_out", il); } diff --git a/src/models/minicpm3.cpp b/src/models/minicpm3.cpp index 297cc34ba5..89dd710515 100644 --- a/src/models/minicpm3.cpp +++ b/src/models/minicpm3.cpp @@ -5,10 +5,10 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap const int64_t n_embd_base = 256; const float scale_embd = 12.0f; const float scale_depth = 1.4f; - const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k)); + const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k())); - const uint32_t n_embd_head_qk_rope = hparams.n_rot; - const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const uint32_t n_embd_head_qk_rope = hparams.n_rot(); + const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot(); const uint32_t kv_lora_rank = hparams.n_lora_kv; @@ -51,21 +51,21 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap LLM_NORM_RMS, il); cb(q, "q", il); - // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens} + // {q_lora_rank, n_head * hparams.n_embd_head_k()} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k(), n_tokens} q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q); cb(q, "q", il); // split into {n_head * n_embd_head_qk_nope, n_tokens} ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, - ggml_row_size(q->type, hparams.n_embd_head_k), - ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, hparams.n_embd_head_k()), + ggml_row_size(q->type, hparams.n_embd_head_k() * n_head), 0); cb(q_nope, "q_nope", il); // and {n_head * n_embd_head_qk_rope, n_tokens} ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, - ggml_row_size(q->type, hparams.n_embd_head_k), - ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, hparams.n_embd_head_k()), + ggml_row_size(q->type, hparams.n_embd_head_k() * n_head), ggml_row_size(q->type, n_embd_head_qk_nope)); cb(q_pe, "q_pe", il); @@ -97,15 +97,15 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap // split into {n_head * n_embd_head_qk_nope, n_tokens} ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, - ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v), - ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)), + ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v()), + ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v())), 0); cb(k_nope, "k_nope", il); // and {n_head * n_embd_head_v, n_tokens} - ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens, - ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)), - ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head), + ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v(), n_head, n_tokens, + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())), + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())*n_head), ggml_row_size(kv->type, (n_embd_head_qk_nope))); cb(v_states, "v_states", il); diff --git a/src/models/minimax-m2.cpp b/src/models/minimax-m2.cpp index f7001badf7..83d0916c08 100644 --- a/src/models/minimax-m2.cpp +++ b/src/models/minimax-m2.cpp @@ -1,11 +1,10 @@ - #include "models.h" llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - // GGML_ASSERT(n_embd_head == hparams.n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64 + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + // GGML_ASSERT(n_embd_head == n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64 ggml_tensor * cur; ggml_tensor * inpL; @@ -91,7 +90,7 @@ llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_ model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/mistral3.cpp b/src/models/mistral3.cpp index 0b67223591..42a5117ff0 100644 --- a/src/models/mistral3.cpp +++ b/src/models/mistral3.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_mistral3::llm_build_mistral3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -127,7 +127,7 @@ llm_build_mistral3::llm_build_mistral3(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/models.h b/src/models/models.h index 3a7177189d..edf6bb20d9 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -3,7 +3,7 @@ #include "llama-model.h" #include "llama-graph.h" -// note: almost all graphs require atleast sqrtf, so include cmath globally +// note: almost all graphs require at least sqrtf, so include cmath globally #include // @@ -44,6 +44,26 @@ struct llm_build_delta_net_base : public llm_graph_context { ggml_tensor * b, ggml_tensor * s, int il); + + // use the ggml_gated_delta_net fused operator + std::pair build_delta_net_fused( + ggml_tensor * q, + ggml_tensor * k, + ggml_tensor * v, + ggml_tensor * g, + ggml_tensor * b, + ggml_tensor * s, + int il); + + // choose one of two implementations above based on the number of tokens + std::pair build_delta_net( + ggml_tensor * q, + ggml_tensor * k, + ggml_tensor * v, + ggml_tensor * g, + ggml_tensor * b, + ggml_tensor * s, + int il); }; struct llm_build_rwkv6_base : public llm_graph_context { @@ -434,6 +454,10 @@ struct llm_build_neo_bert : public llm_graph_context { llm_build_neo_bert(const llama_model & model, const llm_graph_params & params); }; +struct llm_build_eurobert : public llm_graph_context { + llm_build_eurobert(const llama_model & model, const llm_graph_params & params); +}; + template struct llm_build_olmo2 : public llm_graph_context { llm_build_olmo2(const llama_model & model, const llm_graph_params & params); diff --git a/src/models/modern-bert.cpp b/src/models/modern-bert.cpp index 32066c712b..26020584c6 100644 --- a/src/models/modern-bert.cpp +++ b/src/models/modern-bert.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/mpt.cpp b/src/models/mpt.cpp index 2328e027a7..ce44a805f5 100644 --- a/src/models/mpt.cpp +++ b/src/models/mpt.cpp @@ -3,10 +3,10 @@ llm_build_mpt::llm_build_mpt(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * pos; diff --git a/src/models/nemotron-h.cpp b/src/models/nemotron-h.cpp index d61d62a8c9..d3fccfb70d 100644 --- a/src/models/nemotron-h.cpp +++ b/src/models/nemotron-h.cpp @@ -2,8 +2,8 @@ llm_build_nemotron_h::llm_build_nemotron_h(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; @@ -107,16 +107,25 @@ ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor * ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const llama_model & model, int il) { if (model.layers[il].ffn_gate_inp == nullptr) { cur = build_ffn(cur, - model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s, NULL, NULL, NULL, - model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s, NULL, LLM_FFN_RELU_SQR, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); } else { - ggml_tensor * ffn_inp = cur; + ggml_tensor * inp_emb = cur; + ggml_tensor * inp_latent = cur; + + if (model.layers[il].ffn_latent_down) { + inp_latent = ggml_mul_mat(ctx0, model.layers[il].ffn_latent_down, cur); + } + + ggml_tensor * router_logits = build_lora_mm(model.layers[il].ffn_gate_inp, cur); + cb(router_logits, "ffn_moe_logits", il); + ggml_tensor * moe_out = - build_moe_ffn(ffn_inp, + build_moe_ffn(inp_latent, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, nullptr, // no gate @@ -124,15 +133,23 @@ ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const lla model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_RELU_SQR, hparams.expert_weights_norm, - true, hparams.expert_weights_scale, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, - il); + il, + router_logits, nullptr, + model.layers[il].ffn_up_exps_s, + nullptr, // no gate + model.layers[il].ffn_down_exps_s); cb(moe_out, "ffn_moe_out", il); - ggml_tensor * ffn_shexp = build_ffn(ffn_inp, - model.layers[il].ffn_up_shexp, NULL, NULL, - NULL /* no gate */ , NULL, NULL, - model.layers[il].ffn_down_shexp, NULL, NULL, + if (model.layers[il].ffn_latent_up) { + moe_out = ggml_mul_mat(ctx0, model.layers[il].ffn_latent_up, moe_out); + } + + ggml_tensor * ffn_shexp = build_ffn(inp_emb, + model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s, + NULL /* no gate */ , NULL, NULL, + model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s, NULL, LLM_FFN_RELU_SQR, LLM_FFN_PAR, il); cb(ffn_shexp, "ffn_shexp", il); diff --git a/src/models/nemotron.cpp b/src/models/nemotron.cpp index fcead041f0..34aa6fa5ec 100644 --- a/src/models/nemotron.cpp +++ b/src/models/nemotron.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_nemotron::llm_build_nemotron(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - //GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + //GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/neo-bert.cpp b/src/models/neo-bert.cpp index 7c32bfca5f..2fdf4a3692 100644 --- a/src/models/neo-bert.cpp +++ b/src/models/neo-bert.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_neo_bert::llm_build_neo_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/olmo.cpp b/src/models/olmo.cpp index bbd623f111..26f4b6ee62 100644 --- a/src/models/olmo.cpp +++ b/src/models/olmo.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_olmo::llm_build_olmo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/olmo2.cpp b/src/models/olmo2.cpp index 713552dab8..5076359e3f 100644 --- a/src/models/olmo2.cpp +++ b/src/models/olmo2.cpp @@ -2,10 +2,10 @@ template llm_build_olmo2::llm_build_olmo2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/olmoe.cpp b/src/models/olmoe.cpp index b8b6988f89..83a56a0b3b 100644 --- a/src/models/olmoe.cpp +++ b/src/models/olmoe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_olmoe::llm_build_olmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -92,7 +92,7 @@ llm_build_olmoe::llm_build_olmoe(const llama_model & model, const llm_graph_para nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/openai-moe-iswa.cpp b/src/models/openai-moe-iswa.cpp index 527e8967c5..f5c613de43 100644 --- a/src/models/openai-moe-iswa.cpp +++ b/src/models/openai-moe-iswa.cpp @@ -106,7 +106,7 @@ llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model, nullptr, n_expert, n_expert_used, LLM_FFN_SWIGLU_OAI_MOE, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/openelm.cpp b/src/models/openelm.cpp index fbf682ec83..5df6fe3e3c 100644 --- a/src/models/openelm.cpp +++ b/src/models/openelm.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_openelm::llm_build_openelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/orion.cpp b/src/models/orion.cpp index bb02273bfe..48c01efe36 100644 --- a/src/models/orion.cpp +++ b/src/models/orion.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_orion::llm_build_orion(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/paddleocr.cpp b/src/models/paddleocr.cpp index 39a368df53..340455c2d5 100644 --- a/src/models/paddleocr.cpp +++ b/src/models/paddleocr.cpp @@ -5,10 +5,10 @@ llm_build_paddleocr::llm_build_paddleocr(const llama_model & model, const llm_gr // NOTE: same with qwen2vl.cpp, but bias tensors are optional - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/pangu-embedded.cpp b/src/models/pangu-embedded.cpp index 664572a500..1cf0938e68 100644 --- a/src/models/pangu-embedded.cpp +++ b/src/models/pangu-embedded.cpp @@ -2,10 +2,10 @@ llm_build_pangu_embedded::llm_build_pangu_embedded(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/phi2.cpp b/src/models/phi2.cpp index 22dbf61076..32d40d71fb 100644 --- a/src/models/phi2.cpp +++ b/src/models/phi2.cpp @@ -2,10 +2,10 @@ llm_build_phi2::llm_build_phi2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * attn_norm_output; diff --git a/src/models/phi3.cpp b/src/models/phi3.cpp index c8e5da33db..3d11a9459c 100644 --- a/src/models/phi3.cpp +++ b/src/models/phi3.cpp @@ -2,10 +2,10 @@ template llm_build_phi3::llm_build_phi3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; @@ -114,7 +114,7 @@ llm_build_phi3::llm_build_phi3(const llama_model & model, const llm_graph_ nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/plamo.cpp b/src/models/plamo.cpp index 04ff709f9c..b7a7121104 100644 --- a/src/models/plamo.cpp +++ b/src/models/plamo.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_plamo::llm_build_plamo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/plamo2.cpp b/src/models/plamo2.cpp index 3af236843b..f02acbc186 100644 --- a/src/models/plamo2.cpp +++ b/src/models/plamo2.cpp @@ -27,7 +27,7 @@ llm_build_plamo2::llm_build_plamo2(const llama_model & model, const llm_graph_pa cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); // check if this layer is Mamba or Attention - bool is_mamba_layer = hparams.is_recurrent(il); + const bool is_mamba_layer = hparams.is_recurrent(il); if (is_mamba_layer) { // PLaMo-2 Mamba layer @@ -106,9 +106,9 @@ ggml_tensor * llm_build_plamo2::build_plamo2_attn_layer(llm_graph_input_attn_kv cb(qkv, "wqkv", il); // split QKV tensor into Q, K, V - const int64_t n_embd_head_q = hparams.n_embd_head_k; - const int64_t n_embd_head_k = hparams.n_embd_head_k; - const int64_t n_embd_head_v = hparams.n_embd_head_v; + const int64_t n_embd_head_q = hparams.n_embd_head_k(); + const int64_t n_embd_head_k = hparams.n_embd_head_k(); + const int64_t n_embd_head_v = hparams.n_embd_head_v(); int32_t n_head = hparams.n_head(il); int32_t n_head_kv = hparams.n_head_kv(il); @@ -171,6 +171,8 @@ ggml_tensor * llm_build_plamo2::build_plamo2_mamba_layer(llm_graph_input_rs * in GGML_ASSERT(n_seqs != 0); GGML_ASSERT(ubatch.equal_seqs()); GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); + GGML_ASSERT(d_inner % n_head == 0); + GGML_ASSERT(n_group == 0); ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); diff --git a/src/models/plamo3.cpp b/src/models/plamo3.cpp index 55c8064679..32af6e0466 100644 --- a/src/models/plamo3.cpp +++ b/src/models/plamo3.cpp @@ -3,8 +3,8 @@ template llm_build_plamo3::llm_build_plamo3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t head_dim_q = hparams.n_embd_head_k; - const int64_t head_dim_v = hparams.n_embd_head_v; + const int64_t head_dim_q = hparams.n_embd_head_k(); + const int64_t head_dim_v = hparams.n_embd_head_v(); ggml_tensor * cur; ggml_tensor * inpL = build_inp_embd(model.tok_embd); diff --git a/src/models/plm.cpp b/src/models/plm.cpp index 612a487c56..bcb651ce54 100644 --- a/src/models/plm.cpp +++ b/src/models/plm.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k)); + const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k())); - const uint32_t n_embd_head_qk_rope = hparams.n_rot; - const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const uint32_t n_embd_head_qk_rope = hparams.n_rot(); + const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot(); const uint32_t kv_lora_rank = hparams.n_lora_kv; @@ -38,15 +38,15 @@ llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params & // split into {n_head * n_embd_head_qk_nope, n_tokens} ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, - ggml_row_size(q->type, hparams.n_embd_head_k), - ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, hparams.n_embd_head_k()), + ggml_row_size(q->type, hparams.n_embd_head_k() * n_head), 0); cb(q_nope, "q_nope", il); // and {n_head * n_embd_head_qk_rope, n_tokens} ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, - ggml_row_size(q->type, hparams.n_embd_head_k), - ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, hparams.n_embd_head_k()), + ggml_row_size(q->type, hparams.n_embd_head_k() * n_head), ggml_row_size(q->type, n_embd_head_qk_nope)); cb(q_pe, "q_pe", il); @@ -78,23 +78,23 @@ llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params & // split into {n_head * n_embd_head_qk_nope, n_tokens} ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, - ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v), - ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)), + ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v()), + ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v())), 0); cb(k_nope, "k_nope", il); // and {n_head * n_embd_head_v, n_tokens} - ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens, - ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)), - ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head), + ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v(), n_head, n_tokens, + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())), + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())*n_head), ggml_row_size(kv->type, (n_embd_head_qk_nope))); cb(v_states, "v_states", il); v_states = ggml_cont(ctx0, v_states); cb(v_states, "v_states", il); - v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens, - ggml_row_size(kv->type, hparams.n_embd_head_v * n_head), + v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v() * n_head, n_tokens, + ggml_row_size(kv->type, hparams.n_embd_head_v() * n_head), 0); cb(v_states, "v_states", il); diff --git a/src/models/qwen.cpp b/src/models/qwen.cpp index 31fd9b7376..7390f1320b 100644 --- a/src/models/qwen.cpp +++ b/src/models/qwen.cpp @@ -2,9 +2,9 @@ llm_build_qwen::llm_build_qwen(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen2.cpp b/src/models/qwen2.cpp index 3da4dea3c1..58c1062250 100644 --- a/src/models/qwen2.cpp +++ b/src/models/qwen2.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen2::llm_build_qwen2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen2moe.cpp b/src/models/qwen2moe.cpp index 49142b7123..60761789dc 100644 --- a/src/models/qwen2moe.cpp +++ b/src/models/qwen2moe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen2moe::llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -94,7 +94,7 @@ llm_build_qwen2moe::llm_build_qwen2moe(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/qwen2vl.cpp b/src/models/qwen2vl.cpp index 9be38675cf..9004bab9db 100644 --- a/src/models/qwen2vl.cpp +++ b/src/models/qwen2vl.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen2vl::llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen3.cpp b/src/models/qwen3.cpp index c1f34624c0..35cea5e329 100644 --- a/src/models/qwen3.cpp +++ b/src/models/qwen3.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen3::llm_build_qwen3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -41,13 +41,13 @@ llm_build_qwen3::llm_build_qwen3(const llama_model & model, const llm_graph_para // self-attention { // compute Q and K and RoPE them - ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); cb(Qcur, "Qcur", il); - ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s); cb(Kcur, "Kcur", il); - ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s); cb(Vcur, "Vcur", il); Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); @@ -79,6 +79,9 @@ llm_build_qwen3::llm_build_qwen3(const llama_model & model, const llm_graph_para cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + if (model.layers[il].wo_s) { + cur = ggml_mul(ctx0, cur, model.layers[il].wo_s); + } } if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); @@ -94,9 +97,9 @@ llm_build_qwen3::llm_build_qwen3(const llama_model & model, const llm_graph_para cb(cur, "ffn_norm", il); cur = build_ffn(cur, - model.layers[il].ffn_up, NULL, NULL, - model.layers[il].ffn_gate, NULL, NULL, - model.layers[il].ffn_down, NULL, NULL, + model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp index 56eefd7de2..3108bf331a 100644 --- a/src/models/qwen35.cpp +++ b/src/models/qwen35.cpp @@ -4,9 +4,9 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_params & params) : llm_build_delta_net_base(params), model(model) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); @@ -29,6 +29,8 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_pa cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il); cb(cur, "attn_norm", il); + ggml_build_forward_expand(gf, cur); + // Determine layer type and build appropriate attention mechanism if (hparams.is_recurrent(il)) { // Linear attention layer (gated delta net) @@ -88,11 +90,11 @@ std::pair llm_build_qwen35::build_qkvz( const int64_t n_seqs = ubatch.n_seqs; const int64_t n_seq_tokens = ubatch.n_seq_tokens; - ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input); + ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input, model.layers[il].wqkv_s); qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs); cb(qkv_mixed, "linear_attn_qkv_mixed", il); - ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input); + ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input, model.layers[il].wqkv_gate_s); cb(z, "z", il); return { qkv_mixed, z }; @@ -115,13 +117,13 @@ ggml_tensor * llm_build_qwen35::build_layer_attn( ggml_tensor * inp_pos, int * sections, int il) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention // Qwen3Next uses a single Q projection that outputs query + gate - ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ] + ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); // [ (n_embd_head * 2) * n_head, n_tokens ] cb(Qcur_full, "Qcur_full", il); ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens, @@ -133,10 +135,10 @@ ggml_tensor * llm_build_qwen35::build_layer_attn( Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il); cb(Qcur, "Qcur_normed", il); - ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s); cb(Kcur, "Kcur", il); - ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s); cb(Vcur, "Vcur", il); // Apply K normalization @@ -184,7 +186,7 @@ ggml_tensor * llm_build_qwen35::build_layer_attn( cur = ggml_mul(ctx0, cur, gate_sigmoid); cb(cur, "attn_gated", il); - cur = build_lora_mm(model.layers[il].wo, cur); + cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s); cb(cur, "attn_output", il); return cur; @@ -215,13 +217,13 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( ggml_tensor * qkv_mixed = qkvz.first; ggml_tensor * z = qkvz.second; - ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur); + ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur, model.layers[il].ssm_beta_s); beta = ggml_reshape_4d(ctx0, beta, 1, num_v_heads, n_seq_tokens, n_seqs); cb(beta, "beta", il); beta = ggml_sigmoid(ctx0, beta); - ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur); + ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur, model.layers[il].ssm_alpha_s); alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs); cb(alpha, "alpha", il); @@ -269,7 +271,6 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( cb(state_update_target, "state_update_target", il); ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target)); - cb(conv_states_all, "conv_states_updated", il); ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs); state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs); @@ -320,9 +321,9 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); // if head keys and value keys are different, repeat to force tensors into matching shapes - if (num_k_heads != num_v_heads) { + // note: need explicit repeat only if we are not using the fused GDN + if (num_k_heads != num_v_heads && (!cparams.fused_gdn_ar || !cparams.fused_gdn_ch)) { GGML_ASSERT(num_v_heads % num_k_heads == 0); - // TODO: try to avoid these explicit repeats by utilizing op broadcast q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs); k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs); } @@ -331,13 +332,8 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( cb(k_conv, "k_conv_predelta", il); cb(v_conv, "v_conv_predelta", il); - // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens - std::pair attn_out; // pair of (output, new_state) - if (n_seq_tokens == 1) { - attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il); - } else { - attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il); - } + auto attn_out = build_delta_net(q_conv, k_conv, v_conv, gate, beta, state, il); + ggml_tensor * output = attn_out.first; ggml_tensor * new_state = attn_out.second; cb(output, "attn_output", il); @@ -360,7 +356,7 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( cb(final_output, "final_output", il); // Output projection - cur = build_lora_mm(model.layers[il].ssm_out, final_output); + cur = build_lora_mm(model.layers[il].ssm_out, final_output, model.layers[il].ssm_out_s); cb(cur, "linear_attn_out", il); // Reshape back to original dimensions @@ -374,9 +370,9 @@ ggml_tensor * llm_build_qwen35::build_layer_ffn(ggml_tensor * cur, const int il) GGML_ASSERT(model.layers[il].ffn_gate_inp == nullptr); cur = build_ffn(cur, - model.layers[il].ffn_up, NULL, NULL, - model.layers[il].ffn_gate, NULL, NULL, - model.layers[il].ffn_down, NULL, NULL, + model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp index c7295e3364..165e2412e5 100644 --- a/src/models/qwen35moe.cpp +++ b/src/models/qwen35moe.cpp @@ -4,9 +4,9 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params) : llm_build_delta_net_base(params), model(model) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); @@ -29,6 +29,8 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_gr cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il); cb(cur, "attn_norm", il); + ggml_build_forward_expand(gf, cur); + // Determine layer type and build appropriate attention mechanism if (hparams.is_recurrent(il)) { // Linear attention layer (gated delta net) @@ -88,11 +90,11 @@ std::pair llm_build_qwen35moe::build_qkvz( const int64_t n_seqs = ubatch.n_seqs; const int64_t n_seq_tokens = ubatch.n_seq_tokens; - ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input); + ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input, model.layers[il].wqkv_s); qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs); cb(qkv_mixed, "linear_attn_qkv_mixed", il); - ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input); + ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input, model.layers[il].wqkv_gate_s); cb(z, "z", il); return { qkv_mixed, z }; @@ -115,13 +117,13 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn( ggml_tensor * inp_pos, int * sections, int il) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention // Qwen3Next uses a single Q projection that outputs query + gate - ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ] + ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); // [ (n_embd_head * 2) * n_head, n_tokens ] cb(Qcur_full, "Qcur_full", il); ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens, @@ -133,10 +135,10 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn( Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il); cb(Qcur, "Qcur_normed", il); - ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s); cb(Kcur, "Kcur", il); - ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s); cb(Vcur, "Vcur", il); // Apply K normalization @@ -184,7 +186,7 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn( cur = ggml_mul(ctx0, cur, gate_sigmoid); cb(cur, "attn_gated", il); - cur = build_lora_mm(model.layers[il].wo, cur); + cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s); cb(cur, "attn_output", il); return cur; @@ -215,13 +217,13 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( ggml_tensor * qkv_mixed = qkvz.first; ggml_tensor * z = qkvz.second; - ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur); + ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur, model.layers[il].ssm_beta_s); beta = ggml_reshape_4d(ctx0, beta, 1, num_v_heads, n_seq_tokens, n_seqs); cb(beta, "beta", il); beta = ggml_sigmoid(ctx0, beta); - ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur); + ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur, model.layers[il].ssm_alpha_s); alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs); cb(alpha, "alpha", il); @@ -269,7 +271,6 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( cb(state_update_target, "state_update_target", il); ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target)); - cb(conv_states_all, "conv_states_updated", il); ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs); state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs); @@ -320,9 +321,9 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); // if head keys and value keys are different, repeat to force tensors into matching shapes - if (num_k_heads != num_v_heads) { + // note: need explicit repeat only if we are not using the fused GDN + if (num_k_heads != num_v_heads && (!cparams.fused_gdn_ar || !cparams.fused_gdn_ch)) { GGML_ASSERT(num_v_heads % num_k_heads == 0); - // TODO: try to avoid these explicit repeats by utilizing op broadcast q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs); k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs); } @@ -331,13 +332,8 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( cb(k_conv, "k_conv_predelta", il); cb(v_conv, "v_conv_predelta", il); - // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens - std::pair attn_out; // pair of (output, new_state) - if (n_seq_tokens == 1) { - attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il); - } else { - attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il); - } + auto attn_out = build_delta_net(q_conv, k_conv, v_conv, gate, beta, state, il); + ggml_tensor * output = attn_out.first; ggml_tensor * new_state = attn_out.second; cb(output, "attn_output", il); @@ -360,7 +356,7 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( cb(final_output, "final_output", il); // Output projection - cur = build_lora_mm(model.layers[il].ssm_out, final_output); + cur = build_lora_mm(model.layers[il].ssm_out, final_output, model.layers[il].ssm_out_s); cb(cur, "linear_attn_out", il); // Reshape back to original dimensions @@ -375,20 +371,28 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int ggml_tensor * moe_out = build_moe_ffn(cur, - model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, - model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, nullptr, - n_expert, n_expert_used, LLM_FFN_SILU, - true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); + n_expert, n_expert_used, + LLM_FFN_SILU, true, + hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, + nullptr, model.layers[il].ffn_gate_up_exps, + model.layers[il].ffn_up_exps_s, + model.layers[il].ffn_gate_exps_s, + model.layers[il].ffn_down_exps_s); cb(moe_out, "ffn_moe_out", il); // Add shared experts if present - following Qwen3Next reference implementation if (model.layers[il].ffn_up_shexp != nullptr) { ggml_tensor * ffn_shexp = build_ffn(cur, - model.layers[il].ffn_up_shexp, NULL, NULL, - model.layers[il].ffn_gate_shexp, NULL, NULL, - model.layers[il].ffn_down_shexp, NULL, NULL, + model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s, + model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s, + model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(ffn_shexp, "ffn_shexp", il); diff --git a/src/models/qwen3moe.cpp b/src/models/qwen3moe.cpp index c0b6ff5df9..95e8269baf 100644 --- a/src/models/qwen3moe.cpp +++ b/src/models/qwen3moe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -41,13 +41,13 @@ llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_grap // self_attention { // compute Q and K and RoPE them - ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); cb(Qcur, "Qcur", il); - ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s); cb(Kcur, "Kcur", il); - ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s); cb(Vcur, "Vcur", il); Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); @@ -79,6 +79,9 @@ llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_grap cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + if (model.layers[il].wo_s) { + cur = ggml_mul(ctx0, cur, model.layers[il].wo_s); + } } if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); @@ -102,9 +105,13 @@ llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, - il); + il, + nullptr, nullptr, + model.layers[il].ffn_up_exps_s, + model.layers[il].ffn_gate_exps_s, + model.layers[il].ffn_down_exps_s); cb(moe_out, "ffn_moe_out", il); cur = moe_out; diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp index 974120ea6f..cc479dd075 100644 --- a/src/models/qwen3next.cpp +++ b/src/models/qwen3next.cpp @@ -21,6 +21,8 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il); cb(cur, "attn_norm", il); + ggml_build_forward_expand(gf, cur); + // Determine layer type and build appropriate attention mechanism if (hparams.is_recurrent(il)) { // Linear attention layer (gated delta net) @@ -98,8 +100,8 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn( ggml_tensor * cur, ggml_tensor * inp_pos, int il) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention @@ -354,7 +356,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear( cb(state_update_target, "state_update_target", il); ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target)); - cb(conv_states_all, "conv_states_updated", il); ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs); state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs); @@ -405,6 +406,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear( //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); // if head keys and value keys are different, repeat to force tensors into matching shapes + // TODO: avoid repeats for fused GDN, needs broadcast configuration for GDN op [TAG_GGML_GDN_BCAST] if (num_k_heads != num_v_heads) { GGML_ASSERT(num_v_heads % num_k_heads == 0); int64_t repeat_factor = num_v_heads / num_k_heads; @@ -430,13 +432,8 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear( cb(k_conv, "k_conv_predelta", il); cb(v_conv, "v_conv_predelta", il); - // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens - std::pair attn_out; // pair of (output, new_state) - if (n_seq_tokens == 1) { - attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il); - } else { - attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il); - } + auto attn_out = build_delta_net(q_conv, k_conv, v_conv, gate, beta, state, il); + ggml_tensor * output = attn_out.first; ggml_tensor * new_state = attn_out.second; cb(output, "attn_output", il); @@ -474,11 +471,16 @@ ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, const int // MoE branch ggml_tensor * moe_out = build_moe_ffn(cur, - model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, - model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, nullptr, - n_expert, n_expert_used, LLM_FFN_SILU, - true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); + n_expert, n_expert_used, + LLM_FFN_SILU, true, + hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, + nullptr, model.layers[il].ffn_gate_up_exps); cb(moe_out, "ffn_moe_out", il); // Add shared experts if present - following Qwen3Next reference implementation diff --git a/src/models/qwen3vl-moe.cpp b/src/models/qwen3vl-moe.cpp index e5e1a2150c..195daea66c 100644 --- a/src/models/qwen3vl-moe.cpp +++ b/src/models/qwen3vl-moe.cpp @@ -4,10 +4,10 @@ llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_ const size_t n_deepstack_layers = hparams.n_deepstack_layers; const int64_t n_embd = hparams.n_embd; - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -99,7 +99,7 @@ llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_ nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/qwen3vl.cpp b/src/models/qwen3vl.cpp index 0f8315b324..bbd5f42ba5 100644 --- a/src/models/qwen3vl.cpp +++ b/src/models/qwen3vl.cpp @@ -4,10 +4,10 @@ llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_ const size_t n_deepstack_layers = hparams.n_deepstack_layers; const int64_t n_embd = hparams.n_embd; - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/refact.cpp b/src/models/refact.cpp index ff5eb2841d..140700d9e2 100644 --- a/src/models/refact.cpp +++ b/src/models/refact.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_refact::llm_build_refact(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/rnd1.cpp b/src/models/rnd1.cpp index 46b3dc3efc..c8e1f43400 100644 --- a/src/models/rnd1.cpp +++ b/src/models/rnd1.cpp @@ -2,10 +2,10 @@ // RND1 is a Qwen3Moe AR model converted to diffusion model. llm_build_rnd1::llm_build_rnd1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -93,7 +93,7 @@ llm_build_rnd1::llm_build_rnd1(const llama_model & model, const llm_graph_params nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/seed-oss.cpp b/src/models/seed-oss.cpp index 0dc33c50ba..a4d0b75d84 100644 --- a/src/models/seed-oss.cpp +++ b/src/models/seed-oss.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_seed_oss::llm_build_seed_oss(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/smallthinker.cpp b/src/models/smallthinker.cpp index 4c497ca76f..e2155aacef 100644 --- a/src/models/smallthinker.cpp +++ b/src/models/smallthinker.cpp @@ -2,10 +2,10 @@ template llm_build_smallthinker::llm_build_smallthinker(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){ - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; @@ -93,7 +93,7 @@ llm_build_smallthinker::llm_build_smallthinker(const llama_model & model, nullptr, n_expert, n_expert_used, LLM_FFN_RELU, true, - false, 0.0, + hparams.expert_weights_scale, static_cast(hparams.expert_gating_func), il, probs); diff --git a/src/models/smollm3.cpp b/src/models/smollm3.cpp index 97c30deed5..e267fd8f32 100644 --- a/src/models/smollm3.cpp +++ b/src/models/smollm3.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_smollm3::llm_build_smollm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/stablelm.cpp b/src/models/stablelm.cpp index bed1915c00..ff5aced93b 100644 --- a/src/models/stablelm.cpp +++ b/src/models/stablelm.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_stablelm::llm_build_stablelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/starcoder.cpp b/src/models/starcoder.cpp index e197af4a8c..941cee9821 100644 --- a/src/models/starcoder.cpp +++ b/src/models/starcoder.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_starcoder::llm_build_starcoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/starcoder2.cpp b/src/models/starcoder2.cpp index e40ef2cb74..a5965aceb3 100644 --- a/src/models/starcoder2.cpp +++ b/src/models/starcoder2.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_starcoder2::llm_build_starcoder2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/step35-iswa.cpp b/src/models/step35-iswa.cpp index f8737815a6..176209cd93 100644 --- a/src/models/step35-iswa.cpp +++ b/src/models/step35-iswa.cpp @@ -52,7 +52,7 @@ llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const ll // RoPE (partial rotary factors per layer) const bool is_swa = hparams.is_swa(il); ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il); - const int64_t n_rot_l = is_swa ? hparams.n_rot : (hparams.n_rot / 2); + const int64_t n_rot_l = hparams.n_rot(il); Qcur = ggml_rope_ext( ctx0, Qcur, inp_pos, rope_factors, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, @@ -119,9 +119,6 @@ llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const ll cb(cur, "ffn_out", il); } else { // MoE routed experts - const bool norm_w = hparams.expert_weights_norm; - const float w_scale = hparams.expert_weights_scale; - const bool scale_w = w_scale != 0.0f; ggml_tensor * moe_out = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, @@ -129,8 +126,8 @@ llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const ll model.layers[il].ffn_down_exps, model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, - LLM_FFN_SILU, - norm_w, scale_w, w_scale, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/t5-dec.cpp b/src/models/t5-dec.cpp index 297e450de7..8ca8372bd4 100644 --- a/src/models/t5-dec.cpp +++ b/src/models/t5-dec.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_t5_dec::llm_build_t5_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); //const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/t5-enc.cpp b/src/models/t5-enc.cpp index 70e1d80dcd..395dfb5104 100644 --- a/src/models/t5-enc.cpp +++ b/src/models/t5-enc.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_t5_enc::llm_build_t5_enc(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/xverse.cpp b/src/models/xverse.cpp index 364797dd31..3a8dfafcce 100644 --- a/src/models/xverse.cpp +++ b/src/models/xverse.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_xverse::llm_build_xverse(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/unicode.cpp b/src/unicode.cpp index 1475b53b65..122c8ca04a 100644 --- a/src/unicode.cpp +++ b/src/unicode.cpp @@ -773,7 +773,7 @@ static std::vector unicode_regex_split_custom(const std::string & text, // tiny_aya digit grouping pattern from tokenizer.json: // {"type": "Split", "pattern": {"Regex": "\\d{1,3}(?=(?:\\d{3})*\\b)"}, "behavior": "Isolated"} // Splits digits into groups of 3 from the right (e.g., 1234567 -> 1, 234, 567) - // TODO: Revisit this regex, incase there are any subtle tokenization differences with the original regex. + // TODO: Revisit this regex, in case there are any subtle tokenization differences with the original regex. bpe_offsets = unicode_regex_split_custom_afmoe(text, offsets); } diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 350bffc315..9582164b58 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -149,10 +149,11 @@ endif () if (NOT WIN32 OR NOT BUILD_SHARED_LIBS) # these tests are disabled on Windows because they use internal functions not exported with LLAMA_API (when building with shared libraries) llama_build_and_test(test-sampling.cpp) + llama_build_and_test(test-reasoning-budget.cpp) llama_build_and_test(test-grammar-parser.cpp) llama_build_and_test(test-grammar-integration.cpp) llama_build_and_test(test-llama-grammar.cpp) - llama_build_and_test(test-chat.cpp) + llama_build_and_test(test-chat.cpp WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}) # TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8 if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") llama_build_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}) @@ -185,13 +186,15 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS) #llama_test(test-tokenizer-1-spm NAME test-tokenizer-1-baichuan ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-baichuan.gguf) # llama_build_and_test(test-double-float.cpp) # SLOW + + llama_build_and_test(test-llama-archs.cpp) endif() -llama_build_and_test(test-chat-parser.cpp) llama_build_and_test(test-chat-peg-parser.cpp peg-parser/simple-tokenize.cpp) -llama_build_and_test(test-chat-template.cpp) llama_build_and_test(test-jinja.cpp) llama_test(test-jinja NAME test-jinja-py ARGS -py LABEL python) +llama_build_and_test(test-chat-auto-parser.cpp WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}) +llama_build_and_test(test-chat-template.cpp) llama_build_and_test(test-json-partial.cpp) llama_build_and_test(test-log.cpp) llama_build_and_test( @@ -201,6 +204,7 @@ llama_build_and_test( peg-parser/test-gbnf-generation.cpp peg-parser/test-json-parser.cpp peg-parser/test-json-serialization.cpp + peg-parser/test-python-dict-parser.cpp peg-parser/test-unicode.cpp peg-parser/tests.h ) @@ -256,6 +260,22 @@ endif() set(LLAMA_TEST_NAME test-mtmd-c-api) llama_build_and_test(test-mtmd-c-api.c) target_link_libraries(${LLAMA_TEST_NAME} PRIVATE mtmd) +unset(LLAMA_TEST_NAME) + +# GGUF model data fetcher library for tests that need real model metadata +# Only compile when cpp-httplib has SSL support (CPPHTTPLIB_OPENSSL_SUPPORT) +if (TARGET cpp-httplib) + get_target_property(_cpp_httplib_defs cpp-httplib INTERFACE_COMPILE_DEFINITIONS) + if (_cpp_httplib_defs MATCHES "CPPHTTPLIB_OPENSSL_SUPPORT") + add_library(gguf-model-data STATIC gguf-model-data.cpp) + target_link_libraries(gguf-model-data PRIVATE common cpp-httplib) + target_include_directories(gguf-model-data PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}) + + add_executable(test-gguf-model-data test-gguf-model-data.cpp) + target_link_libraries(test-gguf-model-data PRIVATE gguf-model-data common) + llama_test(test-gguf-model-data LABEL "model") + endif() +endif() # dummy executable - not installed get_filename_component(TEST_TARGET test-c.c NAME_WE) @@ -264,3 +284,6 @@ target_link_libraries(${TEST_TARGET} PRIVATE llama) llama_build_and_test(test-alloc.cpp) target_include_directories(test-alloc PRIVATE ${PROJECT_SOURCE_DIR}/ggml/src) + +llama_build(export-graph-ops.cpp) +target_include_directories(export-graph-ops PRIVATE ${PROJECT_SOURCE_DIR}/ggml/src) diff --git a/tests/export-graph-ops.cpp b/tests/export-graph-ops.cpp new file mode 100644 index 0000000000..754089d068 --- /dev/null +++ b/tests/export-graph-ops.cpp @@ -0,0 +1,169 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" +#include "../src/llama-ext.h" +#include "ggml.h" + +#include +#include +#include +#include +#include + +struct input_tensor { + ggml_type type; + std::array ne; + std::array nb; + + input_tensor(ggml_type type, int64_t * ne, size_t * nb): type(type) { + memcpy(this->ne.data(), ne, 4 * sizeof(int64_t)); + memcpy(this->nb.data(), nb, 4 * sizeof(size_t)); + } + + bool operator<(const input_tensor &b) const { + return std::tie(type, ne, nb) < + std::tie(b.type, b.ne, b.nb); + } + + void serialize(std::ostream& out) const { + out << type << ' '; + for (size_t i = 0; i < 4; i++) { + out << ne[i] << ' '; + } + for (size_t i = 0; i < 4; i++) { + out << nb[i] << ' '; + } + } +}; + +struct test_object { + ggml_op op; + ggml_type type; + std::array ne; + std::vector op_params; + std::vector sources; + std::string name; + + void serialize(std::ostream& out) const { + out << op << ' ' << type << ' '; + for (size_t i = 0; i < 4; i++) { + out << ne[i] << ' '; + } + + out << op_params.size() << ' '; + for (size_t i = 0; i < op_params.size(); i++) { + out << op_params[i] << ' '; + } + + out << sources.size() << ' '; + for (size_t s = 0; s < sources.size(); s++) { + sources[s].serialize(out); + } + + if (!name.empty()) { + out << name; + } else { + out << '-'; + } + + out << '\n'; + } + + bool operator<(const test_object &b) const { + return std::tie(op, type, ne, op_params, sources) < + std::tie(b.op, b.type, b.ne, b.op_params, b.sources); + } +}; + +static void extract_graph_ops(ggml_cgraph * cgraph, const char * label, std::set & tests) { + int n_nodes = ggml_graph_n_nodes(cgraph); + int n_skipped = 0; + int n_before = (int) tests.size(); + for (int i = 0; i < n_nodes; i++) { + ggml_tensor * node = ggml_graph_node(cgraph, i); + + if (node->op == GGML_OP_NONE || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE) { + n_skipped++; + continue; + } + + test_object test; + + test.op = node->op; + test.type = node->type; + memcpy(&test.ne, node->ne, 4 * sizeof(int64_t)); + + test.op_params.resize(GGML_MAX_OP_PARAMS / sizeof(int32_t)); + memcpy(test.op_params.data(), node->op_params, GGML_MAX_OP_PARAMS); + + for (size_t s = 0; s < GGML_MAX_SRC; s++) { + if (node->src[s] == nullptr) { + break; + } + + test.sources.emplace_back(node->src[s]->type, node->src[s]->ne, node->src[s]->nb); + } + + test.name = node->name; + tests.insert(test); + } + + int n_new = (int) tests.size() - n_before; + LOG_INF("%s: %d unique ops, %d total nodes, %d skipped (view ops)\n", + label, n_new, n_nodes, n_skipped); +} + +int main(int argc, char ** argv) { + common_params params; + params.out_file = "tests.txt"; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_GRAPH_OPS)) { + return 1; + } + + common_init(); + + // Load CPU-only + ggml_backend_dev_t cpu_device = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + params.devices = { cpu_device, nullptr }; + params.fit_params = false; + params.n_gpu_layers = 0; + + params.warmup = false; + + auto init_result = common_init_from_params(params); + + llama_context * ctx = init_result->context(); + + const uint32_t n_seqs = llama_n_seq_max(ctx); + const uint32_t n_tokens = std::min(llama_n_ctx(ctx), llama_n_ubatch(ctx)); + + std::set tests; + + auto * gf_pp = llama_graph_reserve(ctx, n_tokens, n_seqs, n_tokens); + if (!gf_pp) { + throw std::runtime_error("failed to reserve prompt processing graph"); + } + extract_graph_ops(gf_pp, "pp", tests); + + auto * gf_tg = llama_graph_reserve(ctx, n_seqs, n_seqs, n_seqs); + if (!gf_tg) { + throw std::runtime_error("failed to reserve token generation graph"); + } + extract_graph_ops(gf_tg, "tg", tests); + + LOG_INF("%d unique ops total\n", (int) tests.size()); + + std::ofstream f(params.out_file); + + if (!f.is_open()) { + throw std::runtime_error("Unable to open output file"); + } + + for (const auto& test : tests) { + test.serialize(f); + } + + return 0; +} diff --git a/tests/gguf-model-data.cpp b/tests/gguf-model-data.cpp new file mode 100644 index 0000000000..3bc82c88da --- /dev/null +++ b/tests/gguf-model-data.cpp @@ -0,0 +1,613 @@ +// GGUF binary parser adapted from the huggingface/gguf package. +// Reference: https://github.com/huggingface/huggingface.js + +#include "gguf-model-data.h" + +#include "common.h" +#include "gguf.h" + +#include +#include +#include +#include +#include + +#include "http.h" +#define JSON_ASSERT GGML_ASSERT +#include + +// Equivalent of RangeView +struct gguf_buf_reader { + const char * data; + size_t size; + size_t pos; + + gguf_buf_reader(const std::vector & buf) : data(buf.data()), size(buf.size()), pos(0) {} + + bool has_n_bytes(size_t n) const { + return pos + n <= size; + } + + template + bool read_val(T & out) { + if (!has_n_bytes(sizeof(T))) { + return false; + } + memcpy(&out, data + pos, sizeof(T)); + pos += sizeof(T); + return true; + } + + bool read_str(std::string & out) { + uint64_t len; + if (!read_val(len)) { + return false; + } + if (!has_n_bytes((size_t)len)) { + return false; + } + out.assign(data + pos, (size_t)len); + pos += (size_t)len; + return true; + } + + bool skip(size_t n) { + if (!has_n_bytes(n)) { + return false; + } + pos += n; + return true; + } +}; + +static size_t gguf_val_type_size(int32_t vtype) { + switch (vtype) { + case GGUF_TYPE_UINT8: return 1; + case GGUF_TYPE_INT8: return 1; + case GGUF_TYPE_UINT16: return 2; + case GGUF_TYPE_INT16: return 2; + case GGUF_TYPE_UINT32: return 4; + case GGUF_TYPE_INT32: return 4; + case GGUF_TYPE_FLOAT32: return 4; + case GGUF_TYPE_BOOL: return 1; + case GGUF_TYPE_UINT64: return 8; + case GGUF_TYPE_INT64: return 8; + case GGUF_TYPE_FLOAT64: return 8; + default: return 0; // string/array handled separately + } +} + +// Equivalent of readMetadataValue(), skips unused values rather than storing +static bool gguf_skip_value(gguf_buf_reader & r, int32_t vtype) { + if (vtype == GGUF_TYPE_STRING) { + std::string tmp; + return r.read_str(tmp); + } + if (vtype == GGUF_TYPE_ARRAY) { + int32_t elem_type; + uint64_t count; + if (!r.read_val(elem_type)) { + return false; + } + if (!r.read_val(count)) { + return false; + } + if (elem_type == GGUF_TYPE_STRING) { + for (uint64_t i = 0; i < count; i++) { + std::string tmp; + if (!r.read_str(tmp)) { + return false; + } + } + return true; + } + if (elem_type == GGUF_TYPE_ARRAY) { + // nested arrays - recurse + for (uint64_t i = 0; i < count; i++) { + if (!gguf_skip_value(r, GGUF_TYPE_ARRAY)) { + return false; + } + } + return true; + } + size_t elem_sz = gguf_val_type_size(elem_type); + if (elem_sz == 0) { + return false; + } + return r.skip((size_t)count * elem_sz); + } + size_t sz = gguf_val_type_size(vtype); + if (sz == 0) { + return false; + } + return r.skip(sz); +} + +static bool gguf_read_uint32_val(gguf_buf_reader & r, int32_t vtype, uint32_t & out) { + if (vtype == GGUF_TYPE_UINT8) { + uint8_t v; + if (!r.read_val(v)) { + return false; + } + out = v; + return true; + } + if (vtype == GGUF_TYPE_INT8) { + int8_t v; + if (!r.read_val(v)) { + return false; + } + out = (uint32_t)v; + return true; + } + if (vtype == GGUF_TYPE_UINT16) { + uint16_t v; + if (!r.read_val(v)) { + return false; + } + out = v; + return true; + } + if (vtype == GGUF_TYPE_INT16) { + int16_t v; + if (!r.read_val(v)) { + return false; + } + out = (uint32_t)v; + return true; + } + if (vtype == GGUF_TYPE_UINT32) { + uint32_t v; + if (!r.read_val(v)) { + return false; + } + out = v; + return true; + } + if (vtype == GGUF_TYPE_INT32) { + int32_t v; + if (!r.read_val(v)) { + return false; + } + out = (uint32_t)v; + return true; + } + if (vtype == GGUF_TYPE_UINT64) { + uint64_t v; + if (!r.read_val(v)) { + return false; + } + out = (uint32_t)v; + return true; + } + if (vtype == GGUF_TYPE_INT64) { + int64_t v; + if (!r.read_val(v)) { + return false; + } + out = (uint32_t)v; + return true; + } + return false; +} + +// Follows the same header -> KV -> tensor parsing sequence as gguf() huggingface/gguf +static std::optional gguf_parse_meta(const std::vector & buf) { + gguf_buf_reader r(buf); + + // Header: magic(4) + version(4) + tensor_count(8) + kv_count(8) = 24 bytes minimum + uint32_t magic_raw; + if (!r.read_val(magic_raw)) { + return std::nullopt; + } + if (memcmp(&magic_raw, "GGUF", 4) != 0) { + fprintf(stderr, "gguf_parse_meta: invalid magic\n"); + return std::nullopt; + } + + uint32_t version; + if (!r.read_val(version)) { + return std::nullopt; + } + if (version < 2 || version > 3) { + fprintf(stderr, "gguf_parse_meta: unsupported version %u\n", version); + return std::nullopt; + } + + int64_t tensor_count_raw; + int64_t kv_count_raw; + if (!r.read_val(tensor_count_raw)) { + return std::nullopt; + } + if (!r.read_val(kv_count_raw)) { + return std::nullopt; + } + + uint64_t tensor_count = (uint64_t)tensor_count_raw; + uint64_t kv_count = (uint64_t)kv_count_raw; + + gguf_remote_model model; + + std::string arch_prefix; + + // Parse KV pairs + for (uint64_t i = 0; i < kv_count; i++) { + std::string key; + if (!r.read_str(key)) { + return std::nullopt; + } + + int32_t vtype; + if (!r.read_val(vtype)) { + return std::nullopt; + } + + if (key == "general.architecture" && vtype == GGUF_TYPE_STRING) { + if (!r.read_str(model.architecture)) { + return std::nullopt; + } + arch_prefix = model.architecture + "."; + continue; + } + + // Extract split.count for proper handling of split files + if (key == "split.count") { + uint32_t v; + if (!gguf_read_uint32_val(r, vtype, v)) { + return std::nullopt; + } + model.n_split = (uint16_t)v; + continue; + } + + // Extract split.tensors.count so we can verify we have all tensors + if (key == "split.tensors.count") { + uint32_t v; + if (!gguf_read_uint32_val(r, vtype, v)) { + return std::nullopt; + } + model.n_split_tensors = v; + continue; + } + + if (!arch_prefix.empty()) { + uint32_t * target = nullptr; + + if (key == arch_prefix + "embedding_length") { target = &model.n_embd; } + else if (key == arch_prefix + "feed_forward_length") { target = &model.n_ff; } + else if (key == arch_prefix + "block_count") { target = &model.n_layer; } + else if (key == arch_prefix + "attention.head_count") { target = &model.n_head; } + else if (key == arch_prefix + "attention.head_count_kv") { target = &model.n_head_kv; } + else if (key == arch_prefix + "expert_count") { target = &model.n_expert; } + else if (key == arch_prefix + "attention.key_length") { target = &model.n_embd_head_k; } + else if (key == arch_prefix + "attention.value_length") { target = &model.n_embd_head_v; } + + if (target) { + if (!gguf_read_uint32_val(r, vtype, *target)) { + return std::nullopt; + } + continue; + } + } + + if (!gguf_skip_value(r, vtype)) { + return std::nullopt; + } + } + + // Parse tensor info entries + model.tensors.reserve((size_t)tensor_count); + for (uint64_t i = 0; i < tensor_count; i++) { + gguf_remote_tensor t; + + if (!r.read_str(t.name)) { + return std::nullopt; + } + if (!r.read_val(t.n_dims)) { + return std::nullopt; + } + + if (t.n_dims > 4) { + fprintf(stderr, "gguf_parse_meta: tensor '%s' has %u dims (max 4)\n", t.name.c_str(), t.n_dims); + return std::nullopt; + } + + for (uint32_t d = 0; d < t.n_dims; d++) { + if (!r.read_val(t.ne[d])) { + return std::nullopt; + } + } + + int32_t type_raw; + if (!r.read_val(type_raw)) { + return std::nullopt; + } + t.type = (ggml_type)type_raw; + + uint64_t offset; + if (!r.read_val(offset)) { + return std::nullopt; + } + + // Infer n_vocab from token_embd.weight + if (t.name == "token_embd.weight") { + model.n_vocab = (uint32_t)t.ne[1]; + } + + model.tensors.push_back(std::move(t)); + } + + return model; +} + +// cache handling for local download +static std::string get_default_cache_dir() { + return fs_get_cache_directory() + "gguf-headers/"; +} + +static std::string sanitize_for_path(const std::string & s) { + std::string out = s; + for (char & c : out) { + if (c == '/' || c == '\\' || c == ':') { + c = '_'; + } + } + return out; +} + +static bool read_file(const std::string & path, std::vector & out) { + std::ifstream f(path, std::ios::binary | std::ios::ate); + if (!f.good()) { + return false; + } + auto sz = f.tellg(); + if (sz <= 0) { + return false; + } + out.resize((size_t)sz); + f.seekg(0); + f.read(out.data(), sz); + return f.good(); +} + +static bool write_file(const std::string & path, const std::vector & data) { + std::ofstream f(path, std::ios::binary | std::ios::trunc); + if (!f.good()) { + return false; + } + f.write(data.data(), (std::streamsize)data.size()); + return f.good(); +} + +// HuggingFace file auto-detection and HTTP download +static std::pair> gguf_http_get( + const std::string & url, + const httplib::Headers & headers = {}, + int timeout_sec = 60) { + try { + auto [cli, parts] = common_http_client(url); + + if (timeout_sec > 0) { + cli.set_read_timeout(timeout_sec, 0); + cli.set_write_timeout(timeout_sec, 0); + } + cli.set_connection_timeout(30, 0); + + std::vector body; + auto res = cli.Get(parts.path, headers, + [&](const char * data, size_t len) { + body.insert(body.end(), data, data + len); + return true; + }, nullptr); + + if (!res) { + fprintf(stderr, "gguf_fetch: HTTP request failed for %s (error %d)\n", + url.c_str(), (int)res.error()); + return {-1, {}}; + } + return {res->status, std::move(body)}; + } catch (const std::exception & e) { + fprintf(stderr, "gguf_fetch: HTTP error: %s\n", e.what()); + return {-1, {}}; + } +} + +// Find the filename for given repo/quant. +// For split models, returns the first shard (the one containing "00001-of-") +// split_prefix is set to the portion before "-00001-of-XXXXX.gguf" when a split file is found +static std::string detect_gguf_filename(const std::string & repo, const std::string & quant, + std::string & split_prefix) { + split_prefix.clear(); + std::string api_url = "https://huggingface.co/api/models/" + repo; + + auto [code, body] = gguf_http_get(api_url, {}, 30); + if (code != 200 || body.empty()) { + fprintf(stderr, "gguf_fetch: failed to query HF API for %s (HTTP %ld)\n", repo.c_str(), code); + return ""; + } + + nlohmann::json j; + try { + j = nlohmann::json::parse(body.begin(), body.end()); + } catch (...) { + fprintf(stderr, "gguf_fetch: failed to parse HF API response\n"); + return ""; + } + + if (!j.contains("siblings") || !j["siblings"].is_array()) { + fprintf(stderr, "gguf_fetch: unexpected HF API response format\n"); + return ""; + } + + std::vector matches; + std::string quant_upper = quant; + for (char & c : quant_upper) { c = (char)toupper(c); } + + for (const auto & sibling : j["siblings"]) { + if (!sibling.contains("rfilename")) { continue; } + std::string fname = sibling["rfilename"].get(); + if (fname.size() < 5 || fname.substr(fname.size() - 5) != ".gguf") { + continue; + } + + std::string fname_upper = fname; + for (char & c : fname_upper) { c = (char)toupper(c); } + if (fname_upper.find(quant_upper) != std::string::npos) { + matches.push_back(fname); + } + } + + if (matches.empty()) { + fprintf(stderr, "gguf_fetch: no .gguf files matching '%s' in %s\n", quant.c_str(), repo.c_str()); + return ""; + } + + std::sort(matches.begin(), matches.end()); + + // Prefer non-split, non-supplementary file + for (const auto & m : matches) { + if (m.find("-of-") == std::string::npos && m.find("mmproj") == std::string::npos) { + return m; + } + } + + // Return the first shard (00001-of-) and extract the prefix + for (const auto & m : matches) { + auto pos = m.find("-00001-of-"); + if (pos != std::string::npos) { + split_prefix = m.substr(0, pos); + return m; + } + } + + return matches[0]; +} + +static std::optional fetch_and_parse( + const std::string & repo, + const std::string & filename, + const std::string & cache_path) { + std::string url = "https://huggingface.co/" + repo + "/resolve/main/" + filename; + + // Progressive download inspired by RangeView.fetchChunk() + // Start at 2MB, double each time, cap at 64MB + size_t chunk_size = 2 * 1024 * 1024; + const size_t max_chunk = 64 * 1024 * 1024; + + while (chunk_size <= max_chunk) { + fprintf(stderr, "gguf_fetch: downloading %zu bytes from %s\n", chunk_size, filename.c_str()); + + char range_buf[64]; + snprintf(range_buf, sizeof(range_buf), "bytes=0-%zu", chunk_size - 1); + httplib::Headers headers = {{"Range", range_buf}}; + + auto [code, body] = gguf_http_get(url, headers, 120); + if (code != 200 && code != 206) { + fprintf(stderr, "gguf_fetch: HTTP %ld fetching %s\n", code, url.c_str()); + return std::nullopt; + } + + if (body.empty()) { + fprintf(stderr, "gguf_fetch: empty response\n"); + return std::nullopt; + } + + auto result = gguf_parse_meta(body); + if (result.has_value()) { + write_file(cache_path, body); + return result; + } + + if (code == 200) { + fprintf(stderr, "gguf_fetch: server returned full response but metadata parse failed\n"); + return std::nullopt; + } + + // Parse failed, try larger chunk + chunk_size *= 2; + } + + fprintf(stderr, "gguf_fetch: metadata exceeds 64MB, giving up\n"); + return std::nullopt; +} + +// Try cache first, then fetch and parse a single GGUF shard. +static std::optional fetch_or_cached( + const std::string & repo, + const std::string & filename, + const std::string & cdir, + const std::string & repo_part) { + std::string fname_part = sanitize_for_path(filename); + std::string cache_path = cdir + "/" + repo_part + "--" + fname_part + ".partial"; + + { + std::vector cached; + if (std::filesystem::exists(cache_path) && read_file(cache_path, cached)) { + auto result = gguf_parse_meta(cached); + if (result.has_value()) { + fprintf(stderr, "gguf_fetch: loaded from cache: %s\n", cache_path.c_str()); + return result; + } + } + } + + fs_create_directory_with_parents(cdir); + return fetch_and_parse(repo, filename, cache_path); +} + +std::optional gguf_fetch_model_meta( + const std::string & repo, + const std::string & quant, + const std::string & cache_dir) { + std::string cdir = cache_dir.empty() ? get_default_cache_dir() : cache_dir; + std::string repo_part = sanitize_for_path(repo); + + std::string split_prefix; + std::string filename = detect_gguf_filename(repo, quant, split_prefix); + if (filename.empty()) { + return std::nullopt; + } + + auto model_opt = fetch_or_cached(repo, filename, cdir, repo_part); + if (!model_opt.has_value()) { + fprintf(stderr, "gguf_fetch: failed to fetch %s\n", filename.c_str()); + return std::nullopt; + } + + auto & model = model_opt.value(); + + // If the model is split across multiple files we need to fetch the remaining shards metadata + if (model.n_split > 1) { + if (split_prefix.empty()) { + fprintf(stderr, "gguf_fetch: model reports %u splits but filename has no split pattern\n", model.n_split); + return std::nullopt; + } + + fprintf(stderr, "gguf_fetch: split model with %u shards, fetching remaining %u...\n", + model.n_split, model.n_split - 1); + + for (int i = 2; i <= model.n_split; i++) { + char num_buf[6], total_buf[6]; + snprintf(num_buf, sizeof(num_buf), "%05d", i); + snprintf(total_buf, sizeof(total_buf), "%05d", (int)model.n_split); + std::string shard_name = split_prefix + "-" + num_buf + "-of-" + total_buf + ".gguf"; + + auto shard = fetch_or_cached(repo, shard_name, cdir, repo_part); + if (!shard.has_value()) { + fprintf(stderr, "gguf_fetch: failed to fetch shard %d: %s\n", i, shard_name.c_str()); + return std::nullopt; + } + + model.tensors.insert(model.tensors.end(), + std::make_move_iterator(shard->tensors.begin()), + std::make_move_iterator(shard->tensors.end())); + } + + if (model.n_split_tensors > 0 && model.tensors.size() != model.n_split_tensors) { + fprintf(stderr, "gguf_fetch: WARNING: expected %u tensors from split.tensors.count, got %zu\n", + model.n_split_tensors, model.tensors.size()); + } + } + + return model_opt; +} diff --git a/tests/gguf-model-data.h b/tests/gguf-model-data.h new file mode 100644 index 0000000000..ed433791ad --- /dev/null +++ b/tests/gguf-model-data.h @@ -0,0 +1,42 @@ +#pragma once + +#include "ggml.h" + +#include +#include +#include +#include + +struct gguf_remote_tensor { + std::string name; + ggml_type type = GGML_TYPE_F32; + int64_t ne[4] = {1, 1, 1, 1}; // dimensions, unused dims = 1 + uint32_t n_dims = 0; +}; + +struct gguf_remote_model { + // Selected KV metadata + std::string architecture; // general.architecture + uint32_t n_embd = 0; // .embedding_length + uint32_t n_ff = 0; // .feed_forward_length + uint32_t n_vocab = 0; // inferred from token_embd.weight ne[1] + uint32_t n_layer = 0; // .block_count + uint32_t n_head = 0; // .attention.head_count + uint32_t n_head_kv = 0; // .attention.head_count_kv + uint32_t n_expert = 0; // .expert_count (0 if absent) + uint32_t n_embd_head_k = 0; // .attention.key_length + uint32_t n_embd_head_v = 0; // .attention.value_length + uint16_t n_split = 0; // split.count (0 = not split) + uint32_t n_split_tensors = 0; // split.tensors.count (0 if not split) + + std::vector tensors; +}; + +// Fetch model metadata from HuggingFace with local caching. +// repo: e.g., "ggml-org/Qwen3-32B-GGUF" +// quant: e.g., "Q8_0" -- auto-detects filename (including first shard of split models) +// Returns nullopt if download fails or network is unavailable. +std::optional gguf_fetch_model_meta( + const std::string & repo, + const std::string & quant = "Q8_0", + const std::string & cache_dir = ""); // empty = default diff --git a/tests/peg-parser/test-basic.cpp b/tests/peg-parser/test-basic.cpp index 1bda6f2e69..b6af61491d 100644 --- a/tests/peg-parser/test-basic.cpp +++ b/tests/peg-parser/test-basic.cpp @@ -1,3 +1,4 @@ +#include "peg-parser.h" #include "tests.h" void test_basic(testing & t) { @@ -119,7 +120,7 @@ void test_basic(testing & t) { return p.literal("hello") + p.optional(p.literal(" world")); }); - auto ctx = common_peg_parse_context("hello", false); + auto ctx = common_peg_parse_context("hello"); auto result = parser.parse(ctx); t.assert_equal("optional_absent", true, result.success()); t.assert_equal("optional_absent_end", 5u, result.end); @@ -131,7 +132,7 @@ void test_basic(testing & t) { return p.literal("hello") + p.optional(p.literal(" world")); }); - auto ctx = common_peg_parse_context("hello ", true); + auto ctx = common_peg_parse_context("hello ", COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); t.assert_equal("partial_match_need_more", true, result.need_more_input()); }); @@ -214,7 +215,7 @@ void test_basic(testing & t) { t.test("sequence_partial_match_1", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.literal("") + p.literal(""); }); - auto ctx = common_peg_parse_context("") + p.literal(""); }); - auto ctx = common_peg_parse_context("") + p.literal(""); }); - auto ctx = common_peg_parse_context("I am common_chat_combinator_parser", true); + auto ctx = common_peg_parse_context("I am common_chat_combinator_parser", COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); t.assert_equal("sequence_no_match", true, result.fail()); }); @@ -259,7 +260,7 @@ void test_basic(testing & t) { t.test("choices_partial_match_1", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.literal("option1") | p.literal("option2"); }); - auto ctx = common_peg_parse_context("opt", true); + auto ctx = common_peg_parse_context("opt", COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); t.assert_equal("choices_partial_match_1", true, result.need_more_input()); }); @@ -269,7 +270,7 @@ void test_basic(testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.literal("choice_a") | p.literal("choice_b"); }); - auto ctx = common_peg_parse_context("choice", true); + auto ctx = common_peg_parse_context("choice", COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); t.assert_equal("choices_partial_match_2", true, result.need_more_input()); }); @@ -278,7 +279,7 @@ void test_basic(testing & t) { t.test("choices_full_match_1", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.literal("first") | p.literal("second"); }); - auto ctx = common_peg_parse_context("first", false); + auto ctx = common_peg_parse_context("first"); auto result = parser.parse(ctx); t.assert_equal("choices_full_match_1", true, result.success()); }); @@ -287,7 +288,7 @@ void test_basic(testing & t) { t.test("choices_full_match_2", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.literal("alpha") | p.literal("beta"); }); - auto ctx = common_peg_parse_context("beta", false); + auto ctx = common_peg_parse_context("beta"); auto result = parser.parse(ctx); t.assert_equal("choices_full_match_2", true, result.success()); }); @@ -296,7 +297,7 @@ void test_basic(testing & t) { t.test("choices_no_match", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.literal("good") | p.literal("better"); }); - auto ctx = common_peg_parse_context("best", false); + auto ctx = common_peg_parse_context("best"); auto result = parser.parse(ctx); t.assert_equal("choices_no_match", true, result.fail()); }); @@ -305,7 +306,7 @@ void test_basic(testing & t) { t.test("zero_or_more_partial_match_1", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.zero_or_more(p.literal("ab")); }); - auto ctx = common_peg_parse_context("a", true); + auto ctx = common_peg_parse_context("a", COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); t.assert_equal("zero_or_more_partial_match_1", true, result.need_more_input()); }); @@ -314,7 +315,7 @@ void test_basic(testing & t) { t.test("zero_or_more_partial_match_2", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.zero_or_more(p.literal("xy")); }); - auto ctx = common_peg_parse_context("xyx", true); + auto ctx = common_peg_parse_context("xyx", COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); t.assert_equal("zero_or_more_partial_match_2", true, result.need_more_input()); }); @@ -323,7 +324,7 @@ void test_basic(testing & t) { t.test("zero_or_more_full_match", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.zero_or_more(p.literal("test")); }); - auto ctx = common_peg_parse_context("test", false); + auto ctx = common_peg_parse_context("test"); auto result = parser.parse(ctx); t.assert_equal("zero_or_more_full_match", true, result.success()); }); @@ -332,7 +333,7 @@ void test_basic(testing & t) { t.test("one_or_more_partial_match_1", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.one_or_more(p.literal("repeat")); }); - auto ctx = common_peg_parse_context("rep", true); + auto ctx = common_peg_parse_context("rep", COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); t.assert_equal("one_or_more_partial_match_1", true, result.need_more_input()); }); @@ -341,7 +342,7 @@ void test_basic(testing & t) { t.test("one_or_more_partial_match_2", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.one_or_more(p.literal("ab")); }); - auto ctx = common_peg_parse_context("aba", true); + auto ctx = common_peg_parse_context("aba", COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); t.assert_equal("one_or_more_partial_match_2", true, result.need_more_input()); }); @@ -350,7 +351,7 @@ void test_basic(testing & t) { t.test("one_or_more_full_match", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.one_or_more(p.literal("single")); }); - auto ctx = common_peg_parse_context("single", false); + auto ctx = common_peg_parse_context("single"); auto result = parser.parse(ctx); t.assert_equal("one_or_more_full_match", true, result.success()); }); @@ -359,7 +360,7 @@ void test_basic(testing & t) { t.test("one_or_more_no_match", [&](testing & t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.one_or_more(p.literal("()")); }); - auto ctx = common_peg_parse_context("success", false); + auto ctx = common_peg_parse_context("success"); auto result = parser.parse(ctx); t.assert_equal("one_or_more_no_match", true, result.fail()); }); @@ -375,7 +376,7 @@ void test_basic(testing & t) { return p.rule("value", p.ref("number") | p.ref("list")); }); - common_peg_parse_context ctx("1", false); + common_peg_parse_context ctx("1"); auto result = value_parser.parse(ctx); t.assert_equal("result_is_success", true, result.success()); @@ -389,7 +390,7 @@ void test_basic(testing & t) { return p.rule("value", p.ref("number") | p.ref("list")); }); - common_peg_parse_context ctx("[1]", false); + common_peg_parse_context ctx("[1]"); auto result = value_parser.parse(ctx); t.assert_equal("result_is_success", true, result.success()); @@ -403,7 +404,7 @@ void test_basic(testing & t) { return p.rule("value", p.ref("number") | p.ref("list")); }); - common_peg_parse_context ctx("[[2]]", false); + common_peg_parse_context ctx("[[2]]"); auto result = value_parser.parse(ctx); t.assert_equal("result_is_success", true, result.success()); @@ -417,7 +418,7 @@ void test_basic(testing & t) { return p.rule("value", p.ref("number") | p.ref("list")); }); - common_peg_parse_context ctx("[[[3]]]", false); + common_peg_parse_context ctx("[[[3]]]"); auto result = value_parser.parse(ctx); t.assert_equal("result_is_success", true, result.success()); @@ -431,7 +432,7 @@ void test_basic(testing & t) { return p.rule("value", p.ref("number") | p.ref("list")); }); - common_peg_parse_context ctx("[[", true); + common_peg_parse_context ctx("[[", COMMON_PEG_PARSE_FLAG_LENIENT); auto result = value_parser.parse(ctx); t.assert_equal("result_is_need_more_input", true, result.need_more_input()); @@ -445,10 +446,26 @@ void test_basic(testing & t) { return p.rule("value", p.ref("number") | p.ref("list")); }); - common_peg_parse_context ctx("[a]", false); + common_peg_parse_context ctx("[a]"); auto result = value_parser.parse(ctx); t.assert_equal("result_is_fail", true, result.fail()); }); + + // Test markers + t.test("marker", [](testing &t) { + auto bracket_parser = build_peg_parser([](common_peg_parser_builder & p) { + return p.marker(); + }); + + common_peg_parse_context ctx_square("[marker]"); + common_peg_parse_context ctx_sharp(""); + + auto result_square = bracket_parser.parse(ctx_square); + auto result_sharp = bracket_parser.parse(ctx_sharp); + + t.assert_true("result_square_is_success", result_square.success()); + t.assert_true("result_sharp_is_success", result_sharp.success()); + }); }); } diff --git a/tests/peg-parser/test-json-parser.cpp b/tests/peg-parser/test-json-parser.cpp index 48351cd66f..5dd00115ce 100644 --- a/tests/peg-parser/test-json-parser.cpp +++ b/tests/peg-parser/test-json-parser.cpp @@ -46,7 +46,7 @@ void test_json_parser(testing &t) { auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); }); std::string input = R"({"name": "test", "value": )"; - common_peg_parse_context ctx(input, true); + common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = json.parse(ctx); @@ -58,7 +58,7 @@ void test_json_parser(testing &t) { auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); }); std::string input = R"([1, 2, 3, )"; - common_peg_parse_context ctx(input, true); + common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = json.parse(ctx); @@ -70,7 +70,7 @@ void test_json_parser(testing &t) { auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); }); std::string input = R"({"data": {"nested": )"; - common_peg_parse_context ctx(input, true); + common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = json.parse(ctx); @@ -84,7 +84,7 @@ void test_json_parser(testing &t) { t.test("success", [&](testing &t) { std::string input = R"("name": "bob")"; - common_peg_parse_context ctx(input, false); + common_peg_parse_context ctx(input); auto result = parser.parse(ctx); t.assert_true("success", result.success()); @@ -92,7 +92,7 @@ void test_json_parser(testing &t) { t.test("partial", [&](testing &t) { std::string input = R"("name": "bo)"; - common_peg_parse_context ctx(input, true); + common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); t.assert_true("need more input", result.need_more_input()); @@ -100,7 +100,7 @@ void test_json_parser(testing &t) { t.test("failed", [&](testing &t) { std::string input = R"([])"; - common_peg_parse_context ctx(input, false); + common_peg_parse_context ctx(input); auto result = parser.parse(ctx); t.assert_true("fail", result.fail()); diff --git a/tests/peg-parser/test-python-dict-parser.cpp b/tests/peg-parser/test-python-dict-parser.cpp new file mode 100644 index 0000000000..1a549106b8 --- /dev/null +++ b/tests/peg-parser/test-python-dict-parser.cpp @@ -0,0 +1,318 @@ +#include "tests.h" + +void test_python_dict_parser(testing &t) { + // Test parsing a simple Python dict object with single quotes + t.test("simple Python dict object parsing", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{'name': 'test', 'value': 42, 'flag': True}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test parsing a Python array with mixed types + t.test("Python array with mixed types", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "[1, 'hello', True, None, 3.14]"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test parsing nested Python dict with objects and arrays + t.test("nested Python dict with objects and arrays", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = + "{'users': [{'id': 1, 'name': 'Alice'}, {'id': 2, 'name': 'Bob'}], 'count': 2, 'metadata': {'version': '1.0', 'tags': ['admin', 'user']}}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test parsing Python dict with escaped single quotes + t.test("Python dict with escaped single quotes", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{'message': 'It\\'s working!'}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test parsing Python dict with double quotes inside single quotes + t.test("Python dict with double quotes inside single quotes", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{'quote': 'He said \"Hello\"'}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test the example from the requirements + t.test("complex Python dict example from requirements", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{ 'obj' : { 'something': 1, 'other \"something\"' : 'foo\\'s bar' } }"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test need_more_input() parsing - incomplete object + t.test("need_more_input() parsing - incomplete object", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{'name': 'test', 'value': "; + common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_need_more_input", true, result.need_more_input()); + }); + + // Test need_more_input() parsing - incomplete single-quoted string + t.test("need_more_input() parsing - incomplete single-quoted string", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{'name': 'test"; + common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_need_more_input", true, result.need_more_input()); + }); + + // Test unicode in Python dict strings + t.test("unicode in Python dict strings", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{'message': 'Hello, 世界!'}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test Python dict with unicode escapes + t.test("Python dict with unicode escapes", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{'unicode': 'Hello\\u0041'}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test that Python parser accepts double-quoted strings too + t.test("Python parser accepts double-quoted strings", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{\"name\": \"test\"}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test Python parser with mixed quote styles + t.test("Python parser with mixed quote styles", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + std::string input = "{\"name\": 'test', 'value': \"hello\"}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + + t.assert_equal("result_is_success", true, result.success()); + t.assert_equal("result_end", input.size(), result.end); + }); + + // Test Python True/False/None + t.test("Python True/False/None", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { return p.python_value(); }); + + t.test("True", [&](testing &t) { + std::string input = "True"; + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + + t.test("False", [&](testing &t) { + std::string input = "False"; + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + + t.test("None", [&](testing &t) { + std::string input = "None"; + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + + t.test("rejects JSON-style true/false/null", [&](testing &t) { + for (const auto & kw : {"true", "false", "null"}) { + std::string input = kw; + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + t.assert_true(std::string("rejects ") + kw, result.fail()); + } + }); + }); + + // Test single-quoted string content parser directly + t.test("single-quoted string content parser", [](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { + return p.sequence({ p.literal("'"), p.string_content('\''), p.literal("'"), p.space() }); + }); + + t.test("simple string", [&](testing &t) { + std::string input = "'hello'"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + + t.test("string with escaped single quote", [&](testing &t) { + std::string input = "'it\\'s'"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + + t.test("string with double quotes", [&](testing &t) { + std::string input = "'say \"hello\"'"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + + t.test("incomplete string", [&](testing &t) { + std::string input = "'hello"; + common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT); + + auto result = parser.parse(ctx); + t.assert_true("need_more_input", result.need_more_input()); + }); + }); + + // Test json() with pre-registered flexible json-string rule (python dict support) + t.test("json() parser with flexible json-string rule", [](testing &t) { + t.test("json() rejects single quotes by default", [&](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { + return p.json(); + }); + + std::string input = "{'name': 'test'}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + t.assert_true("fail", result.fail()); + }); + + t.test("json() accepts single quotes with pre-registered flexible json-string rule", [&](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { + // Pre-register json-string rule with both quote styles + p.rule("json-string", [&]() { + return p.choice({ p.double_quoted_string(), p.single_quoted_string() }); + }); + return p.json(); + }); + + std::string input = "{'name': 'test'}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + + t.test("json() still accepts double quotes with flexible json-string rule", [&](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { + p.rule("json-string", [&]() { + return p.choice({ p.double_quoted_string(), p.single_quoted_string() }); + }); + return p.json(); + }); + + std::string input = "{\"name\": \"test\"}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + + t.test("json() accepts mixed quote styles with flexible json-string rule", [&](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { + p.rule("json-string", [&]() { + return p.choice({ p.double_quoted_string(), p.single_quoted_string() }); + }); + return p.json(); + }); + + std::string input = "{\"name\": 'test', 'value': \"hello\"}"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + + t.test("complex nested structure with flexible json-string rule", [&](testing &t) { + auto parser = build_peg_parser([](common_peg_parser_builder & p) { + p.rule("json-string", [&]() { + return p.choice({ p.double_quoted_string(), p.single_quoted_string() }); + }); + return p.json(); + }); + + std::string input = "{ 'obj' : { 'something': 1, 'other \"something\"' : 'foo\\'s bar' } }"; + common_peg_parse_context ctx(input); + + auto result = parser.parse(ctx); + t.assert_true("success", result.success()); + t.assert_equal("end", input.size(), result.end); + }); + }); +} diff --git a/tests/peg-parser/test-unicode.cpp b/tests/peg-parser/test-unicode.cpp index 19d9b9e41c..24663d7017 100644 --- a/tests/peg-parser/test-unicode.cpp +++ b/tests/peg-parser/test-unicode.cpp @@ -58,7 +58,7 @@ void test_unicode(testing &t) { std::string test_name = "case " + std::to_string(i) + ": " + hex_dump(tc.input); t.test(test_name, [&](testing &t) { - common_peg_parse_context ctx(tc.input, true); + common_peg_parse_context ctx(tc.input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); // Assert result type matches @@ -101,7 +101,7 @@ void test_unicode(testing &t) { std::string test_name = "case " + std::to_string(i) + ": " + hex_dump(tc.input); t.test(test_name, [&](testing &t) { - common_peg_parse_context ctx(tc.input, true); + common_peg_parse_context ctx(tc.input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); // Assert result type matches @@ -142,7 +142,7 @@ void test_unicode(testing &t) { std::string test_name = "case " + std::to_string(i) + ": " + hex_dump(tc.input); t.test(test_name, [&](testing &t) { - common_peg_parse_context ctx(tc.input, true); + common_peg_parse_context ctx(tc.input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); // Assert result type matches @@ -187,7 +187,7 @@ void test_unicode(testing &t) { std::string test_name = "case " + std::to_string(i) + ": " + hex_dump(tc.input); t.test(test_name, [&](testing &t) { - common_peg_parse_context ctx(tc.input, true); + common_peg_parse_context ctx(tc.input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); // Assert result type matches @@ -225,7 +225,7 @@ void test_unicode(testing &t) { std::string test_name = "case " + std::to_string(i) + ": " + hex_dump(tc.input); t.test(test_name, [&](testing &t) { - common_peg_parse_context ctx(tc.input, false); + common_peg_parse_context ctx(tc.input); auto result = parser.parse(ctx); assert_result_equal(t, tc.expected_result, result.type); @@ -259,7 +259,7 @@ void test_unicode(testing &t) { std::string test_name = "case " + std::to_string(i) + ": " + hex_dump(tc.input); t.test(test_name, [&](testing &t) { - common_peg_parse_context ctx(tc.input, true); + common_peg_parse_context ctx(tc.input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); assert_result_equal(t, tc.expected_result, result.type); @@ -293,7 +293,7 @@ void test_unicode(testing &t) { std::string test_name = "case " + std::to_string(i) + ": " + hex_dump(tc.input); t.test(test_name, [&](testing &t) { - common_peg_parse_context ctx(tc.input, false); + common_peg_parse_context ctx(tc.input); auto result = parser.parse(ctx); assert_result_equal(t, tc.expected_result, result.type); @@ -327,10 +327,10 @@ void test_unicode(testing &t) { t.test(test_name, [&](testing &t) { auto parser = build_peg_parser([](common_peg_parser_builder& p) { - return p.sequence({p.json_string_content(), p.literal("\"")}); + return p.sequence({p.string_content('"'), p.literal("\"")}); }); - common_peg_parse_context ctx(tc.input, false); + common_peg_parse_context ctx(tc.input); auto result = parser.parse(ctx); assert_result_equal(t, tc.expected_result, result.type); @@ -364,10 +364,10 @@ void test_unicode(testing &t) { t.test(test_name, [&](testing &t) { auto parser = build_peg_parser([](common_peg_parser_builder& p) { - return p.json_string_content(); + return p.string_content('"'); }); - common_peg_parse_context ctx(tc.input, true); + common_peg_parse_context ctx(tc.input, COMMON_PEG_PARSE_FLAG_LENIENT); auto result = parser.parse(ctx); assert_result_equal(t, tc.expected_result, result.type); @@ -390,9 +390,6 @@ void test_unicode(testing &t) { // Invalid continuation byte {std::string("\xC3\x28"), "", COMMON_PEG_PARSE_RESULT_FAIL}, - - // Overlong encoding (security issue) - {std::string("\xC0\x80"), "", COMMON_PEG_PARSE_RESULT_FAIL}, }; for (size_t i = 0; i < test_cases.size(); i++) { @@ -401,10 +398,10 @@ void test_unicode(testing &t) { t.test(test_name, [&](testing &t) { auto parser = build_peg_parser([](common_peg_parser_builder& p) { - return p.json_string_content(); + return p.string_content('"'); }); - common_peg_parse_context ctx(tc.input, false); + common_peg_parse_context ctx(tc.input); auto result = parser.parse(ctx); assert_result_equal(t, tc.expected_result, result.type); @@ -430,10 +427,10 @@ void test_unicode(testing &t) { t.test(test_name, [&](testing &t) { auto parser = build_peg_parser([](common_peg_parser_builder& p) { - return p.sequence({p.json_string_content(), p.literal("\"")}); + return p.sequence({p.string_content('"'), p.literal("\"")}); }); - common_peg_parse_context ctx(tc.input, false); + common_peg_parse_context ctx(tc.input); auto result = parser.parse(ctx); assert_result_equal(t, tc.expected_result, result.type); diff --git a/tests/peg-parser/tests.h b/tests/peg-parser/tests.h index 4d3f4e9eaf..debd4286c5 100644 --- a/tests/peg-parser/tests.h +++ b/tests/peg-parser/tests.h @@ -22,3 +22,4 @@ void test_json_parser(testing &t); void test_gbnf_generation(testing &t); void test_unicode(testing &t); void test_json_serialization(testing &t); +void test_python_dict_parser(testing &t); diff --git a/tests/test-alloc.cpp b/tests/test-alloc.cpp index 95e09c97b0..7ae739ad2e 100644 --- a/tests/test-alloc.cpp +++ b/tests/test-alloc.cpp @@ -285,7 +285,7 @@ static void test_max_size_too_many_tensors() { GGML_ASSERT(backend.context->allocated_total() <= 16 + 16); } -// Scenario where there is some space left in the first buffer, but not enough to accomodate +// Scenario where there is some space left in the first buffer, but not enough to accommodate // a larger tensor, so a second buffer is required static void test_max_size_tensor_too_large() { dummy_backend backend = dummy_backend_init(32); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index e8e237c6ec..c9896cc11e 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -31,10 +31,12 @@ #include #include #include +#include #include #include #include #include +#include #include #include #include @@ -1868,9 +1870,9 @@ struct test_case { }; -// ################################### -// ## Section 2: GGML Op Defintions ## -// ################################### +// #################################### +// ## Section 2: GGML Op Definitions ## +// #################################### // The following is an example showing the bare minimum for creating a test for a GGML op. @@ -2977,6 +2979,7 @@ struct test_bin_bcast : public test_case { const std::array nr; int nf; // number of fused ops, nf == 1 -> single op (no fusion) bool perm1; // permute src1? + bool src_overlap; // src0 and src1 are overlapping views of the same buffer bool run_whole_graph() override { return nf > 1; } @@ -2992,8 +2995,8 @@ struct test_bin_bcast : public test_case { std::array ne = {10, 10, 1, 1}, std::array nr = {1, 2, 1, 1}, int nf = 1, - bool perm1 = false) - : op(op), type(type), ne(ne), nr(nr), nf(nf), perm1(perm1) {} + bool perm1 = false, bool src_overlap = false) + : op(op), type(type), ne(ne), nr(nr), nf(nf), perm1(perm1), src_overlap(src_overlap) {} ggml_tensor * build_graph(ggml_context * ctx) override { GGML_ASSERT(nf <= 16); @@ -3008,6 +3011,8 @@ struct test_bin_bcast : public test_case { b[i] = ggml_new_tensor_4d(ctx, type, ne[p[0]], ne[p[1]], ne[p[2]], ne[p[3]]); b[i] = ggml_permute(ctx, b[i], p[0], p[1], p[2], p[3]); + } else if (src_overlap) { + b[i] = ggml_view_4d(ctx, a, ne[0], ne[1], ne[2], 2 * (ne[3] / 3), a->nb[1], a->nb[2], a->nb[3], (ne[3] / 3) * a->nb[3]); } else { b[i] = ggml_new_tensor(ctx, type, 4, ne.data()); } @@ -3021,7 +3026,13 @@ struct test_bin_bcast : public test_case { ggml_set_param(b[0]); } - ggml_tensor * out = a; + ggml_tensor *out; + + if (src_overlap) { + out = ggml_view_4d(ctx, a, ne[0], ne[1], ne[2], 2 * (ne[3] / 3), a->nb[1], a->nb[2], a->nb[3], 0); + } else { + out = a; + } for (int i = 0; i < nf; ++i) { out = op(ctx, out, b[i]); @@ -3656,6 +3667,51 @@ struct test_rwkv_wkv6 : public test_case { } }; +// GGML_OP_GATED_DELTA_NET +struct test_gated_delta_net : public test_case { + const ggml_type type; + + const int64_t head_count; + const int64_t head_size; + const int64_t n_seq_tokens; + const int64_t n_seqs; + const int v_repeat; + const bool permuted; + const bool kda; + + std::string vars() override { + return VARS_TO_STR8(type, head_count, head_size, n_seq_tokens, n_seqs, v_repeat, permuted, kda); + } + + test_gated_delta_net(ggml_type type = GGML_TYPE_F32, + int64_t head_count = 4, int64_t head_size = 16, int64_t n_seq_tokens = 1, int64_t n_seqs = 1, + int v_repeat = 1, bool permuted = false, bool kda = false) + : type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs), + v_repeat(v_repeat), permuted(permuted), kda(kda) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * q; + ggml_tensor * k; + ggml_tensor * v; + if (permuted) { + // create with dims 1 and 2 swapped, then permute back to get non-contiguous layout + q = ggml_permute(ctx, ggml_new_tensor_4d(ctx, type, head_size, n_seq_tokens, head_count, n_seqs), 0, 2, 1, 3); + k = ggml_permute(ctx, ggml_new_tensor_4d(ctx, type, head_size, n_seq_tokens, head_count, n_seqs), 0, 2, 1, 3); + v = ggml_permute(ctx, ggml_new_tensor_4d(ctx, type, head_size, n_seq_tokens, head_count * v_repeat, n_seqs), 0, 2, 1, 3); + } else { + q = ggml_new_tensor_4d(ctx, type, head_size, head_count, n_seq_tokens, n_seqs); + k = ggml_new_tensor_4d(ctx, type, head_size, head_count, n_seq_tokens, n_seqs); + v = ggml_new_tensor_4d(ctx, type, head_size, head_count * v_repeat, n_seq_tokens, n_seqs); + } + const int64_t g_ne0 = kda ? head_size : 1; + ggml_tensor * g = ggml_new_tensor_4d(ctx, type, g_ne0, head_count * v_repeat, n_seq_tokens, n_seqs); + ggml_tensor * beta = ggml_new_tensor_4d(ctx, type, 1, head_count * v_repeat, n_seq_tokens, n_seqs); + ggml_tensor * state = ggml_new_tensor_2d(ctx, type, head_size * v_repeat * head_size * head_count, n_seqs); + ggml_tensor * out = ggml_gated_delta_net(ctx, q, k, v, g, beta, state); + return out; + } +}; + // GGML_OP_GATED_LINEAR_ATTN struct test_gla : public test_case { const ggml_type type; @@ -6213,7 +6269,7 @@ struct test_flash_attn_ext : public test_case { void initialize_tensors(ggml_context * ctx) override { for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { if (strcmp(t->name, "s") == 0) { - // make the sink values more noticable in order to trigger a test failure when the implementation is wrong + // make the sink values more noticeable in order to trigger a test failure when the implementation is wrong init_tensor_uniform(t, -10.0f, 10.0f); } else if (strcmp(t->name, "m") == 0) { init_tensor_kq_mask(t); @@ -6594,6 +6650,236 @@ struct test_diag : public test_case { } }; +// Deserializable generic test case +struct input_tensor { + ggml_type type; + std::array ne; + std::array nb; // strides (0 = use default contiguous strides) +}; + +static bool is_non_contiguous(const input_tensor & src) { + if (src.nb[0] == 0) { + return false; + } + const size_t default_nb0 = ggml_type_size(src.type); + const size_t default_nb1 = default_nb0 * (src.ne[0] / ggml_blck_size(src.type)); + const size_t default_nb2 = default_nb1 * src.ne[1]; + const size_t default_nb3 = default_nb2 * src.ne[2]; + return src.nb[0] != default_nb0 || + src.nb[1] != default_nb1 || + src.nb[2] != default_nb2 || + src.nb[3] != default_nb3; +} + +static std::string var_to_str(const std::vector& sources) { + std::ostringstream oss; + bool first = true; + for (const auto& src : sources) { + if (!first) oss << ","; + oss << ggml_type_name(src.type) << "[" << src.ne[0] << "," << src.ne[1] << "," << src.ne[2] << "," << src.ne[3] << "]"; + if (is_non_contiguous(src)) { + oss << "nb[" << src.nb[0] << "," << src.nb[1] << "," << src.nb[2] << "," << src.nb[3] << "]"; + } + first = false; + } + return oss.str(); +} + +static std::string var_to_str(const std::array& params) { + std::ostringstream oss; + oss << "["; + bool first = true; + for (size_t i = 0; i < params.size(); ++i) { + if (params[i] != 0) { + if (!first) oss << ","; + oss << i << ":" << params[i]; + first = false; + } + } + oss << "]"; + return oss.str(); +} + + +struct test_generic_op : public test_case { + const ggml_op op; + const ggml_type type; + const std::array ne; + const std::array op_params; + + const std::vector sources; + const std::string name; + + std::string vars() override { + if (name.empty()) { + return VARS_TO_STR4(type, ne, op_params, sources); + } + + return VARS_TO_STR5(name, type, ne, op_params, sources); + } + + test_generic_op(ggml_op op, ggml_type type, std::array ne, + std::array op_params, + std::vector sources, std::string name = "") + : op(op), type(type), ne(ne), op_params(op_params), sources(sources), name(std::move(name)) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + const size_t source_count = std::min(sources.size(), (size_t)GGML_MAX_SRC); + + std::array source_tensors; + for (size_t i = 0; i < source_count; ++i) { + const input_tensor& src = sources[i]; + + if (is_non_contiguous(src)) { + size_t total_size; + const size_t blck_size = ggml_blck_size(src.type); + if (blck_size == 1) { + total_size = ggml_type_size(src.type); + for (int d = 0; d < 4; d++) { + total_size += (src.ne[d] - 1) * src.nb[d]; + } + } else { + total_size = src.ne[0] * src.nb[0] / blck_size; + for (int d = 1; d < 4; d++) { + total_size += (src.ne[d] - 1) * src.nb[d]; + } + } + + // Convert bytes to elements, padded to block size for quantized types + const size_t type_size = ggml_type_size(src.type); + size_t backing_elements = (total_size * blck_size + type_size - 1) / type_size; + backing_elements = ((backing_elements + blck_size - 1) / blck_size) * blck_size; + ggml_tensor * backing = ggml_new_tensor_1d(ctx, src.type, backing_elements); + source_tensors[i] = ggml_view_4d(ctx, backing, + src.ne[0], src.ne[1], src.ne[2], src.ne[3], + src.nb[1], src.nb[2], src.nb[3], 0); + // nb[0] does not get set by view_4d, so set it manually + source_tensors[i]->nb[0] = src.nb[0]; + } else { + source_tensors[i] = ggml_new_tensor_4d(ctx, src.type, src.ne[0], src.ne[1], src.ne[2], src.ne[3]); + } + } + + // Ops with an inplace flag create a view of src[0] as their output. + bool inplace = false; + if (op == GGML_OP_SET || op == GGML_OP_ACC) { + inplace = op_params[4] != 0; + } else if (op == GGML_OP_ADD_REL_POS) { + inplace = op_params[0] != 0; + } + + ggml_tensor * out; + if (inplace && source_count > 0) { + out = ggml_view_tensor(ctx, source_tensors[0]); + } else { + out = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); + } + out->op = op; + for (size_t i = 0; i < source_count; ++i) { + out->src[i] = source_tensors[i]; + } + + memcpy(out->op_params, op_params.data(), GGML_MAX_OP_PARAMS); + ggml_set_name(out, "out"); + + return out; + } + + double max_nmse_err() override { + switch (op) { + case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: + case GGML_OP_OUT_PROD: + case GGML_OP_CONV_TRANSPOSE_2D: + case GGML_OP_IM2COL: + case GGML_OP_CONV_2D: + case GGML_OP_CONV_3D: + case GGML_OP_SET_ROWS: + case GGML_OP_CPY: + return 5e-4; + case GGML_OP_SOFT_MAX: + return 1e-6; + case GGML_OP_RWKV_WKV7: + return 5e-3; + case GGML_OP_FLASH_ATTN_EXT: + { + // Scale error with kv length to account for accumulating floating point error + const int64_t kv = sources[1].ne[1]; + return 5e-4 * std::max(1.0, kv / 20000.0); + } + default: + return 1e-7; + } + } + + void initialize_tensors(ggml_context * ctx) override { + ggml_tensor * out = ggml_get_tensor(ctx, "out"); + + std::random_device rd; + std::default_random_engine rng(rd()); + + for (size_t i = 0; i < sources.size() && i < GGML_MAX_SRC; i++) { + ggml_tensor * t = out->src[i]; + if (!t) { + break; + } + + // FLASH_ATTN_EXT: src[3] is the KQ mask + if (op == GGML_OP_FLASH_ATTN_EXT && i == 3) { + init_tensor_kq_mask(t); + continue; + } + + if (t->type == GGML_TYPE_I32 || t->type == GGML_TYPE_I64) { + if (op == GGML_OP_GET_ROWS || op == GGML_OP_GET_ROWS_BACK) { + const int64_t num_rows = sources[0].ne[1]; + const int64_t nels = ggml_nelements(t); + std::vector data(nels); + std::uniform_int_distribution dist(0, num_rows - 1); + for (int64_t i = 0; i < nels; i++) { + data[i] = dist(rng); + } + ggml_backend_tensor_set(t, data.data(), 0, nels * sizeof(int32_t)); + } else if (op == GGML_OP_SET_ROWS) { + init_set_rows_row_ids(t, ne[1]); + } else if (op == GGML_OP_ROPE) { + const int mode = op_params[2]; + const int64_t nels = (mode & GGML_ROPE_TYPE_MROPE) ? ne[2] * 4 : ne[2]; + std::vector data(nels); + std::uniform_int_distribution dist(0, ne[2] - 1); + for (int64_t i = 0; i < nels; i++) { + data[i] = dist(rng); + } + ggml_backend_tensor_set(t, data.data(), 0, nels * sizeof(int32_t)); + } else if (op == GGML_OP_MUL_MAT_ID || op == GGML_OP_ADD_ID) { + const int64_t n_expert = (op == GGML_OP_MUL_MAT_ID) ? sources[0].ne[2] : sources[1].ne[1]; + for (int64_t r = 0; r < ggml_nrows(t); r++) { + std::vector data(t->ne[0]); + for (int32_t i = 0; i < t->ne[0]; i++) { + data[i] = i % n_expert; + } + std::shuffle(data.begin(), data.end(), rng); + ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t)); + } + } else if (op == GGML_OP_SSM_SCAN) { + for (int64_t r = 0; r < ggml_nrows(t); r++) { + std::vector data(t->ne[0]); + for (int32_t i = 0; i < t->ne[0]; i++) { + data[i] = i; + } + std::shuffle(data.begin(), data.end(), rng); + ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t)); + } + } else { + init_tensor_uniform(t); + } + } else { + init_tensor_uniform(t); + } + } + } +}; + enum llm_norm_type { LLM_NORM, @@ -7527,9 +7813,9 @@ static std::vector> make_test_cases_eval() { } } - auto add_test_bin_bcast = [&](ggml_type type, std::array ne, std::array nr, bool perm1 = false) { + auto add_test_bin_bcast = [&](ggml_type type, std::array ne, std::array nr, bool perm1 = false, bool src_overlap = false) { for (auto op : {ggml_add, ggml_sub, ggml_mul, ggml_div}) { - test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr, 1, perm1)); + test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr, 1, perm1, src_overlap)); } }; for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) { @@ -7549,6 +7835,12 @@ static std::vector> make_test_cases_eval() { add_test_bin_bcast(type, {10, 5, 4, 3}, {2, 2, 2, 2}, perm1); } + // src_overlap + add_test_bin_bcast(type, {10, 5, 4, 6}, {1, 1, 1, 1}, false, true); + add_test_bin_bcast(type, {10, 5, 4, 5}, {1, 1, 1, 1}, false, true); + add_test_bin_bcast(type, {1, 1, 120, 120}, {1, 1, 1, 1}, false, true); + add_test_bin_bcast(type, {1, 1, 4, 320}, {1, 1, 1, 1}, false, true); + // test case for k_bin_bcast_unravel in CUDA backend add_test_bin_bcast(type, {1, 1, 65536, 1}, {256, 1, 1, 1}); @@ -7596,7 +7888,7 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_softcap(GGML_TYPE_F32, {10, 10, 10, 10}, 50.0f)); test_cases.emplace_back(new test_silu_back()); - for (float eps : { 0.0f, 1e-6f, 1e-4f, 1e-1f }) { + for (float eps : { 0.0f, 1e-6f, 1e-4f, 1e-1f, 10.f }) { for (uint32_t n : { 64, 1025 }) { for (bool v : { false, true }) { test_cases.emplace_back(new test_norm(GGML_TYPE_F32, { n, 5, 4, 3 }, v, eps)); @@ -7648,6 +7940,9 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {d_conv, d_inner, 1, 1}, {d_conv, d_inner, 1, 1})); test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {2 * d_conv, d_inner, 1, 1}, {d_conv, d_inner, 1, 1})); test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {d_conv, d_inner, 4, 1}, {d_conv, d_inner, 1, 1})); + // long token (n_t > 32, exercises the long_token kernel path) + test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {d_conv - 1 + 64, d_inner, 1, 1}, {d_conv, d_inner, 1, 1})); + test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {d_conv - 1 + 64, d_inner, 4, 1}, {d_conv, d_inner, 1, 1})); } } @@ -7791,10 +8086,6 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 67, {1, 1}, {4, 1}, {0, 2, 1, 3})); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 16, 32, 32, { 1, 1}, {1, 1}, {0, 1, 2, 3}, 64, 3)); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 64, 77, 77, {12,1}, {1,1})); - test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 2, 1, 3, {128, 1024}, {1, 1})); - test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 2, 3, 4, {128, 1024}, {1, 1})); - test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 2, 1, 3, {128*1024, 1}, {1, 1}, {0, 2, 1, 3})); - test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 2, 1, 3, {128*1024, 1}, {1, 1}, {0, 1, 2, 3}, 64)); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0, GGML_TYPE_F32, 576, 512, 576, {1,1}, {1,1})); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0, GGML_TYPE_F32, 1, 2048, 8192, {1, 1}, {1, 1})); @@ -7802,6 +8093,8 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_mul_mat(type_a, GGML_TYPE_F32, 1, 64, 256, {1, 1}, {1, 1})); } + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0, GGML_TYPE_F32, 6, 4096, 5120, {1, 1}, {1, 1})); + #if 0 // test the mat-mat path for Metal for (int k = 1; k < 512; ++k) { @@ -8283,11 +8576,12 @@ static std::vector> make_test_cases_eval() { } } - for (int hsk : { 40, 64, 72, 80, 96, 128, 192, 256, 576 }) { + for (int hsk : { 40, 64, 72, 80, 96, 128, 192, 256, 320, 576 }) { for (int hsv : { 40, 64, 72, 80, 96, 128, 192, 256, 512 }) { - if (hsk != 192 && hsk != 576 && hsk != hsv) continue; + if (hsk != 192 && hsk != 320 && hsk != 576 && hsk != hsv) continue; if (hsk == 192 && (hsv != 128 && hsv != 192)) continue; if (hsk == 576 && hsv != 512) continue; // DeepSeek MLA + if (hsk == 320 && hsv != 256) continue; // MLA for (bool mask : { true, false } ) { for (bool sinks : { true, false } ) { @@ -8296,12 +8590,13 @@ static std::vector> make_test_cases_eval() { for (float logit_softcap : {0.0f, 10.0f}) { if (hsk != 128 && logit_softcap != 0.0f) continue; for (int nh : { 1, 4 }) { - if (nh == 1 && hsk != 576) continue; // GLM 4.7 Flash + if (nh == 1 && hsk != 320 && hsk != 576) continue; // GLM 4.7 Flash for (int nr3 : { 1, 3, }) { if (hsk > 64 && nr3 > 1) continue; // skip broadcast for large head sizes - for (int nr2 : { 1, 4, 12, 20 }) { + for (int nr2 : { 1, 4, 12, 20, 32 }) { if (nr2 == 12 && hsk != 128) continue; if (nr2 == 20 && (nh != 1 || hsk != 576)) continue; + if (nr2 == 32 && (nh != 1 || hsk != 320)) continue; //for (int kv : { 1, 17, 31, 33, 61, 113, 65, 127, 129, 130, 255, 260, 371, 380, 407, 512, 1024, }) { for (int kv : { 113, 512, 1024, }) { if (nr2 != 1 && kv != 512) continue; @@ -8385,6 +8680,26 @@ static std::vector> make_test_cases_eval() { } } + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 128, 1, 1)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 16, 1, 1)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 16, 1, 1, 1, true, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 16, 1, 1, 1, false, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 16, 64, 1, 2)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 1)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 2)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 8, 32, 4, 2, 2)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 2, 1, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 1, 1, true)); + // KDA (vector gate) + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 1, 1, 1, false, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 1, 2, 1, false, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 16, 1, 2, 1, false, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 32, 4, 1, 1, false, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 2, 1, false, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 8, 32, 4, 2, 2, false, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 2, 1, true, true)); + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 16, 4, 2, 1, true, true)); + #if 0 // these tests are disabled to save execution time, sbut they can be handy for debugging test_cases.emplace_back(new test_llama(2, true)); @@ -8650,11 +8965,92 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {128, 16, 2, 3}, 2)); test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 2, 3}, {64, 16, 2, 3}, 3)); + // GATED_DELTA_NET: realistic model configurations + // TG: n_seq_tokens=1 (autoregressive) + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 128, 1, 1)); // Qwen3.5-like: 32 heads, d=128 + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 16, 64, 1, 1)); // smaller model + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 128, 1, 1, 1, false, true)); // KDA + // PP: n_seq_tokens=64,256 (prompt processing) + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 128, 64, 1)); // PP-64 + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 128, 256, 1)); // PP-256 + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 128, 512, 1)); // PP-512 + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 128, 1024, 1)); // PP-1024 + // Small model configs (fewer heads = less GPU occupancy for autoregressive) + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 128, 64, 1)); // 4h PP-64 + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 128, 256, 1)); // 4h PP-256 + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 128, 512, 1)); // 4h PP-512 + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 128, 1024, 1)); // 4h PP-1024 + test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 128, 64, 1, 1, false, true)); // KDA PP-64 + + return test_cases; +} + +static std::vector> make_test_cases_from_file(const char * path) { + std::ifstream f(path); + + if (!f.is_open()) { + throw std::runtime_error("Unable to read test file"); + } + + std::vector> test_cases; + + std::string line; + + while (std::getline(f, line)) { + std::istringstream iss(line); + + ggml_op op; + ggml_type type; + std::array ne; + std::array op_params = {}; + std::string name; + uint64_t tmp; + + iss >> tmp; + op = (ggml_op)tmp; + iss >> tmp; + type = (ggml_type)tmp; + + for (size_t i = 0; i < 4; i++) { + iss >> ne[i]; + } + + iss >> tmp; + for (size_t i = 0; i < tmp && i < op_params.size(); i++) { + iss >> op_params[i]; + } + + iss >> tmp; + + size_t num_src = std::min((uint64_t)GGML_MAX_SRC, tmp); + std::vector sources(num_src); + for (size_t i = 0; i < num_src; i++) { + input_tensor& src = sources[i]; + iss >> tmp; + src.type = (ggml_type)tmp; + + for (size_t i = 0; i < 4; i++) { + iss >> src.ne[i]; + } + for (size_t i = 0; i < 4; i++) { + iss >> src.nb[i]; + } + } + + iss >> name; + + if (name.length() == 1 && name[0] == '-') { + name = ""; + } + + test_cases.emplace_back(new test_generic_op(op, type, ne, op_params, sources, std::move(name))); + } + return test_cases; } static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_names_filter, const char * params_filter, - printer * output_printer) { + printer * output_printer, const char * test_file_path) { auto filter_test_cases = [](std::vector> & test_cases, const char * params_filter) { if (params_filter == nullptr) { return; @@ -8672,9 +9068,26 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } }; + std::vector> test_cases; + + if (test_file_path == nullptr) { + switch (mode) { + case MODE_TEST: + case MODE_GRAD: + case MODE_SUPPORT: + test_cases = make_test_cases_eval(); + break; + case MODE_PERF: + test_cases = make_test_cases_perf(); + break; + } + } else { + test_cases = make_test_cases_from_file(test_file_path); + } + + filter_test_cases(test_cases, params_filter); + if (mode == MODE_TEST) { - auto test_cases = make_test_cases_eval(); - filter_test_cases(test_cases, params_filter); ggml_backend_t backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, NULL); if (backend_cpu == NULL) { test_operation_info info("", "", "CPU"); @@ -8714,8 +9127,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } if (mode == MODE_GRAD) { - auto test_cases = make_test_cases_eval(); - filter_test_cases(test_cases, params_filter); size_t n_ok = 0; for (auto & test : test_cases) { if (test->eval_grad(backend, op_names_filter, output_printer)) { @@ -8728,8 +9139,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } if (mode == MODE_PERF) { - auto test_cases = make_test_cases_perf(); - filter_test_cases(test_cases, params_filter); for (auto & test : test_cases) { test->eval_perf(backend, op_names_filter, output_printer); } @@ -8737,9 +9146,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } if (mode == MODE_SUPPORT) { - auto test_cases = make_test_cases_eval(); - filter_test_cases(test_cases, params_filter); - // Filter out fusion cases test_cases.erase( std::remove_if(test_cases.begin(), test_cases.end(), [](const std::unique_ptr & tc) { @@ -8858,7 +9264,8 @@ static void show_test_coverage() { } static void usage(char ** argv) { - printf("Usage: %s [mode] [-o ] [-b ] [-p ] [--output ] [--list-ops] [--show-coverage]\n", argv[0]); + printf("Usage: %s [mode] [-o ] [-b ] [-p ] [--output ] [--list-ops]", argv[0]); + printf(" [--show-coverage] [--test-file ]\n"); printf(" valid modes:\n"); printf(" - test (default, compare with CPU backend for correctness)\n"); printf(" - grad (compare gradients from backpropagation with method of finite differences)\n"); @@ -8869,6 +9276,7 @@ static void usage(char ** argv) { printf(" --output specifies output format (default: console, options: console, sql, csv)\n"); printf(" --list-ops lists all available GGML operations\n"); printf(" --show-coverage shows test coverage\n"); + printf(" --test-file reads test operators from a test file generated by llama-export-graph-ops\n"); } int main(int argc, char ** argv) { @@ -8877,6 +9285,7 @@ int main(int argc, char ** argv) { const char * op_names_filter = nullptr; const char * backend_filter = nullptr; const char * params_filter = nullptr; + const char * test_file_path = nullptr; for (int i = 1; i < argc; i++) { if (strcmp(argv[i], "test") == 0) { @@ -8924,6 +9333,13 @@ int main(int argc, char ** argv) { } else if (strcmp(argv[i], "--show-coverage") == 0) { show_test_coverage(); return 0; + } else if (strcmp(argv[i], "--test-file") == 0) { + if (i + 1 < argc) { + test_file_path = argv[++i]; + } else { + usage(argv); + return 1; + } } else { usage(argv); return 1; @@ -8976,7 +9392,7 @@ int main(int argc, char ** argv) { false, "", ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024, true)); - bool ok = test_backend(backend, mode, op_names_filter, params_filter, output_printer.get()); + bool ok = test_backend(backend, mode, op_names_filter, params_filter, output_printer.get(), test_file_path); if (ok) { n_ok++; diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp index c10bde91b6..d4cd62c71e 100644 --- a/tests/test-backend-sampler.cpp +++ b/tests/test-backend-sampler.cpp @@ -361,7 +361,7 @@ static void test_backend_temp_sampling(const test_params & params) { GGML_ASSERT(false && "Failed to decode token"); } - // Verfify sequence 0 + // Verify sequence 0 { int32_t batch_idx = test_ctx.idx_for_seq(0); int n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx.get(), batch_idx); @@ -379,7 +379,7 @@ static void test_backend_temp_sampling(const test_params & params) { } - // Verfify sequence 1 + // Verify sequence 1 { int32_t batch_idx = test_ctx.idx_for_seq(1); @@ -395,7 +395,7 @@ static void test_backend_temp_sampling(const test_params & params) { } } - // lambda to testing non-positive temperature values. + // lambda for testing non-positive temperature values. auto test_argmax_temp = [&](float temp) { printf("\nTesting temperature = %.1f\n", temp); @@ -454,7 +454,7 @@ static void test_backend_temp_ext_sampling(const test_params & params) { } } - // lambda to testing non-positive temp/delta/exponent values. + // lambda for testing non-positive temp/delta/exponent values. auto test_argmax_temp = [&](float temp, float delta, float exponent) { printf("\nTesting temperature = %.1f, delta = %1.f, exponent = %1.f\n", temp, delta, exponent); @@ -530,7 +530,7 @@ static void test_backend_min_p_sampling(const test_params & params) { printf("min-p cpu sampled token id:%d, string: '%s'\n", token, token_str.c_str()); GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab); - // Decode and sampler 10 more tokens + // Decode and sample 10 more tokens for (int i = 0; i < 10; i++) { int32_t loop_idx = test_ctx.idx_for_seq(seq_id); llama_token token = llama_sampler_sample(chain.get(), test_ctx.ctx.get(), loop_idx); @@ -582,7 +582,7 @@ static void test_backend_top_p_sampling(const test_params & params) { printf("top-p cpu sampled token id:%d, string: '%s'\n", token, token_str.c_str()); GGML_ASSERT(token >= 0 && token < test_ctx.n_vocab); - // Decode and sampler 10 more tokens + // Decode and sample 10 more tokens for (int i = 0; i < 10; i++) { int32_t loop_idx = test_ctx.idx_for_seq(seq_id); llama_token token = llama_sampler_sample(chain.get(), test_ctx.ctx.get(), loop_idx); @@ -619,7 +619,7 @@ static void test_backend_multi_sequence_sampling(const test_params & params) { GGML_ASSERT(false && "Failed to decode token"); } - // Verfiy sequence 0 + // Verify sequence 0 { int32_t batch_idx = test_ctx.idx_for_seq(0); llama_token token = llama_get_sampled_token_ith(test_ctx.ctx.get(), batch_idx); @@ -763,7 +763,7 @@ static void test_backend_logit_bias_sampling(const test_params & params) { printf("backend logit bias sampling test PASSED\n"); } -// This test verifies that it is possible to have two different backend sampler, +// This test verifies that it is possible to have two different backend samplers, // one that uses the backend dist sampler, and another that uses CPU dist sampler. static void test_backend_mixed_sampling(const test_params & params) { struct llama_sampler_chain_params chain_params_0 = llama_sampler_chain_default_params(); @@ -791,7 +791,7 @@ static void test_backend_mixed_sampling(const test_params & params) { GGML_ASSERT(false && "Failed to decode token"); } - // Verfiy sequence 0 that used the dist backend sampler. + // Verify sequence 0 that used the dist backend sampler. { int32_t batch_idx = test_ctx.idx_for_seq(0); llama_token token = llama_get_sampled_token_ith(test_ctx.ctx.get(), batch_idx); @@ -802,7 +802,7 @@ static void test_backend_mixed_sampling(const test_params & params) { //GGML_ASSERT(llama_get_sampled_logits_count_ith(test_ctx.ctx.get(), batch_idx) == 0); } - // Verfiy sequence 1 that used the top-k backend sampler. + // Verify sequence 1 that used the top-k backend sampler. { int32_t batch_idx = test_ctx.idx_for_seq(1); float * logits = llama_get_sampled_logits_ith(test_ctx.ctx.get(), batch_idx); @@ -934,7 +934,7 @@ static void test_backend_cpu_mixed_batch(const test_params & params) { // samplers. llama_set_sampler(test_ctx.ctx.get(), 0, nullptr); - // Create a CPU sampler and verify we can sampler from it. + // Create a CPU sampler and verify we can sample from it. struct llama_sampler_chain_params chain_params = llama_sampler_chain_default_params(); llama_sampler_ptr chain(llama_sampler_chain_init(chain_params)); llama_sampler_chain_add(chain.get(), llama_sampler_init_greedy()); diff --git a/tests/test-chat-auto-parser.cpp b/tests/test-chat-auto-parser.cpp new file mode 100644 index 0000000000..eaa57872a8 --- /dev/null +++ b/tests/test-chat-auto-parser.cpp @@ -0,0 +1,1889 @@ +#include "chat-auto-parser-helpers.h" +#include "chat-auto-parser.h" +#include "chat-peg-parser.h" +#include "chat.h" +#include "peg-parser.h" +#include "testing.h" + +#include +#include +#include +#include + +using namespace autoparser; + +static void test_calculate_diff_split_basic(testing & t); +static void test_calculate_diff_split_identical(testing & t); +static void test_calculate_diff_split_common_prefix(testing & t); +static void test_calculate_diff_split_common_suffix(testing & t); +static void test_calculate_diff_split_common_both(testing & t); +static void test_calculate_diff_split_empty_cases(testing & t); +static void test_calculate_diff_split_no_common(testing & t); +static void test_calculate_diff_split_single_char(testing & t); +static void test_calculate_diff_split_overlaps(testing & t); +static void test_calculate_diff_split_tag_boundaries(testing & t); +static void test_calculate_diff_split(testing & t); + +static void test_until_common_prefix_basic(testing & t); +static void test_until_common_prefix(testing & t); + +static void test_after_common_suffix_basic(testing & t); +static void test_after_common_suffix(testing & t); + +static void test_analyze_tool_call_pure_json(testing & t); +static void test_analyze_tool_call_function_name_markers(testing & t); +static void test_analyze_tool_call_full_markers(testing & t); +static void test_analyze_tool_call_edge_cases(testing & t); + +static void test_compare_variants_basic(testing & t); +static void test_compare_variants_messages_modifier(testing & t); +static void test_compare_variants_tools_modifier(testing & t); +static void test_compare_variants_both_modifiers(testing & t); +static void test_compare_variants_template_failure(testing & t); +static void test_compare_variants_identity(testing & t); +static void test_compare_variants(testing & t); + +// Seed-OSS template tool calling analysis tests +static void test_seed_oss_tool_analysis(testing & t); +static void test_seed_oss_tool_presence(testing & t); +static void test_seed_oss_call_count(testing & t); +static void test_seed_oss_function_names(testing & t); +static void test_seed_oss_argument_count(testing & t); +static void test_seed_oss_args_presence(testing & t); +static void test_seed_oss_tool_with_reasoning(testing & t); + +// Nemotron template analysis tests +static void test_nemotron_analysis(testing & t); +static void test_nemotron_reasoning_detection(testing & t); +static void test_nemotron_tool_format(testing & t); + +// CohereForAI template analysis tests +static void test_cohere_reasoning_detection(testing & t); +static void test_cohere_analysis(testing & t); + +// Marker separation +static void test_marker_separation(testing & t); + +// standard_json_tools format tests +static void test_standard_json_tools_formats(testing & t); +static void test_standard_json_tools_openai(testing & t); +static void test_standard_json_tools_cohere(testing & t); +static void test_standard_json_tools_function_key(testing & t); + +// normalize_quotes_to_json tests +static void test_normalize_quotes_to_json(testing & t); +static void test_normalize_quotes_with_embedded_quotes(testing & t); + +// TAG_WITH_TAGGED argument parsing tests +static void test_tagged_args_with_embedded_quotes(testing & t); + +int main(int argc, char * argv[]) { + testing t(std::cout); + t.verbose = true; + + // usage: test-chat-auto-parser-helpers [filter_regex] + + if (argc > 1) { + t.set_filter(argv[1]); + } + + t.test("diff_split", test_calculate_diff_split); + t.test("common_prefix", test_until_common_prefix); + t.test("common_suffix", test_after_common_suffix); + t.test("compare_variants", test_compare_variants); + t.test("segments", test_marker_separation); + t.test("seed_oss_diffs", test_seed_oss_tool_analysis); + t.test("cohere", test_cohere_analysis); + t.test("nemotron", test_nemotron_analysis); + t.test("standard_json_tools", test_standard_json_tools_formats); + t.test("normalize_quotes_to_json", test_normalize_quotes_to_json); + t.test("tagged_args_embedded_quotes", test_tagged_args_with_embedded_quotes); + + return t.summary(); +} + +static void test_marker_separation(testing & t) { + auto single_square_marker = segmentize_markers("pre_marker[marker]post_marker"); + auto single_diag_marker = segmentize_markers("pre_markerpost_marker"); + auto paired_markers = segmentize_markers("world"); + auto double_different_markers = segmentize_markers("[hello][world]"); + auto in_between = segmentize_markers("imdabada[hey]"); + + t.test("single_square_marker", [&] (testing & t) { + t.assert_equal("first is text", segment_type::TEXT, single_square_marker[0].type); + t.assert_equal("second is marker", segment_type::MARKER, single_square_marker[1].type); + t.assert_equal("last is text", segment_type::TEXT, single_square_marker[2].type); + + t.assert_equal("first is 'pre_marker'", "pre_marker", single_square_marker[0].value); + t.assert_equal("second is '[marker]'", "[marker]", single_square_marker[1].value); + t.assert_equal("last is 'post_marker'", "post_marker", single_square_marker[2].value); + }); + + t.test("single_diagonal_marker", [&] (testing & t) { + t.assert_equal("first is text", segment_type::TEXT, single_diag_marker[0].type); + t.assert_equal("second is marker", segment_type::MARKER, single_diag_marker[1].type); + t.assert_equal("last is text", segment_type::TEXT, single_diag_marker[2].type); + + t.assert_equal("first is 'pre_marker'", "pre_marker", single_diag_marker[0].value); + t.assert_equal("second is ''", "", single_diag_marker[1].value); + t.assert_equal("last is 'post_marker'", "post_marker", single_diag_marker[2].value); + }); + + t.test("paired_markers", [&] (testing & t) { + t.assert_equal("first is marker", segment_type::MARKER, paired_markers[0].type); + t.assert_equal("second is text", segment_type::TEXT, paired_markers[1].type); + t.assert_equal("third is marker", segment_type::MARKER, paired_markers[2].type); + + t.assert_equal("first is ''", "", paired_markers[0].value); + t.assert_equal("second is 'world'", "world", paired_markers[1].value); + t.assert_equal("third is ''", "", paired_markers[2].value); + }); + + t.test("double_different_markers", [&] (testing & t) { + t.assert_equal("first is marker", segment_type::MARKER, double_different_markers[0].type); + t.assert_equal("second is marker", segment_type::MARKER, double_different_markers[1].type); + t.assert_equal("third is marker", segment_type::MARKER, double_different_markers[2].type); + t.assert_equal("fourth is marker", segment_type::MARKER, double_different_markers[3].type); + + t.assert_equal("first is ''", "", double_different_markers[0].value); + t.assert_equal("second is '[hello]'", "[hello]", double_different_markers[1].value); + t.assert_equal("third is ''", "", double_different_markers[2].value); + t.assert_equal("fourth is '[world]'", "[world]", double_different_markers[3].value); + }); + + t.test("in_between", [&] (testing & t) { + t.assert_equal("first is text", segment_type::TEXT, in_between[0].type); + t.assert_equal("second is marker", segment_type::MARKER, in_between[1].type); + t.assert_equal("third is text", segment_type::TEXT, in_between[2].type); + t.assert_equal("fourth is marker", segment_type::MARKER, in_between[3].type); + t.assert_equal("fifth is text", segment_type::TEXT, in_between[4].type); + t.assert_equal("sixth is marker", segment_type::MARKER, in_between[5].type); + + t.assert_equal("first is 'im'", "im", in_between[0].value); + t.assert_equal("second is ''", "", in_between[1].value); + t.assert_equal("third is 'daba'", "daba", in_between[2].value); + t.assert_equal("fourth is ''", "", in_between[3].value); + t.assert_equal("fifth is 'da'", "da", in_between[4].value); + t.assert_equal("sixth is '[hey]'", "[hey]", in_between[5].value); + }); +} + +static void test_calculate_diff_split(testing & t) { + t.test("calculate_diff_split basic", test_calculate_diff_split_basic); + t.test("calculate_diff_split identical", test_calculate_diff_split_identical); + t.test("calculate_diff_split common prefix", test_calculate_diff_split_common_prefix); + t.test("calculate_diff_split common suffix", test_calculate_diff_split_common_suffix); + t.test("calculate_diff_split common both", test_calculate_diff_split_common_both); + t.test("calculate_diff_split empty cases", test_calculate_diff_split_empty_cases); + t.test("calculate_diff_split no common", test_calculate_diff_split_no_common); + t.test("calculate_diff_split single char", test_calculate_diff_split_single_char); + t.test("calculate_diff_split overlaps", test_calculate_diff_split_overlaps); + t.test("calculate_diff_split tag boundaries", test_calculate_diff_split_tag_boundaries); +} + +static void test_calculate_diff_split_basic(testing & t) { + diff_split result = calculate_diff_split("hello world", "hello test"); + t.assert_equal("prefix should be 'hello '", "hello ", result.prefix); + t.assert_equal("left should be 'world'", "world", result.left); + t.assert_equal("right should be 'test'", "test", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("abc", "xyz"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'abc'", "abc", result.left); + t.assert_equal("right should be 'xyz'", "xyz", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("prefixA suffix", "prefixB suffix"); + t.assert_equal("prefix should be 'prefix'", "prefix", result.prefix); + t.assert_equal("left should be 'A'", "A", result.left); + t.assert_equal("right should be 'B'", "B", result.right); + t.assert_equal("suffix should be ' suffix'", " suffix", result.suffix); +} + +static void test_calculate_diff_split_identical(testing & t) { + diff_split result = calculate_diff_split("hello", "hello"); + t.assert_equal("prefix should be 'hello'", "hello", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("", ""); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("a", "a"); + t.assert_equal("prefix should be 'a'", "a", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("", ""); + t.assert_equal("prefix should be ''", "", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); +} + +static void test_calculate_diff_split_common_prefix(testing & t) { + diff_split result = calculate_diff_split("abcdef", "abcxyz"); + t.assert_equal("prefix should be 'abc'", "abc", result.prefix); + t.assert_equal("left should be 'def'", "def", result.left); + t.assert_equal("right should be 'xyz'", "xyz", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("same", "sameagain"); + t.assert_equal("prefix should be 'same'", "same", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be 'again'", "again", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("test", "testing"); + t.assert_equal("prefix should be 'test'", "test", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be 'ing'", "ing", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); +} + +static void test_calculate_diff_split_common_suffix(testing & t) { + diff_split result = calculate_diff_split("123end", "456end"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be '123'", "123", result.left); + t.assert_equal("right should be '456'", "456", result.right); + t.assert_equal("suffix should be 'end'", "end", result.suffix); + + result = calculate_diff_split("start", "end"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'start'", "start", result.left); + t.assert_equal("right should be 'end'", "end", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("abcsuffix", "xyzsuffix"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'abc'", "abc", result.left); + t.assert_equal("right should be 'xyz'", "xyz", result.right); + t.assert_equal("suffix should be 'suffix'", "suffix", result.suffix); +} + +static void test_calculate_diff_split_common_both(testing & t) { + diff_split result = calculate_diff_split("helloXworld", "helloYworld"); + t.assert_equal("prefix should be 'hello'", "hello", result.prefix); + t.assert_equal("left should be 'X'", "X", result.left); + t.assert_equal("right should be 'Y'", "Y", result.right); + t.assert_equal("suffix should be 'world'", "world", result.suffix); + + result = calculate_diff_split("ABCmiddleXYZ", "ABCdifferentXYZ"); + t.assert_equal("prefix should be 'ABC'", "ABC", result.prefix); + t.assert_equal("left should be 'middle'", "middle", result.left); + t.assert_equal("right should be 'different'", "different", result.right); + t.assert_equal("suffix should be 'XYZ'", "XYZ", result.suffix); + + result = calculate_diff_split("startAend", "startBend"); + t.assert_equal("prefix should be 'start'", "start", result.prefix); + t.assert_equal("left should be 'A'", "A", result.left); + t.assert_equal("right should be 'B'", "B", result.right); + t.assert_equal("suffix should be 'end'", "end", result.suffix); + + // Edge case: common prefix and suffix overlap + result = calculate_diff_split("aa", "ab"); + t.assert_equal("prefix should be 'a'", "a", result.prefix); + t.assert_equal("left should be 'a'", "a", result.left); + t.assert_equal("right should be 'b'", "b", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); +} + +static void test_calculate_diff_split_empty_cases(testing & t) { + // Empty left, non-empty right + diff_split result = calculate_diff_split("", "hello"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be 'hello'", "hello", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + // Non-empty left, empty right + result = calculate_diff_split("hello", ""); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'hello'", "hello", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + // Both empty + result = calculate_diff_split("", ""); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + // Left single char, empty right + result = calculate_diff_split("a", ""); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'a'", "a", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + // Empty left, right single char + result = calculate_diff_split("", "a"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be 'a'", "a", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); +} + +static void test_calculate_diff_split_no_common(testing & t) { + diff_split result = calculate_diff_split("abc", "xyz"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'abc'", "abc", result.left); + t.assert_equal("right should be 'xyz'", "xyz", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("left", "right"); + // The algorithm finds "t" as a common suffix since both strings end with 't' + // This is the algorithm's actual behavior - it finds maximal common suffix + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'lef'", "lef", result.left); + t.assert_equal("right should be 'righ'", "righ", result.right); + t.assert_equal("suffix should be 't'", "t", result.suffix); + + result = calculate_diff_split("123", "456"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be '123'", "123", result.left); + t.assert_equal("right should be '456'", "456", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); +} + +static void test_calculate_diff_split_single_char(testing & t) { + diff_split result = calculate_diff_split("a", "b"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'a'", "a", result.left); + t.assert_equal("right should be 'b'", "b", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("a", "a"); + t.assert_equal("prefix should be 'a'", "a", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("a", "ab"); + t.assert_equal("prefix should be 'a'", "a", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be 'b'", "b", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("ab", "a"); + t.assert_equal("prefix should be 'a'", "a", result.prefix); + t.assert_equal("left should be 'b'", "b", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); +} + +static void test_calculate_diff_split_overlaps(testing & t) { + // One string is substring of another + diff_split result = calculate_diff_split("test", "testing"); + t.assert_equal("prefix should be 'test'", "test", result.prefix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be 'ing'", "ing", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + result = calculate_diff_split("testing", "test"); + t.assert_equal("prefix should be 'test'", "test", result.prefix); + t.assert_equal("left should be 'ing'", "ing", result.left); + t.assert_equal("right should be empty", "", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + // Similar strings with one extra char at start + result = calculate_diff_split("Xtest", "Ytest"); + // The algorithm finds "test" as a common suffix since both strings end with "test" + // This is the algorithm's actual behavior - it finds maximal common suffix + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'X'", "X", result.left); + t.assert_equal("right should be 'Y'", "Y", result.right); + t.assert_equal("suffix should be 'test'", "test", result.suffix); + + // Similar strings with one extra char at end + result = calculate_diff_split("testX", "testY"); + t.assert_equal("prefix should be 'test'", "test", result.prefix); + t.assert_equal("left should be 'X'", "X", result.left); + t.assert_equal("right should be 'Y'", "Y", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + // Strings that are reverses + result = calculate_diff_split("abc", "cba"); + t.assert_equal("prefix should be empty", "", result.prefix); + t.assert_equal("left should be 'abc'", "abc", result.left); + t.assert_equal("right should be 'cba'", "cba", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); +} + +static void test_calculate_diff_split_tag_boundaries(testing & t) { + // Test with unclosed XML tags + diff_split result = calculate_diff_split("testcontent"); + // The fix_tag_boundaries should move incomplete tags appropriately + t.assert_true("prefix should start with 'test'", result.prefix.find("test") == 0); + t.assert_true("should handle tag boundaries", result.left != "" || result.right != "" || result.suffix != ""); + + // Test with unclosed brackets + result = calculate_diff_split("test[", "test]value"); + t.assert_true("should handle bracket boundaries", result.left != "" || result.right != "" || result.suffix != ""); + + // Test with partial tags on both sides + result = calculate_diff_split("prefix", "prefixsuffix"); + // fix_tag_boundaries moves the incomplete '<' from prefix to left/right + t.assert_equal("prefix should be 'prefix'", "prefix", result.prefix); + t.assert_equal("left should be ''", "", result.left); + t.assert_equal("right should be 'suffix'", "suffix", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + // Test with complex nested tags + result = calculate_diff_split("prefix
content
", "prefix
different
"); + // Algorithm finds "ent" as a common suffix because both strings end with it + // This is the actual algorithm behavior, though not semantically ideal + t.assert_equal("prefix should be 'prefix
'", "prefix
", result.prefix); + t.assert_equal("left should be 'cont'", "cont", result.left); + t.assert_equal("right should be 'differ'", "differ", result.right); + t.assert_equal("suffix should be 'ent
'", "ent
", result.suffix); + + // Test with unclosed angle bracket + result = calculate_diff_split("Hello ", "Hello test"); + t.assert_equal("prefix should be 'Hello '", "Hello ", result.prefix); + t.assert_true("left should contain ''", result.left.find("") != std::string::npos); + t.assert_equal("right should be 'test'", "test", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + // Test with unclosed square bracket + result = calculate_diff_split("test [array]", "test other"); + t.assert_equal("prefix should be 'test '", "test ", result.prefix); + t.assert_true("left should contain '[array]'", result.left.find("[array]") != std::string::npos); + t.assert_equal("right should be 'other'", "other", result.right); + t.assert_equal("suffix should be empty", "", result.suffix); + + // Test empty prefix and suffix with tags + result = calculate_diff_split("left", "righ"); + t.assert_equal("prefix should be ''", "", result.prefix); + t.assert_equal("left should be 'left'", "left", result.left); + t.assert_equal("right should be 'righ'", "righ", result.right); + t.assert_equal("suffix should be ''", "", result.suffix); + + { + // real case from template tests, simplified + std::string left = "PREFIXSure"; + std::string right = "PREFIXLemme thinkSure"; + result = calculate_diff_split(left, right); + t.assert_equal("prefix should be PREFIX", "PREFIX", result.prefix); + t.assert_equal("suffix should be Sure", "Sure", result.suffix); + t.assert_equal("left should be empty", "", result.left); + t.assert_equal("right should be Lemme think", "Lemme think", result.right); + } + + { + // Real case: special tokens with |> boundary issue + // The suffix starts with |> which should be moved to complete <|END_RESPONSE and <|END_ACTION + std::string prefix = "SOME_PREFIX"; + std::string suffix = "|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"; + std::string left_diff = "<|START_RESPONSE|>Let me help you.<|END_RESPONSE"; + std::string right_diff = + "<|START_THINKING|><|END_THINKING|><|START_ACTION|>[\n" + " {\"tool_call_id\": \"0\", \"tool_name\": \"test_function_name\", " + "\"parameters\": {\"param1\": \"value1\", \"param2\": \"value2\"}}\n" + "]<|END_ACTION"; + + std::string left = prefix + left_diff + suffix; + std::string right = prefix + right_diff + suffix; + result = calculate_diff_split(left, right); + + t.assert_equal("special token prefix", prefix, result.prefix); + // The |> should be moved from suffix to complete the tokens + t.assert_equal("special token left", "<|START_RESPONSE|>Let me help you.<|END_RESPONSE|>", result.left); + t.assert_true("special token right ends with |>", result.right.find("<|END_ACTION|>") != std::string::npos); + t.assert_equal("special token suffix", "<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", + result.suffix); + } +} + +static void test_until_common_prefix(testing & t) { + t.test("until_common_prefix basic", test_until_common_prefix_basic); +} + +static void test_until_common_prefix_basic(testing & t) { + // Test case from the user request + std::string result = until_common_prefix("", "", ""); + t.assert_equal("untilCommonPrefix should return ''", "", result); + + // Additional test cases to ensure robustness + // Test with different common prefix lengths + result = until_common_prefix("prefixsuffix", "different", "other"); + t.assert_equal("should return 'prefix'", "prefix", result); + + // Test when common prefix is at the start + result = until_common_prefix("rest", "left", "right"); + t.assert_equal("should return empty string when common prefix at start", "", result); + + // Test when there's no common prefix + result = until_common_prefix("something", "left", "right"); + t.assert_equal("should return empty string when no common prefix", "", result); + + // Test with empty strings + result = until_common_prefix("test", "", "right"); + t.assert_equal("should return empty string when left is empty", "", result); + + // Test with longer common prefix + result = until_common_prefix("abcXYZrest", "left", "right"); + t.assert_equal("should return 'abcXYZ'", "abcXYZ", result); +} + +static void test_after_common_suffix(testing & t) { + t.test("after_common_suffix basic", test_after_common_suffix_basic); +} + +static void test_after_common_suffix_basic(testing & t) { + // Test case from the user request + std::string result = after_common_suffix("100", + "100", + "535"); + t.assert_equal("afterCommonSuffix should return ''", "", result); + + // Test when common suffix is at the end + result = after_common_suffix("rest", "left", "right"); + t.assert_equal("should return empty string when common suffix at end", "", result); + + // Test with empty strings + result = after_common_suffix("test", "left", ""); + t.assert_equal("should return empty string when right is empty", "", result); + + // Test case with XML-like structure similar to the main example + result = after_common_suffix("value", + "value", + "different"); + t.assert_equal("should return ''", "", result); + + // Test with longer common suffix appearing at the end of full + result = after_common_suffix("prefixrest", "prefixleft", "prefixright"); + t.assert_equal("should return '' when common suffix is at end of full", "", result); + + // Test with common suffix appearing in middle but not at end + result = after_common_suffix("content", "value", "other"); + t.assert_equal("should return '' when common suffix appears before end", "", result); + + // Test with multi-character common suffix at the very end of full + result = after_common_suffix("startend", "prefixleft", "prefixright"); + t.assert_equal("should return '' when common suffix is at end of full", "", result); +} + +static void test_compare_variants(testing & t) { + t.test("compare_variants basic", test_compare_variants_basic); + t.test("compare_variants messages modifier", test_compare_variants_messages_modifier); + t.test("compare_variants tools modifier", test_compare_variants_tools_modifier); + t.test("compare_variants both modifiers", test_compare_variants_both_modifiers); + t.test("compare_variants template failure", test_compare_variants_template_failure); + t.test("compare_variants identity", test_compare_variants_identity); +} + +static void test_compare_variants_basic(testing & t) { + // Create a simple template that just echoes messages + common_chat_template tmpl("{{ messages[0]['content'] }}", "", ""); + + template_params params; + params.messages = json::array({ + json {{"role", "user"}, {"content", "Hello"}} + }); + + auto modifier = [](template_params & p) { + p.messages[0]["content"] = "World"; + }; + + auto result = ::compare_variants(tmpl, params, modifier); + + if (!t.assert_true("result should have value", result.has_value())) { + return; + } + // The template might not output anything if messages is empty or format is different + // Check that we get a valid result + t.assert_true("prefix or left should have content", !result->diff.prefix.empty() || !result->diff.left.empty()); +} + +static void test_compare_variants_messages_modifier(testing & t) { + // Test with messages modifier only + common_chat_template tmpl("{% for message in messages %}{{ message['role'] }}:{{ message['content'] }}{% endfor %}", "", ""); + + template_params params; + params.messages = json::array({ + json {{"role", "user"}, {"content", "A"}} + }); + + auto modifier = [](template_params & p) { + p.messages[0]["content"] = "B"; + }; + + std::optional result = ::compare_variants(tmpl, params, modifier); + + if (!t.assert_true("result should have value", result.has_value())) { + return; + } + t.assert_equal("left should be 'A'", "A", result->diff.left); + t.assert_equal("right should be 'B'", "B", result->diff.right); +} + +static void test_compare_variants_tools_modifier(testing & t) { + // Test with tools modifier only + common_chat_template tmpl( + "{% for tool in tools %}{{ tool['name'] }}{% endfor %}", "", ""); + + template_params params; + params.tools = json::array({ + json {{"name", "foo"}} + }); + + auto modifier = [](template_params & p) { + p.tools[0]["name"] = "bar"; + }; + + auto result = ::compare_variants(tmpl, params, modifier); + + if (!t.assert_true("result should have value", result.has_value())) { + return; + } + t.assert_equal("left should be 'foo'", "foo", result->diff.left); + t.assert_equal("right should be 'bar'", "bar", result->diff.right); +} + +static void test_compare_variants_both_modifiers(testing & t) { + // Test with both messages and tools modifiers using the for loop approach + common_chat_template tmpl( + "{% for message in messages %}{{ message['role'] }}:{{ message['content'] }}{% endfor %}", "", ""); + + template_params params; + params.messages = json::array({ + json {{"role", "user"}, {"content", "A"}} + }); + + auto modifier = [](template_params & p) { + p.messages[0]["content"] = "B"; + p.messages[0]["role"] = "newuser"; + }; + + auto result = ::compare_variants(tmpl, params, modifier); + + if (!t.assert_true("result should have value", result.has_value())) { + return; + } + t.assert_equal("left should be 'user:A'", "user:A", result->diff.left); + t.assert_equal("right should be 'newuser:B'", "newuser:B", result->diff.right); +} + +static void test_compare_variants_template_failure(testing & t) { + // Test with template that causes failure during application (not construction) + // We use a valid template syntax but one that will fail during application + common_chat_template tmpl("{{ messages[0]['nonexistent_field'] }}", "", ""); + + template_params params; + params.messages = json::array({ + json {{"role", "user"}, {"content", "Hello"}} + }); + + auto modifier = [](template_params & p) { + p.messages[0]["content"] = "World"; + }; + + auto result = ::compare_variants(tmpl, params, modifier); + + t.assert_true("result should be nullopt on template failure", !result.has_value()); +} + +static void test_compare_variants_identity(testing & t) { + // Test with identity modifier (no change) + common_chat_template tmpl("{{ messages[0]['content'] }}", "", ""); + + template_params params; + params.messages = json::array({ + json {{"role", "user"}, {"content", "Hello"}} + }); + + // No modifier - should use identity + auto result = ::compare_variants(tmpl, params, nullptr); + + if (!t.assert_true("result should have value", result.has_value())) { + return; + } + t.assert_equal("prefix should be 'Hello'", "Hello", result->diff.prefix); + t.assert_equal("left should be empty", "", result->diff.left); + t.assert_equal("right should be empty", "", result->diff.right); + t.assert_equal("suffix should be empty", "", result->diff.suffix); +} + +// ============================================================================ +// Seed-OSS Template Tool Calling Analysis Tests +// ============================================================================ + +static void test_seed_oss_tool_analysis(testing & t) { + t.test("Seed-OSS tool presence", test_seed_oss_tool_presence); + t.test("Seed-OSS call count", test_seed_oss_call_count); + t.test("Seed-OSS function names", test_seed_oss_function_names); + t.test("Seed-OSS argument count", test_seed_oss_argument_count); + t.test("Seed-OSS args presence", test_seed_oss_args_presence); + t.test("Seed-OSS tool with reasoning", test_seed_oss_tool_with_reasoning); +} + +// Helper to load Seed-OSS template +static common_chat_template load_seed_oss_template(testing & t) { + std::string template_path = "models/templates/ByteDance-Seed-OSS.jinja"; + std::ifstream fin(template_path, std::ios::binary); + std::ostringstream buf; + if (fin.is_open()) { + buf << fin.rdbuf(); + } + std::string template_source = buf.str(); + common_chat_template tmpl(template_source, "", ""); + t.assert_true("Seed-OSS template loaded successfully", template_source.length() > 0); + return tmpl; +} + +// Helper to build tool call JSON +static json build_tool_call(const std::string & name, const json & args, const std::string & id = "call_001") { + return json{ + {"id", id}, + {"type", "function"}, + {"function", json{ + {"name", name}, + {"arguments", args} + }} + }; +} + +// Helper to build tools definition +static json build_tools_definition() { + json parameters_schema = json::object(); + parameters_schema["type"] = "object"; + parameters_schema["properties"] = json::object(); + parameters_schema["properties"]["param1"] = json::object({ + {"type", "string"}, + {"description", "First parameter"} + }); + parameters_schema["properties"]["param2"] = json::object({ + {"type", "string"}, + {"description", "Second parameter"} + }); + parameters_schema["required"] = json::array({"param1", "param2"}); + + return json::array({ + json{ + {"type", "function"}, + {"function", json{ + {"name", "test_function_name"}, + {"description", "A test function for debugging"}, + {"parameters", parameters_schema} + }} + } + }); +} + +// T1: Compare with/without tool call (user, assistant) +static void test_seed_oss_tool_presence(testing & t) { + common_chat_template tmpl = load_seed_oss_template(t); + + json assistant_no_tools = json{ + {"role", "assistant"}, + {"content", "Let me help you."} + }; + + json assistant_with_tools = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_function_name", json::object({{"param1", "value1"}, {"param2", "value2"}})) + })} + }; + + json user_msg = json{ + {"role", "user"}, + {"content", "Hello, please help me."} + }; + + template_params params_no_tools; + params_no_tools.messages = json::array({user_msg, assistant_no_tools}); + params_no_tools.tools = build_tools_definition(); + params_no_tools.add_generation_prompt = false; + params_no_tools.enable_thinking = true; + + template_params params_with_tools; + params_with_tools.messages = json::array({user_msg, assistant_with_tools}); + params_with_tools.tools = build_tools_definition(); + params_with_tools.add_generation_prompt = false; + params_with_tools.enable_thinking = true; + + auto result = ::compare_variants(tmpl, params_no_tools, + [&](template_params & p) { + p.messages = params_with_tools.messages; + }); + + if (!t.assert_true("T1 result should have value", result.has_value())) { + return; + } + + const auto & diff = result->diff; + t.assert_true("T1 prefix should contain system", diff.prefix.find("system") != std::string::npos); + t.assert_true("T1 prefix should contain user", diff.prefix.find("user") != std::string::npos); + t.assert_true("T1 prefix should contain assistant", diff.prefix.find("assistant") != std::string::npos); + + // Left should be the assistant content without tool + t.assert_equal("T1 left should contain 'Let me help you.'", "Let me help you.", diff.left); + + // Right should contain the tool call markers + t.assert_true("T1 right should contain tool_call begin", diff.right.find("") != std::string::npos); + t.assert_true("T1 right should contain function tag", diff.right.find("") != std::string::npos); + t.assert_true("T1 right should contain parameter=param1", diff.right.find("") != std::string::npos); + t.assert_true("T1 right should contain parameter=param2", diff.right.find("") != std::string::npos); + t.assert_true("T1 right should contain value1", diff.right.find("value1") != std::string::npos); + t.assert_true("T1 right should contain value2", diff.right.find("value2") != std::string::npos); + t.assert_true("T1 right should contain tool_call end", diff.right.find("") != std::string::npos); + + // Suffix should be the eos token + t.assert_equal("T1 suffix should be ''", "", diff.suffix); +} + +// T2: Compare one vs two tool calls +static void test_seed_oss_call_count(testing & t) { + common_chat_template tmpl = load_seed_oss_template(t); + + json assistant_one_call = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_function_name", json::object({{"param1", "value1"}, {"param2", "value2"}})) + })} + }; + + json assistant_two_calls = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_function_name", json::object({{"param1", "value1"}, {"param2", "value2"}})), + build_tool_call("test_function_name", json::object({{"param1", "value3"}, {"param2", "value4"}}), "call_002") + })} + }; + + json user_msg = json{ + {"role", "user"}, + {"content", "Hello, please help me."} + }; + + template_params params_one; + params_one.messages = json::array({user_msg, assistant_one_call}); + params_one.tools = build_tools_definition(); + params_one.add_generation_prompt = false; + params_one.enable_thinking = true; + + auto result = ::compare_variants(tmpl, params_one, + [&](template_params & p) { + p.messages = json::array({user_msg, assistant_two_calls}); + }); + + if (!t.assert_true("T2 result should have value", result.has_value())) { + return; + } + + const auto & diff = result->diff; + + // Prefix should include the first tool call + t.assert_true("T2 prefix should contain first tool_call begin", diff.prefix.find("") != std::string::npos); + t.assert_true("T2 prefix should contain first function", diff.prefix.find("") != std::string::npos); + t.assert_true("T2 prefix should contain value1", diff.prefix.find("value1") != std::string::npos); + t.assert_true("T2 prefix should contain value2", diff.prefix.find("value2") != std::string::npos); + t.assert_true("T2 prefix should contain first tool_call end", diff.prefix.find("") != std::string::npos); + + // Left should be empty (no second tool call in variant A) + t.assert_equal("T2 left should be empty", "", diff.left); + + // Right should contain the second tool call + t.assert_true("T2 right should contain second tool_call begin", diff.right.find("") != std::string::npos); + t.assert_true("T2 right should contain second function", diff.right.find("") != std::string::npos); + t.assert_true("T2 right should contain value3", diff.right.find("value3") != std::string::npos); + t.assert_true("T2 right should contain value4", diff.right.find("value4") != std::string::npos); + t.assert_true("T2 right should contain second tool_call end", diff.right.find("") != std::string::npos); + + // Suffix should end with the eos token + t.assert_equal("T2 suffix should end with ''", "", diff.suffix.substr(diff.suffix.length() - 10, 10)); +} + +// T3: Compare different function names +static void test_seed_oss_function_names(testing & t) { + common_chat_template tmpl = load_seed_oss_template(t); + + // Build tools with two different function names + json parameters_schema = json::object(); + parameters_schema["type"] = "object"; + parameters_schema["properties"] = json::object(); + parameters_schema["properties"]["arg1"] = json::object({ + {"type", "string"}, + {"description", "Argument 1"} + }); + parameters_schema["required"] = json::array({"arg1"}); + + json tools = json::array({ + json{ + {"type", "function"}, + {"function", json{ + {"name", "func_alpha"}, + {"description", "First function"}, + {"parameters", parameters_schema} + }} + }, + json{ + {"type", "function"}, + {"function", json{ + {"name", "func_beta"}, + {"description", "Second function"}, + {"parameters", parameters_schema} + }} + } + }); + + json assistant_func_alpha = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("func_alpha", json::object({{"arg1", "test_value"}})) + })} + }; + + json assistant_func_beta = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("func_beta", json::object({{"arg1", "test_value"}})) + })} + }; + + json user_msg = json{ + {"role", "user"}, + {"content", "Hello"} + }; + + template_params params_alpha; + params_alpha.messages = json::array({user_msg, assistant_func_alpha}); + params_alpha.tools = tools; + params_alpha.add_generation_prompt = false; + params_alpha.enable_thinking = true; + + auto result = ::compare_variants(tmpl, params_alpha, + [&](template_params & p) { + p.messages = json::array({user_msg, assistant_func_beta}); + }); + + if (!t.assert_true("T3 result should have value", result.has_value())) { + return; + } + + const auto & diff = result->diff; + + bool func_alpha_in_left = diff.left.find("func_alpha") != std::string::npos; + bool func_alpha_in_prefix = diff.prefix.find("func_alpha") != std::string::npos; + bool func_beta_in_right = diff.right.find("func_beta") != std::string::npos; + bool func_beta_in_prefix = diff.prefix.find("func_beta") != std::string::npos; + bool func_beta_in_suffix = diff.suffix.find("func_beta") != std::string::npos; + + // Left should contain func_alpha (or be in prefix) + t.assert_true("T3 left should contain func_alpha (or prefix)", func_alpha_in_left || func_alpha_in_prefix); + + // Right should contain func_beta + t.assert_true("T3 right should contain func_beta", func_beta_in_right || func_beta_in_prefix || func_beta_in_suffix); + + // Both should have the same parameter value (in common parts, not in diffs) + // Since both have same args, test_value will be in prefix/suffix + t.assert_true("T3 diff should contain test_value (in prefix or suffix)", + diff.prefix.find("test_value") != std::string::npos || diff.suffix.find("test_value") != std::string::npos); +} + +// T4: Compare different argument counts (zero, one, two parameters) +static void test_seed_oss_argument_count(testing & t) { + common_chat_template tmpl = load_seed_oss_template(t); + + // Build tools with 0, 1, or 2 required parameters + json params_2_required = json::object(); + params_2_required["type"] = "object"; + params_2_required["properties"] = json::object(); + params_2_required["properties"]["arg1"] = json::object({ + {"type", "string"}, + {"description", "Argument 1"} + }); + params_2_required["properties"]["arg2"] = json::object({ + {"type", "string"}, + {"description", "Argument 2"} + }); + params_2_required["required"] = json::array({"arg1", "arg2"}); + + json params_1_required = json::object(); + params_1_required["type"] = "object"; + params_1_required["properties"] = json::object(); + params_1_required["properties"]["arg1"] = json::object({ + {"type", "string"}, + {"description", "Argument 1"} + }); + params_1_required["required"] = json::array({"arg1"}); + + json tools = json::array({ + json{ + {"type", "function"}, + {"function", json{ + {"name", "test_func"}, + {"description", "Test function"}, + {"parameters", params_2_required} + }} + } + }); + + // Test: zero args vs one arg + json assistant_zero_args = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_func", json::object()) + })} + }; + + json assistant_one_arg = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_func", json::object({{"arg1", "value1"}})) + })} + }; + + json assistant_two_args = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_func", json::object({{"arg1", "value1"}, {"arg2", "value2"}})) + })} + }; + + json user_msg = json{ + {"role", "user"}, + {"content", "Hello"} + }; + + // Test zero vs one + template_params params_zero; + params_zero.messages = json::array({user_msg, assistant_zero_args}); + params_zero.tools = tools; + params_zero.add_generation_prompt = false; + params_zero.enable_thinking = true; + + auto result_zero_one = ::compare_variants(tmpl, params_zero, + [&](template_params & p) { + p.messages = json::array({user_msg, assistant_one_arg}); + }); + + if (!t.assert_true("T4 zero vs one result should have value", result_zero_one.has_value())) { + return; + } + t.assert_true("T4 zero vs one left should be empty or minimal", result_zero_one->diff.left.empty() || result_zero_one->diff.left == ""); + t.assert_true("T4 zero vs one right should contain arg1", result_zero_one->diff.right.find("arg1") != std::string::npos); + + // Test one vs two + template_params params_one; + params_one.messages = json::array({user_msg, assistant_one_arg}); + params_one.tools = tools; + params_one.add_generation_prompt = false; + params_one.enable_thinking = true; + + auto result_one_two = ::compare_variants(tmpl, params_one, + [&](template_params & p) { + p.messages = json::array({user_msg, assistant_two_args}); + }); + + if (!t.assert_true("T4 one vs two result should have value", result_one_two.has_value())) { + return; + } + + const auto & diff4 = result_one_two->diff; + t.assert_true("T4 one vs two left should contain arg1 (or prefix)", + diff4.left.find("arg1") != std::string::npos || diff4.prefix.find("arg1") != std::string::npos); + t.assert_true("T4 one vs two right should contain arg1 (or prefix)", + diff4.right.find("arg1") != std::string::npos || diff4.prefix.find("arg1") != std::string::npos); + t.assert_true("T4 one vs two right should contain arg2 (or prefix/suffix)", + diff4.right.find("arg2") != std::string::npos || diff4.prefix.find("arg2") != std::string::npos || diff4.suffix.find("arg2") != std::string::npos); +} + +// T5: Compare different argument values +static void test_seed_oss_args_presence(testing & t) { + common_chat_template tmpl = load_seed_oss_template(t); + + json assistant_same_arg = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_function_name", json::object({{"param1", "value1"}})) + })} + }; + + json assistant_other_arg = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_function_name", json::object({{"param2", "value2"}})) + })} + }; + + json assistant_both_args = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_function_name", json::object({{"param1", "value1"}, {"param2", "value2"}})) + })} + }; + + json user_msg = json{ + {"role", "user"}, + {"content", "Hello"} + }; + + template_params params_same; + params_same.messages = json::array({user_msg, assistant_same_arg}); + params_same.tools = build_tools_definition(); + params_same.add_generation_prompt = false; + params_same.enable_thinking = true; + + // Test same arg vs other arg + auto result_same_other = ::compare_variants(tmpl, params_same, + [&](template_params & p) { + p.messages = json::array({user_msg, assistant_other_arg}); + }); + + if (!t.assert_true("T5 same vs other result should have value", result_same_other.has_value())) { + return; + } + const auto & diff5a = result_same_other->diff; + t.assert_true("T5 same vs other left should contain param1 (or prefix/suffix)", + diff5a.left.find("param1") != std::string::npos || diff5a.prefix.find("param1") != std::string::npos || diff5a.suffix.find("param1") != std::string::npos); + t.assert_true("T5 same vs other left should contain value1 (or prefix/suffix)", + diff5a.left.find("value1") != std::string::npos || diff5a.prefix.find("value1") != std::string::npos); + t.assert_true("T5 same vs other right should contain param2 (or prefix/suffix)", + diff5a.right.find("param2") != std::string::npos || diff5a.prefix.find("param2") != std::string::npos || diff5a.suffix.find("param2") != std::string::npos); + t.assert_true("T5 same vs other right should contain value2 (or prefix/suffix)", + diff5a.right.find("value2") != std::string::npos || diff5a.prefix.find("value2") != std::string::npos || diff5a.suffix.find("value2") != std::string::npos); + + // Test same arg vs both args + auto result_same_both = ::compare_variants(tmpl, params_same, + [&](template_params & p) { + p.messages = json::array({user_msg, assistant_both_args}); + }); + + if (!t.assert_true("T5 same vs both result should have value", result_same_both.has_value())) { + return; + } + const auto & diff5b = result_same_both->diff; + t.assert_true("T5 same vs both left should contain param1 (or prefix/suffix)", + diff5b.left.find("param1") != std::string::npos || diff5b.prefix.find("param1") != std::string::npos || diff5b.suffix.find("param1") != std::string::npos); + t.assert_true("T5 same vs both right should contain param1 (or prefix/suffix)", + diff5b.right.find("param1") != std::string::npos || diff5b.prefix.find("param1") != std::string::npos || diff5b.suffix.find("param1") != std::string::npos); + t.assert_true("T5 same vs both right should contain param2 (or prefix/suffix)", + diff5b.right.find("param2") != std::string::npos || diff5b.prefix.find("param2") != std::string::npos || diff5b.suffix.find("param2") != std::string::npos); +} + +// T6: Tool call with vs without reasoning_content +static void test_seed_oss_tool_with_reasoning(testing & t) { + common_chat_template tmpl = load_seed_oss_template(t); + + json assistant_tool_only = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_function_name", json::object({{"param1", "value1"}, {"param2", "value2"}})) + })} + }; + + json assistant_tool_with_reasoning = json{ + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + build_tool_call("test_function_name", json::object({{"param1", "value1"}, {"param2", "value2"}})) + })}, + {"reasoning_content", "I need to call the tool first."} + }; + + json user_msg = json{ + {"role", "user"}, + {"content", "Hello, please help me."} + }; + + template_params params_tool_only; + params_tool_only.messages = json::array({user_msg, assistant_tool_only}); + params_tool_only.tools = build_tools_definition(); + params_tool_only.add_generation_prompt = false; + params_tool_only.enable_thinking = true; + + auto result = ::compare_variants(tmpl, params_tool_only, + [&](template_params & p) { + p.messages = json::array({user_msg, assistant_tool_with_reasoning}); + }); + + if (!t.assert_true("T6 result should have value", result.has_value())) { + return; + } + + const auto & diff = result->diff; + + // Left should be empty (no reasoning in variant A) + t.assert_equal("T6 left should be empty", "", diff.left); + + // Right should contain the thinking token with reasoning content + t.assert_true("T6 right should contain think begin", diff.right.find("") != std::string::npos); + t.assert_true("T6 right should contain reasoning content", diff.right.find("I need to call the tool first.") != std::string::npos); + t.assert_true("T6 right should contain think end", diff.right.find("") != std::string::npos); + + // Prefix should contain the assistant role + t.assert_true("T6 prefix should contain assistant", diff.prefix.find("assistant") != std::string::npos); + + // Suffix should contain the tool call + t.assert_true("T6 suffix should contain tool_call begin", diff.suffix.find("") != std::string::npos); + t.assert_true("T6 suffix should contain function name", diff.suffix.find("test_function_name") != std::string::npos); + t.assert_true("T6 suffix should contain eos", diff.suffix.find("") != std::string::npos); +} + +static common_chat_template load_template(testing & t, const std::string & template_path) { + std::ifstream fin(template_path, std::ios::binary); + std::ostringstream buf; + if (fin.is_open()) { + buf << fin.rdbuf(); + } + std::string template_source = buf.str(); + common_chat_template tmpl(template_source, "", ""); + t.assert_true("Nemotron template loaded successfully", template_source.length() > 0); + return tmpl; +} + +// ============================================================================ +// Nemotron Template Analysis Tests +// ============================================================================ +static common_chat_template load_nemotron_template(testing & t) { + return load_template(t, "models/templates/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja"); +} + +static void test_nemotron_analysis(testing & t) { + t.test("Nemotron reasoning detection", test_nemotron_reasoning_detection); + t.test("Nemotron tool format", test_nemotron_tool_format); +} + +static void test_nemotron_reasoning_detection(testing & t) { + common_chat_template tmpl = load_nemotron_template(t); + + // Test the comparison manually to see what's happening + json user_msg = json{ { "role", "user" }, { "content", "Hello" } }; + json assistant_no_reasoning = json{ + { "role", "assistant" }, + { "content", "I can help." } + }; + json assistant_with_reasoning = json{ + { "role", "assistant" }, + { "content", "I can help." }, + { "reasoning_content", "Let me think about this." } + }; + + template_params params; + params.messages = json::array({ user_msg, assistant_no_reasoning }); + params.add_generation_prompt = false; + params.enable_thinking = true; + + // Run differential analysis + struct autoparser analysis; + analysis.analyze_template(tmpl); + + // Check reasoning markers + t.assert_equal("reasoning_start should be ''", "", analysis.reasoning.start); + t.assert_equal("reasoning_end should be '\\n'", "\n", analysis.reasoning.end); + + // Check reasoning mode detection + // Nemotron uses forced closed reasoning with add_generation_prompt + t.assert_equal("reasoning should be FORCED_CLOSED", reasoning_mode::FORCED_CLOSED, analysis.reasoning.mode); + + // Make sure reasoning markers don't spill over to content markers + t.assert_equal("content start should be empty", "", analysis.content.start); + t.assert_equal("content end should be empty", "", analysis.content.end); + + t.assert_equal("content should be PLAIN", content_mode::PLAIN, analysis.content.mode); +} + +static void test_nemotron_tool_format(testing & t) { + common_chat_template tmpl = load_nemotron_template(t); + + // Run differential analysis + struct autoparser analysis; + analysis.analyze_template(tmpl); + + // Check tool markers - Nemotron uses per-call wrapping (each call individually wrapped) + t.assert_equal("tool_section_start should be empty (per-call format)", "", analysis.tools.format.section_start); + t.assert_equal("tool_section_end should be empty (per-call format)", "", analysis.tools.format.section_end); + t.assert_equal("per_call_start should be '\\n'", "\n", analysis.tools.format.per_call_start); + t.assert_equal("per_call_end should be ''", "", analysis.tools.format.per_call_end); + t.assert_true("should support parallel calls", analysis.jinja_caps.supports_parallel_tool_calls); + + // Check function markers + t.assert_equal("func_name_prefix should be '\\n'", ">\n", analysis.tools.function.name_suffix); + t.assert_equal("func_close should be '\\n'", "\n", analysis.tools.function.close); + + // Check argument markers (note: markers retain trailing newlines for proper parsing) + t.assert_equal("arg_name_prefix should be '\\n'", ">\n", analysis.tools.arguments.name_suffix); + t.assert_equal("arg_value_suffix should be '\\n'", "\n", analysis.tools.arguments.value_suffix); + + // Check format classification + t.assert_true("tool format should be TAG_WITH_TAGGED", analysis.tools.format.mode == tool_format::TAG_WITH_TAGGED); + + // Verify tool support + t.assert_true("should support tools", analysis.jinja_caps.supports_tools); +} + +static common_chat_template load_cohere_template(testing & t) { + return load_template(t, "models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja"); +} + +static void test_cohere_analysis(testing & t) { + t.test("Cohere reasoning detection", test_cohere_reasoning_detection); +} + +static void test_cohere_reasoning_detection(testing & t) { + common_chat_template tmpl = load_cohere_template(t); + + // Run differential analysis + struct autoparser analysis; + analysis.analyze_template(tmpl); + + // Check reasoning markers - Cohere uses special token format + t.assert_equal("reasoning_start should be '<|START_THINKING|>'", "<|START_THINKING|>", analysis.reasoning.start); + t.assert_equal("reasoning_end should be '<|END_THINKING|>'", "<|END_THINKING|>", analysis.reasoning.end); + + // Check reasoning mode - Cohere only shows reasoning with tool calls (TOOLS_ONLY) + t.assert_equal("reasoning should be TOOLS_ONLY", reasoning_mode::TOOLS_ONLY, analysis.reasoning.mode); + + // Check content markers - Cohere wraps all content with START/END_RESPONSE + t.assert_equal("content_start should be '<|START_RESPONSE|>'", "<|START_RESPONSE|>", analysis.content.start); + t.assert_equal("content_end should be '<|END_RESPONSE|>'", "<|END_RESPONSE|>", analysis.content.end); + + // Content is always wrapped (both with and without tools) + t.assert_equal("content should be ALWAYS_WRAPPED", content_mode::ALWAYS_WRAPPED, analysis.content.mode); +} + +static void test_tool_format_cohere(testing & t) { + common_chat_template tmpl = load_cohere_template(t); + + // Run differential analysis + struct autoparser analysis; + analysis.analyze_template(tmpl); + + // Check tool section markers - Cohere uses ACTION markers + t.assert_equal("tool_section_start should be '<|START_ACTION|>'", "<|START_ACTION|>", analysis.tools.format.section_start); + t.assert_equal("tool_section_end should be '<|END_ACTION|>'", "<|END_ACTION|>", analysis.tools.format.section_end); + + // JSON_NATIVE format has no per-call markers + t.assert_equal("per_call_start should be empty", "", analysis.tools.format.per_call_start); + t.assert_equal("per_call_end should be empty", "", analysis.tools.format.per_call_end); + + // JSON_NATIVE format has empty function markers (no XML-style markers) + t.assert_equal("func_name_prefix should be empty", "", analysis.tools.function.name_prefix); + t.assert_equal("func_name_suffix should be empty", "", analysis.tools.function.name_suffix); + t.assert_equal("func_close should be empty", "", analysis.tools.function.close); + + // JSON_NATIVE format has empty args markers + t.assert_equal("args_start should be empty", "", analysis.tools.arguments.start); + t.assert_equal("args_end should be empty", "", analysis.tools.arguments.end); + + // JSON_NATIVE format has empty argument markers + t.assert_equal("arg_name_prefix should be empty", "", analysis.tools.arguments.name_prefix); + t.assert_equal("arg_name_suffix should be empty", "", analysis.tools.arguments.name_suffix); + t.assert_equal("arg_value_prefix should be empty", "", analysis.tools.arguments.value_prefix); + t.assert_equal("arg_value_suffix should be empty", "", analysis.tools.arguments.value_suffix); + t.assert_equal("arg_separator should be empty", "", analysis.tools.arguments.separator); + + // Check JSON field names - Cohere uses non-standard names + t.assert_equal("name_field should be 'tool_name'", "tool_name", analysis.tools.format.name_field); + t.assert_equal("args_field should be 'parameters'", "parameters", analysis.tools.format.args_field); + // This isn't a real tool call id field, i.e. with the OpenAI tool call ID format + t.assert_equal("id_field should be 'tool_call_id'", "", analysis.tools.format.id_field); + + // Check format classification + t.assert_equal("tool format should be JSON_NATIVE", tool_format::JSON_NATIVE, analysis.tools.format.mode); + + // Check flags + t.assert_true("should support tools", analysis.jinja_caps.supports_tools); + t.assert_true("should support parallel calls", analysis.jinja_caps.supports_parallel_tool_calls); + t.assert_true("should not require nonnull content", !analysis.content.requires_nonnull_content); + t.assert_true("tools_array_wrapped should be true", analysis.tools.format.tools_array_wrapped); +} + +// ============================================================================ +// standard_json_tools Format Tests +// ============================================================================ + +// Helper to build tools definition for tests +static json build_test_tools() { + json parameters_schema = json::object(); + parameters_schema["type"] = "object"; + parameters_schema["properties"] = json::object(); + parameters_schema["properties"]["location"] = json::object({ + {"type", "string"}, + {"description", "The city and state"} + }); + parameters_schema["properties"]["unit"] = json::object({ + {"type", "string"}, + {"description", "Temperature unit"}, + {"enum", json::array({"celsius", "fahrenheit"})} + }); + parameters_schema["required"] = json::array({"location"}); + + return json::array({ + json{ + {"type", "function"}, + {"function", json{ + {"name", "get_current_weather"}, + {"description", "Get the current weather in a given location"}, + {"parameters", parameters_schema} + }} + } + }); +} + +static void test_standard_json_tools_formats(testing & t) { + t.test("OpenAI format", test_standard_json_tools_openai); + t.test("Cohere format", test_standard_json_tools_cohere); + t.test("function-as-key format", test_standard_json_tools_function_key); +} + +// Test 1: OpenAI Standard Format +// {"id": "call_abc", "function": {"name": "get_weather", "arguments": {"location": "NYC"}}} +static void test_standard_json_tools_openai(testing & t) { + json tools = build_test_tools(); + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + auto tool_call = p.standard_json_tools( + "", "", tools, + /* parallel */ true, + /* force */ false, + /* name_key */ "function.name", + /* args_key */ "function.arguments", + /* array_wrapped */ false, + /* function_is_key */ false, + /* call_id_key */ "id", + /* gen_call_id_key */ "", + /* parameters_order */ {} + ); + return p.content(p.until("")) + p.optional(tool_call) + p.end(); + }); + + std::string input = + "Let me check the weather." + "" + R"({"id": "call_abc123", "function": {"name": "get_current_weather", "arguments": {"location": "NYC"}}})" + ""; + + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + + if (!t.assert_true("parse success", result.success())) { + return; + } + + common_chat_msg msg; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + t.assert_equal("tool calls count", 1u, msg.tool_calls.size()); + if (!msg.tool_calls.empty()) { + t.assert_equal("tool name", "get_current_weather", msg.tool_calls[0].name); + t.assert_equal("tool id", "call_abc123", msg.tool_calls[0].id); + } + t.assert_true("content present", msg.content.find("Let me check the weather") != std::string::npos); +} + +// Test 2: Cohere Format +// {"tool_call_id": 0, "tool_name": "get_weather", "parameters": {"location": "NYC"}} +static void test_standard_json_tools_cohere(testing & t) { + json tools = build_test_tools(); + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + auto tool_call = p.standard_json_tools( + "<|START_ACTION|>[", "]<|END_ACTION|>", tools, + /* parallel */ true, + /* force */ false, + /* name_key */ "tool_name", + /* args_key */ "parameters", + /* array_wrapped */ false, // Brackets are part of section markers + /* function_is_key */ false, + /* call_id_key */ "", + /* gen_call_id_key */ "tool_call_id", + /* parameters_order */ {"tool_call_id", "tool_name", "parameters"} + ); + return p.content(p.until("<|START_ACTION|>")) + p.optional(tool_call) + p.end(); + }); + + std::string input = + "Let me search for that." + "<|START_ACTION|>[" + R"({"tool_call_id": 0, "tool_name": "get_current_weather", "parameters": {"location": "NYC", "unit": "celsius"}})" + "]<|END_ACTION|>"; + + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + + if (!t.assert_true("parse success", result.success())) { + return; + } + + common_chat_msg msg; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + t.assert_equal("tool calls count", 1u, msg.tool_calls.size()); + if (!msg.tool_calls.empty()) { + t.assert_equal("tool name", "get_current_weather", msg.tool_calls[0].name); + t.assert_equal("tool id", "0", msg.tool_calls[0].id); + } + t.assert_true("content present", msg.content.find("Let me search") != std::string::npos); +} + +// Test 3: Function-as-Key Format +// {"get_current_weather": {"id": "call-0001", "args": {"location": "NYC"}}} +static void test_standard_json_tools_function_key(testing & t) { + json tools = build_test_tools(); + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + auto tool_call = p.standard_json_tools( + "[", "]", tools, + /* parallel */ true, + /* force */ false, + /* name_key */ "", // Name is the key itself + /* args_key */ "args", + /* array_wrapped */ false, + /* function_is_key */ true, + /* call_id_key */ "id", + /* gen_call_id_key */ "", + /* parameters_order */ {} + ); + return p.content(p.until("")) + p.optional(tool_call) + p.end(); + }); + + std::string input = + "I'll call the weather function." + "[" + R"({"get_current_weather": {"id": "call-0001", "args": {"location": "NYC", "unit": "celsius"}}})" + "]"; + + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + + if (!t.assert_true("parse success", result.success())) { + return; + } + + common_chat_msg msg; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + t.assert_equal("tool calls count", 1u, msg.tool_calls.size()); + if (!msg.tool_calls.empty()) { + t.assert_equal("tool name", "get_current_weather", msg.tool_calls[0].name); + t.assert_equal("tool id", "call-0001", msg.tool_calls[0].id); + } + t.assert_true("content present", msg.content.find("I'll call the weather") != std::string::npos); +} + +// ============================================================================ +// normalize_quotes_to_json Tests +// ============================================================================ + +// Copy of the function for isolated testing (original is static in chat-peg-parser.cpp) +static std::string normalize_quotes_to_json(const std::string & input) { + std::string result; + result.reserve(input.size() + 16); + + bool in_single_quoted = false; + bool in_double_quoted = false; + + for (size_t i = 0; i < input.size(); ++i) { + char c = input[i]; + + if (c == '\\' && i + 1 < input.size()) { + char next = input[i + 1]; + + if (in_single_quoted) { + if (next == '\'') { + result += '\''; + ++i; + continue; + } + if (next == '"') { + result += "\\\""; + ++i; + continue; + } + result += c; + result += next; + ++i; + continue; + } + + if (in_double_quoted) { + result += c; + result += next; + ++i; + continue; + } + + result += c; + continue; + } + + if (c == '"') { + if (in_single_quoted) { + result += "\\\""; + } else { + in_double_quoted = !in_double_quoted; + result += c; + } + } else if (c == '\'') { + if (in_double_quoted) { + result += c; + } else if (in_single_quoted) { + in_single_quoted = false; + result += '"'; + } else { + in_single_quoted = true; + result += '"'; + } + } else { + result += c; + } + } + + return result; +} + +static void test_normalize_quotes_to_json(testing & t) { + t.test("basic single to double quotes", [](testing & t) { + std::string input = "{'key': 'value'}"; + std::string expected = "{\"key\": \"value\"}"; + std::string result = normalize_quotes_to_json(input); + t.assert_equal("basic conversion", expected, result); + }); + + t.test("escaped single quote inside single-quoted string", [](testing & t) { + std::string input = "{'code': 'print(\\'hello\\')'}"; + std::string expected = "{\"code\": \"print('hello')\"}"; + std::string result = normalize_quotes_to_json(input); + t.assert_equal("escaped single quote", expected, result); + }); + + t.test("double quote inside single-quoted string", [](testing & t) { + std::string input = "{'msg': 'He said \"hi\"'}"; + std::string expected = "{\"msg\": \"He said \\\"hi\\\"\"}"; + std::string result = normalize_quotes_to_json(input); + t.assert_equal("double quote escaping", expected, result); + }); + + t.test("nested backslash escapes", [](testing & t) { + std::string input = "{'path': 'C:\\\\Users\\\\test'}"; + std::string expected = "{\"path\": \"C:\\\\Users\\\\test\"}"; + std::string result = normalize_quotes_to_json(input); + t.assert_equal("backslash escaping", expected, result); + }); + + t.test("newline escapes", [](testing & t) { + std::string input = "{'text': 'line1\\nline2'}"; + std::string expected = "{\"text\": \"line1\\nline2\"}"; + std::string result = normalize_quotes_to_json(input); + t.assert_equal("newline escaping", expected, result); + }); + + t.test("mixed quotes", [](testing & t) { + std::string input = "{\"already_double\": 'single_value'}"; + std::string expected = "{\"already_double\": \"single_value\"}"; + std::string result = normalize_quotes_to_json(input); + t.assert_equal("mixed quotes", expected, result); + }); + + t.test("embedded quotes - the test case", test_normalize_quotes_with_embedded_quotes); +} + +// Test case that mirrors the Seed-OSS failing test scenario +static void test_normalize_quotes_with_embedded_quotes(testing & t) { + // This is similar to the Seed-OSS template test case + // The input has embedded double quotes like "14" and "bar" inside string values + std::string input = "{'filename': 'foo.cpp', 'oldString': 'def foo(arg = \"14\"):\\n return arg + \"bar\"\\n', 'newString': 'def foo(arg = \"15\"):\\n pass\\n'}"; + + // Expected: Python single quotes -> JSON double quotes, internal double quotes escaped + std::string expected = "{\"filename\": \"foo.cpp\", \"oldString\": \"def foo(arg = \\\"14\\\"):\\n return arg + \\\"bar\\\"\\n\", \"newString\": \"def foo(arg = \\\"15\\\"):\\n pass\\n\"}"; + + std::string result = normalize_quotes_to_json(input); + + t.assert_equal("normalize quotes with embedded double quotes", expected, result); + + // Also verify the result is valid JSON + try { + json parsed = json::parse(result); + t.assert_true("result is valid JSON", true); + t.assert_equal("filename field", "foo.cpp", parsed["filename"].get()); + t.assert_true("oldString contains embedded quotes", + parsed["oldString"].get().find("\"14\"") != std::string::npos); + t.assert_true("newString contains embedded quotes", + parsed["newString"].get().find("\"15\"") != std::string::npos); + } catch (const std::exception & e) { + t.assert_true(std::string("JSON parse failed: ") + e.what(), false); + } +} + +// ============================================================================ +// TAG_WITH_TAGGED Argument Parsing Tests +// ============================================================================ + +// Build tools definition for edit function +static json build_edit_tool() { + json parameters_schema = json::object(); + parameters_schema["type"] = "object"; + parameters_schema["properties"] = json::object(); + parameters_schema["properties"]["filename"] = json::object({ + {"type", "string"}, + {"description", "Path of file to edit"} + }); + parameters_schema["properties"]["oldString"] = json::object({ + {"type", "string"}, + {"description", "String to replace"} + }); + parameters_schema["properties"]["newString"] = json::object({ + {"type", "string"}, + {"description", "New (replacement) value"} + }); + parameters_schema["required"] = json::array({"filename", "oldString", "newString"}); + + return json::array({ + json{ + {"type", "function"}, + {"function", json{ + {"name", "edit"}, + {"description", "Edit a file"}, + {"parameters", parameters_schema} + }} + } + }); +} + +// Test that reproduces the Seed-OSS template issue with embedded quotes +static void test_tagged_args_with_embedded_quotes(testing & t) { + json tools = build_edit_tool(); + + // Build a parser for TAG_WITH_TAGGED format like Seed-OSS/Nemotron + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + // Build tool choice for the edit function + auto tool_choice = p.choice(); + + for (const auto & tool_def : tools) { + if (!tool_def.contains("function")) { continue; } + const auto & function = tool_def.at("function"); + std::string name = function.at("name"); + const auto & params = function.at("parameters"); + + if (!params.contains("properties") || !params.at("properties").is_object()) { continue; } + + const auto & properties = params.at("properties"); + + // Build argument parsers + std::vector arg_parsers; + for (const auto & [param_name, param_schema] : properties.items()) { + auto arg = p.tool_arg( + p.tool_arg_open(p.literal("")) + + p.space() + + p.tool_arg_string_value(p.until("")) + + p.space() + + p.tool_arg_close(p.literal("")) + ); + arg_parsers.push_back(p.optional(p.rule("arg-" + param_name, arg))); + } + + // Build arg sequence with space() between + common_peg_parser args_seq = p.eps(); + for (size_t i = 0; i < arg_parsers.size(); i++) { + if (i > 0) { + args_seq = args_seq + p.space(); + } + args_seq = args_seq + arg_parsers[i]; + } + + auto func_parser = + p.tool_open(p.literal("")) + + p.space() + args_seq + p.space() + + p.tool_close(p.literal("")); + + tool_choice |= p.rule("tool-" + name, p.tool(func_parser)); + } + + auto tool_section = + p.literal("") + p.space() + + tool_choice + + p.space() + p.literal(""); + + return p.content(p.until("")) + p.optional(tool_section) + p.end(); + }); + + // The exact input from the failing test + std::string input = + "\n" + "\n" + "\n" + "foo.cpp\n" + "\n" + "" + "def foo(arg = \"14\"):\n" + " return arg + \"bar\"\n" + "\n" + "\n" + "" + "def foo(arg = \"15\"):\n" + " pass\n" + "\n" + "\n" + "\n" + ""; + + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + + if (!t.assert_true("parse success", result.success())) { + return; + } + + common_chat_msg msg; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + t.assert_equal("tool calls count", 1u, msg.tool_calls.size()); + + if (!msg.tool_calls.empty()) { + t.assert_equal("tool name", "edit", msg.tool_calls[0].name); + + // Parse the arguments as JSON to verify they're valid + std::string args = msg.tool_calls[0].arguments; + + try { + json parsed = json::parse(args); + t.assert_true("arguments is valid JSON", true); + + // Verify each field has proper value + t.assert_equal("filename", "foo.cpp", parsed.value("filename", "")); + + std::string oldString = parsed.value("oldString", ""); + t.assert_true("oldString contains embedded quotes", + oldString.find("\"14\"") != std::string::npos); + t.assert_true("oldString contains bar with quotes", + oldString.find("\"bar\"") != std::string::npos); + + std::string newString = parsed.value("newString", ""); + t.assert_true("newString contains embedded quotes", + newString.find("\"15\"") != std::string::npos); + + } catch (const std::exception & e) { + t.assert_true(std::string("arguments should be valid JSON: ") + e.what(), false); + } + } +} + diff --git a/tests/test-chat-parser.cpp b/tests/test-chat-parser.cpp deleted file mode 100644 index 6f44a2b421..0000000000 --- a/tests/test-chat-parser.cpp +++ /dev/null @@ -1,617 +0,0 @@ -// Tests chat handling, including grammar generation and parsing for tool calling, for various templates. -// -// Also acts as a CLI to generate a Markdown summary of the formats of Jinja templates, -// e.g. given Minja (http://github.com/google/minja) checked out in parent dir: -// -// cmake -B build && cmake --build build --parallel && ./build/bin/test-chat ../minja/build/tests/*.jinja 2>/dev/null -// -#include -#include -#include - -#include "chat-parser.h" -#include "common.h" -#include "log.h" -#include "regex-partial.h" - -template -static void assert_equals(const std::string_view label, const T & expected, const T & actual) { - if (expected != actual) { - std::cerr << label << std::endl; - std::cerr << "Expected: " << expected << std::endl; - std::cerr << "Actual: " << actual << std::endl; - std::cerr << std::flush; - throw std::runtime_error("Test failed"); - } -} - -template -static void assert_equals(const T & expected, const T & actual) { - assert_equals("", expected, actual); -} -static void assert_equals(const char * expected, const std::string & actual) { - return assert_equals(expected, actual); -} - -static void assert_throws(const std::function & fn, const std::string & expected_exception_pattern = "") { - try { - fn(); - } catch (const std::exception & e) { - if (expected_exception_pattern.empty()) { - return; - } - std::regex expected_exception_regex(expected_exception_pattern); - std::string actual_message = e.what(); - if (std::regex_search(actual_message, expected_exception_regex)) { - return; - } - throw std::runtime_error("Exception doesn't match expected pattern: " + actual_message + " (pattern: " + expected_exception_pattern + ")"); - throw std::runtime_error("Exception of unexpected type: " + std::string(e.what())); - } - throw std::runtime_error("Exception was expected but not thrown"); -} - -static void test_reasoning() { - //common_log_set_verbosity_thold(LOG_DEFAULT_DEBUG); - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - params.reasoning_format = COMMON_REASONING_FORMAT_NONE; - params.reasoning_in_content = false; - params.thinking_forced_open = false; - common_chat_msg_parser builder("CogitoErgo sum", /* is_partial= */ false, params); - assert_equals(false, builder.try_parse_reasoning("", "")); - assert_equals("CogitoErgo sum", builder.consume_rest()); - } - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = false; - common_chat_msg_parser builder("CogitoErgo sum", /* is_partial= */ false, params); - assert_equals(true, builder.try_parse_reasoning("", "")); - assert_equals(std::string("Cogito"), builder.result().reasoning_content); - assert_equals("Ergo sum", builder.consume_rest()); - } - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - params.reasoning_format = COMMON_REASONING_FORMAT_NONE; - params.reasoning_in_content = false; - params.thinking_forced_open = false; - common_chat_msg_parser builder("CogitoErgo sum", /* is_partial= */ false, params); - assert_equals(false, builder.try_parse_reasoning("", "")); - assert_equals("CogitoErgo sum", builder.consume_rest()); - } - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = true; - common_chat_msg_parser builder("CogitoErgo sum", /* is_partial= */ false, params); - assert_equals(true, builder.try_parse_reasoning("", "")); - assert_equals(std::string("Cogito"), builder.result().reasoning_content); - assert_equals("Ergo sum", builder.consume_rest()); - } - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = true; - params.thinking_forced_open = true; - common_chat_msg_parser builder("CogitoErgo sum", /* is_partial= */ false, params); - assert_equals(true, builder.try_parse_reasoning("", "")); - assert_equals("Cogito", builder.result().content); - assert_equals("Ergo sum", builder.consume_rest()); - } - { - const std::string variant("content_only_inline_think"); - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = false; - params.parse_tool_calls = false; - const std::string input = "PenseBonjour"; - auto msg = common_chat_parse(input, false, params); - assert_equals(variant, std::string("Pense"), msg.reasoning_content); - assert_equals(variant, std::string("Bonjour"), msg.content); - } - { - const std::string variant("llama_3_inline_think"); - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_LLAMA_3_X; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = false; - params.parse_tool_calls = false; - const std::string input = "PlanRéponse"; - auto msg = common_chat_parse(input, false, params); - assert_equals(variant, std::string("Plan"), msg.reasoning_content); - assert_equals(variant, std::string("Réponse"), msg.content); - } - // Test DeepSeek V3.1 parsing - reasoning content followed by "" and then regular content - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = true; - params.parse_tool_calls = true; - const std::string variant("deepseek_v3_1_reasoning_format_deepseek"); - common_chat_msg_parser builder("REASONINGok", /* is_partial= */ false, params); - assert_equals(variant, true, builder.try_parse_reasoning("", "")); - assert_equals(variant, std::string("REASONING"), builder.result().reasoning_content); - assert_equals(variant, std::string("ok"), builder.consume_rest()); - } - // Test DeepSeek V3.1 parsing - reasoning_format none - reasoning content followed by "" and then regular content - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_NONE; - params.reasoning_in_content = false; - params.thinking_forced_open = true; - params.parse_tool_calls = true; - const std::string variant("deepseek_v3_1_reasoning_format_none"); - const std::string input = "REASONINGok"; - auto msg = common_chat_parse(input, false, params); - assert_equals(variant, std::string("REASONINGok"), msg.content); - assert_equals(variant, std::string(""), msg.reasoning_content); - } -} - -static void test_regex() { - auto test_throws = [](const std::string & input, const std::string & regex, const std::string & expected_exception_pattern = "") { - common_chat_msg_parser builder(input, /* is_partial= */ false, {}); - assert_throws([&]() { builder.consume_regex(common_regex(regex)); }, expected_exception_pattern); - }; - - test_throws("Hello, world!", "abc", "^abc$"); - test_throws("Hello, world!", "e", "^e$"); - - { - common_chat_msg_parser builder("Hello, world!", /* is_partial= */ false, {}); - builder.consume_regex(common_regex("Hello")); - assert_equals(", world!", builder.consume_rest()); - } - - { - // When in non partial mode, we can say whether the regex was consumed or not. - common_chat_msg_parser builder("Hello,", /* is_partial= */ false, {}); - assert_equals(false, builder.try_consume_regex(common_regex("Hello, world!")).has_value()); - } - { - common_chat_msg_parser builder("Hello,", /* is_partial= */ false, {}); - auto res = builder.try_consume_regex(common_regex("H(el)l(?:o, world!)?")); - assert_equals(true, res.has_value()); - // Verify captures - assert_equals(2, res->groups.size()); - assert_equals("Hell", builder.str(res->groups[0])); - assert_equals("el", builder.str(res->groups[1])); - // Verify position is after the match - assert_equals(4, builder.pos()); - assert_equals("o,", builder.consume_rest()); - } - { - // But in partial mode, we have a partial final match / can't decide, so we throw a partial exception. - common_chat_msg_parser builder("Hello,", /* is_partial= */ true, {}); - assert_throws([&]() { - builder.try_consume_regex(common_regex("Hello, world!")); - }, "^Hello, world!$"); - } - - // Now regardless of the mode, we can tell these aren't a match. - for (const auto is_partial : {false, true}) { - common_chat_msg_parser builder("Hello,", is_partial, {}); - assert_equals(false, builder.try_consume_regex(common_regex("a(b|c)(d|e)f")).has_value()); - } - for (const auto is_partial : {false, true}) { - common_chat_msg_parser builder("Hello,", is_partial, {}); - assert_equals(false, builder.try_consume_literal("Oh")); - } -} - -const std::vector barely_healable_jsons = { - "{", - "{\"", - "{\"\\", - "{\"n", - "{\"name\"", - "{\"name\":", - "{\"name\":\"", - "{\"name\":\"\\", - "{\"name\":\"python", - "{\"name\":\"python\\", - "{\",", - "{\":", - "{\"[", - "{\"]", - "{\"{", - "{\"}", - "{\"1", - "{\"name\":\",", - "{\"name\":\":", - "{\"name\":\"[", - "{\"name\":\"]", - "{\"name\":\"{", - "{\"name\":\"}", - "{\"name\":\"1", -}; - -static void test(const std::string & input, bool is_partial, const std::vector> & args_paths, const std::vector> & content_paths, const std::string & expected) { - common_chat_msg_parser builder(input, is_partial, {}); - auto js = builder.try_consume_json_with_dumped_args(args_paths, content_paths); - assert_equals(true, js.has_value()); - assert_equals(is_partial, js->is_partial); - assert_equals(expected, args_paths.size() == 1 && args_paths[0].empty() ? js->value.get() : js->value.dump()); -} - -static void test_deepseek_v3_1_tool_calls() { - //common_log_set_verbosity_thold(LOG_DEFAULT_DEBUG); - // variant: happy path for when it works as the model card says it should - const std::string variant("simple"); - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = false; - params.parse_tool_calls = true; - const std::string input = "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>"; - auto msg = common_chat_parse(input, false, params); - assert_equals(variant, 1, msg.tool_calls.size()); - assert_equals(variant, std::string("get_time"), msg.tool_calls[0].name); - // JSON arguments are dumped without spaces - assert_equals(variant, std::string("{\"city\":\"Tokyo\"}"), msg.tool_calls[0].arguments); - assert_equals(variant, std::string(""), msg.content); - assert_equals(variant, std::string(""), msg.reasoning_content); - - // variant: simple + thinking open - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = true; - params.parse_tool_calls = true; - const std::string variant("simple_thinking"); - const std::string in = "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>"; - auto m = common_chat_parse(in, false, params); - assert_equals(variant, 1, m.tool_calls.size()); - assert_equals(variant, std::string("get_time"), m.tool_calls[0].name); - assert_equals(variant, std::string("{\"city\":\"Tokyo\"}"), m.tool_calls[0].arguments); - assert_equals(variant, std::string(""), m.content); - assert_equals(variant, std::string("REASONING"), m.reasoning_content); - } - // variant: simple + multiple tool calls - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = false; - params.parse_tool_calls = true; - const std::string variant("simple_multiple_tool_calls"); - const std::string in = "CONTENT<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Paris\"}<｜tool▁call▁end｜><｜tool▁call▁begin｜>get_weather<｜tool▁sep｜>{\"city\": \"Paris\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>"; - auto m = common_chat_parse(in, false, params); - assert_equals(variant, 2, m.tool_calls.size()); - assert_equals(variant, std::string("get_time"), m.tool_calls[0].name); - assert_equals(variant, std::string("{\"city\":\"Paris\"}"), m.tool_calls[0].arguments); - assert_equals(variant, std::string("get_weather"), m.tool_calls[1].name); - assert_equals(variant, std::string("{\"city\":\"Paris\"}"), m.tool_calls[1].arguments); - assert_equals(variant, std::string("CONTENT"), m.content); - assert_equals(variant, std::string(""), m.reasoning_content); - } - - - // variant: thinking forced open + tool call in reasoning content - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = true; - params.parse_tool_calls = true; - const std::string variant("thinking_forced_open_tool_call_in_reasoning"); - const std::string in = "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time2<｜tool▁sep｜>{\"city\": \"Tokyo2\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>"; - auto m = common_chat_parse(in, false, params); - assert_equals(variant, 1, m.tool_calls.size()); - assert_equals(variant, std::string("get_time"), m.tool_calls[0].name); - assert_equals(variant, std::string("{\"city\":\"Tokyo\"}"), m.tool_calls[0].arguments); - assert_equals(variant, std::string(""), m.content); - assert_equals(variant, std::string("REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time2<｜tool▁sep｜>{\"city\": \"Tokyo2\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>REASONING"), m.reasoning_content); - } - - // variant: thinking forced open + tool call in reasoning content + no closing think + not partial - // This is a bit of a fine tuning issue on the model's part IMO. It really should not be attempting - // to make tool calls in reasoning content according to the model card, but it does sometimes, so - // add the reasoning content as regular content and parse the tool calls. - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = true; - params.parse_tool_calls = true; - const std::string variant("thinking_forced_open_tool_call_in_reasoning_no_closing_think_not_partial"); - const std::string in = "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>"; - auto m = common_chat_parse(in, false, params); - assert_equals(variant, std::string("REASONING"), m.content); - assert_equals(variant, std::string(""), m.reasoning_content); - assert_equals(variant, 1, m.tool_calls.size()); - assert_equals(variant, std::string("get_time"), m.tool_calls[0].name); - assert_equals(variant, std::string("{\"city\":\"Tokyo\"}"), m.tool_calls[0].arguments); - } - - // variant: thinking forced open + tool call in reasoning content + no closing think + partial - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = true; - params.parse_tool_calls = true; - const std::string variant("thinking_forced_open_tool_call_in_reasoning_no_closing_think_partial"); - const std::string in = "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>"; - auto m = common_chat_parse(in, /* is_partial= */ true, params); - assert_equals(variant, std::string("REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>"), m.reasoning_content); - assert_equals(variant, std::string(""), m.content); - assert_equals(variant, 0, m.tool_calls.size()); - } - - // variant: thinking not forced open + reasoning + regular content + no tool calls - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = true; - params.parse_tool_calls = true; - const std::string variant("thinking_forced_open_reasoning_regular_content_no_tool_calls"); - const std::string in = "REASONINGCONTENT"; - auto m = common_chat_parse(in, false, params); - assert_equals(variant, 0, m.tool_calls.size()); - assert_equals(variant, std::string("CONTENT"), m.content); - assert_equals(variant, std::string("REASONING"), m.reasoning_content); - } - // variant: thinking not forced open + missing reasoning + no tool calls - { - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1; - params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; - params.reasoning_in_content = false; - params.thinking_forced_open = false; - params.parse_tool_calls = true; - const std::string variant("thinking_not_forced_open_missing_reasoning_no_tool_calls"); - const std::string in = "CONTENT"; - auto m = common_chat_parse(in, false, params); - assert_equals(variant, 0, m.tool_calls.size()); - assert_equals(variant, std::string("CONTENT"), m.content); - assert_equals(variant, std::string(""), m.reasoning_content); - } -} - -static void test_with_args(const std::string & input, const std::string & expected, bool parse_as_partial = true, bool is_partial = true) { - common_chat_msg_parser builder(input, parse_as_partial, {}); - auto js = builder.try_consume_json_with_dumped_args({{"args"}}, {}); - assert_equals(true, js.has_value()); - assert_equals(is_partial, js->is_partial); - assert_equals(expected, js->value.dump()); -} - -static void test_json_with_dumped_args_no_args() { - // Normal JSON, nothing to heal, nothing to dump - test("{\"name\": \"python\"}", false, {}, {}, "{\"name\":\"python\"}"); - // Full json is args - test("{\"name\": \"python\"}", false, {{}}, {}, "{\"name\":\"python\"}"); - - // If the arguments are further down, don't heal partial content. - for (const auto & src : barely_healable_jsons) { - test(src, true, {{"arguments"}}, {}, "{}"); - } - // But heal content that isn't partial. - test("{\"name\": \"python\"", true, {{"arguments"}}, {}, "{\"name\":\"python\"}"); -} - -static void test_json_with_dumped_args() { - - // Partial content. - test("{\"content\": \"t", true, {}, {{"content"}}, "{\"content\":\"t\"}"); - test("{\"content\": \"", true, {}, {{"content"}}, "{\"content\":\"\"}"); - test("{\"content\": ", true, {}, {{"content"}}, "{}"); - - // If the entire JSON is the arguments, healing it them dumping it produces the same output as the input (just reformatted). - test("{\"name\": \"python", true, {{}}, {}, "{\"name\":\"python"); - for (const auto & src : barely_healable_jsons) { - test(src, true, {{}}, {}, src); - } - - // Full JSON w/ args - for (auto parse_as_partial : {true, false}) { - test_with_args( - R"({"name": "python", "args": {"arg1": 1}})", - R"({"name":"python","args":"{\"arg1\":1}"})", - parse_as_partial, - /* is_partial= */ false - ); - } - - // Partial JSON w/ partial args - test_with_args( - R"({"foo": "bar", "args": {")", - R"({"foo":"bar","args":"{\""})" - ); - // Partial args broken in object key - test_with_args( - R"({"foo": "bar", "args": {"ar)", - R"({"foo":"bar","args":"{\"ar"})" - ); - // Partial args broken after object key - test_with_args( - R"({"foo": "bar", "args": {"arg1")", - R"({"foo":"bar","args":"{\"arg1\""})" - ); - // Partial args broken before object value - test_with_args( - R"({"foo": "bar", "args": {"arg1":)", - R"({"foo":"bar","args":"{\"arg1\":"})" - ); - // Partial args broken before object value (space) - test_with_args( - R"({"foo": "bar", "args": {"arg1": )", - R"({"foo":"bar","args":"{\"arg1\":"})" - ); - // Partial args broken in object value that may not be complete (int) - test_with_args( - R"({"foo": "bar", "args": {"arg1": 1)", - R"({"foo":"bar","args":"{\"arg1\":"})" - ); - // Partial args broken in object value that is complete (int) - test_with_args( - R"({"foo": "bar", "args": {"arg1": 1 )", - R"({"foo":"bar","args":"{\"arg1\":1"})" - ); - // Partial args broken in object value that is incomplete (string) - test_with_args( - R"({"foo": "bar", "args": {"arg1": ")", - R"({"foo":"bar","args":"{\"arg1\":\""})" - ); - // Partial args broken in object value that is complete (string) - test_with_args( - R"({"foo": "bar", "args": {"arg1": "1")", - R"({"foo":"bar","args":"{\"arg1\":\"1\""})" - ); - // Partial args broken on array opening - test_with_args( - R"({"foo": "bar", "args": [)", - R"({"foo":"bar","args":"["})" - ); - // Partial args broken on array value that is incomplete (int) - test_with_args( - R"({"foo": "bar", "args": [1)", - R"({"foo":"bar","args":"["})" - ); - // Partial args broken on array value that is complete (int) - test_with_args( - R"({"foo": "bar", "args": [1 )", - R"({"foo":"bar","args":"[1"})" - ); - // Partial args broken on array value that is complete (string) - test_with_args( - R"({"foo": "bar", "args": ["1")", - R"({"foo":"bar","args":"[\"1\""})" - ); - // Partial args broken after array value - test_with_args( - R"({"foo": "bar", "args": [1,)", - R"({"foo":"bar","args":"[1,"})" - ); - // Partial args broken on nested array - test_with_args( - R"({"foo": "bar", "args": {"arg1": [)", - R"({"foo":"bar","args":"{\"arg1\":["})" - ); - - // Unicode tests - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\u)", - R"({"foo":"bar","args":"{\"arg1\":\"\\u"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\u0)", - R"({"foo":"bar","args":"{\"arg1\":\"\\u0"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\u00)", - R"({"foo":"bar","args":"{\"arg1\":\"\\u00"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\u000)", - R"({"foo":"bar","args":"{\"arg1\":\"\\u000"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\u0000)", - R"({"foo":"bar","args":"{\"arg1\":\"\\u0000"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\ud8)", - R"({"foo":"bar","args":"{\"arg1\":\"\\ud8"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\ud80)", - R"({"foo":"bar","args":"{\"arg1\":\"\\ud80"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\ud800)", - R"({"foo":"bar","args":"{\"arg1\":\"\\ud800"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\ud800\)", - R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\ud800\u)", - R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\u"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\ud800\ud)", - R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\ud"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\ud800\udc)", - R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\udc"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\ud800\udc0)", - R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\udc0"})" - ); - test_with_args( - R"({"foo": "bar", "args": {"arg1": "\ud800\udc00)", - R"({"foo":"bar","args":"{\"arg1\":\"\\ud800\\udc00"})" - ); -} - -static void test_positions() { - { - common_chat_msg_parser builder("Hello, world!", /* is_partial= */ false, {}); - assert_equals(0, builder.pos()); - assert_throws([&]() { builder.move_to(100); }); - assert_equals(0, builder.pos()); - assert_throws([&]() { builder.move_back(1); }); - assert_equals(0, builder.pos()); - - builder.move_to(8); - assert_equals(8, builder.pos()); - builder.move_back(1); - assert_equals(7, builder.pos()); - assert_equals("world!", builder.consume_rest()); - - builder.move_to(0); - assert_equals(0, builder.pos()); - - assert_throws([&]() { builder.finish(); }); - assert_equals(0, builder.pos()); - - builder.move_to(builder.input().size()); - builder.finish(); - } - { - common_chat_msg_parser builder("Hello, world!", /* is_partial= */ true, {}); - - builder.move_to(builder.input().size()); - assert_equals(builder.input().size(), builder.pos()); - builder.finish(); - } -} - -int main() { - test_positions(); - test_json_with_dumped_args_no_args(); - test_json_with_dumped_args(); - test_reasoning(); - test_regex(); - test_deepseek_v3_1_tool_calls(); - std::cout << "All tests passed!\n"; - return 0; -} diff --git a/tests/test-chat-peg-parser.cpp b/tests/test-chat-peg-parser.cpp index f767c73c27..dc8724be34 100644 --- a/tests/test-chat-peg-parser.cpp +++ b/tests/test-chat-peg-parser.cpp @@ -1,8 +1,3 @@ -#include -#include -#include - -#include "chat-parser.h" #include "chat-peg-parser.h" #include "chat.h" #include "common.h" @@ -10,6 +5,11 @@ #include "peg-parser.h" #include "testing.h" #include "peg-parser/simple-tokenize.h" + +#include +#include +#include + #include "nlohmann/json.hpp" using json = nlohmann::ordered_json; @@ -17,9 +17,12 @@ using json = nlohmann::ordered_json; static json create_tools(); static void test_example_native(testing & t); static void test_example_qwen3_coder(testing & t); +static void test_example_qwen3_non_coder(testing & t); static void test_command7_parser_compare(testing & t); +static void test_prefix_tool_names(testing & t); +static void test_tagged_peg_parser(testing & t); -int main(int argc, char *argv[]) { +int main(int argc, char * argv[]) { testing t(std::cout); if (argc >= 2) { t.set_filter(argv[1]); @@ -32,7 +35,10 @@ int main(int argc, char *argv[]) { t.test("native", test_example_native); t.test("qwen3 coder", test_example_qwen3_coder); + t.test("qwen3 non-coder", test_example_qwen3_non_coder); t.test("comparison", test_command7_parser_compare); + t.test("prefix tool names", test_prefix_tool_names); + t.test("tagged peg parser", test_tagged_peg_parser); return t.summary(); } @@ -41,87 +47,75 @@ static json create_tools() { json tools = json::array(); json tool_weather = { - {"type", "function"}, - {"function", { - {"name", "get_current_weather"}, - {"description", "Get the current weather in a given location"}, - {"parameters", { - {"type", "object"}, - {"properties", { - {"location", { - {"type", "string"}, - {"description", "The city and state, e.g. San Francisco, CA"} - }}, - {"unit", { - {"type", "string"}, - {"enum", {"celsius", "fahrenheit"}}, - {"description", "The temperature unit to use. Infer this from the users location."} - }} - }}, - {"required", {"location", "unit"}}, - }}, - }} + { "type", "function" }, + { "function", + { + { "name", "get_current_weather" }, + { "description", "Get the current weather in a given location" }, + { "parameters", + { + { "type", "object" }, + { "properties", + { { "location", + { { "type", "string" }, { "description", "The city and state, e.g. San Francisco, CA" } } }, + { "unit", + { { "type", "string" }, + { "enum", { "celsius", "fahrenheit" } }, + { "description", + "The temperature unit to use. Infer this from the users location." } } } } }, + { "required", { "location", "unit" } }, + } }, + } } }; tools.push_back(tool_weather); json tool_forecast = { - {"type", "function"}, - {"function", { - {"name", "get_forecast"}, - {"description", "Get the weather forecast for a given location"}, - {"parameters", { - {"type", "object"}, - {"properties", { - {"location", { - {"type", "string"}, - {"description", "The city and state, e.g. San Francisco, CA"} - }}, - {"unit", { - {"type", "string"}, - {"enum", {"celsius", "fahrenheit"}}, - {"description", "The temperature unit to use. Infer this from the users location."} - }}, - {"days", { - {"type", "integer"}, - {"description", "Number of days to forecast (1-10)"}, - {"minimum", 1}, - {"maximum", 10} - }} - }}, - {"required", {"location", "unit"}}, - }}, - }} + { "type", "function" }, + { "function", + { + { "name", "get_forecast" }, + { "description", "Get the weather forecast for a given location" }, + { "parameters", + { + { "type", "object" }, + { "properties", + { { "location", + { { "type", "string" }, { "description", "The city and state, e.g. San Francisco, CA" } } }, + { "unit", + { { "type", "string" }, + { "enum", { "celsius", "fahrenheit" } }, + { "description", "The temperature unit to use. Infer this from the users location." } } }, + { "days", + { { "type", "integer" }, + { "description", "Number of days to forecast (1-10)" }, + { "minimum", 1 }, + { "maximum", 10 } } } } }, + { "required", { "location", "unit" } }, + } }, + } } }; tools.push_back(tool_forecast); json tool_search = { - {"type", "function"}, - {"function", { - {"name", "search_knowledge_base"}, - {"description", "Search the internal technical documentation knowledge base."}, - {"parameters", { - {"type", "object"}, - {"properties", { - {"query", { - {"type", "string"}, - {"description", "The search query string."} - }}, - {"max_results", { - {"type", "integer"}, - {"description", "The maximum number of results to return."}, - {"default", 5} - }}, - {"category", { - {"type", "string"}, - {"enum", {"api", "troubleshooting", "billing", "general"}}, - {"description", "Filter search by specific category."} - }} - }}, - {"required", {"query", "category"}}, - {"additionalProperties", false} - }}, - {"strict", true} - }} + { "type", "function" }, + { "function", + { { "name", "search_knowledge_base" }, + { "description", "Search the internal technical documentation knowledge base." }, + { "parameters", + { { "type", "object" }, + { "properties", + { { "query", { { "type", "string" }, { "description", "The search query string." } } }, + { "max_results", + { { "type", "integer" }, + { "description", "The maximum number of results to return." }, + { "default", 5 } } }, + { "category", + { { "type", "string" }, + { "enum", { "api", "troubleshooting", "billing", "general" } }, + { "description", "Filter search by specific category." } } } } }, + { "required", { "query", "category" } }, + { "additionalProperties", false } } }, + { "strict", true } } } }; tools.push_back(tool_search); @@ -131,39 +125,39 @@ static json create_tools() { struct tool_argument { std::string name; std::string type; - bool is_required; - json schema; + bool is_required; + json schema; }; struct tool_definition { - std::string name; + std::string name; std::vector arguments; - json schema; + json schema; }; // Test fictitious model output that emits arguments as JSON. static void test_example_native(testing & t) { struct test_case { // Parameters - std::string name; - json tools; + std::string name; + json tools; common_chat_tool_choice tool_choice; common_reasoning_format reasoning_format; - json json_schema; - bool parallel_tool_calls; - bool thinking_forced_open; - std::string input; + json json_schema; + bool parallel_tool_calls; + bool thinking_forced_open; + std::string input; // Expect - std::string expect_reasoning; - std::string expect_content; + std::string expect_reasoning; + std::string expect_content; std::vector expect_tool_calls; }; auto build_parser = [](const test_case & tc) { - return build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) { + return build_chat_peg_parser([&](common_chat_peg_builder & p) { auto reasoning_in_content = (tc.reasoning_format == COMMON_REASONING_FORMAT_NONE); - auto reasoning = p.eps(); + auto reasoning = p.eps(); if (tc.thinking_forced_open) { // If thinking is forced open, expect a closing tag reasoning = p.reasoning(p.until("")) + "" + p.space(); @@ -174,231 +168,188 @@ static void test_example_native(testing & t) { // tool calling parser if (tc.tools.is_array() && !tc.tools.empty()) { - auto tools = p.choice(); - for (const auto & tool : tc.tools) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - const auto & schema = function.at("parameters"); + auto tool_call = + p.standard_json_tools("[", "]", tc.tools, tc.parallel_tool_calls, + tc.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED); - auto tool_name = p.json_member("name", "\"" + p.tool_name(p.literal(name)) + "\""); - auto tool_args = p.json_member("arguments", p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema))); - - tools |= p.rule("tool-" + name, p.tool_open(p.literal("{")) << tool_name << "," << tool_args << "}"); - }; - - auto parallel_calls = p.eps(); - if (tc.parallel_tool_calls) { - parallel_calls = p.zero_or_more("," << tools); - } - - auto tool_call = p.trigger_rule("tool-call", - p.sequence({ - p.literal("["), - tools, - parallel_calls, - p.literal("]") - }) - ); - - return p.sequence({ - (reasoning_in_content ? p.eps() : reasoning), - p.content(p.until("")), - p.optional(p.space() + tool_call), - p.space(), - p.end() - }); + return p.sequence({ (reasoning_in_content ? p.eps() : reasoning), p.content(p.until("")), + p.optional(p.space() + tool_call), p.space(), p.end() }); } // response_format parser if (tc.json_schema.is_object() && !tc.json_schema.empty()) { - return p.sequence({ - (reasoning_in_content ? p.eps() : reasoning), - p.content(p.schema(p.json(), "response-output", tc.json_schema)), - p.space(), - p.end() - }); + return p.sequence({ (reasoning_in_content ? p.eps() : reasoning), + p.content(p.schema(p.json(), "response-output", tc.json_schema)), p.space(), + p.end() }); } // Content-only parser - return p.sequence({ - (reasoning_in_content ? p.eps() : reasoning), - p.content(p.rest()), - p.end() - }); + return p.sequence({ (reasoning_in_content ? p.eps() : reasoning), p.content(p.rest()), p.end() }); }); }; std::vector test_cases = std::vector{ { - /* .name = */ "content with thinking_forced_open = false", - /* .tools = */ {}, - /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .json_schema = */ {}, - /* .parallel_tool_calls = */ false, - /* .thinking_forced_open = */ false, - /* .input = */ ( - "The user said hello, I must say hello back\nHello" - ), - /* .expect_reasoning = */ "The user said hello, I must say hello back", - /* .expect_content = */ "Hello", - /* .expect_tool_calls = */ {}, - }, + /* .name = */ "content with thinking_forced_open = false", + /* .tools = */ {}, + /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, + /* .json_schema = */ {}, + /* .parallel_tool_calls = */ false, + /* .thinking_forced_open = */ false, + /* .input = */ ("The user said hello, I must say hello back\nHello"), + /* .expect_reasoning = */ "The user said hello, I must say hello back", + /* .expect_content = */ "Hello", + /* .expect_tool_calls = */ {}, + }, { - /* .name = */ "content with thinking_forced_open = false and no reasoning", - /* .tools = */ {}, - /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .json_schema = */ {}, - /* .parallel_tool_calls = */ false, - /* .thinking_forced_open = */ false, - /* .input = */ ( - "Hello" - ), - /* .expect_reasoning = */ "", - /* .expect_content = */ "Hello", - /* .expect_tool_calls = */ {}, - }, + /* .name = */ "content with thinking_forced_open = false and no reasoning", + /* .tools = */ {}, + /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, + /* .json_schema = */ {}, + /* .parallel_tool_calls = */ false, + /* .thinking_forced_open = */ false, + /* .input = */ ("Hello"), + /* .expect_reasoning = */ "", + /* .expect_content = */ "Hello", + /* .expect_tool_calls = */ {}, + }, { - /* .name = */ "content with thinking_forced_open = false and reasoning_format = none", - /* .tools = */ {}, - /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE, - /* .json_schema = */ {}, - /* .parallel_tool_calls = */ false, - /* .thinking_forced_open = */ true, - /* .input = */ ( - "The user said hello, I must say hello back\nHello" - ), - /* .expect_reasoning = */ "", - /* .expect_content = */ "The user said hello, I must say hello back\nHello", - /* .expect_tool_calls = */ {}, - }, + /* .name = */ "content with thinking_forced_open = false and reasoning_format = none", + /* .tools = */ {}, + /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE, + /* .json_schema = */ {}, + /* .parallel_tool_calls = */ false, + /* .thinking_forced_open = */ true, + /* .input = */ ("The user said hello, I must say hello back\nHello"), + /* .expect_reasoning = */ "", + /* .expect_content = */ "The user said hello, I must say hello back\nHello", + /* .expect_tool_calls = */ {}, + }, { - /* .name = */ "content with thinking_forced_open = true", - /* .tools = */ {}, - /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .json_schema = */ {}, - /* .parallel_tool_calls = */ false, - /* .thinking_forced_open = */ true, - /* .input = */ ( - "The user said hello, I must say hello back\nHello" - ), - /* .expect_reasoning = */ "The user said hello, I must say hello back", - /* .expect_content = */ "Hello", - /* .expect_tool_calls = */ {}, - }, + /* .name = */ "content with thinking_forced_open = true", + /* .tools = */ {}, + /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, + /* .json_schema = */ {}, + /* .parallel_tool_calls = */ false, + /* .thinking_forced_open = */ true, + /* .input = */ ("The user said hello, I must say hello back\nHello"), + /* .expect_reasoning = */ "The user said hello, I must say hello back", + /* .expect_content = */ "Hello", + /* .expect_tool_calls = */ {}, + }, { - /* .name = */ "content with thinking_forced_open = true and reasoning_format = none", - /* .tools = */ {}, - /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE, - /* .json_schema = */ {}, - /* .parallel_tool_calls = */ false, - /* .thinking_forced_open = */ true, - /* .input = */ ( - "The user said hello, I must say hello back\nHello" - ), - /* .expect_reasoning = */ "", - /* .expect_content = */ "The user said hello, I must say hello back\nHello", - /* .expect_tool_calls = */ {}, - }, + /* .name = */ "content with thinking_forced_open = true and reasoning_format = none", + /* .tools = */ {}, + /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE, + /* .json_schema = */ {}, + /* .parallel_tool_calls = */ false, + /* .thinking_forced_open = */ true, + /* .input = */ ("The user said hello, I must say hello back\nHello"), + /* .expect_reasoning = */ "", + /* .expect_content = */ "The user said hello, I must say hello back\nHello", + /* .expect_tool_calls = */ {}, + }, { - /* .name = */ "tools with tool_choice = auto and no parallel_tool_calls", - /* .tools = */ create_tools(), - /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_AUTO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .json_schema = */ {}, - /* .parallel_tool_calls = */ false, - /* .thinking_forced_open = */ true, - /* .input = */ ( - "I must get the weather in New York\n" - "[" - R"({"name": "get_current_weather", "arguments": {"location": "New York City, NY", "unit": "fahrenheit"}})" - "]" - ), - /* .expect_reasoning = */ "I must get the weather in New York", - /* .expect_content = */ "", - /* .expect_tool_calls = */ {{ + /* .name = */ "tools with tool_choice = auto and no parallel_tool_calls", + /* .tools = */ create_tools(), + /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_AUTO, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, + /* .json_schema = */ {}, + /* .parallel_tool_calls = */ false, + /* .thinking_forced_open = */ true, + /* .input = */ + ("I must get the weather in New York\n" + "[" + R"({"name": "get_current_weather", "arguments": {"location": "New York City, NY", "unit": "fahrenheit"}})" + "]"), + /* .expect_reasoning = */ "I must get the weather in New York", + /* .expect_content = */ "", + /* .expect_tool_calls = */ + { { /* .name = */ "get_current_weather", /* .arguments = */ R"({"location": "New York City, NY", "unit": "fahrenheit"})", /* .id = */ "", - }}, - }, + } }, + }, { - /* .name = */ "tools with tool_choice = auto and parallel_tool_calls", - /* .tools = */ create_tools(), - /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_AUTO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .json_schema = */ {}, - /* .parallel_tool_calls = */ true, - /* .thinking_forced_open = */ true, - /* .input = */ ( - "I must get the weather in New York and San Francisco and a 3 day forecast of each.\nLet me search that for you." - "[" - R"({"name": "get_current_weather", "arguments": {"location": "New York City, NY", "unit": "fahrenheit"}})" - ", " - R"({"name": "get_current_weather", "arguments": {"location": "San Francisco, CA", "unit": "fahrenheit"}})" - ", " - R"({"name": "get_forecast", "arguments": {"location": "New York City, NY", "unit": "fahrenheit", "days": 3}})" - ", " - R"({"name": "get_forecast", "arguments": {"location": "San Francisco, CA", "unit": "fahrenheit", "days": 3}})" - "]" - ), - /* .expect_reasoning = */ "I must get the weather in New York and San Francisco and a 3 day forecast of each.", - /* .expect_content = */ "Let me search that for you.", - /* .expect_tool_calls = */ {{ - /* .name = */ "get_current_weather", - /* .arguments = */ R"({"location": "New York City, NY", "unit": "fahrenheit"})", - /* .id = */ "", - }, { - /* .name = */ "get_current_weather", - /* .arguments = */ R"({"location": "San Francisco, CA", "unit": "fahrenheit"})", - /* .id = */ "", - }, { - /* .name = */ "get_forecast", - /* .arguments = */ R"({"location": "New York City, NY", "unit": "fahrenheit", "days": 3})", - /* .id = */ "", - }, { - /* .name = */ "get_forecast", - /* .arguments = */ R"({"location": "San Francisco, CA", "unit": "fahrenheit", "days": 3})", - /* .id = */ "", - }}, - }, + /* .name = */ "tools with tool_choice = auto and parallel_tool_calls", + /* .tools = */ create_tools(), + /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_AUTO, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, + /* .json_schema = */ {}, + /* .parallel_tool_calls = */ true, + /* .thinking_forced_open = */ true, + /* .input = */ + ("I must get the weather in New York and San Francisco and a 3 day forecast of each.\nLet me " + "search that for you." + "[" + R"({"name": "get_current_weather", "arguments": {"location": "New York City, NY", "unit": "fahrenheit"}})" + ", " + R"({"name": "get_current_weather", "arguments": {"location": "San Francisco, CA", "unit": "fahrenheit"}})" + ", " + R"({"name": "get_forecast", "arguments": {"location": "New York City, NY", "unit": "fahrenheit", "days": 3}})" + ", " + R"({"name": "get_forecast", "arguments": {"location": "San Francisco, CA", "unit": "fahrenheit", "days": 3}})" + "]"), + /* .expect_reasoning = */ + "I must get the weather in New York and San Francisco and a 3 day forecast of each.", /* .expect_content = */ "Let me search that for you.", + /* .expect_tool_calls = */ + { { + /* .name = */ "get_current_weather", + /* .arguments = */ R"({"location": "New York City, NY", "unit": "fahrenheit"})", + /* .id = */ "", + }, + { + /* .name = */ "get_current_weather", + /* .arguments = */ R"({"location": "San Francisco, CA", "unit": "fahrenheit"})", + /* .id = */ "", + }, + { + /* .name = */ "get_forecast", + /* .arguments = */ R"({"location": "New York City, NY", "unit": "fahrenheit", "days": 3})", + /* .id = */ "", + }, + { + /* .name = */ "get_forecast", + /* .arguments = */ R"({"location": "San Francisco, CA", "unit": "fahrenheit", "days": 3})", + /* .id = */ "", + } }, + }, { - /* .name = */ "response_format with thinking_forced_open = true", - /* .tools = */ {}, - /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .json_schema = */ { - {"type", "object"}, - {"properties", { - {"invoice_number", {{"type", "string"}}}, - {"amount", {{"type", "number"}}}, - {"due_date", {{"type", "string"}}} - }}, - {"required", {"invoice_number", "amount", "due_date"}} - }, - /* .parallel_tool_calls = */ false, - /* .thinking_forced_open = */ true, - /* .input = */ ( - "I must produce the invoice in the requested format\n" - R"({"invoice_number": "INV-2025-001", "amount": 1250.50, "due_date": "2025-12-31"})" - ), - /* .expect_reasoning = */ "I must produce the invoice in the requested format", - /* .expect_content = */ R"({"invoice_number": "INV-2025-001", "amount": 1250.50, "due_date": "2025-12-31"})", - /* .expect_tool_calls = */ {}, - }, + /* .name = */ "response_format with thinking_forced_open = true", + /* .tools = */ {}, + /* .tool_choice = */ COMMON_CHAT_TOOL_CHOICE_NONE, + /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, + /* .json_schema = */ + { { "type", "object" }, + { "properties", + { { "invoice_number", { { "type", "string" } } }, + { "amount", { { "type", "number" } } }, + { "due_date", { { "type", "string" } } } } }, + { "required", { "invoice_number", "amount", "due_date" } } }, + /* .parallel_tool_calls = */ false, + /* .thinking_forced_open = */ true, + /* .input = */ + ("I must produce the invoice in the requested format\n" + R"({"invoice_number": "INV-2025-001", "amount": 1250.50, "due_date": "2025-12-31"})"), + /* .expect_reasoning = */ "I must produce the invoice in the requested format", + /* .expect_content = */ + R"({"invoice_number": "INV-2025-001", "amount": 1250.50, "due_date": "2025-12-31"})", /* .expect_tool_calls = */ {}, + }, }; for (const auto & tc : test_cases) { t.test(tc.name, [&](testing & t) { - auto parser = build_parser(tc); - auto lazy = !tc.tools.empty() && tc.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + auto parser = build_parser(tc); + auto lazy = !tc.tools.empty() && tc.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; auto grammar = build_grammar([&](const common_grammar_builder & builder) { - for (auto const & def : tc.tools) { - auto function = def.at("function"); + for (const auto & def : tc.tools) { + auto function = def.at("function"); auto parameters = function.at("parameters"); builder.resolve_refs(parameters); }; @@ -406,17 +357,17 @@ static void test_example_native(testing & t) { }); t.log("Grammar:"); - for (auto const & line : string_split(grammar, "\n")) { + for (const auto & line : string_split(grammar, "\n")) { t.log(line); } - common_peg_parse_context ctx(tc.input, false); - auto result = parser.parse(ctx); + common_peg_parse_context ctx(tc.input); + auto result = parser.parse(ctx); t.assert_true("success", result.success()); common_chat_msg msg; - auto mapper = common_chat_peg_native_mapper(msg); + auto mapper = common_chat_peg_mapper(msg); mapper.from_ast(ctx.ast, result); t.assert_equal("content equal", tc.expect_content, msg.content); @@ -431,16 +382,16 @@ static void test_example_native(testing & t) { } static void test_example_qwen3_coder(testing & t) { - auto tools = create_tools(); - auto parser = build_chat_peg_constructed_parser([&](common_chat_peg_constructed_builder & p) { + auto tools = create_tools(); + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { auto content = p.rule("content", p.content(p.until(""))); std::vector tool_parsers; - for (auto const & def : tools) { - auto function = def.at("function"); - std::string name = function.at("name"); - auto parameters = function.at("parameters"); - auto properties = parameters.at("properties"); + for (const auto & def : tools) { + auto function = def.at("function"); + std::string name = function.at("name"); + auto parameters = function.at("parameters"); + auto properties = parameters.at("properties"); std::set required_properties; if (function.contains("required")) { @@ -450,59 +401,36 @@ static void test_example_qwen3_coder(testing & t) { std::vector arg_parsers; for (const auto & [param_name, param_schema] : properties.items()) { bool is_required = required_properties.find(param_name) != required_properties.end(); - auto type = param_schema.value("type", "object"); + auto type = param_schema.value("type", "object"); - auto arg = p.tool_arg(p.sequence({ - p.tool_arg_open(""), - (type == "string" ? - p.tool_arg_string_value( - p.schema( - p.until_one_of({ - "\n\n" - }), - "tool-" + name + "-arg-" + param_name + "-schema", - param_schema, - true - ) - ) : p.tool_arg_json_value( - p.schema( - p.json(), - "tool-" + name + "-arg-" + param_name + "-schema", - param_schema - ) - ) - ), - p.tool_arg_close( - "\n" + - p.peek(p.literal("")) - ) - })); + auto arg = p.tool_arg( + p.sequence({ p.tool_arg_open(""), + (type == "string" ? + p.tool_arg_string_value(p.schema( + p.until_one_of({ "\n\n" }), + "tool-" + name + "-arg-" + param_name + "-schema", param_schema, true)) : + p.tool_arg_json_value(p.schema( + p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema))), + p.tool_arg_close("\n" + + p.peek(p.literal(""))) })); - arg_parsers.push_back(is_required ? - p.rule("tool-" + name + "-arg-" + param_name, arg) : - p.optional(p.rule("tool-" + name + "-arg-" + param_name, arg))); + arg_parsers.push_back(is_required ? p.rule("tool-" + name + "-arg-" + param_name, arg) : + p.optional(p.rule("tool-" + name + "-arg-" + param_name, arg))); } - tool_parsers.push_back(p.rule("tool-" + name, - p.tool_open("") - << p.sequence(arg_parsers) - << p.tool_close(p.literal("")) - )); + tool_parsers.push_back(p.rule("tool-" + name, p.tool_open("") + << p.sequence(arg_parsers) + << p.tool_close(p.literal("")))); }; - auto tool_call = p.trigger_rule("tool-call", - "" - << p.choice(tool_parsers) - << "" - ); + auto tool_call = p.trigger_rule("tool-call", "" << p.choice(tool_parsers) << ""); return content + p.zero_or_more(p.space() + tool_call) + p.end(); }); auto grammar = build_grammar([&](const common_grammar_builder & builder) { - for (auto const & def : tools) { - auto function = def.at("function"); + for (const auto & def : tools) { + auto function = def.at("function"); auto parameters = function.at("parameters"); builder.resolve_refs(parameters); }; @@ -510,11 +438,11 @@ static void test_example_qwen3_coder(testing & t) { }); t.log("Grammar:"); - for (auto const & line : string_split(grammar, "\n")) { + for (const auto & line : string_split(grammar, "\n")) { t.log(line); } - t.test("incremental parsing", [&](testing &t) { + t.test("incremental parsing", [&](testing & t) { std::string input = "Let me search the knowledge base for cat pictures." "\n" @@ -530,7 +458,7 @@ static void test_example_qwen3_coder(testing & t) { for (auto it = tokens.begin(); it != tokens.end(); it++) { std::string in = std::accumulate(tokens.begin(), it + 1, std::string()); - common_peg_parse_context ctx(in, it + 1 < tokens.end()); + common_peg_parse_context ctx(in, (it + 1 < tokens.end()) ? COMMON_PEG_PARSE_FLAG_LENIENT : COMMON_PEG_PARSE_FLAG_NONE); auto result = parser.parse(ctx); if (!t.assert_equal("not fail", false, result.fail())) { @@ -538,7 +466,7 @@ static void test_example_qwen3_coder(testing & t) { } common_chat_msg msg; - auto mapper = common_chat_peg_constructed_mapper(msg); + auto mapper = common_chat_peg_mapper(msg); mapper.from_ast(ctx.ast, result); //t.log("Input: " + input); @@ -554,7 +482,105 @@ static void test_example_qwen3_coder(testing & t) { try { // This shouldn't emit any runtime errors auto diffs = common_chat_msg_diff::compute_diffs(prev, msg); - } catch(const std::exception & e) { + } catch (const std::exception & e) { + t.log(in.substr(0, result.end) + "[failed->]" + in.substr(result.end)); + t.assert_true(std::string("failed with ") + e.what(), false); + } + + prev = msg; + } + }); +} + +static void test_example_qwen3_non_coder(testing & t) { + auto tools = create_tools(); + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + // tool calling parser using standard JSON format + auto tool_call = p.standard_json_tools("", "", tools, true, false); + + return p.sequence({ p.content(p.until("")), p.optional(p.space() + tool_call), p.end() }); + }); + + auto grammar = build_grammar([&](const common_grammar_builder & builder) { + for (const auto & def : tools) { + auto function = def.at("function"); + auto parameters = function.at("parameters"); + builder.resolve_refs(parameters); + }; + parser.build_grammar(builder); + }); + + t.log("Grammar:"); + for (const auto & line : string_split(grammar, "\n")) { + t.log(line); + } + + t.test("tool call parsing", [&](testing & t) { + std::string input = + "I need to get the weather.\n" + "" + "{\"name\": \"get_current_weather\", \"arguments\": {\"location\": \"New York City, NY\", \"unit\": " + "\"fahrenheit\"}}" + ""; + + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + + t.assert_true("success", result.success()); + + common_chat_msg msg; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + t.assert_equal("content", "I need to get the weather.\n", msg.content); + t.assert_equal("reasoning", "", msg.reasoning_content); + t.assert_equal("tool calls count", 1u, msg.tool_calls.size()); + if (!msg.tool_calls.empty()) { + t.assert_equal("tool name", "get_current_weather", msg.tool_calls[0].name); + t.assert_equal("tool args", "{\"location\": \"New York City, NY\", \"unit\": \"fahrenheit\"}", + msg.tool_calls[0].arguments); + } + }); + + t.test("incremental parsing", [&](testing & t) { + std::string input = + "I need to get the weather.\n" + "" + "{\"name\": \"get_current_weather\", \"arguments\": {\"location\": \"New York City, NY\", \"unit\": " + "\"fahrenheit\"}}" + ""; + + std::vector tokens = simple_tokenize(input); + + common_chat_msg prev; + for (auto it = tokens.begin(); it != tokens.end(); it++) { + std::string in = std::accumulate(tokens.begin(), it + 1, std::string()); + + common_peg_parse_context ctx(in, (it + 1 < tokens.end()) ? COMMON_PEG_PARSE_FLAG_LENIENT : COMMON_PEG_PARSE_FLAG_NONE); + + auto result = parser.parse(ctx); + if (!t.assert_equal("not fail", false, result.fail())) { + t.log(in.substr(0, result.end) + "[failed->]" + in.substr(result.end)); + } + + common_chat_msg msg; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + //t.log("Input: " + input); + t.log("==========================================="); + t.log("Iteration " + std::to_string(in.size())); + t.log("Reasoning: " + msg.reasoning_content); + t.log("Content : " + msg.content); + for (const auto & tc : msg.tool_calls) { + t.log("Tool name: " + tc.name); + t.log("Tool args: " + tc.arguments); + } + + try { + // This shouldn't emit any runtime errors + auto diffs = common_chat_msg_diff::compute_diffs(prev, msg); + } catch (const std::exception & e) { t.log(in.substr(0, result.end) + "[failed->]" + in.substr(result.end)); t.assert_true(std::string("failed with ") + e.what(), false); } @@ -565,38 +591,37 @@ static void test_example_qwen3_coder(testing & t) { } void test_command7_parser_compare(testing & t) { - auto parser = build_chat_peg_native_parser([](common_chat_peg_native_builder & p) { - auto thinking = p.reasoning_block( - "<|START_THINKING|>" << p.reasoning(p.until("<|END_THINKING|>")) << "<|END_THINKING|>"); + auto parser = build_chat_peg_parser([](common_chat_peg_builder & p) { + auto thinking = + p.reasoning_block("<|START_THINKING|>" << p.reasoning(p.until("<|END_THINKING|>")) << "<|END_THINKING|>"); auto response = "<|START_RESPONSE|>" << p.content(p.until("<|END_RESPONSE|>")) << "<|END_RESPONSE|>"; - auto tool_call_id = p.atomic("\"tool_call_id\"" << (":" << ("\"" + p.tool_id(p.json_string_content()) + "\""))); - auto tool_call_name = p.atomic("\"tool_name\"" << (":" << ("\"" + p.tool_name(p.json_string_content()) + "\""))); + auto tool_call_id = p.atomic("\"tool_call_id\"" << (":" << ("\"" + p.tool_id(p.string_content('"')) + "\""))); + auto tool_call_name = + p.atomic("\"tool_name\"" << (":" << ("\"" + p.tool_name(p.string_content('"')) + "\""))); auto tool_call_args = "\"parameters\"" << (":" << p.tool_args(p.json())); auto tool_call_fields = p.rule("tool-call-fields", tool_call_id | tool_call_name | tool_call_args); - auto tool_call = p.rule("tool-call", p.tool( - p.tool_open(p.literal("{")) - << tool_call_fields - << p.zero_or_more( p.literal(",") << tool_call_fields) - << p.tool_close(p.literal("}")) - )); + auto tool_call = + p.rule("tool-call", p.tool(p.tool_open(p.literal("{")) + << tool_call_fields << p.zero_or_more(p.literal(",") << tool_call_fields) + << p.tool_close(p.literal("}")))); - auto tool_calls = p.rule("tool-calls", - "<|START_ACTION|>" - << ("[" << tool_call << p.zero_or_more(p.literal(",") << tool_call) << "]") - << "<|END_ACTION|>"); + auto tool_calls = p.rule( + "tool-calls", "<|START_ACTION|>" << ("[" << tool_call << p.zero_or_more(p.literal(",") << tool_call) << "]") + << "<|END_ACTION|>"); return p.optional(thinking) << (tool_calls | response) + p.end(); }); - auto test_current = [&](const common_peg_arena & p, const std::string & input, bool is_partial, bool print_results) { - common_peg_parse_context ctx(input, is_partial); - auto result = p.parse(ctx); + auto test_current = [&](const common_peg_arena & p, const std::string & input, bool is_partial, + bool print_results) { + common_peg_parse_context ctx(input, is_partial ? COMMON_PEG_PARSE_FLAG_LENIENT : COMMON_PEG_PARSE_FLAG_NONE); + auto result = p.parse(ctx); common_chat_msg msg; - auto mapper = common_chat_peg_native_mapper(msg); + auto mapper = common_chat_peg_mapper(msg); mapper.from_ast(ctx.ast, result); if (print_results) { @@ -614,79 +639,19 @@ void test_command7_parser_compare(testing & t) { } }; - auto test_legacy = [&](const std::string & input, bool need_more_input, bool print_results) { - // Original common_chat_combinator_parser taken from chat.cpp - common_chat_parser_params params; - params.format = COMMON_CHAT_FORMAT_GENERIC; - params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - params.reasoning_in_content = false; - params.thinking_forced_open = false; - common_chat_msg_parser builder( - input, - /* .is_partial = */ need_more_input, - params - ); + std::string reasoning = + "To plan an effective trip to Japan that includes both historical sites and modern attractions within a " + "budget of $4000 for a two-week stay, we need to:\n\n" + "1. Identify key historical sites and modern attractions in Japan.\n" + "2. Find affordable accommodation options that provide a balance between comfort and cost.\n" + "3. Determine the best modes of transportation for getting around Japan.\n" + "4. Create a day-by-day itinerary that ensures the user gets to see a variety of attractions without " + "overspending.\n" + "5. Provide a detailed cost breakdown that includes accommodation, transportation, meals, and entry fees " + "to attractions."; - builder.try_parse_reasoning("<|START_THINKING|>", "<|END_THINKING|>"); - - static const common_regex start_action_regex("<\\|START_ACTION\\|>"); - static const common_regex end_action_regex("<\\|END_ACTION\\|>"); - static const common_regex start_response_regex("<\\|START_RESPONSE\\|>"); - static const common_regex end_response_regex("<\\|END_RESPONSE\\|>"); - - if (auto res = builder.try_find_regex(start_action_regex)) { - // If we didn't extract thoughts, prelude includes them. - auto tool_calls = builder.consume_json_with_dumped_args({ { "parameters" } }); - for (const auto & tool_call : tool_calls.value) { - std::string name = tool_call.contains("tool_name") ? tool_call.at("tool_name") : ""; - std::string id = tool_call.contains("tool_call_id") ? tool_call.at("tool_call_id") : ""; - std::string arguments = tool_call.contains("parameters") ? tool_call.at("parameters") : ""; - if (!builder.add_tool_call(name, id, arguments) || tool_calls.is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - } - if (tool_calls.is_partial) { - throw common_chat_msg_partial_exception("incomplete tool call"); - } - builder.consume_regex(end_action_regex); - } else if (auto res = builder.try_find_regex(start_response_regex)) { - if (!builder.try_find_regex(end_response_regex)) { - builder.add_content(builder.consume_rest()); - throw common_chat_msg_partial_exception(end_response_regex.str()); - } - } else { - builder.add_content(builder.consume_rest()); - } - - if (print_results) { - std::cout << "== Parsed (legacy) ==\n"; - std::cout << "=== Reasoning ===\n"; - std::cout << builder.result().reasoning_content << "\n"; - std::cout << "\n\n=== Content ===\n"; - std::cout << builder.result().content << "\n"; - std::cout << "\n\n=== Tool Calls ===\n"; - for (const auto & tc : builder.result().tool_calls) { - std::cout << "id: " << tc.id << "\n"; - std::cout << "name: " << tc.name << "\n"; - std::cout << "args: " << tc.arguments << "\n"; - } - } - }; - - std::string reasoning = "To plan an effective trip to Japan that includes both historical sites and modern attractions within a " - "budget of $4000 for a two-week stay, we need to:\n\n" - "1. Identify key historical sites and modern attractions in Japan.\n" - "2. Find affordable accommodation options that provide a balance between comfort and cost.\n" - "3. Determine the best modes of transportation for getting around Japan.\n" - "4. Create a day-by-day itinerary that ensures the user gets to see a variety of attractions without " - "overspending.\n" - "5. Provide a detailed cost breakdown that includes accommodation, transportation, meals, and entry fees " - "to attractions."; - - std::vector> tool_calls = {{ - "call_0", - "plan_trip", - nlohmann::json::parse(R"({ + std::vector> tool_calls = { + { "call_0", "plan_trip", nlohmann::json::parse(R"({ "destination": "Japan", "duration": 14, "budget": 4000, @@ -694,8 +659,8 @@ void test_command7_parser_compare(testing & t) { "accommodation_preferences": "affordable", "transportation_preferences": "efficient", "meal_preferences": "local cuisine" - })") - }}; + })") } + }; std::vector tokens; @@ -712,10 +677,10 @@ void test_command7_parser_compare(testing & t) { auto json = nlohmann::json::array(); for (const auto & tc : tool_calls) { - auto tc_json = nlohmann::json::object(); + auto tc_json = nlohmann::json::object(); tc_json["tool_call_id"] = std::get<0>(tc); - tc_json["tool_name"] = std::get<1>(tc); - tc_json["parameters"] = std::get<2>(tc); + tc_json["tool_name"] = std::get<1>(tc); + tc_json["parameters"] = std::get<2>(tc); json.push_back(tc_json); } @@ -727,42 +692,284 @@ void test_command7_parser_compare(testing & t) { std::string input = std::accumulate(tokens.begin(), tokens.end(), std::string()); - // Run tests - t.test("legacy_parse", [&](testing & /* t */) { - test_legacy(input, false, false); + t.test("current_parse", [&](testing & /* t */) { test_current(parser, input, false, false); }); + t.bench("current_parse_benchmark complete", [&]() { test_current(parser, input, false, false); }, 100); + t.bench( + "current_parse_benchmark incremental", + [&]() { + std::string in; + for (auto i = 0u; i < tokens.size(); i++) { + in += tokens[i]; + test_current(parser, in, i + 1 < tokens.size(), false); + } + }, + 20); +} + +// Test that tool names that are proper prefixes of other tool names don't cause +// premature matching during incremental parsing. +// For example, "special_function" should not match when parsing "special_function_with_opt". +static void test_prefix_tool_names(testing & t) { + // Create tools where one name is a proper prefix of another + json tools = json::array(); + + json tool_short = { + { "type", "function" }, + { "function", + { + { "name", "special_function" }, + { "description", "A special function" }, + { "parameters", + { + { "type", "object" }, + { "properties", + { + { "arg1", { { "type", "integer" } } }, + } }, + { "required", { "arg1" } }, + } }, + } } + }; + tools.push_back(tool_short); + + json tool_long = { + { "type", "function" }, + { "function", + { + { "name", "special_function_with_opt" }, + { "description", "A special function with optional params" }, + { "parameters", + { + { "type", "object" }, + { "properties", + { + { "arg1", { { "type", "integer" } } }, + { "arg2", { { "type", "integer" } } }, + } }, + { "required", { "arg1" } }, + } }, + } } + }; + tools.push_back(tool_long); + + // Use standard_constructed_tools which had the prefix matching bug + std::map markers = { + { "tool_call_start_marker", "" }, + { "tool_call_end_marker", "" }, + { "function_opener", "" }, + { "function_name_suffix", ">" }, + { "parameter_key_prefix", "" }, + { "parameter_closer", "" }, + }; + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + auto content = p.rule("content", p.content(p.until(""))); + auto tool_call = p.standard_constructed_tools(markers, tools, false, false); + return content + p.zero_or_more(p.space() + tool_call) + p.end(); }); - t.test("current_parse", [&](testing & /* t */) { - test_current(parser, input, false, false); + // Test parsing the long tool name - this should NOT trigger the short tool name + t.test("parse long tool name", [&](testing & t) { + std::string input = + "Let me call the function." + "" + "" + "42" + "" + ""; + + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + + t.assert_true("success", result.success()); + + common_chat_msg msg; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + t.assert_equal("content", "Let me call the function.", msg.content); + t.assert_equal("tool calls count", 1u, msg.tool_calls.size()); + if (!msg.tool_calls.empty()) { + t.assert_equal("tool name", "special_function_with_opt", msg.tool_calls[0].name); + } }); - // Run benchmarks - t.bench("legacy_parse_benchmark complete", [&]() { - test_legacy(input, false, false); - }); + // Test incremental parsing - the key test case + // This ensures that when incrementally parsing "special_function_with_opt", + // we don't prematurely emit "special_function" as a tool call + t.test("incremental parse long tool name", [&](testing & t) { + std::string input = + "Let me call the function." + "" + "" + "42" + "" + ""; - t.bench("legacy_parse_benchmark incremental", [&]() { - std::string in; - for (auto i = 0u; i < tokens.size(); i++) { - in += tokens[i]; + std::vector tokens = simple_tokenize(input); + + common_chat_msg prev; + for (auto it = tokens.begin(); it != tokens.end(); it++) { + std::string in = std::accumulate(tokens.begin(), it + 1, std::string()); + + common_peg_parse_context ctx(in, (it + 1 < tokens.end()) ? COMMON_PEG_PARSE_FLAG_LENIENT : COMMON_PEG_PARSE_FLAG_NONE); + auto result = parser.parse(ctx); + + if (!t.assert_equal("not fail", false, result.fail())) { + t.log(in.substr(0, result.end) + "[failed->]" + in.substr(result.end)); + return; + } + + common_chat_msg msg; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + // The critical check: during incremental parsing, we should never + // see "special_function" as the tool name when parsing "special_function_with_opt" + for (const auto & tc : msg.tool_calls) { + if (!t.assert_equal("tool name should not be short prefix", false, + tc.name == "special_function")) { + t.log("Premature tool name match at input: " + in); + return; + } + } try { - test_legacy(in, i + 1 < tokens.size(), false); - } catch (common_chat_msg_partial_exception & /* e */) { - // Do nothing, this is expected + auto diffs = common_chat_msg_diff::compute_diffs(prev, msg); + } catch (const std::exception & e) { + t.log(in.substr(0, result.end) + "[failed->]" + in.substr(result.end)); + t.assert_true(std::string("diff failed with ") + e.what(), false); + return; } - } - }, 20); - t.bench("current_parse_benchmark complete", [&]() { - test_current(parser, input, false, false); - }, 100); - - t.bench("current_parse_benchmark incremental", [&]() { - std::string in; - for (auto i = 0u; i < tokens.size(); i++) { - in += tokens[i]; - test_current(parser, in, i + 1 < tokens.size(), false); + prev = msg; } - }, 20); + + // Final check: the complete parse should have the correct tool name + t.assert_equal("final tool calls count", 1u, prev.tool_calls.size()); + if (!prev.tool_calls.empty()) { + t.assert_equal("final tool name", "special_function_with_opt", prev.tool_calls[0].name); + } + }); + + // Test parsing the short tool name still works + t.test("parse short tool name", [&](testing & t) { + std::string input = + "Let me call the function." + "" + "" + "42" + "" + ""; + + common_peg_parse_context ctx(input); + auto result = parser.parse(ctx); + + t.assert_true("success", result.success()); + + common_chat_msg msg; + auto mapper = common_chat_peg_mapper(msg); + mapper.from_ast(ctx.ast, result); + + t.assert_equal("content", "Let me call the function.", msg.content); + t.assert_equal("tool calls count", 1u, msg.tool_calls.size()); + if (!msg.tool_calls.empty()) { + t.assert_equal("tool name", "special_function", msg.tool_calls[0].name); + } + }); +} + +static void test_tagged_peg_parser(testing & t) { + t.test("basic tag extraction", [&](testing & t) { + auto parser = build_tagged_peg_parser([](common_peg_parser_builder & p) { + return p.tag("greeting", p.until(" ")) + " " + p.tag("name", p.rest()) + p.end(); + }); + + auto result = parser.parse_and_extract("Hello World"); + t.assert_true("success", result.result.success()); + t.assert_equal("greeting tag", "Hello", result.tags.at("greeting")); + t.assert_equal("name tag", "World", result.tags.at("name")); + }); + + t.test("duplicate tags overwrite", [&](testing & t) { + auto parser = build_tagged_peg_parser([](common_peg_parser_builder & p) { + return p.tag("item", p.until(",")) + "," + p.tag("item", p.rest()) + p.end(); + }); + + auto result = parser.parse_and_extract("first,second"); + t.assert_true("success", result.result.success()); + t.assert_equal("item tag", "second", result.tags.at("item")); + }); + + t.test("no tags extracted", [&](testing & t) { + auto parser = build_tagged_peg_parser([](common_peg_parser_builder & p) { + return p.rest() + p.end(); + }); + + auto result = parser.parse_and_extract("Hello"); + t.assert_true("success", result.result.success()); + t.assert_equal("empty tags", 0u, result.tags.size()); + }); + + t.test("structured extraction", [&](testing & t) { + auto parser = build_tagged_peg_parser([](common_peg_parser_builder & p) { + auto header = p.tag("header", p.until("\n")); + auto body = p.tag("body", p.rest()); + return header + "\n" + body + p.end(); + }); + + auto result = parser.parse_and_extract("Title\nBody content here"); + t.assert_true("success", result.result.success()); + t.assert_equal("header", "Title", result.tags.at("header")); + t.assert_equal("body", "Body content here", result.tags.at("body")); + }); + + t.test("partial parse", [&](testing & t) { + auto parser = build_tagged_peg_parser([](common_peg_parser_builder & p) { + return p.tag("prefix", p.until(":")) + ":" + p.tag("value", p.rest()) + p.end(); + }); + + auto result = parser.parse_and_extract("key:val", COMMON_PEG_PARSE_FLAG_LENIENT); + t.assert_true("not fail", !result.result.fail()); + t.assert_equal("prefix tag", "key", result.tags.at("prefix")); + t.assert_equal("value tag", "val", result.tags.at("value")); + }); + + t.test("find in the middle", [&](testing & t) { + auto parser = build_tagged_peg_parser([](common_peg_parser_builder & p) { + return p.choice({ p.literal("{"), p.literal(":") }) + p.space() + p.literal("\"") + p.atomic(p.literal("fun_name")); + }); + + std::string tpl = "This is a very long jinja template string. We have tools. We will try to call them now: { \"fun_name\" : { \"arg\" : 1 }"; + auto result = parser.parse_anywhere_and_extract(tpl); + t.assert_true("success", result.result.success()); + }); + + t.test("fail find in the middle", [&](testing & t) { + auto parser = build_tagged_peg_parser([](common_peg_parser_builder & p) { + return p.choice({ p.literal("{"), p.literal(":") }) + p.space() + p.literal("\"") + p.atomic(p.literal("fun_name")); + }); + + std::string tpl = "This is a very long jinja template string. We have tools. We will try to call them now: 1"; + auto result = parser.parse_anywhere_and_extract(tpl); + t.assert_true("failure", result.result.fail()); + }); + + t.test("find function tag with name", [&](testing &t) { + std::string haystack = "\n\n\n\nXXXX\n\n\nYYYY\n\n\n\n"; + auto parser = build_tagged_peg_parser([](common_peg_parser_builder & p) { + std::string needle = "foofoo"; + return p.tag("fun_marker", p.choice({ + p.tag("fun_pre", p.literal("<") + p.until_one_of({ ">", needle })) + p.literal(needle) + + p.tag("fun_post", p.negate(p.space() + p.literal("<")) + p.until(">") + p.literal(">")) + p.space(), + p.tag("fun_pre", p.literal("[") + p.until_one_of({ "]", needle })) + p.literal(needle) + + p.tag("fun_post", p.negate(p.space() + p.literal("[") + p.until("]") + p.literal("]")) + p.space()) })); + }); + auto result = parser.parse_anywhere_and_extract(haystack); + t.assert_true("success", result.result.success()); + t.assert_equal("fun_pre should be ''", ">", result.tags["fun_post"]); + }); } diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 27b537a036..6cc132131c 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -1,4 +1,5 @@ #include +#include #include #include #include @@ -21,17 +22,16 @@ using json = nlohmann::ordered_json; -int main_automated_tests(void); +static int main_automated_tests(void); -void run_multiple(std::string dir_path, bool stop_on_first_failure, json input, bool use_common = false); -void run_single(std::string contents, json input, bool use_common = false, const std::string & output_path = ""); +static void run_multiple(const std::string& dir_path, bool stop_on_first_failure, const json& input, bool use_common = false); +static void run_single(const std::string& contents, json input, bool use_common = false, const std::string & output_path = ""); - - -std::string HELP = R"( +static std::string HELP = R"( Usage: test-chat-template [OPTIONS] PATH_TO_TEMPLATE Options: -h, --help Show this help message and exit. + --with-tools Add a tool and a tool call to the default JSON input --json Path to the JSON input file. --stop-on-first-fail Stop testing on the first failure (default: false). --no-common Use direct Jinja engine instead of common chat templates (default: use common). @@ -41,7 +41,7 @@ If PATH_TO_TEMPLATE is a directory, runs all .jinja files in that directory. If PATH_TO_TEMPLATE is omitted, runs automated tests (default CI mode). )"; -std::string DEFAULT_JSON = R"({ +static std::string DEFAULT_JSON = R"({ "messages": [ { "role": "user", @@ -57,12 +57,65 @@ std::string DEFAULT_JSON = R"({ "add_generation_prompt": true })"; +static std::string DEFAULT_JSON_WITH_TOOLS = R"({ + "messages": [ + { + "role": "user", + "content": "Hello, how are you?" + }, + { + "role": "assistant", + "content": "I am fine, thank you!" + }, + { + "role": "user", + "content": "Call a tool!" + }, + { + "role": "assistant", + "tool_calls": [ + { + "id": "call00001", + "type": "function", + "function": { + "name": "test", + "arguments": { "arg": "hello" } + } + } + ] + } + ], + "tools": [ + { + "type": "function", + "function": { + "name": "test", + "description": "Test", + "parameters": { + "type": "object", + "properties": { + "arg": { + "type": "string" + } + } + }, + "required": ["arg"] + } + } + ], + "bos_token": "", + "eos_token": "", + "add_generation_prompt": true +})"; + + int main(int argc, char ** argv) { std::vector args(argv, argv + argc); std::string tmpl_path; std::string json_path; std::string output_path; + std::string & json_to_use = DEFAULT_JSON; bool stop_on_first_fail = false; bool use_common = true; @@ -70,9 +123,12 @@ int main(int argc, char ** argv) { if (args[i] == "--help" || args[i] == "-h") { std::cout << HELP << "\n"; return 0; - } else if (args[i] == "--json" && i + 1 < args.size()) { + } + if (args[i] == "--json" && i + 1 < args.size()) { json_path = args[i + 1]; i++; + } else if (args[i] == "--with-tools") { + json_to_use = DEFAULT_JSON_WITH_TOOLS; } else if (args[i] == "--stop-on-first-fail") { stop_on_first_fail = true; } else if (args[i] == "--output" && i + 1 < args.size()) { @@ -105,7 +161,7 @@ int main(int argc, char ** argv) { std::istreambuf_iterator()); input_json = json::parse(content); } else { - input_json = json::parse(DEFAULT_JSON); + input_json = json::parse(json_to_use); } std::filesystem::path p(tmpl_path); @@ -125,7 +181,7 @@ int main(int argc, char ** argv) { return 0; } -void run_multiple(std::string dir_path, bool stop_on_first_fail, json input, bool use_common) { +void run_multiple(const std::string& dir_path, bool stop_on_first_fail, const json& input, bool use_common) { std::vector failed_tests; // list all files in models/templates/ and run each @@ -180,7 +236,7 @@ static std::string format_using_common( common_chat_templates_inputs inputs; inputs.use_jinja = true; inputs.messages = messages; - inputs.tools = tools; + inputs.tools = std::move(tools); inputs.add_generation_prompt = true; auto output = common_chat_templates_apply(tmpls.get(), inputs).prompt; output = normalize_newlines(output); @@ -209,7 +265,7 @@ static jinja::value_string format_using_direct_engine( jinja::runtime runtime(ctx); const jinja::value results = runtime.execute(ast); - auto parts = runtime.gather_string_parts(results); + auto parts = jinja::runtime::gather_string_parts(results); std::cout << "\n=== RESULTS ===\n"; for (const auto & part : parts->as_string().parts) { @@ -220,7 +276,7 @@ static jinja::value_string format_using_direct_engine( } -void run_single(std::string contents, json input, bool use_common, const std::string & output_path) { +void run_single(const std::string& contents, json input, bool use_common, const std::string & output_path) { jinja::enable_debug(true); jinja::value_string output_parts; @@ -560,7 +616,7 @@ int main_automated_tests(void) { supported_tmpl.resize(res); res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size()); std::cout << "Built-in chat templates:\n"; - for (auto tmpl : supported_tmpl) { + for (const auto *tmpl : supported_tmpl) { std::cout << " " << tmpl << "\n"; } @@ -592,6 +648,7 @@ int main_automated_tests(void) { } std::vector messages; + messages.reserve(conversation.size()); for (const auto & msg : conversation) { messages.push_back(simple_msg(msg.role, msg.content)); } @@ -622,58 +679,6 @@ int main_automated_tests(void) { } } - // TODO: llama_chat_format_single will be deprecated, remove these tests later - - // test llama_chat_format_single for system message - std::cout << "\n\n=== llama_chat_format_single (system message) ===\n\n"; - std::vector chat2; - auto sys_msg = simple_msg("system", "You are a helpful assistant"); - - auto fmt_sys = [&](std::string tmpl_str) { - auto tmpls = common_chat_templates_init(/* model= */ nullptr, tmpl_str); - auto output = common_chat_format_single(tmpls.get(), chat2, sys_msg, false, /* use_jinja= */ false); - std::cout << "fmt_sys(" << tmpl_str << ") : " << output << "\n"; - std::cout << "-------------------------\n"; - return output; - }; - assert(fmt_sys("chatml") == "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n"); - assert(fmt_sys("mistral-v1") == " [INST] You are a helpful assistant\n\n"); - assert(fmt_sys("mistral-v3") == "[INST] You are a helpful assistant\n\n"); - assert(fmt_sys("mistral-v3-tekken") == "[INST]You are a helpful assistant\n\n"); - assert(fmt_sys("mistral-v7") == "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT]"); - assert(fmt_sys("llama2") == "[INST] You are a helpful assistant\n"); - assert(fmt_sys("llama2-sys") == "[INST] <>\nYou are a helpful assistant\n<>\n\n"); - assert(fmt_sys("mistral") == "[INST] You are a helpful assistant\n"); // for old pre-v1 templates - assert(fmt_sys("gemma") == ""); // for gemma, system message is merged with user message - assert(fmt_sys("llama3") == "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|>"); - assert(fmt_sys("gigachat") == "You are a helpful assistant<|message_sep|>"); - - - // test llama_chat_format_single for user message - std::cout << "\n\n=== llama_chat_format_single (user message) ===\n\n"; - chat2.push_back(simple_msg("system", "You are a helpful assistant")); - chat2.push_back(simple_msg("user", "Hello")); - chat2.push_back(simple_msg("assistant", "I am assistant")); - auto new_msg = simple_msg("user", "How are you"); - - auto fmt_single = [&](const std::string & tmpl_str) { - auto tmpls = common_chat_templates_init(/* model= */ nullptr, tmpl_str.c_str()); - auto output = common_chat_format_single(tmpls.get(), chat2, new_msg, true, /* use_jinja= */ false); - std::cout << "fmt_single(" << tmpl_str << ") : " << output << "\n"; - std::cout << "-------------------------\n"; - return output; - }; - assert(fmt_single("chatml") == "\n<|im_start|>user\nHow are you<|im_end|>\n<|im_start|>assistant\n"); - assert(fmt_single("mistral-v1") == " [INST] How are you [/INST]"); - assert(fmt_single("mistral-v3") == "[INST] How are you[/INST]"); - assert(fmt_single("mistral-v3-tekken") == "[INST]How are you[/INST]"); - assert(fmt_single("mistral-v7") == "[INST] How are you[/INST]"); - assert(fmt_single("llama2") == "[INST] How are you [/INST]"); - assert(fmt_single("mistral") == "[INST] How are you [/INST]"); // for old pre-v1 templates - assert(fmt_single("gemma") == "\nuser\nHow are you\nmodel\n"); - assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"); - // assert(fmt_single("gigachat") == "user<|role_sep|>How are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>"); - std::cout << "\nOK: All tests passed successfully.\n"; return 0; diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp index f3d19118b5..3a6297e148 100644 --- a/tests/test-chat.cpp +++ b/tests/test-chat.cpp @@ -5,18 +5,22 @@ // // cmake -B build && cmake --build build --parallel && ./build/bin/test-chat ../minja/build/tests/*.jinja 2>/dev/null // +#include "../src/llama-grammar.h" +#include "../src/unicode.h" +#include "chat-auto-parser.h" #include "chat.h" - +#include "common.h" +#include "ggml.h" #include "log.h" -#include "../src/unicode.h" -#include "../src/llama-grammar.h" - -#include - +#include +#include #include -#include #include +#include +#include +#include +#include #include using json = nlohmann::ordered_json; @@ -33,6 +37,7 @@ static std::ostream & operator<<(std::ostream & os, const common_chat_msg_diff & os << "}"; return os; } + // operator<< for vector: static std::ostream & operator<<(std::ostream & os, const std::vector & diffs) { os << "[\n"; @@ -42,6 +47,7 @@ static std::ostream & operator<<(std::ostream & os, const std::vector -bool equals(const common_chat_msg & expected, const common_chat_msg & actual) { +template <> bool equals(const common_chat_msg & expected, const common_chat_msg & actual) { return normalize(expected) == normalize(actual); } template static void assert_equals(const T & expected, const T & actual) { if (!equals(expected, actual)) { - std::cerr << "Expected:```\n" << expected << "\n```" << std::endl; - std::cerr << "Actual:```\n" << actual << "\n```" << std::endl; - std::cerr << std::flush; + std::ostringstream oss_expected; + oss_expected << expected; + std::ostringstream oss_actual; + oss_actual << actual; + LOG_ERR("Expected: %s\n", oss_expected.str().c_str()); + LOG_ERR("Actual: %s\n", oss_actual.str().c_str()); + common_log_flush(common_log_main()); throw std::runtime_error("Test failed"); } } static std::string read_file(const std::string & path) { - std::cerr << "# Reading: " << path << '\n' << std::flush; std::ifstream fs(path, std::ios_base::binary); if (!fs.is_open()) { fs = std::ifstream("../" + path, std::ios_base::binary); @@ -118,6 +125,207 @@ static std::unique_ptr build_grammar(const std::string & grammar_ llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0)); } +// Helper to format a code point as a readable string +static std::string format_codepoint(uint32_t cp) { + if (cp >= 32 && cp < 127) { + return std::string("'") + static_cast(cp) + "'"; + } else if (cp == '\n') { + return "'\\n'"; + } else if (cp == '\r') { + return "'\\r'"; + } else if (cp == '\t') { + return "'\\t'"; + } else { + return "U+" + std::to_string(cp); + } +} + +// Helper to format expected element from grammar stack +static std::string format_expected_element(const llama_grammar_rules & /* rules*/, const llama_grammar_element * elem) { + if (!elem) { + return ""; + } + + switch (elem->type) { + case LLAMA_GRETYPE_END: + return ""; + case LLAMA_GRETYPE_ALT: + return ""; + case LLAMA_GRETYPE_RULE_REF: + { + // Find rule name - just show rule ID for now + return "value) + ">"; + } + case LLAMA_GRETYPE_CHAR: + { + std::string result; + const llama_grammar_element * pos = elem; + bool first = true; + + do { + if (!first) { + result += " | "; + } + first = false; + + if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) { + // Range like [a-z] + result += "[" + format_codepoint(pos->value) + "-" + format_codepoint(pos[1].value) + "]"; + pos += 2; + } else { + result += format_codepoint(pos->value); + pos += 1; + } + } while (pos->type == LLAMA_GRETYPE_CHAR_ALT); + + return result; + } + case LLAMA_GRETYPE_CHAR_NOT: + { + std::string result = "[^"; + const llama_grammar_element * pos = elem; + bool first = true; + + do { + if (!first) { + result += " "; + } + first = false; + + if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) { + result += format_codepoint(pos->value) + "-" + format_codepoint(pos[1].value); + pos += 2; + } else { + result += format_codepoint(pos->value); + pos += 1; + } + } while (pos->type == LLAMA_GRETYPE_CHAR_ALT); + + return result + "]"; + } + case LLAMA_GRETYPE_CHAR_ANY: + return ""; + case LLAMA_GRETYPE_TOKEN: + return "value) + ">"; + case LLAMA_GRETYPE_TOKEN_NOT: + return "value) + ">"; + default: + return ""; + } +} + +// Get description of what the grammar expects at current position +static std::string get_expected_description(const llama_grammar_rules & rules, const llama_grammar_stacks & stacks) { + if (stacks.empty()) { + return ""; + } + + std::string result; + std::set seen; + + for (const auto & stack : stacks) { + if (stack.empty()) { + if (seen.insert("").second) { + if (!result.empty()) { + result += " OR "; + } + result += ""; + } + continue; + } + + const llama_grammar_element * elem = stack.back(); + std::string desc = format_expected_element(rules, elem); + if (seen.insert(desc).second) { + if (!result.empty()) { + result += " OR "; + } + result += desc; + } + } + + return result; +} + +// Result of a detailed grammar match attempt +struct grammar_match_result { + bool success = false; // Did the string fully match the grammar? + size_t matched_bytes = 0; // Bytes successfully matched before failure + size_t matched_codepoints = 0; // Codepoints successfully matched before failure + size_t total_bytes = 0; // Total bytes in input + size_t total_codepoints = 0; // Total codepoints in input + std::string matched_prefix; // The portion that was successfully matched + std::string failing_char; // The character that caused failure (if any) + std::string expected_description; // What the grammar expected at failure point + bool incomplete = false; // True if matched all input but grammar expects more +}; + +// Detailed version of match_string that returns failure information +static grammar_match_result match_string_detailed(const std::string & input, llama_grammar * grammar) { + grammar_match_result result; + result.total_bytes = input.size(); + + const auto cpts = unicode_cpts_from_utf8(input); + result.total_codepoints = cpts.size(); + + auto & stacks_cur = llama_grammar_get_stacks(grammar); + const auto & rules = llama_grammar_get_rules(grammar); + + size_t byte_pos = 0; + + for (size_t i = 0; i < cpts.size(); i++) { + const auto & cpt = cpts[i]; + + // Get expected before accepting (for error reporting) + std::string expected_before = get_expected_description(rules, stacks_cur); + + llama_grammar_accept(grammar, cpt); + + // Calculate byte position for this codepoint + size_t cpt_bytes = 0; + if (cpt < 0x80) { + cpt_bytes = 1; + } else if (cpt < 0x800) { + cpt_bytes = 2; + } else if (cpt < 0x10000) { + cpt_bytes = 3; + } else { + cpt_bytes = 4; + } + + if (stacks_cur.empty()) { + // Grammar failed to match at this point + result.matched_bytes = byte_pos; + result.matched_codepoints = i; + result.matched_prefix = input.substr(0, byte_pos); + result.failing_char = format_codepoint(cpt); + result.expected_description = expected_before; + result.incomplete = false; + return result; + } + + byte_pos += cpt_bytes; + } + + // All input matched - check if grammar is complete + result.matched_bytes = input.size(); + result.matched_codepoints = cpts.size(); + result.matched_prefix = input; + + if (std::any_of(stacks_cur.begin(), stacks_cur.end(), [](const auto & stack) { return stack.empty(); })) { + // An empty stack means that the grammar has been completed + result.success = true; + result.incomplete = false; + } else { + // Grammar expects more input + result.success = false; + result.incomplete = true; + result.expected_description = get_expected_description(rules, stacks_cur); + } + + return result; +} + // TODO: extract to common helper (copied from test-grammar-integration.cpp) static bool match_string(const std::string & input, llama_grammar * grammar) { const auto cpts = unicode_cpts_from_utf8(input); @@ -146,11 +354,13 @@ static std::string renormalize_json(const std::string & json_str) { auto json_obj = json::parse(json_str); return json_obj.dump(); } catch (const std::exception & e) { - std::cerr << "Failed to parse JSON: " << e.what() << '\n'; - return json_str; + return ""; // ignore parial JSON contents for comparison purposes } } -static void assert_msg_equals(const common_chat_msg & expected, const common_chat_msg & actual, bool ignore_whitespace_differences = false) { + +static void assert_msg_equals(const common_chat_msg & expected, + const common_chat_msg & actual, + bool ignore_whitespace_differences = false) { assert_equals(expected.role, actual.role); if (ignore_whitespace_differences) { assert_equals(string_strip(expected.content), string_strip(actual.content)); @@ -183,7 +393,7 @@ static void assert_msg_equals(const common_chat_msg & expected, const common_cha } } -common_chat_tool special_function_tool { +static common_chat_tool special_function_tool{ /* .name = */ "special_function", /* .description = */ "I'm special", /* .parameters = */ R"({ @@ -197,7 +407,7 @@ common_chat_tool special_function_tool { "required": ["arg1"] })", }; -common_chat_tool special_function_tool_with_optional_param { +static common_chat_tool special_function_tool_with_optional_param{ /* .name = */ "special_function_with_opt", /* .description = */ "I'm special but have optional stuff", /* .parameters = */ R"({ @@ -215,7 +425,15 @@ common_chat_tool special_function_tool_with_optional_param { "required": ["arg1"] })", }; -common_chat_tool python_tool { +static common_chat_tool empty_args_tool{ + /* .name = */ "empty_args", + /* .description = */ "A tool that takes no arguments", + /* .parameters = */ R"({ + "type": "object", + "properties": {} + })", +}; +static common_chat_tool python_tool{ /* .name = */ "python", /* .description = */ "an ipython interpreter", /* .parameters = */ R"({ @@ -229,7 +447,53 @@ common_chat_tool python_tool { "required": ["code"] })", }; -common_chat_tool todo_list_tool { + +static common_chat_tool html_tool{ + /* .name = */ "html", + /* .description = */ "an html validator", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "markup": { + "type": "string", + "description": "HTML markup to validate." + } + }, + "required": ["markup"] + })", +}; + +static common_chat_tool get_time_tool{ + /* .name = */ "get_time", + /* .description = */ "Get the current time in a city", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "city": { + "type": "string", + "description": "City name" + } + }, + "required": ["city"] + })", +}; + +static common_chat_tool get_weather_tool{ + /* .name = */ "get_weather", + /* .description = */ "Get the current weather in a city", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "city": { + "type": "string", + "description": "City name" + } + }, + "required": ["city"] + })", +}; + +static common_chat_tool todo_list{ /* .name = */ "todo_list", /* .description = */ "Create or update the todo list", /* .parameters = */ R"({ @@ -243,158 +507,460 @@ common_chat_tool todo_list_tool { "required": ["todos"] })", }; -common_chat_tool code_interpreter_tool { - /* .name = */ "code_interpreter", - /* .description = */ "an ipython interpreter", + +static common_chat_tool edit_tool{ + /* .name = */ "edit", + /* .description = */ "Edit file", /* .parameters = */ R"({ "type": "object", "properties": { - "code": { + "filename": { "type": "string", - "description": "Python code to execute." + "description": "Path of file to edit" + }, + "oldString": { + "type": "string", + "description": "String to replace" + }, + "newString": { + "type": "string", + "description": "New (replacement) value" } }, - "required": ["code"] + "required": ["filename", "oldString", "newString"] })", }; -std::vector tools { special_function_tool, special_function_tool_with_optional_param, python_tool }; -std::vector llama_3_1_tools { special_function_tool, code_interpreter_tool }; -struct delta_data { - std::string delta; - common_chat_params params; +static common_chat_tool magic_tool{ + /* .name = */ "magic", + /* .description = */ "Magic tool that takes a hash", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "name": { + "type": "string" + }, + "ref": { + "type": "string" + } + }, + "required": ["name", "ref"] + })", }; -static common_chat_msg simple_assist_msg(const std::string & content, const std::string & reasoning_content = "", const std::string & tool_name = "", const std::string & arguments = "", const std::string & id = "") { +static common_chat_tool magic_int_tool{ + /* .name = */ "magic_int", + /* .description = */ "Magic tool that takes a hash", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "ref": { + "type": "integer" + }, + "name": { + "type": "string" + } + }, + "required": ["ref"] + })", +}; + +static common_chat_tool amount_tool{ + /* .name = */ "amount", + /* .description = */ "Amount converter", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "orig": { + "type": "number" + } + }, + "required": ["orig"] + })", +}; + +static common_chat_tool imaginary_number_tool{ + /* .name = */ "imaginary_number", + /* .description = */ "Imaginary number converter", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "number": { + "type": "object", + "properties": { + "real": { + "type": "number" + }, + "imaginary": { + "type": "number" + } + }, + "required": ["real", "imaginary"] + } + }, + "required": ["number"] + })", +}; + +static common_chat_tool string_param_tool{ + /* .name = */ "string_param", + /* .description = */ "Tool with string parameter for testing", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "text": { + "type": "string", + "description": "A text parameter" + } + }, + "required": [] + })", +}; + +static common_chat_tool quoted_unquoted_tool{ + /* .name = */ "quoted_unquoted", + /* .description = */ "Tool with two string parameters, one for quoted string, one for unquoted", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "quoted": { + "type": "string", + "description": "Quoted value" + }, + "unquoted": { + "type": "string", + "description": "Unquoted value" + } + }, + "required": ["quoted", "unquoted"] + })", +}; + + +static common_chat_tool tool_2req_4opt{ + /* .name = */ "tool_2req_4opt", + /* .description = */ "Tool with 2 required and 4 optional params", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "req1": { "type": "string", "description": "Required string" }, + "req2": { "type": "integer", "description": "Required int" }, + "opt1": { "type": "string", "description": "Optional string 1" }, + "opt2": { "type": "integer", "description": "Optional int 1" }, + "opt3": { "type": "string", "description": "Optional string 2" }, + "opt4": { "type": "integer", "description": "Optional int 2" } + }, + "required": ["req1", "req2"] + })", +}; + +static common_chat_tool tool_2req_5opt{ + /* .name = */ "tool_2req_5opt", + /* .description = */ "Tool with 2 required and 5 optional params", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "req1": { "type": "string", "description": "Required string" }, + "req2": { "type": "integer", "description": "Required int" }, + "opt1": { "type": "string", "description": "Optional string 1" }, + "opt2": { "type": "integer", "description": "Optional int 1" }, + "opt3": { "type": "string", "description": "Optional string 2" }, + "opt4": { "type": "integer", "description": "Optional int 2" }, + "opt5": { "type": "string", "description": "Optional string 3" } + }, + "required": ["req1", "req2"] + })", +}; + +static std::vector tools{ special_function_tool, special_function_tool_with_optional_param, + python_tool, html_tool, todo_list }; + +const common_chat_msg message_user{ + "user", + "Hey there!", + /* .content_parts = */ {}, + /* .tool_calls = */ {}, + /* .reasoning_content = */ "", + /* .tool_name = */ "", + /* .tool_call_id = */ "", +}; + +const common_chat_msg message_user_parts{ + "user", + /* .content = */ "", + /* .content_parts = */ + { + { "text", "Hey" }, + { "text", "there" }, + }, + /* .tool_calls = */ + { }, + /* .reasoning_content = */ + "", + /* .tool_name = */ "", + /* .tool_call_id = */ "", +}; + +static common_chat_msg simple_assist_msg(const std::string & content, + const std::string & reasoning_content = "", + const std::string & tool_name = "", + const std::string & arguments = "", + const std::string & id = "") { common_chat_msg msg; - msg.role = "assistant"; - msg.content = content; + msg.role = "assistant"; + msg.content = content; msg.reasoning_content = reasoning_content; - if (!tool_name.empty()) { + if (!tool_name.empty() || !id.empty()) { msg.tool_calls.push_back({ tool_name, arguments, id }); } return msg; } -static delta_data init_delta(const struct common_chat_templates * tmpls, const std::vector & end_tokens, - const common_chat_msg & user_message, - const common_chat_msg & delta_message, - const std::vector & tools, - const common_chat_tool_choice & tool_choice) { - common_chat_templates_inputs inputs; - inputs.parallel_tool_calls = true; - inputs.messages.push_back(user_message); - inputs.tools = tools; - inputs.tool_choice = tool_choice; - auto params_prefix = common_chat_templates_apply(tmpls, inputs); - - inputs.messages.push_back(delta_message); - inputs.add_generation_prompt = false; - auto params_full = common_chat_templates_apply(tmpls, inputs); - - std::string prefix = params_prefix.prompt; - std::string full = params_full.prompt; - - if (full == prefix) { - throw std::runtime_error("Full message is the same as the prefix"); - } - - size_t common_prefix_length = 0; - for (size_t i = 0; i < prefix.size() && i < full.size(); ++i) { - if (prefix[i] != full[i]) { - break; - } - if (prefix[i] == '<') { - // DeepSeek R1's template (as of 20250209) adds a trailing if add_generation_prompt, - // but it removes thinking tags for past messages. - // The prefix and full strings diverge at vs. <｜tool▁calls▁begin｜>, we avoid consuming the leading <. - continue; - } - common_prefix_length = i + 1; - } - auto delta = full.substr(common_prefix_length); - - // Strip end tokens - for (const auto & end_token : end_tokens) { - // rfind to find the last occurrence - auto pos = delta.rfind(end_token); - if (pos != std::string::npos) { - delta = delta.substr(0, pos); - break; - } - } - return { delta, params_full }; +static common_chat_msg message_with_tool_calls(const std::string & tool_name, const std::string & arguments) { + return simple_assist_msg("", "", tool_name, arguments); } -/* - Applies the template to 1 user message w/ add_generation_prompt=true, then w/ the test message w/ add_generation_prompt=false, - gets the diff, removes any end tokens and parses the result w/ the grammar, checking that - the parsed message is the same as the test_message -*/ -static void test_templates(const struct common_chat_templates * tmpls, const std::vector & end_tokens, - const common_chat_msg & test_message, - const std::vector & tools = {}, - const std::string & expected_delta = "", - bool expect_grammar_triggered = true, - bool test_grammar_if_triggered = true, - common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE, - bool ignore_whitespace_differences = false - ) { - common_chat_msg user_message; - user_message.role = "user"; - user_message.content = "Hello, world!"; +static common_chat_msg message_with_tool_calls_and_reasoning(const std::string & tool_name, + const std::string & arguments, + const std::string & reasoning) { + return simple_assist_msg("", reasoning, tool_name, arguments); +} - for (const auto & tool_choice : std::vector {COMMON_CHAT_TOOL_CHOICE_AUTO, COMMON_CHAT_TOOL_CHOICE_REQUIRED}) { - auto data = init_delta(tmpls, end_tokens, user_message, test_message, tools, tool_choice); - if (!expected_delta.empty()) { - if (ignore_whitespace_differences) { - assert_equals(string_strip(expected_delta), string_strip(data.delta)); - } else { - assert_equals(expected_delta, data.delta); +static common_chat_msg message_with_reasoning_content_and_multiple_tool_calls( + const std::string & reasoning, + const std::string & content, + const std::vector> & tool_calls) { + common_chat_msg msg; + msg.role = "assistant"; + msg.content = content; + msg.reasoning_content = reasoning; + for (const auto & [name, args] : tool_calls) { + msg.tool_calls.push_back({ name, args, "" }); + } + return msg; +} + +static common_chat_msg message_with_content_and_tool_call(const std::string & content, + const std::string & tool_name, + const std::string & arguments) { + return simple_assist_msg(content, "", tool_name, arguments); +} + +static common_chat_msg message_with_reasoning_and_tool_call(const std::string & reasoning, + const std::string & tool_name, + const std::string & arguments) { + return simple_assist_msg("", reasoning, tool_name, arguments); +} + +const common_chat_msg message_assist = simple_assist_msg("Hello, world!\nWhat's up?"); +const common_chat_msg message_assist_empty = simple_assist_msg(""); +const common_chat_msg message_assist_thoughts_unparsed_deepseek = + simple_assist_msg("I'm\nthinkingHello, world!\nWhat's up?"); +const common_chat_msg message_assist_thoughts_unparsed_md = + simple_assist_msg("I'm\nthinkingHello, world!\nWhat's up?\n```json\n{}```"); +const common_chat_msg message_assist_thoughts_unparsed_md_partial = + simple_assist_msg("I'm\nthinkingHello, world!\nWhat's up?\n```json\n{}"); + +const common_chat_msg message_assist_thoughts_unparsed_r7b = + simple_assist_msg("<|START_THINKING|>I'm\nthinking<|END_THINKING|>Hello, world!\nWhat's up?"); +const common_chat_msg message_assist_thoughts_unparsed_magistral = + simple_assist_msg("[THINK]raisonnement[/THINK]Réponse"); +const common_chat_msg message_assist_thoughts = simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"); +const common_chat_msg message_assist_thoughts_unopened_unparsed = + simple_assist_msg("I'm\nthinkingHello, world!\nWhat's up?"); +const common_chat_msg message_assist_thoughts_no_content = simple_assist_msg("", "I'm\nthinking"); +const common_chat_msg message_assist_call = simple_assist_msg("", "", "special_function", "{\"arg1\": 1}"); +const common_chat_msg message_assist_call_noopt = + simple_assist_msg("", "", "special_function_with_opt", "{\"arg1\": 1}"); +const common_chat_msg message_assist_call_withopt = + simple_assist_msg("", "", "special_function_with_opt", "{\"arg1\": 1, \"arg2\": 2}"); +const common_chat_msg message_assist_call_content = + simple_assist_msg("Hello, world!\nWhat's up?", "", "special_function", "{\"arg1\":1}"); +const common_chat_msg message_assist_call_empty_args = simple_assist_msg("", "", "special_function"); +const common_chat_msg message_assist_call_cutoff_args = simple_assist_msg("", "", "special_function", "{\"arg"); +const common_chat_msg message_assist_call_thoughts = + simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\":1}"); +const common_chat_msg message_assist_call_thoughts_unparsed = + simple_assist_msg("I'm\nthinking\n\n", "", "special_function", "{\"arg1\": 1}"); +const common_chat_msg message_assist_call_thoughts_content = + simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": 1}"); +const common_chat_msg message_assist_call_id = + simple_assist_msg("", "", "special_function", "{\"arg1\":1}", /* .id = */ "123456789"); +const common_chat_msg message_assist_call_idx = + simple_assist_msg("", "", "special_function", "{\"arg1\":1}", /* .id = */ "0"); +const common_chat_msg message_assist_thoughts_call_idx = + simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\": 1}", /* id = */ "0"); +const common_chat_msg message_assist_thoughts_partial_call = + simple_assist_msg("", "I'm\nthinking", "special_function", "", /* id = */ "0"); +const common_chat_msg message_assist_call_python = simple_assist_msg("", "", "python", "{\"code\":\"print('hey')\"}"); +const common_chat_msg message_assist_call_python_lines = + simple_assist_msg("", "", "python", "{\"code\":\"# This is a program:\\nprint('hey')\"}"); +const common_chat_msg message_assist_call_python_lines_unclosed = + simple_assist_msg("", "", "python", "{\"code\":\"# This is a program:\\nprint('hey')"); +const common_chat_msg message_assist_json_content = + simple_assist_msg("{\n \"response\": \"Hello, world!\\nWhat's up?\"\n}"); + +// Use for PEG parser implementations +struct peg_test_case { + common_chat_templates_inputs params; + std::string input; + common_chat_msg expect; + bool is_partial = false; +}; + +struct make_peg_parser { + common_chat_params params_; + common_peg_arena arena_; + bool detailed_debug_; + + make_peg_parser(common_chat_templates * tmpls, + const common_chat_templates_inputs & inputs, + bool detailed_debug = false) { + detailed_debug_ = detailed_debug; + params_ = common_chat_templates_apply(tmpls, inputs); + arena_.load(params_.parser); + } + + common_chat_msg parse(const std::string & msg, bool is_partial) const { + common_chat_parser_params parser_params; + parser_params.format = params_.format; + parser_params.debug = detailed_debug_; + return common_chat_peg_parse(arena_, msg, is_partial, parser_params); + } +}; + +static void test_peg_parser(common_chat_templates * tmpls, + const std::function & init, + bool detailed_debug) { + // UTF-8-safe truncation helper (same as in test_parser_with_streaming) + constexpr auto utf8_truncate_safe_len = [](const std::string_view s) -> size_t { + auto len = s.size(); + if (len == 0) { + return 0; + } + auto i = len; + for (size_t back = 0; back < 4 && i > 0; ++back) { + --i; + unsigned char c = s[i]; + if ((c & 0x80) == 0) { + return len; + } + if ((c & 0xC0) == 0xC0) { + size_t expected_len = 0; + if ((c & 0xE0) == 0xC0) { + expected_len = 2; + } else if ((c & 0xF0) == 0xE0) { + expected_len = 3; + } else if ((c & 0xF8) == 0xF0) { + expected_len = 4; + } else { + return i; + } + if (len - i >= expected_len) { + return len; + } + return i; } } + return len - std::min(len, size_t(3)); + }; - if (expect_grammar_triggered) { - // TODO @ngxson : refactor common_chat_parse to avoid passing format/reasoning_format every time - common_chat_parser_params params; - params.format = data.params.format; - params.reasoning_format = reasoning_format; - const auto msg = common_chat_parse(data.delta, /* is_partial= */ false, params); - assert_msg_equals(test_message, msg, ignore_whitespace_differences); - } + peg_test_case tc; + init(tc); + if (tc.params.messages.empty()) { + tc.params.messages = { message_user }; + } + if (tc.expect.role.empty()) { + tc.expect.role = "assistant"; + } - if (!test_message.tool_calls.empty()) { - GGML_ASSERT(!data.params.grammar.empty()); - } - if (!data.params.grammar.empty()) { - auto grammar = build_grammar(data.params.grammar); - if (!grammar) { - throw std::runtime_error("Failed to build grammar"); + auto parser = make_peg_parser(tmpls, tc.params, detailed_debug); + if (detailed_debug) { + LOG_DBG("Using parser: \n%s\n", parser.arena_.dump(parser.arena_.root()).c_str()); + } + + common_chat_msg msg_accum; + common_chat_msg msg_prev; + msg_accum.role = msg_prev.role = "assistant"; + + for (size_t i = 1; i <= tc.input.size(); ++i) { + auto is_partial = i < tc.input.size() || tc.is_partial; + // Use UTF-8 safe truncation to avoid corrupting multi-byte characters + size_t safe_len = utf8_truncate_safe_len(std::string_view(tc.input).substr(0, i)); + std::string prefix = tc.input.substr(0, safe_len); + common_chat_msg msg_current = parser.parse(prefix, is_partial); + + for (const auto & diff : common_chat_msg_diff::compute_diffs(msg_prev, msg_current)) { + if (!diff.reasoning_content_delta.empty()) { + msg_accum.reasoning_content += diff.reasoning_content_delta; } - auto earliest_trigger_pos = std::string::npos; - auto constrained = data.delta; - for (const auto & trigger : data.params.grammar_triggers) { - size_t pos = std::string::npos; - std::smatch match; - switch (trigger.type) { - case COMMON_GRAMMAR_TRIGGER_TYPE_WORD: + if (!diff.content_delta.empty()) { + msg_accum.content += diff.content_delta; + } + if (diff.tool_call_index != std::string::npos) { + // During partial parsing, a new tool call may appear with empty name initially + // The name gets filled in as more input is parsed + while (msg_accum.tool_calls.size() <= diff.tool_call_index) { + msg_accum.tool_calls.push_back({ "", "", "" }); + } + // Always update name and id from diff (may change during incremental parsing), but only if the delta + // actually contains them + if (!diff.tool_call_delta.name.empty()) { + msg_accum.tool_calls[diff.tool_call_index].name = diff.tool_call_delta.name; + } + if (!diff.tool_call_delta.id.empty()) { + msg_accum.tool_calls[diff.tool_call_index].id = diff.tool_call_delta.id; + } + if (!diff.tool_call_delta.arguments.empty()) { + msg_accum.tool_calls[diff.tool_call_index].arguments += diff.tool_call_delta.arguments; + } + } + } + try { + assert_msg_equals(msg_current, msg_accum, true); + } catch (std::exception & e) { + throw std::runtime_error((std::string("Error comparing accumulated message to current: ") + e.what()).c_str()); + } + + msg_prev = msg_current; + } + + if (!tc.is_partial) { + assert_msg_equals(tc.expect, parser.parse(tc.input, false), true); + } + assert_msg_equals(tc.expect, msg_accum, true); + + // Test grammar if present in params + if (!parser.params_.grammar.empty()) { + auto grammar = build_grammar(parser.params_.grammar); + if (!grammar) { + throw std::runtime_error("Failed to build grammar: " + parser.params_.grammar); + } + + // Find the earliest trigger position to determine the constrained portion + auto earliest_trigger_pos = std::string::npos; + for (const auto & trigger : parser.params_.grammar_triggers) { + size_t pos = std::string::npos; + std::smatch match; + switch (trigger.type) { + case COMMON_GRAMMAR_TRIGGER_TYPE_WORD: { const auto & word = trigger.value; - pos = constrained.find(word); + pos = tc.input.find(word); break; } - case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN: + case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN: { - const auto & pattern = trigger.value; - if (std::regex_search(constrained, match, std::regex(pattern))) { - pos = match.position(1); + const auto & pattern = std::regex(trigger.value); + if (std::regex_search(tc.input, match, pattern)) { + pos = match.position(pattern.mark_count()); } break; } - case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL: + case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL: { const auto & pattern = trigger.value; - if (std::regex_match(constrained, match, std::regex(pattern))) { + if (std::regex_match(tc.input, match, std::regex(pattern))) { auto mpos = std::string::npos; for (size_t i = 1; i < match.size(); ++i) { if (match[i].length() > 0) { @@ -409,220 +975,168 @@ static void test_templates(const struct common_chat_templates * tmpls, const std } break; } - default: - throw std::runtime_error("Unknown trigger type"); - } - if (pos == std::string::npos) { - continue; - } + default: + throw std::runtime_error("Unknown trigger type"); + } + if (pos != std::string::npos) { if (earliest_trigger_pos == std::string::npos || pos < earliest_trigger_pos) { earliest_trigger_pos = pos; } } - auto grammar_triggered = false; - if (earliest_trigger_pos != std::string::npos) { - constrained = constrained.substr(earliest_trigger_pos); - grammar_triggered = true; - } - if (data.params.grammar_lazy) { - assert_equals(expect_grammar_triggered, grammar_triggered); - } - - if (grammar_triggered && test_grammar_if_triggered && !match_string(constrained, grammar.get())) { - throw std::runtime_error("Failed to match delta against grammar:\n\n" + data.delta + - "\n\nConstrained: " + constrained + - "\n\nGrammar: " + data.params.grammar); - } } - } -} -/** - * Test if streaming=true is consistant with streaming=false for given partial parser - * Also test if there is any problem with partial message - */ -template -static void test_parser_with_streaming(const common_chat_msg & expected, const std::string & raw_message, T parse_msg) { - constexpr auto utf8_truncate_safe_len = [](const std::string_view s) -> size_t { - auto len = s.size(); - if (len == 0) return 0; - auto i = len; - for (size_t back = 0; back < 4 && i > 0; ++back) { - --i; - unsigned char c = s[i]; - if ((c & 0x80) == 0) { - return len; - } else if ((c & 0xC0) == 0xC0) { - size_t expected_len = 0; - if ((c & 0xE0) == 0xC0) expected_len = 2; - else if ((c & 0xF0) == 0xE0) expected_len = 3; - else if ((c & 0xF8) == 0xF0) expected_len = 4; - else return i; - if (len - i >= expected_len) { - return len; + // Determine the constrained portion of input to test against grammar + std::string constrained = tc.input; + bool grammar_triggered = false; + if (earliest_trigger_pos != std::string::npos) { + constrained = tc.input.substr(earliest_trigger_pos); + grammar_triggered = true; + } else if (!parser.params_.grammar_lazy) { + // For non-lazy grammars, the entire input should match + grammar_triggered = true; + } + + // Test the constrained portion against the grammar + if (grammar_triggered && !tc.is_partial) { + auto result = match_string_detailed(constrained, grammar.get()); + if (!result.success) { + std::string error_msg; + if (result.incomplete) { + error_msg = + "Grammar matched all input but expects more:\n\n" + ">>> Input: " + tc.input + + "\n\n>>> Constrained: " + constrained + + "\n\n>>> Matched prefix (" + std::to_string(result.matched_bytes) + " bytes, " + + std::to_string(result.matched_codepoints) + " codepoints): " + + (result.matched_prefix.size() > 100 ? result.matched_prefix.substr(0, 100) + "..." : result.matched_prefix) + + "\n\n>>> Expected next: " + result.expected_description + + "\n\n>>> Grammar: " + parser.params_.grammar; } else { - return i; + error_msg = + "Grammar match failed:\n\n" + ">>> Input: " + tc.input + + "\n\n>>> Constrained: " + constrained + + "\n\n>>> Matched prefix (" + std::to_string(result.matched_bytes) + " bytes, " + + std::to_string(result.matched_codepoints) + " codepoints): " + + (result.matched_prefix.size() > 100 ? result.matched_prefix.substr(0, 100) + "..." : result.matched_prefix) + + "\n\n>>> Failing character: " + result.failing_char + + "\n\n>>> Expected: " + result.expected_description + + "\n\n>>> Grammar: " + parser.params_.grammar; } + throw std::runtime_error(error_msg); } } - return len - std::min(len, size_t(3)); - }; - constexpr auto utf8_truncate_safe_view = [utf8_truncate_safe_len](const std::string_view s) { - return s.substr(0, utf8_truncate_safe_len(s)); - }; - - auto merged = simple_assist_msg(""); - auto last_msg = parse_msg(""); - for (size_t i = 1; i <= raw_message.size(); ++i) { - auto curr_msg = parse_msg(std::string(utf8_truncate_safe_view(std::string_view(raw_message).substr(0, i)))); - if (curr_msg == simple_assist_msg("")) continue; - LOG_INF("Streaming msg: %s\n", common_chat_msgs_to_json_oaicompat({curr_msg}).dump().c_str()); - for (auto diff: common_chat_msg_diff::compute_diffs(last_msg, curr_msg)) { - LOG_INF("Streaming diff: %s\n", common_chat_msg_diff_to_json_oaicompat(diff).dump().c_str()); - if (!diff.reasoning_content_delta.empty()) { - merged.reasoning_content += diff.reasoning_content_delta; - } - if (!diff.content_delta.empty()) { - merged.content += diff.content_delta; - } - if (diff.tool_call_index != std::string::npos) { - if (!diff.tool_call_delta.name.empty()) { - merged.tool_calls.push_back({diff.tool_call_delta.name, "", ""}); - } - if (!diff.tool_call_delta.arguments.empty()) { - GGML_ASSERT(!merged.tool_calls.empty()); - merged.tool_calls.back().arguments += diff.tool_call_delta.arguments; - } - } - LOG_INF("Streaming merged: %s\n", common_chat_msgs_to_json_oaicompat({merged}).dump().c_str()); - } - assert_msg_equals(curr_msg, merged, true); - last_msg = curr_msg; } - assert_msg_equals(expected, parse_msg(raw_message), true); - assert_msg_equals(expected, merged, true); } -const common_chat_msg message_user { - "user", - "Hey there!", - /* .content_parts = */ {}, - /* .tool_calls = */ {}, - /* .reasoning_content = */ "", - /* .tool_name = */ "", - /* .tool_call_id = */ "", +// Global template filter for --template flag +static std::string g_template_filter; + +// Fluent builder for PEG parser tests +class peg_test_builder; + +class peg_tester { + common_chat_templates_ptr tmpls_; + std::string template_path_; + bool detailed_debug_; + friend class peg_test_builder; + + public: + explicit peg_tester(const std::string & template_path, const bool detailed_debug = false) : + tmpls_(read_templates(template_path)), + template_path_(template_path), + detailed_debug_(detailed_debug) {} + + const std::string & template_path() const { return template_path_; } + + peg_test_builder test(const std::string & input); }; -const common_chat_msg message_user_parts { - "user", - /* .content = */ "", - /* .content_parts = */ { - { "text", "Hey" }, - { "text", "there" }, - }, - /* .tool_calls = */ {}, - /* .reasoning_content = */ "", - /* .tool_name = */ "", - /* .tool_call_id = */ "", -}; +class peg_test_builder { + peg_tester & tester_; + peg_test_case tc_; -const common_chat_msg message_assist = simple_assist_msg("Hello, world!\nWhat's up?"); -const common_chat_msg message_assist_empty = simple_assist_msg(""); -const common_chat_msg message_assist_thoughts_unparsed_deepseek = simple_assist_msg("I'm\nthinkingHello, world!\nWhat's up?"); -const common_chat_msg message_assist_thoughts_unparsed_md = simple_assist_msg("I'm\nthinkingHello, world!\nWhat's up?\n```json\n{}```"); -const common_chat_msg message_assist_thoughts_unparsed_md_partial = simple_assist_msg("I'm\nthinkingHello, world!\nWhat's up?\n```json\n{}"); + public: + peg_test_builder(peg_tester & tester, const std::string & input) : tester_(tester) { tc_.input = input; } -const common_chat_msg message_assist_thoughts_unparsed_r7b = simple_assist_msg("<|START_THINKING|>I'm\nthinking<|END_THINKING|>Hello, world!\nWhat's up?"); -const common_chat_msg message_assist_thoughts_unparsed_magistral = simple_assist_msg("[THINK]raisonnement[/THINK]Réponse"); -const common_chat_msg message_assist_thoughts = simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"); -const common_chat_msg message_assist_thoughts_unopened_unparsed = simple_assist_msg("I'm\nthinkingHello, world!\nWhat's up?"); -const common_chat_msg message_assist_thoughts_no_content = simple_assist_msg("", "I'm\nthinking"); -const common_chat_msg message_assist_call = simple_assist_msg("", "", "special_function", "{\"arg1\": 1}"); -const common_chat_msg message_assist_call_noopt = simple_assist_msg("", "", "special_function_with_opt", "{\"arg1\": 1}"); -const common_chat_msg message_assist_call_withopt = simple_assist_msg("", "", "special_function_with_opt", "{\"arg1\": 1, \"arg2\": 2}"); -const common_chat_msg message_assist_call_content = simple_assist_msg("Hello, world!\nWhat's up?", "", "special_function", "{\"arg1\":1}"); -const common_chat_msg message_assist_call_empty_args = simple_assist_msg("", "", "special_function"); -const common_chat_msg message_assist_call_cutoff_args = simple_assist_msg("", "", "special_function", "{\"arg"); -const common_chat_msg message_assist_call_thoughts = simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\":1}"); -const common_chat_msg message_assist_call_thoughts_unparsed = simple_assist_msg("I'm\nthinking\n\n", "", "special_function", "{\"arg1\": 1}"); -const common_chat_msg message_assist_call_thoughts_content = simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": 1}"); -const common_chat_msg message_assist_call_id = simple_assist_msg("", "", "special_function", "{\"arg1\":1}", /* .id = */ "123456789"); -const common_chat_msg message_assist_call_idx = simple_assist_msg("", "", "special_function", "{\"arg1\":1}", /* .id = */ "0"); -const common_chat_msg message_assist_thoughts_call_idx = simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\": 1}", /* id = */ "0"); -const common_chat_msg message_assist_call_python = simple_assist_msg("", "", "python", "{\"code\":\"print('hey')\"}"); -const common_chat_msg message_assist_call_python_lines = simple_assist_msg("", "", "python", "{\"code\":\"# This is a program:\\nprint('hey')\"}"); -const common_chat_msg message_assist_call_python_lines_unclosed = simple_assist_msg("", "", "python", "{\"code\":\"# This is a program:\\nprint('hey')"); -const common_chat_msg message_assist_call_code_interpreter = simple_assist_msg("", "", "code_interpreter", "{\"code\":\"print('hey')\"}"); - -// Use for PEG parser implementations -struct peg_test_case { - common_chat_templates_inputs params; - std::string input; - common_chat_msg expect; -}; - -struct make_peg_parser { - common_chat_params params_; - common_peg_arena arena_; - - make_peg_parser(common_chat_templates * tmpls, const common_chat_templates_inputs & inputs) { - params_ = common_chat_templates_apply(tmpls, inputs); - arena_.load(params_.parser); + // Parameter setters + peg_test_builder & reasoning_format(common_reasoning_format fmt) { + tc_.params.reasoning_format = fmt; + return *this; } - common_chat_msg parse(const std::string & msg, bool is_partial) { - common_chat_parser_params parser_params; - parser_params.format = params_.format; - return common_chat_peg_parse(arena_, msg, is_partial, parser_params); - } -}; - -static void test_peg_parser(common_chat_templates * tmpls, const std::function & init) { - peg_test_case tc; - init(tc); - if (tc.params.messages.empty()) { - tc.params.messages = {message_user}; - } - if (tc.expect.role.empty()) { - tc.expect.role = "assistant"; + peg_test_builder & tools(std::vector tools) { + tc_.params.tools = std::move(tools); + return *this; } - auto parser = make_peg_parser(tmpls, tc.params); + peg_test_builder & enable_thinking(bool val) { + tc_.params.enable_thinking = val; + return *this; + } - common_chat_msg msg_accum; - common_chat_msg msg_prev; - msg_accum.role = msg_prev.role = "assistant"; + peg_test_builder & parallel_tool_calls(bool val) { + tc_.params.parallel_tool_calls = val; + return *this; + } - for (size_t i = 1; i <= tc.input.size(); ++i) { - auto is_partial = i < tc.input.size(); - common_chat_msg msg_current = parser.parse(tc.input.substr(0, i), is_partial); + peg_test_builder & json_schema(const std::string & schema) { + tc_.params.json_schema = schema; + return *this; + } - for (const auto & diff : common_chat_msg_diff::compute_diffs(msg_prev, msg_current)) { - if (!diff.reasoning_content_delta.empty()) { - msg_accum.reasoning_content += diff.reasoning_content_delta; - } - if (!diff.content_delta.empty()) { - msg_accum.content += diff.content_delta; - } - if (diff.tool_call_index != std::string::npos) { - if (!diff.tool_call_delta.name.empty()) { - msg_accum.tool_calls.push_back({diff.tool_call_delta.name, "", diff.tool_call_delta.id}); - } - if (!diff.tool_call_delta.arguments.empty()) { - msg_accum.tool_calls.back().arguments += diff.tool_call_delta.arguments; - } + peg_test_builder & is_partial(bool val) { + tc_.is_partial = val; + return *this; + } + + // Expect setters + peg_test_builder & expect(const common_chat_msg & msg) { + tc_.expect = msg; + return *this; + } + + peg_test_builder & expect_content(const std::string & content) { + tc_.expect.content = content; + return *this; + } + + peg_test_builder & expect_reasoning(const std::string & reasoning) { + tc_.expect.reasoning_content = reasoning; + return *this; + } + + peg_test_builder & expect_tool_calls(std::vector calls) { + tc_.expect.tool_calls = std::move(calls); + return *this; + } + + // Execute the test + void run() { + // Check template filter + if (!g_template_filter.empty()) { + // Case-insensitive substring match + std::string template_path_lower = tester_.template_path(); + std::string filter_lower = g_template_filter; + std::transform(template_path_lower.begin(), template_path_lower.end(), template_path_lower.begin(), + ::tolower); + std::transform(filter_lower.begin(), filter_lower.end(), filter_lower.begin(), ::tolower); + if (template_path_lower.find(filter_lower) == std::string::npos) { + // Skip this test + return; } } - assert_msg_equals(msg_current, msg_accum, true); - msg_prev = msg_current; + LOG_INF("\n\x1b[38;5;126m[%s]\x1b[0m\n%s\n\n", tester_.template_path().c_str(), tc_.input.c_str()); + test_peg_parser(tester_.tmpls_.get(), [this](peg_test_case & t) { t = tc_; }, tester_.detailed_debug_); } +}; - assert_msg_equals(tc.expect, parser.parse(tc.input, false), true); - assert_msg_equals(tc.expect, msg_accum, true); +peg_test_builder peg_tester::test(const std::string & input) { + return peg_test_builder(*this, input); } static void test_msgs_oaicompat_json_conversion() { - printf("[%s]\n", __func__); + LOG_DBG("%s\n", __func__); std::vector msgs{ message_user, message_user_parts, @@ -633,54 +1147,48 @@ static void test_msgs_oaicompat_json_conversion() { message_assist_call_id, message_assist_call_idx, message_assist_call_python, - message_assist_call_code_interpreter, }; for (const auto & msg : msgs) { - auto oai_json = common_chat_msgs_to_json_oaicompat({msg}); - auto msgs2 = common_chat_msgs_parse_oaicompat(oai_json); + auto oai_json = common_chat_msgs_to_json_oaicompat({ msg }); + auto msgs2 = common_chat_msgs_parse_oaicompat(oai_json); assert_equals((size_t) 1, msgs2.size()); - auto msg2 = msgs2[0]; + const auto & msg2 = msgs2[0]; assert_msg_equals(msg, msg2); } - assert_equals( - std::string( - "[\n" - " {\n" - " \"role\": \"user\",\n" - " \"content\": [\n" - " {\n" - " \"type\": \"text\",\n" - " \"text\": \"Hey\"\n" - " },\n" - " {\n" - " \"type\": \"text\",\n" - " \"text\": \"there\"\n" - " }\n" - " ]\n" - " }\n" - "]" - ), - common_chat_msgs_to_json_oaicompat({message_user_parts}).dump(2)); + assert_equals(std::string("[\n" + " {\n" + " \"role\": \"user\",\n" + " \"content\": [\n" + " {\n" + " \"type\": \"text\",\n" + " \"text\": \"Hey\"\n" + " },\n" + " {\n" + " \"type\": \"text\",\n" + " \"text\": \"there\"\n" + " }\n" + " ]\n" + " }\n" + "]"), + common_chat_msgs_to_json_oaicompat({ message_user_parts }).dump(2)); - assert_equals( - std::string( - "[\n" - " {\n" - " \"role\": \"assistant\",\n" - " \"content\": \"\",\n" - " \"tool_calls\": [\n" - " {\n" - " \"type\": \"function\",\n" - " \"function\": {\n" - " \"name\": \"python\",\n" - " \"arguments\": \"{\\\"code\\\":\\\"print('hey')\\\"}\"\n" - " }\n" - " }\n" - " ]\n" - " }\n" - "]" - ), - common_chat_msgs_to_json_oaicompat({message_assist_call_python}).dump(2)); + // Note: content is "" instead of null due to workaround for templates that render null as "None" + assert_equals(std::string("[\n" + " {\n" + " \"role\": \"assistant\",\n" + " \"content\": \"\",\n" + " \"tool_calls\": [\n" + " {\n" + " \"type\": \"function\",\n" + " \"function\": {\n" + " \"name\": \"python\",\n" + " \"arguments\": \"{\\\"code\\\":\\\"print('hey')\\\"}\"\n" + " }\n" + " }\n" + " ]\n" + " }\n" + "]"), + common_chat_msgs_to_json_oaicompat({ message_assist_call_python }).dump(2)); auto res = common_chat_msgs_parse_oaicompat(json::parse("[{\"role\": \"assistant\", \"tool_calls\": []}]")); assert_equals(1, res.size()); @@ -699,16 +1207,15 @@ static void test_msgs_oaicompat_json_conversion() { } static void test_tools_oaicompat_json_conversion() { - printf("[%s]\n", __func__); + LOG_DBG("%s\n", __func__); std::vector tools{ special_function_tool, python_tool, - code_interpreter_tool, }; for (const auto & tool : tools) { - auto oai_json = common_chat_tools_to_json_oaicompat({tool}); - auto tools2 = common_chat_tools_parse_oaicompat(oai_json); + auto oai_json = common_chat_tools_to_json_oaicompat({ tool }); + auto tools2 = common_chat_tools_parse_oaicompat(oai_json); assert_equals((size_t) 1, tools2.size()); auto tool2 = tools2[0]; assert_equals(tool.name, tool2.name); @@ -716,2347 +1223,32 @@ static void test_tools_oaicompat_json_conversion() { assert_equals(json::parse(tool.parameters).dump(2), json::parse(tool2.parameters).dump(2)); } - assert_equals( - std::string( - "[\n" - " {\n" - " \"type\": \"function\",\n" - " \"function\": {\n" - " \"name\": \"special_function\",\n" - " \"description\": \"I'm special\",\n" - " \"parameters\": {\n" - " \"type\": \"object\",\n" - " \"properties\": {\n" - " \"arg1\": {\n" - " \"type\": \"integer\",\n" - " \"description\": \"The arg.\"\n" - " }\n" - " },\n" - " \"required\": [\n" - " \"arg1\"\n" - " ]\n" - " }\n" - " }\n" - " }\n" - "]" - ), - common_chat_tools_to_json_oaicompat({special_function_tool}).dump(2)); - - { - auto tools_no_params = common_chat_tools_parse_oaicompat(json::parse( - R"([{"type": "function", "function": {"name": "test_func", "description": "A test"}}])")); - assert_equals((size_t) 1, tools_no_params.size()); - assert_equals(std::string("test_func"), tools_no_params[0].name); - assert_equals(std::string("A test"), tools_no_params[0].description); - assert_equals(std::string("{}"), tools_no_params[0].parameters); - } - { - auto tools_no_desc = common_chat_tools_parse_oaicompat(json::parse( - R"([{"type": "function", "function": {"name": "test_func", "parameters": {"type": "object"}}}])")); - assert_equals((size_t) 1, tools_no_desc.size()); - assert_equals(std::string("test_func"), tools_no_desc[0].name); - assert_equals(std::string(""), tools_no_desc[0].description); - } - { - auto tools_minimal = common_chat_tools_parse_oaicompat(json::parse( - R"([{"type": "function", "function": {"name": "test_func"}}])")); - assert_equals((size_t) 1, tools_minimal.size()); - assert_equals(std::string("test_func"), tools_minimal[0].name); - assert_equals(std::string(""), tools_minimal[0].description); - assert_equals(std::string("{}"), tools_minimal[0].parameters); - } + assert_equals(std::string("[\n" + " {\n" + " \"type\": \"function\",\n" + " \"function\": {\n" + " \"name\": \"special_function\",\n" + " \"description\": \"I'm special\",\n" + " \"parameters\": {\n" + " \"type\": \"object\",\n" + " \"properties\": {\n" + " \"arg1\": {\n" + " \"type\": \"integer\",\n" + " \"description\": \"The arg.\"\n" + " }\n" + " },\n" + " \"required\": [\n" + " \"arg1\"\n" + " ]\n" + " }\n" + " }\n" + " }\n" + "]"), + common_chat_tools_to_json_oaicompat({ special_function_tool }).dump(2)); } -// for compat; ref: https://github.com/ggml-org/llama.cpp/pull/18961 -struct test_parser_params { - common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; - common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE; - bool reasoning_in_content = false; - bool thinking_forced_open = false; - bool parse_tool_calls = true; -}; - -static common_chat_msg test_chat_parse(const std::string & input, bool is_partial, const test_parser_params & syntax) { - common_chat_parser_params params; - params.format = syntax.format; - params.reasoning_format = syntax.reasoning_format; - params.reasoning_in_content = syntax.reasoning_in_content; - params.thinking_forced_open = syntax.thinking_forced_open; - params.parse_tool_calls = syntax.parse_tool_calls; - return common_chat_parse(input, is_partial, params); -} - -static void test_template_output_parsers() { - printf("[%s]\n", __func__); - - common_chat_templates_inputs inputs_no_tools; - inputs_no_tools.messages = {message_user}; - - common_chat_templates_inputs inputs_tools; - inputs_tools.messages = {message_user}; - inputs_tools.tools = {special_function_tool}; - - common_chat_templates_inputs inputs_tools_builtin; - inputs_tools_builtin.messages = {message_user}; - inputs_tools_builtin.tools = {python_tool}; - - { - // Not supported yet - auto tmpls = read_templates("models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja"); - assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_GENERIC, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - } - { - auto tmpls = read_templates("models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja"); - std::vector end_tokens{ "<|END_OF_TURN_TOKEN|>" }; - - for (const auto & inputs : { inputs_no_tools, inputs_tools }) { - auto params = common_chat_templates_apply(tmpls.get(), inputs); - assert_equals(COMMON_CHAT_FORMAT_COMMAND_R7B, params.format); - assert_equals(false, params.thinking_forced_open); - } - - assert_msg_equals(message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_COMMAND_R7B})); - assert_msg_equals(message_assist, - test_chat_parse( - "<|START_RESPONSE|>Hello, world!\nWhat's up?<|END_RESPONSE|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_COMMAND_R7B})); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" - "<|START_RESPONSE|>Hello, world!\nWhat's up?<|END_RESPONSE|>", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_COMMAND_R7B, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts_unparsed_deepseek, - test_chat_parse( - "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" - "<|START_RESPONSE|>Hello, world!\nWhat's up?<|END_RESPONSE|>", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_COMMAND_R7B, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ true, - /* .thinking_forced_open = */ false, - })); - assert_msg_equals(message_assist_thoughts_unparsed_r7b, - test_chat_parse( - "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" - "<|START_RESPONSE|>Hello, world!\nWhat's up?<|END_RESPONSE|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_COMMAND_R7B})); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" - "<|START_RESPONSE|>Hello, world!\nWhat's up?<|END_RESPONSE|>", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_COMMAND_R7B, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts_call_idx, - test_chat_parse( - "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" - "<|START_ACTION|>[\n" - " {\"tool_call_id\": \"0\", \"tool_name\": \"special_function\", \"parameters\": {\"arg1\": 1}}\n" - "]<|END_ACTION|>", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_COMMAND_R7B, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts_no_content, - test_chat_parse( - "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" - "<|START_ACTION|>[\n" - " {\"tool_call_id\": \"0\", \"tool_name\": \"special", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_COMMAND_R7B, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - test_templates(tmpls.get(), end_tokens, message_assist_call_idx, tools, - "<|START_THINKING|><|END_THINKING|>" - "<|START_ACTION|>[\n" - " {\"tool_call_id\": \"0\", \"tool_name\": \"special_function\", \"parameters\": {\"arg1\": 1}}\n" - "]<|END_ACTION|>", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ true, - COMMON_REASONING_FORMAT_DEEPSEEK); - test_templates(tmpls.get(), end_tokens, message_assist, tools, - "<|START_RESPONSE|>Hello, world!\n" - "What's up?<|END_RESPONSE|>", - /* expect_grammar_triggered= */ false); - } - // TODO @ngxson : generic tool calls is too costly to maintain, consider removing it in the future - { - auto tmpls = read_templates("models/templates/google-gemma-2-2b-it.jinja"); - std::vector end_tokens{ "" }; - - assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_GENERIC, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - assert_equals(COMMON_CHAT_FORMAT_GENERIC, - common_chat_templates_apply( - read_templates("models/templates/microsoft-Phi-3.5-mini-instruct.jinja").get(), - inputs_tools) - .format); - - // Generic tool calls doesn't generate / parse content-only messages symmetrically. - - assert_equals( - simple_assist_msg("{ \"tool_call\" : { \"name\" : \"t"), - test_chat_parse( - "{ \"tool_call\" : { \"name\" : \"t", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GENERIC, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - /* .parse_tool_calls = */ false, - })); - assert_equals( - message_assist_empty, - test_chat_parse( - "{ \"tool_call\" : { \"name\" : \"t", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_GENERIC})); - - assert_equals( - simple_assist_msg("", "", "puppeteer_screenshot", "{\"name\":\"servethehome_homepage\","), - test_chat_parse( - R"({"tool_call": {"name": "puppeteer_screenshot", "arguments": {"name": "servethehome_homepage",)", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_GENERIC})); - - assert_equals( - message_assist_call_empty_args, - test_chat_parse( - "{ \"tool_call\" : { \"name\" : \"special_function\"", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_GENERIC})); - assert_equals( - message_assist_call_cutoff_args, - test_chat_parse( - "{ \"tool_call\" : { \"name\" : \"special_function\", \"arguments\" : { \"arg", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_GENERIC})); - - assert_msg_equals(message_assist, - test_chat_parse( - "{\n" - " \"response\": \"Hello, world!\\nWhat's up?\"\n" - "}", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_GENERIC})); -#if 0 - test_templates(tmpls.get(), end_tokens, message_assist_call_id, tools, - "{\n" - " \"tool_calls\": [\n" - " {\n" - " \"name\": \"special_function\",\n" - " \"arguments\": {\n" - " \"arg1\": 1\n" - " },\n" - " \"id\": \"123456789\"\n" - " }\n" - " ],\n" - " \"content\": \"\"\n" - "}"); -#endif - } - { - auto tmpls = read_templates("models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja"); - std::vector end_tokens{ "" }; - - assert_equals(COMMON_CHAT_FORMAT_MISTRAL_NEMO, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - test_templates( - tmpls.get(), end_tokens, message_assist_call_id, tools, - "[TOOL_CALLS][{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}, \"id\": \"123456789\"}]"); - } - { - assert_msg_equals( - simple_assist_msg("Réponse", "raisonnement"), - test_chat_parse( - message_assist_thoughts_unparsed_magistral.content, - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_MAGISTRAL, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - } - { - auto tmpls = read_templates("models/templates/Qwen-QwQ-32B.jinja"); - std::vector end_tokens{ "<|im_end|>" }; - - assert_equals(COMMON_CHAT_FORMAT_HERMES_2_PRO, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_HERMES_2_PRO, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - } - { - auto tmpls = read_templates("models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja"); - std::vector end_tokens{ "<|im_end|>" }; - - assert_equals(COMMON_CHAT_FORMAT_HERMES_2_PRO, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_HERMES_2_PRO, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - assert_equals( - COMMON_CHAT_FORMAT_HERMES_2_PRO, - common_chat_templates_apply( - read_templates("models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja").get(), - inputs_tools) - .format); - assert_equals( - COMMON_CHAT_FORMAT_HERMES_2_PRO, - common_chat_templates_apply( - read_templates("models/templates/Qwen-Qwen2.5-7B-Instruct.jinja").get(), - inputs_tools) - .format); - - // Test parsing - assert_msg_equals( - simple_assist_msg("", "", "python", ""), - test_chat_parse( - "```json\n" - " { \"name\" : \"python\"", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - simple_assist_msg("Let's call something\n"), - test_chat_parse( - "Let's call something\n" - "{\"name\"", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals( - simple_assist_msg("Let's call something\n"), - test_chat_parse( - "Let's call something\n" - "{\"name", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_call_thoughts, - test_chat_parse( - // QwQ-32B's template adds a trailing if add_generation_prompt - "I'm\nthinking\n" - "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - })); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "\n" - "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals(message_assist_call_content, - test_chat_parse( - "Hello, world!\nWhat's up?\n" - "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "{\"arg1\": 1}", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "\n" - "{\"arg1\": 1}\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "```xml\n" - "\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "\n" - "```", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "```xml\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "```", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "```\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "```", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "```\n" - "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "```", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "```json\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "```", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "```json\n" - "\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}} \n" - " \n" - "``` ", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "\n" - " {\n" - " \"name\": \"special_function\", \"arguments\": {\"arg1\": 1}\n" - " }\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "\n" - " {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals( - message_assist_call, - test_chat_parse( - "{\n \"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - - // Test multiple tool calls - common_chat_msg message_assist_multiple_calls; - message_assist_multiple_calls.role = "assistant"; - message_assist_multiple_calls.content = ""; - message_assist_multiple_calls.tool_calls.push_back({"special_function", "{\"arg1\": 1}", ""}); - message_assist_multiple_calls.tool_calls.push_back({"python", "{\"code\":\"print('hello')\"}", ""}); - - assert_msg_equals( - message_assist_multiple_calls, - test_chat_parse( - "\n" - "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "\n" - "\n" - "{\"name\": \"python\", \"arguments\": {\"code\":\"print('hello')\"}}\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - - assert_msg_equals( - message_assist_multiple_calls, - test_chat_parse( - "{\"arg1\": 1}\n" - "{\"code\":\"print('hello')\"}", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - - assert_msg_equals( - simple_assist_msg( - "This is not a tool call:", - "", - "special_function", - "{\"arg1\": 1}"), - test_chat_parse( - "This is not a tool call:\n" - "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals(message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - assert_msg_equals(message_assist_thoughts_unparsed_deepseek, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_HERMES_2_PRO})); - // assert_msg_equals(message_assist_thoughts_unparsed_deepseek, - // test_chat_parse( - // "I'm\nthinkingHello, world!\nWhat's up?", - // COMMON_CHAT_FORMAT_HERMES_2_PRO)); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts_unparsed_md, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?\n```json\n{}```", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ true, - /* .thinking_forced_open = */ false, - /* .parse_tool_calls = */ false, - })); - assert_msg_equals(message_assist_thoughts_unparsed_md_partial, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?\n```json\n{}```", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ true, - /* .thinking_forced_open = */ false, - })); - assert_msg_equals(message_assist_thoughts_unopened_unparsed, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - })); - - test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "\n" - "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - ""); - - // Test multiple tool calls with template - common_chat_msg message_assist_multiple_calls_template; - message_assist_multiple_calls_template.role = "assistant"; - message_assist_multiple_calls_template.content = ""; - message_assist_multiple_calls_template.tool_calls.push_back({"special_function", "{\"arg1\": 1}", ""}); - message_assist_multiple_calls_template.tool_calls.push_back({"python", "{\"code\":\"print('test')\"}", ""}); - - test_templates(tmpls.get(), end_tokens, message_assist_multiple_calls_template, tools, - "\n" - "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" - "\n" - "\n" - "{\"name\": \"python\", \"arguments\": {\"code\":\"print('test')\"}}\n" - ""); - - test_templates(tmpls.get(), end_tokens, message_assist_call_python_lines, tools, - "\n" - "{\"name\": \"python\", \"arguments\": {\"code\":\"# This is a program:\\nprint('hey')\"}}\n" - ""); - assert_msg_equals( - simple_assist_msg("", /* reasoning_content= */ "nah uhg"), - test_chat_parse( - "nah uhg", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - } - { - auto tmpls = read_templates("models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja"); - std::vector end_tokens{ "<|eom_id|>", "<|eot_id|>" }; - - assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS, - common_chat_templates_apply(tmpls.get(), inputs_tools_builtin).format); - assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS, - common_chat_templates_apply( - read_templates("models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja").get(), - inputs_tools_builtin) - .format); - - assert_equals( - message_assist_call, - test_chat_parse( - "{\"name\": \"special_function\", \"parameters\": {\"arg1\": 1}}", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_LLAMA_3_X})); - - // test_templates(tmpls.get(), end_tokens, message_assist, tools, R"(?)", /* expect_grammar_triggered= */ false); - test_templates(tmpls.get(), end_tokens, message_assist_call_code_interpreter, llama_3_1_tools, - "<|python_tag|>code_interpreter.call(code=\"print('hey')\")"); - test_templates(tmpls.get(), end_tokens, message_assist_call_python, tools, - "<|python_tag|>python.call(code=\"print('hey')\")"); - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "{\"name\": \"special_function\", \"parameters\": {\"arg1\": 1}}"); - } - { - auto tmpls = read_templates("models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja"); - std::vector end_tokens{ "<|eom_id|>", "<|eot_id|>" }; - - assert_equals(COMMON_CHAT_FORMAT_LLAMA_3_X, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - - test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "{\"name\": \"special_function\", \"parameters\": {\"arg1\": 1}}"); - } - { - auto tmpls = read_templates("models/templates/meetkai-functionary-medium-v3.1.jinja"); - std::vector end_tokens{ "<|eom_id|>", "<|eot_id|>" }; - - assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, - common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1, - common_chat_templates_apply(tmpls.get(), inputs_tools).format); - assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, - common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - - for (auto is_partial : { false, true }) { - assert_equals( - message_assist_call, - test_chat_parse( - "{\"arg1\": 1}", - is_partial, - {COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1})); - } - - assert_equals( - message_assist_call, - test_chat_parse( - "{\"arg1\": 1}<", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1})); - - test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "{\"arg1\": 1}"); - } - { - auto tmpls = read_templates("models/templates/meetkai-functionary-medium-v3.2.jinja"); - std::vector end_tokens{ "<|eom_id|>", "<|eot_id|>" }; - - assert_equals(COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - assert_msg_equals( - simple_assist_msg( - "Hello, world!\nnono\nWhat's up?", - "", - "special_function", - "{\"arg1\": 1}"), - test_chat_parse( - "all\n" - "Hello, world!\n" - "nono\n" - "What's up?>>>special_function\n" - "{\"arg1\": 1}\n", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2})); - assert_msg_equals(message_assist_call_python_lines, - test_chat_parse( - "python\n" - "# This is a program:\n" - "print('hey')", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2})); - assert_msg_equals(message_assist_call_python_lines_unclosed, - test_chat_parse( - "python\n" - "# This is a program:\n" - "print('hey')", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2})); - assert_msg_equals(message_assist_call, - test_chat_parse( - "special_function\n" - "{\"arg1\": 1} \n ", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2})); - assert_msg_equals(message_assist, - test_chat_parse( - "all\n" - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2})); - - test_templates(tmpls.get(), end_tokens, message_assist, {}, - "all\n" - "Hello, world!\n" - "What's up?", - /* expect_grammar_triggered= */ false); - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "special_function\n" - "{\"arg1\": 1}"); - } - { - auto tmpls = read_templates("models/templates/fireworks-ai-llama-3-firefunction-v2.jinja"); - std::vector end_tokens{ "<|eot_id|>" }; - - assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_FIREFUNCTION_V2, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - " functools[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]"); - } - { - // Original DeepSeek R1 template. Leaves <｜tool▁calls▁begin｜> and others unclosed. Our logic fixes the prompt. - auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja"); - std::vector end_tokens{ "<｜end▁of▁sentence｜>" }; - - for (const auto & inputs : { inputs_no_tools, inputs_tools }) { - auto params = common_chat_templates_apply(tmpls.get(), inputs); - assert_equals(COMMON_CHAT_FORMAT_DEEPSEEK_R1, params.format); - assert_equals(true, params.thinking_forced_open); - } - - test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - test_templates(tmpls.get(), end_tokens, message_assist_thoughts, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - assert_msg_equals( - simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"), - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - COMMON_CHAT_FORMAT_DEEPSEEK_R1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - })); - assert_msg_equals( - simple_assist_msg("", "I need to remember the correct syntax. It starts with <｜tool▁calls▁begin｜> and ends with"), - test_chat_parse( - "I need to remember the correct syntax. It starts with <｜tool▁calls▁begin｜> and ends with", - /* is_partial= */ true, - { - COMMON_CHAT_FORMAT_DEEPSEEK_R1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - })); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts_unopened_unparsed, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - })); - assert_msg_equals(message_assist_thoughts, - // Latest template update (ast of 20250209) adds a trailing \n if add_generation_prompt is true. - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - })); - // test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - // "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n" - // "```json\n" - // "{\"arg1\": 1}\n" - // // Look what's not here: <｜tool▁calls▁end｜> (also missing the <｜end▁of▁sentence｜>, but that is removed lazily by the test's delta logic) - // "```<｜tool▁call▁end｜>", - // /* expect_grammar_triggered= */ true, - // /* test_grammar_if_triggered= */ false); - } - { - // Replacement DeepSeek R1 template. Makes the Distill Qwen 7B/32B models happy to call tools and all. - auto tmpls = read_templates("models/templates/llama-cpp-deepseek-r1.jinja"); - std::vector end_tokens{ "<｜end▁of▁sentence｜>" }; - - assert_equals(COMMON_CHAT_FORMAT_DEEPSEEK_R1, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_DEEPSEEK_R1, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - test_templates(tmpls.get(), end_tokens, message_assist_thoughts, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - assert_msg_equals(message_assist_thoughts_unparsed_deepseek, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_DEEPSEEK_R1})); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - })); - - assert_msg_equals(message_assist_call_thoughts_unparsed, - test_chat_parse( - "I'm\nthinking\n\n" - "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n" - "```json\n" - "{\"arg1\": 1}\n" - "```<｜tool▁call▁end｜><｜tool▁calls▁end｜>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_DEEPSEEK_R1})); - assert_msg_equals(message_assist_call, - test_chat_parse( - "<｜tool▁calls｜>function<｜tool▁sep｜>special_function\n" - "```json\n" - "{\"arg1\": 1}\n" - "```<｜tool▁call▁end｜><｜tool▁calls▁end｜>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_DEEPSEEK_R1})); - - assert_msg_equals(message_assist_call_thoughts, - test_chat_parse( - "I'm\nthinking\n\n" - "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n" - "```json\n" - "{\"arg1\": 1}\n" - "```<｜tool▁call▁end｜><｜tool▁calls▁end｜>", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n" - "```json\n" - "{\"arg1\": 1}\n" - "```<｜tool▁call▁end｜><｜tool▁calls▁end｜>"); - } - { - auto tmpls = read_templates("models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja"); - std::vector end_tokens{ "<|end_of_text|>" }; - - assert_equals(COMMON_CHAT_FORMAT_GRANITE, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - - assert_equals(COMMON_CHAT_FORMAT_GRANITE, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - // Test parsing regular content - assert_msg_equals(message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_GRANITE})); - assert_msg_equals( - message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_GRANITE})); - - // Test parsing content with thinking - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GRANITE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts_unparsed_deepseek, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_GRANITE})); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GRANITE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GRANITE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals(simple_assist_msg("I'm\nthinkingHello, world!\nWhat's up?"), - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_GRANITE})); - assert_msg_equals(message_assist_empty, - test_chat_parse( - "I'm\nthinking", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GRANITE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - assert_msg_equals( - message_assist_empty, - test_chat_parse( - "I'm\nthinking[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_GRANITE})); - assert_msg_equals( - message_assist_call_empty_args, - test_chat_parse( - "<|tool_call|>[{\"name\": \"special_function\"", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_GRANITE})); - assert_msg_equals( - message_assist_call_cutoff_args, - test_chat_parse( - "<|tool_call|>[{\"name\": \"special_function\", \"arguments\": {\"arg", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_GRANITE})); - assert_msg_equals( - message_assist_call_cutoff_args, - test_chat_parse( - "<|tool_call|>[{\"name\": \"special_function\", \"arguments\": {\"arg", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GRANITE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test parsing tool calls with thinking - assert_msg_equals( - message_assist_call_thoughts, - test_chat_parse( - "I'm\nthinking<|tool_call|>[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}, {", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GRANITE, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test template generation for regular content - test_templates(tmpls.get(), end_tokens, message_assist, tools, - "Hello, world!\nWhat's up?", - /* expect_grammar_triggered= */ false); - // TODO @ngxson : generic tool call should be removed in the future -#if 0 - // Test template generation for tool calls - test_templates(tmpls.get(), end_tokens, message_assist_call_id, tools, - "{\n" - " \"tool_calls\": [\n" - " {\n" - " \"name\": \"special_function\",\n" - " \"arguments\": {\n" - " \"arg1\": 1\n" - " },\n" - " \"id\": \"123456789\"\n" - " }\n" - " ],\n" - " \"content\": \"\"\n" - "}", - /* expect_grammar_triggered= */ false - ); -#endif - } - { - auto tmpls = read_templates("models/templates/openai-gpt-oss-120b.jinja"); - std::vector end_tokens{ "<|return|>", "<|call|>" }; - - assert_equals(COMMON_CHAT_FORMAT_GPT_OSS, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_GPT_OSS, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - assert_msg_equals(simple_assist_msg("", "I'm\nthink"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthink", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("", "I'm\nthinking"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>final<|message|>Hello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>commentary to=functions.special_function <|constrain|>json<|message|>{\"arg1", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>commentary to=functions.special_function<|message|>{\"arg1", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\": 1}"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>commentary to=functions.special_function <|constrain|>json<|message|>{\"arg1\": 1}", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\": 1}"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>analysis to=functions.special_function <|constrain|>json<|message|>{\"arg1\": 1}", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>commentary<|message|>Hello, world!\nWhat's up?", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": 1}"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>commentary<|message|>Hello, world!\nWhat's up?<|end|>" - "<|start|>assistant<|channel|>commentary to=functions.special_function <|constrain|>json<|message|>{\"arg1\": 1}", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - - // Test parse_tool_calls == false - assert_msg_equals( - simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>final<|message|>Hello, world!\nWhat's up?", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ false, - /* .parse_tool_calls = */ false, - })); - assert_msg_equals( - simple_assist_msg("", "I'm\nthinking"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>commentary to=functions.special_function<|message|>{\"arg1", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ false, - /* .parse_tool_calls = */ false, - })); - assert_msg_equals( - simple_assist_msg("", "I'm\nthinking"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>commentary to=functions.special_function <|constrain|>json<|message|>{\"arg1\": 1}", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ false, - /* .parse_tool_calls = */ false, - })); - - // Test reasoning formats - assert_msg_equals( - simple_assist_msg( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>Hello, world!\nWhat's up?"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>final<|message|>Hello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE, - })); - - assert_msg_equals( - simple_assist_msg( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>Hello, world!\nWhat's up?"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant<|channel|>final<|message|>Hello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - /* .reasoning_in_content = */ true, - })); - - // Test tool calling in role header - assert_msg_equals(simple_assist_msg("", "", "special_function", "{\"arg1\": 1}"), - test_chat_parse( - " to=functions.special_function<|channel|>commentary <|constrain|>json<|message|>{\"arg1\": 1}", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("", "", "special_function", "{\"arg1\": 1}"), - test_chat_parse( - " to=functions.special_function<|channel|>analysis <|constrain|>json<|message|>{\"arg1\": 1}", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - assert_msg_equals(simple_assist_msg("", "I'm\nthinking", "special_function", "{\"arg1\": 1}"), - test_chat_parse( - "<|channel|>analysis<|message|>I'm\nthinking<|end|>" - "<|start|>assistant to=functions.special_function<|channel|>analysis <|constrain|>json<|message|>{\"arg1\": 1}", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GPT_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_AUTO, - })); - } - { - // Seed-OSS format tests - auto tmpls = read_templates("models/templates/ByteDance-Seed-OSS.jinja"); - std::vector end_tokens{ "" }; - - assert_equals(COMMON_CHAT_FORMAT_SEED_OSS, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_SEED_OSS, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - - // Test simple reasoning content - assert_msg_equals( - simple_assist_msg("Hello, world!", "I'm thinking about the answer"), - test_chat_parse( - "I'm thinking about the answerHello, world!", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_SEED_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test budget reflection tags - common_chat_msg msg_budget_reflect; - msg_budget_reflect.role = "assistant"; - msg_budget_reflect.content = "Token usage: 45/1000\nI should continue thinking to find the best solution.I need to calculate this step by step."; - msg_budget_reflect.reasoning_content = "Token usage: 45/1000\nI should continue thinking to find the best solution."; - assert_msg_equals( - msg_budget_reflect, - test_chat_parse( - "Token usage: 45/1000\nI should continue thinking to find the best solution." - "Token usage: 45/1000\nI should continue thinking to find the best solution." - "I need to calculate this step by step.", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_SEED_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test tool calls with Seed-OSS format - common_chat_msg msg_tool_call; - msg_tool_call.role = "assistant"; - msg_tool_call.tool_calls.push_back({"calculate_sum", "{\"numbers\": [1, 2, 3]}", ""}); - assert_msg_equals( - msg_tool_call, - test_chat_parse( - "\n" - "\n" - "[1, 2, 3]\n" - "\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_SEED_OSS})); - - // Test reasoning + tool call combination - common_chat_msg msg_reasoning_tool; - msg_reasoning_tool.role = "assistant"; - msg_reasoning_tool.content = ""; - msg_reasoning_tool.reasoning_content = "I need to calculate the sum of these numbers"; - msg_reasoning_tool.tool_calls.push_back({"calculate_sum", "{\"numbers\": [1, 2, 3]}", ""}); - assert_msg_equals( - msg_reasoning_tool, - test_chat_parse( - "I need to calculate the sum of these numbers" - "\n" - "\n" - "[1, 2, 3]\n" - "\n" - "", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_SEED_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test deltas: the number of tool calls in partial parses should never decrease - std::string tool_msg = "\n" - "\n" - "[1, 2, 3]\n" - ""; - std::size_t previousToolCalls = 0; - for (std::size_t i = std::string("").length(); i < tool_msg.length() - 1; i++) { - auto partial = tool_msg.substr(0, i); - auto partial_res = test_chat_parse(partial, true, { COMMON_CHAT_FORMAT_SEED_OSS, COMMON_REASONING_FORMAT_DEEPSEEK }); - if (partial_res.tool_calls.size() < previousToolCalls) { - throw std::runtime_error("Tool call size decreased on partial: " + partial + " from " + std::to_string(previousToolCalls) + " to " + std::to_string(partial_res.tool_calls.size())); - } - previousToolCalls = partial_res.tool_calls.size(); - } - - // Test multiple parameters in tool call - common_chat_msg msg_multi_param; - msg_multi_param.role = "assistant"; - msg_multi_param.tool_calls.push_back({"process_data", "{\"input\": \"test\", \"format\": \"json\"}", ""}); - assert_msg_equals( - msg_multi_param, - test_chat_parse( - "\n" - "\n" - "test\n" - "json\n" - "\n" - "", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_SEED_OSS})); - - // Test partial parsing for incomplete tool call - don't actually add the call until parsing parameters is done - assert_msg_equals( - simple_assist_msg("", "", "calculate_sum", "{\"numbers\":"), - test_chat_parse( - "\n" - "\n" - "[1,\n", - /* is_partial= */ true, - {COMMON_CHAT_FORMAT_SEED_OSS})); - - // Test incomplete reasoning tag - assert_msg_equals( - simple_assist_msg("", "I was thinking"), - test_chat_parse( - "I was thinking", - /* is_partial= */ true, - { - /* .format = */ COMMON_CHAT_FORMAT_SEED_OSS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test content without reasoning - assert_msg_equals( - simple_assist_msg("This is a simple response without reasoning."), - test_chat_parse( - "This is a simple response without reasoning.", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_SEED_OSS})); - } - { - auto tmpls = read_templates("models/templates/NVIDIA-Nemotron-Nano-v2.jinja"); - std::vector end_tokens{ "" }; - - assert_equals(COMMON_CHAT_FORMAT_NEMOTRON_V2, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_NEMOTRON_V2, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - // Test parsing regular content - assert_msg_equals(message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_NEMOTRON_V2})); - - // Test parsing content with thinking - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_NEMOTRON_V2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test parsing tool calls - assert_msg_equals(message_assist_call, - test_chat_parse( - "[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_NEMOTRON_V2})); - - // Test parsing tool calls with thinking - assert_msg_equals(message_assist_call_thoughts, - test_chat_parse( - "I'm\nthinking[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_NEMOTRON_V2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - })); - - // Test tool calls with extra content - assert_msg_equals(message_assist_call_content, - test_chat_parse( - "[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_NEMOTRON_V2} - )); - - // Test tool calls with extra content AND thinking - assert_msg_equals(message_assist_call_thoughts_content, - test_chat_parse( - "I'm\nthinking[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]Hello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_NEMOTRON_V2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - })); - - // Test template generation for regular content - test_templates(tmpls.get(), end_tokens, message_assist, tools, - "Hello, world!\nWhat's up?\n", - /* expect_grammar_triggered= */ false); - - // Test template generation for tool calls - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]", - /* expect_grammar_triggered= */ true - ); - } - { - auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V3.1.jinja"); - std::vector end_tokens{ "<｜end▁of▁sentence｜>" }; - - for (const auto & inputs : { inputs_no_tools, inputs_tools }) { - auto params = common_chat_templates_apply(tmpls.get(), inputs); - assert_equals(COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, params.format); - assert_equals(true, params.thinking_forced_open); - } - - test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - test_templates(tmpls.get(), end_tokens, message_assist_thoughts, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false); - assert_msg_equals( - simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking"), - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - })); - // variant: thinking forced open, reasoning_format none - assert_msg_equals( - simple_assist_msg("REASONINGok", ""), - test_chat_parse( - "REASONINGok", - /* is_partial= */ false, - { - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - /* .parse_tool_calls = */ true, - })); - // variant: happy path for when it works as the model card says it should - assert_msg_equals( - simple_assist_msg("", "", "get_time", "{\"city\":\"Tokyo\"}"), - test_chat_parse( - "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>", - /* is_partial= */ false, - { - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ false, - /* .parse_tool_calls = */ true, - })); - // variant: simple + thinking open - assert_msg_equals( - simple_assist_msg("", "REASONING", "get_time", "{\"city\":\"Tokyo\"}"), - test_chat_parse( - "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>", - /* is_partial= */ false, - { - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - /* .parse_tool_calls = */ true, - })); - // variant: simple + multiple tool calls - common_chat_msg message_assist_multiple_calls; - message_assist_multiple_calls.role = "assistant"; - message_assist_multiple_calls.content = "CONTENT"; - message_assist_multiple_calls.tool_calls.push_back({"get_time", "{\"city\":\"Paris\"}", ""}); - message_assist_multiple_calls.tool_calls.push_back({"get_weather", "{\"city\":\"Paris\"}", ""}); - assert_msg_equals( - message_assist_multiple_calls, - test_chat_parse( - "CONTENT<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Paris\"}<｜tool▁call▁end｜><｜tool▁call▁begin｜>get_weather<｜tool▁sep｜>{\"city\": \"Paris\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>", - /* is_partial= */ false, - { - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ false, - /* .parse_tool_calls = */ true, - })); - // variant: thinking forced open + tool call in reasoning content - assert_msg_equals( - simple_assist_msg("", "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time2<｜tool▁sep｜>{\"city\": \"Tokyo2\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>REASONING", "get_time", "{\"city\":\"Tokyo\"}"), - test_chat_parse( - "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time2<｜tool▁sep｜>{\"city\": \"Tokyo2\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>", - /* is_partial= */ false, - { - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - /* .parse_tool_calls = */ true, - })); - // variant: thinking forced open + tool call in reasoning content + no closing think + not partial - // This is a bit of a fine tuning issue on the model's part IMO. It really should not be attempting - // to make tool calls in reasoning content according to the model card, but it does sometimes, so - // add the reasoning content as regular content and parse the tool calls. - assert_msg_equals( - simple_assist_msg("REASONING", "", "get_time", "{\"city\":\"Tokyo\"}"), - test_chat_parse( - "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>", - /* is_partial= */ false, - { - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - /* .parse_tool_calls = */ true, - })); - // variant: thinking forced open + tool call in reasoning content + no closing think + partial - assert_msg_equals( - simple_assist_msg("", "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>", "", ""), - test_chat_parse( - "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": \"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>", - /* is_partial= */ true, - { - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ true, - /* .parse_tool_calls = */ true, - })); - // variant: thinking not forced open + missing reasoning + no tool calls - assert_msg_equals( - simple_assist_msg("CONTENT", ""), - test_chat_parse( - "CONTENT", - /* is_partial= */ false, - { - COMMON_CHAT_FORMAT_DEEPSEEK_V3_1, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* .reasoning_in_content = */ false, - /* .thinking_forced_open = */ false, - /* .parse_tool_calls = */ true, - })); - } - { - auto tmpls = read_templates("models/templates/Apertus-8B-Instruct.jinja"); - std::vector end_tokens{ "<|assistant_end|>" }; - - assert_equals(COMMON_CHAT_FORMAT_APERTUS, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_APERTUS, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - // Test parsing regular content - assert_msg_equals(message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_APERTUS})); - - // Test parsing content with thinking - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "<|inner_prefix|>I'm\nthinking<|inner_suffix|>Hello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_APERTUS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test parsing tool calls - assert_msg_equals(message_assist_call, - test_chat_parse( - "<|tools_prefix|>[{\"special_function\": {\"arg1\": 1}}]<|tools_suffix|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_APERTUS})); - - // Test parsing tool calls with thinking - assert_msg_equals(message_assist_call_thoughts, - test_chat_parse( - "<|inner_prefix|>I'm\nthinking<|inner_suffix|><|tools_prefix|>[{\"special_function\": {\"arg1\": 1}}]<|tools_suffix|>", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_APERTUS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - })); - - // Test tool calls with extra content - assert_msg_equals(message_assist_call_content, - test_chat_parse( - "<|tools_prefix|>[{\"special_function\": {\"arg1\": 1}}]<|tools_suffix|>Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_APERTUS} - )); - - // Test tool calls with extra content AND thinking - assert_msg_equals(message_assist_call_thoughts_content, - test_chat_parse( - "<|inner_prefix|>I'm\nthinking<|inner_suffix|><|tools_prefix|>[{\"special_function\": {\"arg1\": 1}}]<|tools_suffix|>Hello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_APERTUS, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - })); - - // Test template generation for regular content - test_templates(tmpls.get(), end_tokens, message_assist, tools, - "Hello, world!\nWhat's up?", - /* expect_grammar_triggered= */ false); - - // Test template generation for tool calls - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "<|tools_prefix|>[{\"special_function\": {\"arg1\": 1}}]<|tools_suffix|>", - /* expect_grammar_triggered= */ true - ); - - // TODO @ngxson : not sure why this fails, but not very important for now - // assert_equals(true, common_chat_templates_support_enable_thinking(tmpls.get())); - } - { - // LFM2 format tests - auto tmpls = read_templates("models/templates/llama-cpp-lfm2.jinja"); - std::vector end_tokens{ "<|im_end|>" }; - - auto inputs_tools_forced_json_schema = std::invoke([&]() -> common_chat_templates_inputs { - common_chat_templates_inputs inputs; - inputs.messages = { - std::invoke([&]() -> common_chat_msg { - common_chat_msg msg; - msg.role = "system"; - msg.content = "force json schema.\n"; - return msg; - }), - message_user, - }; - inputs.tools = {special_function_tool}; - return inputs; - }); - - { - auto params = common_chat_templates_apply(tmpls.get(), inputs_no_tools); - assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, params.format); - assert_equals(false, params.grammar_lazy); - assert_equals(std::string(R"(<|im_start|>user -Hey there!<|im_end|> -<|im_start|>assistant -)"), params.prompt); - } - - { - auto params = common_chat_templates_apply(tmpls.get(), inputs_tools); - assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, params.format); - assert_equals(false, params.grammar_lazy); - assert_equals(std::string(R"(<|im_start|>system -List of tools: <|tool_list_start|>[{"type": "function", "function": {"name": "special_function", "description": "I'm special", "parameters": {"type": "object", "properties": {"arg1": {"type": "integer", "description": "The arg."}}, "required": ["arg1"]}}}]<|tool_list_end|><|im_end|> -<|im_start|>user -Hey there!<|im_end|> -<|im_start|>assistant -)"), params.prompt); - assert_equals(true, params.grammar.empty()); - } - - { - auto params = common_chat_templates_apply(tmpls.get(), inputs_tools_forced_json_schema); - assert_equals(COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS, params.format); - assert_equals(true, params.grammar_lazy); - assert_equals(std::string(R"(<|im_start|>system -List of tools: <|tool_list_start|>[{"type": "function", "function": {"name": "special_function", "description": "I'm special", "parameters": {"type": "object", "properties": {"arg1": {"type": "integer", "description": "The arg."}}, "required": ["arg1"]}}}]<|tool_list_end|><|im_end|> -<|im_start|>user -Hey there!<|im_end|> -<|im_start|>assistant -)"), params.prompt); - assert_equals(false, params.grammar.empty()); - } - - // Test parsing regular content - assert_msg_equals(message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS})); - - // Test single tool call with JSON format - common_chat_msg msg_single_tool_call; - msg_single_tool_call.role = "assistant"; - msg_single_tool_call.tool_calls.push_back({"special_function", "{\"arg1\":1}", ""}); - assert_msg_equals( - msg_single_tool_call, - test_chat_parse( - "<|tool_call_start|>[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]<|tool_call_end|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS})); - - // Test tool call with string argument - common_chat_msg msg_tool_call_string; - msg_tool_call_string.role = "assistant"; - msg_tool_call_string.tool_calls.push_back({"get_weather", "{\"location\":\"Paris\"}", ""}); - assert_msg_equals( - msg_tool_call_string, - test_chat_parse( - "<|tool_call_start|>[{\"name\": \"get_weather\", \"arguments\": {\"location\": \"Paris\"}}]<|tool_call_end|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS})); - - // Test tool call with multiple arguments - common_chat_msg msg_multi_args; - msg_multi_args.role = "assistant"; - msg_multi_args.tool_calls.push_back({"calculate", "{\"x\":10,\"y\":20,\"operation\":\"add\"}", ""}); - assert_msg_equals( - msg_multi_args, - test_chat_parse( - "<|tool_call_start|>[{\"name\": \"calculate\", \"arguments\": {\"x\": 10, \"y\": 20, \"operation\": \"add\"}}]<|tool_call_end|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS})); - - // Test multiple tool calls in single array - common_chat_msg msg_multiple_tools; - msg_multiple_tools.role = "assistant"; - msg_multiple_tools.tool_calls.push_back({"get_weather", "{\"location\":\"Paris\"}", ""}); - msg_multiple_tools.tool_calls.push_back({"get_time", "{\"timezone\":\"UTC\"}", ""}); - assert_msg_equals( - msg_multiple_tools, - test_chat_parse( - "<|tool_call_start|>[{\"name\": \"get_weather\", \"arguments\": {\"location\": \"Paris\"}}, {\"name\": \"get_time\", \"arguments\": {\"timezone\": \"UTC\"}}]<|tool_call_end|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS})); - - // Test tool call with content before - common_chat_msg msg_content_before_tool; - msg_content_before_tool.role = "assistant"; - msg_content_before_tool.content = "Let me check the weather for you."; - msg_content_before_tool.tool_calls.push_back({"get_weather", "{\"location\":\"Paris\"}", ""}); - assert_msg_equals( - msg_content_before_tool, - test_chat_parse( - "Let me check the weather for you.<|tool_call_start|>[{\"name\": \"get_weather\", \"arguments\": {\"location\": \"Paris\"}}]<|tool_call_end|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS})); - - // Test tool call with content after - common_chat_msg msg_content_after_tool; - msg_content_after_tool.role = "assistant"; - msg_content_after_tool.content = "Here's the result."; - msg_content_after_tool.tool_calls.push_back({"get_weather", "{\"location\":\"Paris\"}", ""}); - assert_msg_equals( - msg_content_after_tool, - test_chat_parse( - "<|tool_call_start|>[{\"name\": \"get_weather\", \"arguments\": {\"location\": \"Paris\"}}]<|tool_call_end|>Here's the result.", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS})); - - // Test tool call with newlines (common in LLM output) - common_chat_msg msg_tool_call_newlines; - msg_tool_call_newlines.role = "assistant"; - msg_tool_call_newlines.tool_calls.push_back({"get_current_time", "{\"location\":\"Paris\"}", ""}); - assert_msg_equals( - msg_tool_call_newlines, - test_chat_parse( - "<|tool_call_start|>[{\n \"name\": \"get_current_time\",\n \"arguments\": {\n \"location\": \"Paris\"\n }\n}]<|tool_call_end|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS})); - - // Note: LFM2 uses JSON format for tool calls: [{"name": "...", "arguments": {...}}] - // Unlike other formats, LFM2 template does not render tool calls in conversation history, - // so we don't use test_templates() for tool call generation. Instead, the parsing tests - // above verify edge cases and format variations for the tool call output format. - } - - { - auto tmpls = read_templates("models/templates/MiniMax-M2.jinja"); - std::vector end_tokens{ "[e~[" }; - - assert_equals(COMMON_CHAT_FORMAT_MINIMAX_M2, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_MINIMAX_M2, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - // Test parsing regular content - assert_msg_equals(message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_MINIMAX_M2})); - - // Test parsing content with thinking - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_MINIMAX_M2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test parsing tool calls - assert_msg_equals(message_assist_call, - test_chat_parse( - "1", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_MINIMAX_M2})); - - // Test parsing tool calls with thinking - assert_msg_equals(message_assist_call_thoughts, - test_chat_parse( - "I'm\nthinking1", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_MINIMAX_M2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - })); - - // Test tool calls with extra content - assert_msg_equals(message_assist_call_content, - test_chat_parse( - "1Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_MINIMAX_M2} - )); - - // Test tool calls with extra content AND thinking - assert_msg_equals(message_assist_call_thoughts_content, - test_chat_parse( - "I'm\nthinking1Hello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_MINIMAX_M2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - })); - - // Test streaming - test_parser_with_streaming(message_assist_call_thoughts_content, - "I'm\nthinking\nHello, world!\nWhat's up?\n1", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_MINIMAX_M2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming(message_assist_call_thoughts_unparsed, - "I'm\nthinking\n\n1", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_MINIMAX_M2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE - }); }); - test_parser_with_streaming(message_assist_call_thoughts_content, - "I'm\nthinking\n\n\nHello, world!\nWhat's up?\n\n\n\n1\n\n\n", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_MINIMAX_M2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming(message_assist_call_withopt, - "\n\n1\n2\n\n", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_MINIMAX_M2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE - }); }); - - // Test template generation for regular content - test_templates(tmpls.get(), end_tokens, message_assist, tools, - "Hello, world!\nWhat's up?", - /* expect_grammar_triggered= */ false); - - // Test template generation for tool calls - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "\n\n1\n\n", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ true, - /* common_reasoning_format= */ COMMON_REASONING_FORMAT_NONE, - /* ignore_whitespace_differences= */ true - ); - - // Test template generation for tools with optional parameters - test_templates(tmpls.get(), end_tokens, message_assist_call_noopt, tools, - "\n\n1\n\n", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ true, - /* common_reasoning_format= */ COMMON_REASONING_FORMAT_NONE, - /* ignore_whitespace_differences= */ true - ); - test_templates(tmpls.get(), end_tokens, message_assist_call_withopt, tools, - "\n\n1\n2\n\n", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ true, - /* common_reasoning_format= */ COMMON_REASONING_FORMAT_NONE, - /* ignore_whitespace_differences= */ true - ); - } - - { - auto tmpls = read_templates("models/templates/GLM-4.6.jinja"); - std::vector end_tokens{ "<|assistant|>", "<|observation|>" }; - - assert_equals(COMMON_CHAT_FORMAT_GLM_4_5, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_GLM_4_5, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - // Test parsing regular content - assert_msg_equals(message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_GLM_4_5})); - - // Test parsing content with thinking - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "\nI'm\nthinking\nHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GLM_4_5, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - }), true); - - // Test parsing tool calls - assert_msg_equals(message_assist_call, - test_chat_parse( - "\nspecial_function\narg1\n1\n", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_GLM_4_5}), true); - - // Test parsing tool calls with thinking - assert_msg_equals(message_assist_call_thoughts, - test_chat_parse( - "\nI'm\nthinking\nspecial_function\narg1\n1\n", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GLM_4_5, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }), true); - - // Test tool calls with extra content - assert_msg_equals(message_assist_call_content, - test_chat_parse( - "\nspecial_function\narg1\n1\nHello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_GLM_4_5} - ), true); - - // Test tool calls with extra content AND thinking - assert_msg_equals(message_assist_call_thoughts_content, - test_chat_parse( - "\nI'm\nthinkingHello, world!\nWhat's up?\nspecial_function\narg1\n1\n", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_GLM_4_5, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }), true); - - // Test streaming - test_parser_with_streaming(message_assist_call_thoughts_content, - "\nI'm\nthinkingHello, world!\nWhat's up?\nspecial_function\narg1\n1\n", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_GLM_4_5, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming(message_assist_call_thoughts_unparsed, - "\nI'm\nthinking\n\nspecial_function\narg1\n1\n", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_GLM_4_5, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE - }); }); - test_parser_with_streaming(message_assist_call_withopt, - "\n\nspecial_function_with_opt\narg1\n1\narg2\n2\n\n", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_GLM_4_5, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming( - simple_assist_msg("", "", "complex_function", "{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}"), - "complex_function\n" - "name\n" - "John Doe\n" - "age\n" - "30\n" - "active\n" - "true\n" - "score\n" - "95.5\n" - "", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_GLM_4_5}); }); - test_parser_with_streaming( - simple_assist_msg("", "", "web_search", "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}"), - "web_search\n" - "query\n" - "\"From Zero\" Linkin Park album tracklist complete songs\n" - "limit\n" - "3\n" - "type\n" - "text\n" - "", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_GLM_4_5}); }); - - // Test interleaved thinking - test_parser_with_streaming(simple_assist_msg("Hello, world!\n\nWhat's up?", "I'm\nthinkingThinking2", "special_function", "{\"arg1\": 1}"), - "\nI'm\nthinkingHello, world!\nThinking2What's up?\nspecial_function\narg1\n1\n", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_GLM_4_5, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming(simple_assist_msg("\nI'm\nthinkingHello, world!\nThinking2What's up?", "", "special_function", "{\"arg1\": 1}"), - "\nI'm\nthinkingHello, world!\nThinking2What's up?\nspecial_function\narg1\n1\n", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_GLM_4_5, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE - }); }); - - // Test template generation for regular content - test_templates(tmpls.get(), end_tokens, message_assist, tools, - "\n\nHello, world!\nWhat's up?", - /* expect_grammar_triggered= */ false); - - // Test template generation for tool calls - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "\n\nspecial_function\narg1\n1\n\n", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ false, - /* common_reasoning_format= */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* ignore_whitespace_differences= */ true - ); - - // Test template generation for tools with optional parameters - test_templates(tmpls.get(), end_tokens, message_assist_call_noopt, tools, - "\n\nspecial_function_with_opt\narg1\n1\n\n", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ false, - /* common_reasoning_format= */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* ignore_whitespace_differences= */ true - ); - test_templates(tmpls.get(), end_tokens, message_assist_call_withopt, tools, - "\n\nspecial_function_with_opt\narg1\n1\narg2\n2\n\n", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ false, - /* common_reasoning_format= */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* ignore_whitespace_differences= */ true - ); - } - - { - auto tmpls = read_templates("models/templates/Kimi-K2-Thinking.jinja"); - std::vector end_tokens{ "<|im_end|>" }; - - assert_equals(COMMON_CHAT_FORMAT_KIMI_K2, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format); - assert_equals(COMMON_CHAT_FORMAT_KIMI_K2, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - // Test parsing regular content - assert_msg_equals(message_assist, - test_chat_parse( - "Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_KIMI_K2})); - - // Test parsing content with thinking - assert_msg_equals(message_assist_thoughts, - test_chat_parse( - "I'm\nthinkingHello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK, - })); - - // Test parsing tool calls - assert_msg_equals(message_assist_call, - test_chat_parse( - "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_KIMI_K2})); - - // Test parsing tool calls with thinking - assert_msg_equals(message_assist_call_thoughts, - test_chat_parse( - "I'm\nthinking<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - })); - - // Test tool calls with extra content - assert_msg_equals(message_assist_call_content, - test_chat_parse( - "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>Hello, world!\nWhat's up?", - /* is_partial= */ false, - {COMMON_CHAT_FORMAT_KIMI_K2} - )); - - // Test tool calls with extra content AND thinking - assert_msg_equals(message_assist_call_thoughts_content, - test_chat_parse( - "I'm\nthinking<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>Hello, world!\nWhat's up?", - /* is_partial= */ false, - { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - })); - - // Test streaming - test_parser_with_streaming(message_assist_call_thoughts_content, - "I'm\nthinking\nHello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming(message_assist_call_thoughts_unparsed, - "I'm\nthinking\n\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE - }); }); - test_parser_with_streaming(message_assist_call_thoughts_content, - "I'm\nthinking\n\n\nHello, world!\nWhat's up?\n\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>\n", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming(message_assist_call_withopt, - "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function_with_opt:0<|tool_call_argument_begin|>{\"arg1\": 1, \"arg2\": 2}<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_NONE - }); }); - test_parser_with_streaming(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": \"123456\"}"), - "I'm\nthinkingHello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": \"123456\"}<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": [1, 2, \"345\", 6]}"), - "I'm\nthinkingHello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": [1, 2, \"345\", 6]}<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": {\"12\": 34, \"5\": [67, 8], \"9\": \"10\"}}"), - "I'm\nthinkingHello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": {\"12\": 34, \"5\": [67, 8], \"9\": \"10\"}}<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - /* .format = */ COMMON_CHAT_FORMAT_KIMI_K2, - /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming( - simple_assist_msg("", "", "complex_function", "{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}"), - "<|tool_calls_section_begin|><|tool_call_begin|>functions.complex_function:0<|tool_call_argument_begin|>" - "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}" - "<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_KIMI_K2}); }); - test_parser_with_streaming( - simple_assist_msg("", "", "web_search", "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}"), - "<|tool_calls_section_begin|><|tool_call_begin|>functions.web_search:0<|tool_call_argument_begin|>" - "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}" - "<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_KIMI_K2}); }); - test_parser_with_streaming( - simple_assist_msg("", "", "read_file", "{\"args\": [{\"path\": \"src/providers/ThemeProvider.tsx\"}, {\"path\": \"src/components/Header.tsx\"}, {\"path\": \"src/components/ThemeToggle.tsx\"}, {\"path\": \"src/app/globals.css\"}, {\"path\": \"src/app/layout.tsx\"}]}"), - "<|tool_calls_section_begin|><|tool_call_begin|>functions.read_file:0<|tool_call_argument_begin|>" - "{\"args\": [{\"path\": \"src/providers/ThemeProvider.tsx\"}, {\"path\": \"src/components/Header.tsx\"}, {\"path\": \"src/components/ThemeToggle.tsx\"}, {\"path\": \"src/app/globals.css\"}, {\"path\": \"src/app/layout.tsx\"}]}" - "<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_KIMI_K2}); }); - test_parser_with_streaming( - simple_assist_msg( - "Let me start by examining the relevant files to understand the current implementation.", "", - "read_file", - "{\"files\": [{\"path\": \"src/app/Partners.tsx\", \"line_ranges\": [\"1-100\"]}]}"), - "Let me start by examining the relevant files to understand the current implementation." - "<|tool_calls_section_begin|><|tool_call_begin|>functions.read_file:0<|tool_call_argument_begin|>" - "{\"files\":[{\"path\":\"src/app/Partners.tsx\",\"line_ranges\":[\"1-100\"]}]}" - "<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_KIMI_K2}); }); - auto multi_tool_msg = simple_assist_msg("Let me call multiple tools.", "I'm thinking."); - multi_tool_msg.tool_calls.push_back({ "read_file", "{\"files\": [{\"path\": \"src/app/Partners.tsx\", \"line_ranges\": [\"1-100\"]}]}", "" }); - multi_tool_msg.tool_calls.push_back({ "web_search", "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}", "" }); - multi_tool_msg.tool_calls.push_back({ "complex_function", "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}", "" }); - multi_tool_msg.tool_calls.push_back({ "emoji_function", "{\"message\":\"Hello! 👋 🌟 🚀 Testing emojis: 😀😃😄😁 and symbols: ∑∏∆∇\"}", "" }); - test_parser_with_streaming(multi_tool_msg, - "I'm thinking.Let me call multiple tools." - "<|tool_calls_section_begin|>" - "<|tool_call_begin|>functions.read_file:0<|tool_call_argument_begin|>" - "{\"files\":[{\"path\":\"src/app/Partners.tsx\",\"line_ranges\":[\"1-100\"]}]}" - "<|tool_call_end|>" - "<|tool_call_begin|>functions.web_search:1<|tool_call_argument_begin|>" - "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}" - "<|tool_call_end|>" - "<|tool_call_begin|>functions.complex_function:2<|tool_call_argument_begin|>" - "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}" - "<|tool_call_end|>" - "<|tool_call_begin|>functions.emoji_function:3<|tool_call_argument_begin|>" - "{\"message\":\"Hello! 👋 🌟 🚀 Testing emojis: 😀😃😄😁 and symbols: ∑∏∆∇\"}" - "<|tool_call_end|>" - "<|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - COMMON_CHAT_FORMAT_KIMI_K2, - COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming( - simple_assist_msg("", "I'm thinking", "complex_function_in_think", "{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}"), - "I'm thinking<|tool_calls_section_begin|><|tool_call_begin|>functions.complex_function_in_think:0<|tool_call_argument_begin|>" - "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}" - "<|tool_call_end|><|tool_calls_section_end|>", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - COMMON_CHAT_FORMAT_KIMI_K2, - COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - test_parser_with_streaming( - simple_assist_msg("Hello", "I'm thinkingI'm still thinking", "complex_function_in_think", "{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}"), - "I'm thinking<|tool_calls_section_begin|><|tool_call_begin|>functions.complex_function_in_think:0<|tool_call_argument_begin|>" - "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}" - "<|tool_call_end|><|tool_calls_section_end|>I'm still thinkingHello", - [&](const std::string &msg) { return test_chat_parse(msg, /* is_partial= */ true, { - COMMON_CHAT_FORMAT_KIMI_K2, - COMMON_REASONING_FORMAT_DEEPSEEK - }); }); - - // Test template rendering - common_chat_templates_inputs conversation_with_tools = inputs_tools; - conversation_with_tools.messages.push_back(simple_assist_msg("Let's do it", "Think first", "complex_function", "{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}")); - conversation_with_tools.messages.push_back({ - "tool", - "Tool response 1", - /* .content_parts = */ {}, - /* .tool_calls = */ {}, - /* .reasoning_content = */ "", - /* .tool_name = */ "complex_function", - /* .tool_call_id = */ "", - }); - conversation_with_tools.messages.push_back(simple_assist_msg("Continue", "Think next", "web_search", "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}")); - conversation_with_tools.messages.push_back({ - "tool", - "Tool response 2", - /* .content_parts = */ {}, - /* .tool_calls = */ {}, - /* .reasoning_content = */ "", - /* .tool_name = */ "web_search", - /* .tool_call_id = */ "", - }); - conversation_with_tools.messages.push_back(simple_assist_msg("CC", "Think last", "read_file", "{\"args\": [{\"path\": \"src/providers/ThemeProvider.tsx\"}, {\"path\": \"src/components/Header.tsx\"}, {\"path\": \"src/components/ThemeToggle.tsx\"}, {\"path\": \"src/app/globals.css\"}, {\"path\": \"src/app/layout.tsx\"}]}")); - conversation_with_tools.messages.push_back({ - "tool", - "Tool response 3", - /* .content_parts = */ {}, - /* .tool_calls = */ {}, - /* .reasoning_content = */ "", - /* .tool_name = */ "read_file", - /* .tool_call_id = */ "", - }); - assert_equals(common_chat_templates_apply(tmpls.get(), conversation_with_tools).prompt, std::string("<|im_system|>tool_declare<|im_middle|>[{\"type\": \"function\", \"function\": {\"name\": \"special_function\", \"description\": \"I'm special\", \"parameters\": {\"type\": \"object\", \"properties\": {\"arg1\": {\"type\": \"integer\", \"description\": \"The arg.\"}}, \"required\": [\"arg1\"]}}}]<|im_end|><|im_system|>system<|im_middle|>You are Kimi, an AI assistant created by Moonshot AI.<|im_end|><|im_user|>user<|im_middle|>Hey there!<|im_end|><|im_assistant|>assistant<|im_middle|>Think firstLet's do it<|tool_calls_section_begin|><|tool_call_begin|>functions.complex_function:0<|tool_call_argument_begin|>{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}<|tool_call_end|><|tool_calls_section_end|><|im_end|><|im_system|>complex_function<|im_middle|>## Return of functions.complex_function:0\nTool response 1<|im_end|><|im_assistant|>assistant<|im_middle|>Think nextContinue<|tool_calls_section_begin|><|tool_call_begin|>functions.web_search:1<|tool_call_argument_begin|>{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}<|tool_call_end|><|tool_calls_section_end|><|im_end|><|im_system|>web_search<|im_middle|>## Return of functions.web_search:1\nTool response 2<|im_end|><|im_assistant|>assistant<|im_middle|>Think lastCC<|tool_calls_section_begin|><|tool_call_begin|>functions.read_file:2<|tool_call_argument_begin|>{\"args\": [{\"path\": \"src/providers/ThemeProvider.tsx\"}, {\"path\": \"src/components/Header.tsx\"}, {\"path\": \"src/components/ThemeToggle.tsx\"}, {\"path\": \"src/app/globals.css\"}, {\"path\": \"src/app/layout.tsx\"}]}<|tool_call_end|><|tool_calls_section_end|><|im_end|><|im_system|>read_file<|im_middle|>## Return of functions.read_file:2\nTool response 3<|im_end|><|im_assistant|>assistant<|im_middle|>")); - - // Test template generation for regular content - test_templates(tmpls.get(), end_tokens, message_assist, tools, - "Hello, world!\nWhat's up?", - /* expect_grammar_triggered= */ false); - - // Test template generation for tool calls - test_templates(tmpls.get(), end_tokens, message_assist_call, tools, - "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ true, - /* common_reasoning_format= */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* ignore_whitespace_differences= */ true - ); - - // Test template generation for tools with optional parameters - test_templates(tmpls.get(), end_tokens, message_assist_call_noopt, tools, - "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function_with_opt:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ true, - /* common_reasoning_format= */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* ignore_whitespace_differences= */ true - ); - test_templates(tmpls.get(), end_tokens, message_assist_call_withopt, tools, - "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function_with_opt:0<|tool_call_argument_begin|>{\"arg1\": 1, \"arg2\": 2}<|tool_call_end|><|tool_calls_section_end|>", - /* expect_grammar_triggered= */ true, - /* test_grammar_if_triggered= */ true, - /* common_reasoning_format= */ COMMON_REASONING_FORMAT_DEEPSEEK, - /* ignore_whitespace_differences= */ true - ); - } - - { - // Step-3.5-Flash template: uses same XML output format as Qwen3-Coder and Nemotron v3, - // but with support. Routes to the Nemotron v3 PEG parser for streaming and - // schema-aware parameter parsing. - auto tmpls = read_templates("models/templates/stepfun-ai-Step-3.5-Flash.jinja"); - assert_equals(COMMON_CHAT_FORMAT_PEG_CONSTRUCTED, common_chat_templates_apply(tmpls.get(), inputs_tools).format); - - // Grammar and PEG parser should be generated with thinking_forced_open - { - common_chat_templates_inputs inputs; - inputs.messages = { message_user }; - inputs.tools = { special_function_tool }; - auto params = common_chat_templates_apply(tmpls.get(), inputs); - assert_equals(COMMON_CHAT_FORMAT_PEG_CONSTRUCTED, params.format); - assert_equals(true, params.thinking_forced_open); - assert_equals(false, params.grammar.empty()); - assert_equals(false, params.parser.empty()); - auto grammar = build_grammar(params.grammar); - GGML_ASSERT(grammar && "Failed to build Step-3.5-Flash grammar"); - } - } -} - -static void test_template_output_peg_parsers() { - printf("[%s]\n", __func__); +static void test_template_output_peg_parsers(bool detailed_debug) { + LOG_DBG("%s\n", __func__); // JSON schemas const char * invoice_schema = R"({ @@ -3069,687 +1261,1588 @@ static void test_template_output_peg_parsers() { { // Ministral-3-14B-Reasoning-2512 - auto tmpls = read_templates("models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja"); + auto tst = peg_tester("models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja", detailed_debug); - // Test basic message - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "Hello, world!\nWhat's up?"; - t.expect = message_assist; - }); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); - // Test basic message and reasoning with reasoning_format = none - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?"; - t.expect.content = "[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?"; - }); + tst.test("[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?") + .expect_content("[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?") + .run(); - // Test basic message and reasoning with reasoning_format = auto - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?"; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; + tst.test("[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect(message_assist_thoughts) + .run(); - t.expect = message_assist_thoughts; - }); + tst.test(R"([TOOL_CALLS]special_function[ARGS]{"arg1":1})") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); - // Test tool call - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = R"([TOOL_CALLS]special_function[ARGS]{"arg1":1})"; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {special_function_tool}; + tst.test( + "[THINK]I'm\nthinking[/THINK]" + R"([TOOL_CALLS]special_function[ARGS]{"arg1":1})") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call_thoughts) + .run(); - t.expect = message_assist_call; - }); + tst.test(R"([TOOL_CALLS]special_function[ARGS]{"arg1": 1})" + R"([TOOL_CALLS]special_function_with_opt[ARGS]{"arg1": 1, "arg2": 2})") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} }, + }) + .run(); - // Test tool call with reasoning - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "[THINK]I'm\nthinking[/THINK]" - R"([TOOL_CALLS]special_function[ARGS]{"arg1":1})"; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {special_function_tool}; - - t.expect = message_assist_call_thoughts; - }); - - // Test parallel tool calls - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = R"([TOOL_CALLS]special_function[ARGS]{"arg1": 1})" - R"([TOOL_CALLS]special_function_with_opt[ARGS]{"arg1": 1, "arg2": 2})"; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.parallel_tool_calls = true; - t.params.tools = {special_function_tool, special_function_tool_with_optional_param}; - - t.expect.tool_calls = {{ - /* .name = */ "special_function", - /* .arguments = */ R"({"arg1": 1})", - /* .id = */ {}, - }, { - /* .name = */ "special_function_with_opt", - /* .arguments = */ R"({"arg1": 1, "arg2": 2})", - /* .id = */ {}, - }}; - }); - - // Test response format - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "[THINK]I need to output the invoice details in JSON[/THINK]" - "```json\n" - R"({"amount": 123.45, "date": "2025-12-03"})" - "\n```"; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.json_schema = invoice_schema; - - t.expect.reasoning_content = "I need to output the invoice details in JSON"; - t.expect.content =R"({"amount": 123.45, "date": "2025-12-03"})"; - }); - } - - { - // Qwen3-Coder - auto tmpls = read_templates("models/templates/Qwen3-Coder.jinja"); - - // Test basic message - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "Hello, world!\nWhat's up?"; - t.expect = message_assist; - }); - - // Test tool call - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - ""; - t.params.tools = {special_function_tool}; - t.expect = message_assist_call; - }); - - // Test parallel tool calls - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - "\n" - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - "2\n" - "\n" - "\n" - ""; - t.params.parallel_tool_calls = true; - t.params.tools = {special_function_tool, special_function_tool_with_optional_param}; - - t.expect.tool_calls = {{ - /* .name = */ "special_function", - /* .arguments = */ R"({"arg1": 1})", - /* .id = */ {}, - }, { - /* .name = */ "special_function_with_opt", - /* .arguments = */ R"({"arg1": 1, "arg2": 2})", - /* .id = */ {}, - }}; - }); - - // Test tool call with string parameter - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "def hello():\n" - " print(\"Hello, world!\")\n" - "\n" - "hello()\n" - "\n" - "\n" - ""; - t.params.tools = {python_tool}; - - t.expect.tool_calls = {{ - /* .name = */ "python", - /* .arguments = */ "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", - /* .id = */ {}, - }}; - }); - - // Test tool call with JSON parameter - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "[{\"item\": \"Check stuff\", \"selected\": false}, {\"item\": \"Prepare stuff\", \"selected\": true}]\n" - "\n" - "\n" - ""; - t.params.tools = {todo_list_tool}; - - t.expect.tool_calls = {{ - /* .name = */ "todo_list", - /* .arguments = */ "{\"todos\": [{\"item\": \"Check stuff\", \"selected\": false}, {\"item\": \"Prepare stuff\", \"selected\": true}]}", - /* .id = */ {}, - }}; - }); - - // Test tool call with string parameter and no closing tag - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "def hello():\n" - " print(\"Hello, world!\")\n" - "\n" - "hello()\n" - "\n" - ""; - t.params.tools = {python_tool}; - - t.expect.tool_calls = {{ - /* .name = */ "python", - /* .arguments = */ "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", - /* .id = */ {}, - }}; - }); - - // Test response format - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = R"({"amount": 123.45, "date": "2025-12-03"})"; - t.params.json_schema = invoice_schema; - - t.expect.content = R"({"amount": 123.45, "date": "2025-12-03"})"; - }); + tst.test( + "[THINK]I need to output the invoice details in JSON[/THINK]" + "```json\n" + R"({"amount": 123.45, "date": "2025-12-03"})" + "\n```") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .json_schema(invoice_schema) + .expect_reasoning("I need to output the invoice details in JSON") + .expect_content(R"({"amount": 123.45, "date": "2025-12-03"})") + .run(); } { // NVIDIA Nemotron-3 Nano - auto tmpls = read_templates("models/templates/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja"); + auto tst = peg_tester("models/templates/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja", detailed_debug); - // Test basic message - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "Hello, world!\nWhat's up?"; - t.expect = message_assist; - }); + tst.test("Hello, world!\nWhat's up?").enable_thinking(false).expect(message_assist).run(); - // Test basic message and reasoning with reasoning_format = none - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "I'm\nthinking\n\nHello, world!\nWhat's up?"; - t.expect.content = "I'm\nthinking\n\nHello, world!\nWhat's up?"; - }); + tst.test("I'm\nthinking\n\nHello, world!\nWhat's up?") + .enable_thinking(false) + .reasoning_format(COMMON_REASONING_FORMAT_NONE) + .expect_content("I'm\nthinking\n\nHello, world!\nWhat's up?") + .run(); - // Test basic message and reasoning with reasoning_format = auto - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "I'm\nthinking\n\nHello, world!\nWhat's up?"; - t.params.enable_thinking = true; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; + tst.test("I'm\nthinking\n\nHello, world!\nWhat's up?") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect(message_assist_thoughts) + .run(); - t.expect = message_assist_thoughts; - }); + tst.test( + "\n" + "\n" + "\n1\n\n" + "\n" + "") + .enable_thinking(false) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); - // Test tool call - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - ""; - t.params.enable_thinking = false; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {special_function_tool}; + tst.test( + "I'm\nthinking\n\n" + "\n" + "\n" + "\n1\n\n" + "\n" + "") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call_thoughts) + .run(); - t.expect = message_assist_call; - }); + tst.test( + "\n" + "\n" + "\n1\n\n" + "\n" + "\n" + "\n" + "\n" + "\n1\n\n" + "\n2\n\n" + "\n" + "") + .enable_thinking(false) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} }, + }) + .run(); - // Test tool call with reasoning - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "I'm\nthinking\n\n" - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - ""; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {special_function_tool}; + tst.test( + "\n" + "\n" + "\n" + "def hello():\n" + " print(\"Hello, world!\")\n" + "\n" + "hello()\n" + "\n" + "\n" + "") + .enable_thinking(false) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ + python_tool + }) + .expect_tool_calls({ + { "python", "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", {} }, + }) + .run(); - t.expect = message_assist_call_thoughts; - }); - - // Test parallel tool calls - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - "\n" - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - "2\n" - "\n" - "\n" - ""; - t.params.enable_thinking = false; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.parallel_tool_calls = true; - t.params.tools = {special_function_tool, special_function_tool_with_optional_param}; - - t.expect.tool_calls = {{ - /* .name = */ "special_function", - /* .arguments = */ R"({"arg1": 1})", - /* .id = */ {}, - }, { - /* .name = */ "special_function_with_opt", - /* .arguments = */ R"({"arg1": 1, "arg2": 2})", - /* .id = */ {}, - }}; - }); - - // Test tool call with string parameter - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "def hello():\n" - " print(\"Hello, world!\")\n" - "\n" - "hello()\n" - "\n" - "\n" - ""; - t.params.enable_thinking = false; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {python_tool}; - - t.expect.tool_calls = {{ - /* .name = */ "python", - /* .arguments = */ "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", - /* .id = */ {}, - }}; - }); - - // Test tool call with string parameter and no closing tag - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "def hello():\n" - " print(\"Hello, world!\")\n" - "\n" - "hello()\n" - "\n" - ""; - t.params.enable_thinking = false; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {python_tool}; - - t.expect.tool_calls = {{ - /* .name = */ "python", - /* .arguments = */ "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", - /* .id = */ {}, - }}; - }); - - // Test response format - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "I need to output the invoice details in JSON\n" - "\n" - R"({"amount": 123.45, "date": "2025-12-03"})"; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.json_schema = invoice_schema; - - t.expect.reasoning_content = "I need to output the invoice details in JSON"; - t.expect.content = R"({"amount": 123.45, "date": "2025-12-03"})"; - }); + tst.test( + "I need to output the invoice details in JSON\n" + "\n" + R"({"amount": 123.45, "date": "2025-12-03"})") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .enable_thinking(true) + .json_schema(invoice_schema) + .expect_reasoning("I need to output the invoice details in JSON") + .expect_content(R"({"amount": 123.45, "date": "2025-12-03"})") + .run(); } { - // Step-3.5-Flash (uses Nemotron v3 PEG parser with thinking_forced_open) - // Unlike Nemotron, Step-3.5-Flash always emits regardless of enable_thinking, - // so all inputs must include a delimiter. - auto tmpls = read_templates("models/templates/stepfun-ai-Step-3.5-Flash.jinja"); + // CohereForAI Command-R 7B (2024-tool_use) + auto tst = peg_tester("models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja", detailed_debug); - // Test basic message with reasoning - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "I'm\nthinking\n\nHello, world!\nWhat's up?"; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; + tst.test("<|START_RESPONSE|>Hello, world!\nWhat's up?<|END_RESPONSE|>").expect(message_assist).run(); - t.expect = message_assist_thoughts; - }); + tst.test( + "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" + "<|START_RESPONSE|>Hello, world!\nWhat's up?<|END_RESPONSE|>") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(message_assist_thoughts) + .run(); - // Test basic message without thinking content - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "\nHello, world!\nWhat's up?"; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; + tst.test( + "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" + "<|START_RESPONSE|>Hello, world!\nWhat's up?<|END_RESPONSE|>") + .expect(message_assist_thoughts_unparsed_r7b) + .run(); - t.expect = message_assist; - }); + tst.test( + "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" + "<|START_ACTION|>[\n" + " {\"tool_call_id\": \"0\", \"tool_name\": \"special_function\", \"parameters\": {\"arg1\": 1}}\n" + "]<|END_ACTION|>") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect(message_assist_thoughts_call_idx) + .run(); - // Test tool call without thinking content - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - ""; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {special_function_tool}; + tst.test( + "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" + "<|START_ACTION|>[\n" + " {\"tool_call_id\": \"0\", \"tool_name\": \"special_function\", ") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .is_partial(true) + .expect(message_assist_thoughts_partial_call) + .run(); - t.expect = message_assist_call; - }); - - // Test tool call with thinking - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "I'm\nthinking\n\n" - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - ""; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {special_function_tool}; - - t.expect = message_assist_call_thoughts; - }); - - // Test parallel tool calls with thinking - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "I'm\nthinking\n\n" - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - "\n" - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - "2\n" - "\n" - "\n" - ""; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.parallel_tool_calls = true; - t.params.tools = {special_function_tool, special_function_tool_with_optional_param}; - - t.expect.reasoning_content = "I'm\nthinking"; - t.expect.tool_calls = {{ - /* .name = */ "special_function", - /* .arguments = */ R"({"arg1": 1})", - /* .id = */ {}, - }, { - /* .name = */ "special_function_with_opt", - /* .arguments = */ R"({"arg1": 1, "arg2": 2})", - /* .id = */ {}, - }}; - }); - - // Test parallel tool calls without thinking content - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - "\n" - "\n" - "\n" - "\n" - "1\n" - "\n" - "\n" - "2\n" - "\n" - "\n" - ""; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.parallel_tool_calls = true; - t.params.tools = {special_function_tool, special_function_tool_with_optional_param}; - - t.expect.tool_calls = {{ - /* .name = */ "special_function", - /* .arguments = */ R"({"arg1": 1})", - /* .id = */ {}, - }, { - /* .name = */ "special_function_with_opt", - /* .arguments = */ R"({"arg1": 1, "arg2": 2})", - /* .id = */ {}, - }}; - }); - - // Test tool call with code string parameter - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "\n" - "def hello():\n" - " print(\"Hello, world!\")\n" - "\n" - "hello()\n" - "\n" - "\n" - ""; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {python_tool}; - - t.expect.tool_calls = {{ - /* .name = */ "python", - /* .arguments = */ "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", - /* .id = */ {}, - }}; - }); - - // Test tool call with string parameter and no closing tag - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "\n" - "\n" - "\n" - "\n" - "def hello():\n" - " print(\"Hello, world!\")\n" - "\n" - "hello()\n" - "\n" - ""; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.tools = {python_tool}; - - t.expect.tool_calls = {{ - /* .name = */ "python", - /* .arguments = */ "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", - /* .id = */ {}, - }}; - }); - - // Test response format (JSON schema with thinking) - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = - "I need to output the invoice details in JSON\n" - "\n" - R"({"amount": 123.45, "date": "2025-12-03"})"; - t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; - t.params.json_schema = invoice_schema; - - t.expect.reasoning_content = "I need to output the invoice details in JSON"; - t.expect.content = R"({"amount": 123.45, "date": "2025-12-03"})"; - }); + tst.test( + "<|START_THINKING|><|END_THINKING|>" + "<|START_ACTION|>[\n" + " {\"tool_call_id\": \"0\", \"tool_name\": \"special_function\", \"parameters\": {\"arg1\": 1}}\n" + "]<|END_ACTION|>") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect(message_assist_call_idx) + .run(); } { - // Solar-Open-100B - auto tmpls = read_templates("models/templates/upstage-Solar-Open-100B.jinja"); + // Google Gemma 2 2B - does not support tool calling + auto tst = peg_tester("models/templates/google-gemma-2-2b-it.jinja"); - // Test basic message - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|content|>Hello, world!\nWhat's up?"; - t.expect = message_assist; - }); + tst.test("Hello, world!").expect(simple_assist_msg("Hello, world!")).run(); - // Test basic message and reasoning - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|think|>I'm\nthinking<|end|><|begin|>assistant<|content|>Hello, world!\nWhat's up?"; - t.expect = message_assist_thoughts; - }); + tst.test("Line 1\nLine 2\nLine 3").expect(simple_assist_msg("Line 1\nLine 2\nLine 3")).run(); + } - // Test basic message and reasoning_effort = low - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|content|>Hello, world!\nWhat's up?"; - t.params.chat_template_kwargs["reasoning_effort"] = "\"low\""; - t.expect = message_assist; - }); + { + // Qwen-QwQ-32B (reasoning model) + auto tst = peg_tester("models/templates/Qwen-QwQ-32B.jinja"); - // Test tool call - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|tool_calls|>" - "<|tool_call:begin|>123456789" - "<|tool_call:name|>special_function" - "<|tool_call:args|>{\"arg1\":1}" - "<|tool_call:end|>"; + // QwQ always has thinking forced open - input starts after the \n in the prompt + tst.test("Let me think about this...\n\nThe answer is 42.") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect(simple_assist_msg("The answer is 42.", "Let me think about this...")) + .run(); - t.params.chat_template_kwargs["reasoning_effort"] = "\"low\""; - t.params.tools = {special_function_tool}; - t.expect = message_assist_call_id; - }); + tst.test("Hello, world!").expect(simple_assist_msg("Hello, world!")).run(); + } + { + // NousResearch-Hermes-2-Pro and Hermes-3 (tool calling models) + auto tst = peg_tester("models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja", detailed_debug); - // Test tool call with reasoning - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|think|>I'm\nthinking<|end|>" - "<|begin|>assistant<|tool_calls|>" - "<|tool_call:begin|>0" - "<|tool_call:name|>special_function" - "<|tool_call:args|>{\"arg1\":1}" - "<|tool_call:end|>"; + tst.test( + "\n" + "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" + "") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); - t.params.tools = {special_function_tool}; - t.expect = message_assist_thoughts_call_idx; - }); + tst.test( + "Hello, world!\nWhat's up?\n" + "{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n" + "") + .tools({ special_function_tool }) + .expect(message_assist_call_content) + .run(); - // Test tool call with reasoning and tool_choice = required - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|think|>I'm\nthinking<|end|>" - "<|begin|>assistant<|tool_calls|>" - "<|tool_call:begin|>0" - "<|tool_call:name|>special_function" - "<|tool_call:args|>{\"arg1\":1}" - "<|tool_call:end|>"; + // Note: Hermes template doesn't support thinking/reasoning natively + // Note: We only support one tool calling format per template, no alternate formats + } + { + // Test simple content-only template + auto tst = peg_tester("models/templates/google-gemma-2-2b-it.jinja", detailed_debug); - t.params.tools = {special_function_tool}; - t.params.tool_choice = COMMON_CHAT_TOOL_CHOICE_REQUIRED; - t.expect = message_assist_thoughts_call_idx; - }); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + } + { + // IBM Granite (reasoning and tool calling model) + auto tst = peg_tester("models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja", detailed_debug); - // Test tool call without reasoning and tool_choice = required - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|tool_calls|>" - "<|tool_call:begin|>0" - "<|tool_call:name|>special_function" - "<|tool_call:args|>{\"arg1\":1}" - "<|tool_call:end|>"; + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); - t.params.tools = {special_function_tool}; - t.params.tool_choice = COMMON_CHAT_TOOL_CHOICE_REQUIRED; - t.params.chat_template_kwargs["reasoning_effort"] = "\"low\""; - t.expect = message_assist_call_idx; - }); + tst.test("I'm\nthinkingHello, world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(message_assist_thoughts) + .run(); - // Test parallel tool calls - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|think|>I'm\nthinking<|end|>" - "<|begin|>assistant<|tool_calls|>" - "<|tool_call:begin|>0" - "<|tool_call:name|>special_function" - "<|tool_call:args|>{\"arg1\":1}" - "<|tool_call:end|>" - "<|tool_call:begin|>1" - "<|tool_call:name|>special_function_with_opt" - "<|tool_call:args|>{\"arg1\": 1, \"arg2\": 2}" - "<|tool_call:end|>"; + // TODO: pending support for WRAPPED_WITH_REASONING + // tst.test("I'm\nthinkingHello, world!\nWhat's up?") + // .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + // .expect(message_assist_thoughts) + // .run(); + } - t.params.parallel_tool_calls = true; - t.params.tools = {special_function_tool, special_function_tool_with_optional_param}; + { + // ByteDance-Seed-OSS (reasoning and tool calling model) + auto tst = peg_tester("models/templates/ByteDance-Seed-OSS.jinja", detailed_debug); - t.expect.reasoning_content = "I'm\nthinking"; - t.expect.tool_calls = {{ - /* .name = */ "special_function", - /* .arguments = */ R"({"arg1": 1})", - /* .id = */ "0", - }, { - /* .name = */ "special_function_with_opt", - /* .arguments = */ R"({"arg1": 1, "arg2": 2})", - /* .id = */ "1", - }}; - }); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); - // Test response format - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|think|>I need to output the invoice details in JSON<|end|>" - "<|begin|>assistant<|content|>" - R"({"amount": 123.45, "date": "2025-12-03"})"; + tst.test("I'm thinking about the answer\nHello, world!") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(simple_assist_msg("Hello, world!", "I'm thinking about the answer")) + .run(); - t.params.json_schema = invoice_schema; + tst.test( + "\n" + "\n" + "1\n" + "\n" + "") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); - t.expect.reasoning_content = "I need to output the invoice details in JSON"; - t.expect.content =R"({"amount": 123.45, "date": "2025-12-03"})"; - }); + tst.test( + "\n" + "\n" + "1\n" + "\n" + "\n" + "\n" + "\n" + "1\n" + "2\n" + "\n" + "") + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} }, + }) + .run(); - // Test response format no reasoning - test_peg_parser(tmpls.get(), [&](auto & t) { - t.input = "<|content|>" - R"({"amount": 123.45, "date": "2025-12-03"})"; + tst.test( + "\n" + "\n" + "[{\"item\": \"Check stuff\", \"selected\": false}, {\"item\": \"Prepare stuff\", \"selected\": true}]\n" + "\n" + "") + .tools({ + todo_list + }) + .expect_tool_calls({ + { "todo_list", "{\"todos\": [{\"item\": \"Check stuff\", \"selected\": false}, {\"item\": \"Prepare stuff\", \"selected\": true}]}", {} }, + }) + .run(); - t.params.chat_template_kwargs["reasoning_effort"] = "\"low\""; - t.params.json_schema = invoice_schema; + // tool call with inside quotes + tst.test( + "\n" + "\n" + "\n" + "foo.cpp\n" + "\n" + "" + "def foo(arg = \"14\"):\n" + " return arg + \"bar\"\n" + "\n" + "\n" + "" + "def foo(arg = \"15\"):\n" + " pass\n" + "\n" + "\n" + "\n" + "") + .tools({ + edit_tool + }) + .expect_tool_calls({ + { "edit", "{\"filename\": \"foo.cpp\", " + "\"oldString\": \"def foo(arg = \\\"14\\\"):\\n return arg + \\\"bar\\\"\\n\", " + "\"newString\": \"def foo(arg = \\\"15\\\"):\\n pass\\n\"}", {} + } + }) + .run(); + } - t.expect.content =R"({"amount": 123.45, "date": "2025-12-03"})"; - }); + { + // Qwen3-Coder (tool calling with XML-style format) + auto tst = peg_tester("models/templates/Qwen3-Coder.jinja", detailed_debug); + + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + + tst.test( + "\n" + "\n" + "\n" + "1\n" + "\n" + "\n" + "") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + tst.test( + "\n" + "\n" + "\n" + "1\n" + "\n" + "\n" + "\n" + "\n" + "\n" + "\n" + "1\n" + "\n" + "\n" + "2\n" + "\n" + "\n" + "") + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} }, + }) + .run(); + + // Test with code content (multiline) + tst.test( + "\n" + "\n" + "\n" + "def hello():\n" + " print(\"Hello, world!\")\n" + "\n" + "hello()\n" + "\n" + "\n" + "") + .tools({ + python_tool + }) + .expect_tool_calls({ + { "python", "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", {} }, + }) + .run(); + + // Test with code content (asian unicode chars) + tst.test( + "\n" + "\n" + "\n" + "格\n" + "\n" + "\n" + "") + .tools({ + python_tool + }) + .expect_tool_calls({ + { "python", "{\"code\": \"格\"}", {} }, + }) + .run(); + + // Test with HTML tag content + tst.test( + "\n" + "\n" + "\n" + "\n" + " \n" + " Hello!\n" + " \n" + "\n" + "\n" + "\n" + "") + .tools({ + html_tool + }) + .expect_tool_calls({ + { "html", "{\"markup\": \"\\n \\n Hello!\\n \\n\"}", {} }, + }) + .run(); + + // Test with TODO list (array of objects) + tst.test( + "\n" + "\n" + "\n" + "[{\"item\": \"Check stuff\", \"selected\": false}, {\"item\": \"Prepare stuff\", \"selected\": true}]\n" + "\n" + "\n" + "") + .tools({ + todo_list + }) + .expect_tool_calls({ + { "todo_list", "{\"todos\": [{\"item\": \"Check stuff\", \"selected\": false}, {\"item\": \"Prepare stuff\", \"selected\": true}]}", {} }, + }) + .run(); + + // Test flexible optional argument ordering (2 required + 4 optional, reversed optional order) + tst.test( + "\n" + "\n" + "\nhello\n\n" + "\n42\n\n" + "\n100\n\n" + "\n200\n\n" + "\n" + "") + .tools({ tool_2req_4opt }) + .expect_tool_calls({ + { "tool_2req_4opt", R"({"req1": "hello", "req2": 42, "opt4": 100, "opt2": 200})", {} }, + }) + .run(); + + // Test flexible optional argument ordering (2 required + 5 optional, reversed optional order) + tst.test( + "\n" + "\n" + "\nworld\n\n" + "\n7\n\n" + "\nlast\n\n" + "\nmiddle\n\n" + "\nfirst\n\n" + "\n" + "") + .tools({ tool_2req_5opt }) + .expect_tool_calls({ + { "tool_2req_5opt", R"({"req1": "world", "req2": 7, "opt5": "last", "opt3": "middle", "opt1": "first"})", {} }, + }) + .run(); + + // Test flexible optional argument ordering (2 required + 5 optional, all 5 in shuffled order) + tst.test( + "\n" + "\n" + "\ntest\n\n" + "\n99\n\n" + "\nc\n\n" + "\na\n\n" + "\ne\n\n" + "\n4\n\n" + "\n2\n\n" + "\n" + "") + .tools({ tool_2req_5opt }) + .expect_tool_calls({ + { "tool_2req_5opt", R"({"req1": "test", "req2": 99, "opt3": "c", "opt1": "a", "opt5": "e", "opt4": 4, "opt2": 2})", {} }, + }) + .run(); + } + { + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V3.1.jinja", detailed_debug); + tst.test( + "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": " + "\"XYZCITY\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>") + .tools({ get_time_tool }) + .expect(message_with_tool_calls("get_time", "{\"city\":\"XYZCITY\"}")) + .run(); + } + + { + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V3.1.jinja", detailed_debug); + tst.test( + "REASONING<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": " + "\"Tokyo\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ get_time_tool }) + .expect(message_with_tool_calls_and_reasoning("get_time", "{\"city\":\"Tokyo\"}", "REASONING")) + .run(); + } + + { + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V3.1.jinja", detailed_debug); + tst.test( + "REASONINGCONTENT<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{\"city\": " + "\"Paris\"}<｜tool▁call▁end｜><｜tool▁call▁begin｜>get_weather<｜tool▁sep｜>{\"city\": " + "\"Paris\"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>") + .tools({ + get_time_tool, get_weather_tool + }) + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .parallel_tool_calls(true) + .expect(message_with_reasoning_content_and_multiple_tool_calls( + "REASONING", "CONTENT", + { { "get_time", "{\"city\":\"Paris\"}" }, { "get_weather", "{\"city\":\"Paris\"}" } })) + .run(); + } + + { + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V3.1.jinja", detailed_debug); + tst.test("REASONING\nCONTENT") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(simple_assist_msg("CONTENT", "REASONING\n")) + .run(); + } + + { + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V3.1.jinja", detailed_debug); + tst.test("CONTENT").expect(simple_assist_msg("CONTENT", "")).run(); + } + + // GLM-4.6 tests - format: function_name\n...\n...\n + { + auto tst = peg_tester("models/templates/GLM-4.6.jinja", detailed_debug); + tst.test( + "special_function\n" + "arg1\n1\n" + "") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // GLM-4.7-Flash tests - format: function_name...... + // Note: Template uses forced-open thinking mode (prompt ends with ) + { + auto tst = peg_tester("models/templates/GLM-4.7-Flash.jinja", detailed_debug); + + // Pure content (no reasoning) + tst.test("Hello, world!\nWhat's up?") + .enable_thinking(false) + .expect(message_assist) + .run(); + + // Reasoning with content (forced-open mode - input starts after ) + tst.test("I'm\nthinkingHello, world!\nWhat's up?") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(message_assist_thoughts) + .run(); + + // Tool call without reasoning + tst.test( + "special_function" + "arg11" + "") + .enable_thinking(false) + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + // Tool call with reasoning (forced-open mode) + tst.test( + "I'm\nthinking" + "special_function" + "arg11" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect(message_assist_call_thoughts) + .run(); + + tst.test( + "special_function" + "arg11" + "" + "special_function_with_opt" + "arg11" + "arg22" + "") + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} }, + }) + .run(); + } + + // Kimi-K2-Thinking tests - custom parser + // Unique feature: tool call ID embeds function name as functions.: + { + auto tst = peg_tester("models/templates/Kimi-K2-Thinking.jinja", detailed_debug); + + // Basic content only + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + + // Single tool call + tst.test( + "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>" + "{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>") + .tools({ special_function_tool }) + .expect(simple_assist_msg("", "", "special_function", "{\"arg1\": 1}", "functions.special_function:0")) + .run(); + + // Single tool call with reasoning + tst.test( + "I'm thinking about this" + "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>" + "{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(simple_assist_msg("", "I'm thinking about this", "special_function", "{\"arg1\": 1}", "functions.special_function:0")) + .run(); + + // Tool call with content + tst.test( + "Hello, world!\nWhat's up?" + "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>" + "{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>") + .tools({ special_function_tool }) + .expect(simple_assist_msg("Hello, world!\nWhat's up?", "", "special_function", "{\"arg1\": 1}", "functions.special_function:0")) + .run(); + + // Multiple tool calls (parallel) - tests the indexing behavior + tst.test( + "<|tool_calls_section_begin|>" + "<|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|>" + "<|tool_call_begin|>functions.special_function_with_opt:1<|tool_call_argument_begin|>{\"arg1\": 1, \"arg2\": 2}<|tool_call_end|>" + "<|tool_calls_section_end|>") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", "functions.special_function:0" }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", "functions.special_function_with_opt:1" }, + }) + .run(); + + // Multiple tool calls with reasoning + tst.test( + "I need to call two functions" + "<|tool_calls_section_begin|>" + "<|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|>" + "<|tool_call_begin|>functions.python:1<|tool_call_argument_begin|>{\"code\": \"print('hey')\"}<|tool_call_end|>" + "<|tool_calls_section_end|>") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .parallel_tool_calls(true) + .tools({ + special_function_tool, python_tool + }) + .expect_reasoning("I need to call two functions") + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", "functions.special_function:0" }, + { "python", "{\"code\": \"print('hey')\"}", "functions.python:1" }, + }) + .run(); + + // Python tool with multiline code + tst.test( + "<|tool_calls_section_begin|><|tool_call_begin|>functions.python:0<|tool_call_argument_begin|>" + "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}<|tool_call_end|><|tool_calls_section_end|>") + .tools({ python_tool }) + .expect_tool_calls({ + { "python", "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", "functions.python:0" }, + }) + .run(); + + // Tool call with empty arguments + tst.test( + "<|tool_calls_section_begin|><|tool_call_begin|>functions.empty_args:0<|tool_call_argument_begin|>" + "{}<|tool_call_end|><|tool_calls_section_end|>") + .tools({ empty_args_tool }) + .expect(simple_assist_msg("", "", "empty_args", "{}", "functions.empty_args:0")) + .run(); + + // Partial tool call (streaming) + tst.test( + "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>" + "{\"arg1\": ") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .is_partial(true) + .expect(simple_assist_msg("", "", "special_function", "{\"arg1\": ", "functions.special_function:0")) + .run(); + + // Three tool calls to verify counter continues incrementing + tst.test( + "<|tool_calls_section_begin|>" + "<|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|>" + "<|tool_call_begin|>functions.python:1<|tool_call_argument_begin|>{\"code\": \"print(1)\"}<|tool_call_end|>" + "<|tool_call_begin|>functions.html:2<|tool_call_argument_begin|>{\"markup\": \"

test

\"}<|tool_call_end|>" + "<|tool_calls_section_end|>") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .parallel_tool_calls(true) + .tools({ + special_function_tool, python_tool, html_tool + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", "functions.special_function:0" }, + { "python", "{\"code\": \"print(1)\"}", "functions.python:1" }, + { "html", "{\"markup\": \"

test

\"}", "functions.html:2" }, + }) + .run(); + + // Multiple tool calls with reasoning, call *inside thinking block* + tst.test( + "I need to call two functions" + "<|tool_calls_section_begin|>" + "<|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|>" + "<|tool_call_begin|>functions.python:1<|tool_call_argument_begin|>{\"code\": \"print('hey')\"}<|tool_call_end|>" + "<|tool_calls_section_end|>") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .parallel_tool_calls(true) + .tools({ + special_function_tool, python_tool + }) + .expect_reasoning("I need to call two functions") + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", "functions.special_function:0" }, + { "python", "{\"code\": \"print('hey')\"}", "functions.python:1" }, + }) + .run(); + + // Multiple tool calls with reasoning, call *inside thinking block* and *without section markers or end markers + tst.test( + "I need to call two functions" + "<|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}" + "<|tool_call_begin|>functions.python:1<|tool_call_argument_begin|>{\"code\": \"print('hey')\"}") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .parallel_tool_calls(true) + .tools({ + special_function_tool, python_tool + }) + .expect_reasoning("I need to call two functions") + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", "functions.special_function:0" }, + { "python", "{\"code\": \"print('hey')\"}", "functions.python:1" }, + }) + .run(); + + // Real life test - execute_command + tst.test("<|tool_call_begin|>functions.execute_command:0<|tool_call_argument_begin|>{\"command\": \"ls -lah\"" + ", \"cwd\": \"/home/jarvis/development/exllamav3\", \"timeout\": 10}") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .parallel_tool_calls(true) + .tools({ + { + /* .name = */ "execute_command", + /* .description = */ "Execute shell command", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "command": { + "type": "string", + "description": "Shell command to execute" + }, + "cwd": { + "type": "string", + "description": "Working directory" + }, + "timeout": { + "type": "integer", + "description": "The timeout in seconds" + } + }, + "required": ["command"] + })" + } + }). + expect_tool_calls({ + { + "execute_command", + R"({"command": "ls -lah", "cwd": "/home/jarvis/development/exllamav3", "timeout": 10})", + "functions.execute_command:0" + } + }) + .run(); + } + + { + auto kimi_id_special_func_tool_call = + simple_assist_msg("", "", "special_function", "{\"arg1\": 1}", "functions.special_function:0"); + + // Kimi-K2 old template + auto tst = peg_tester("models/templates/moonshotai-Kimi-K2.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test( + "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>" + "{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>") + .tools({ special_function_tool }) + .expect(kimi_id_special_func_tool_call) + .run(); + + // Kimi-K2-Instruct + auto tst2 = peg_tester("models/templates/Kimi-K2-Instruct.jinja", detailed_debug); + tst2.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst2.test( + "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>" + "{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>") + .tools({ special_function_tool }) + .expect(kimi_id_special_func_tool_call) + .run(); + } + + // LFM2-8B-A1B tests - uses <|tool_list_start|>/<|tool_list_end|> and <|tool_call_start|>[name(args)]<|tool_call_end|> + { + auto tst = peg_tester("models/templates/LFM2-8B-A1B.jinja", detailed_debug); + + // Basic content only + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + + // Single tool call without reasoning + tst.test("<|tool_call_start|>[special_function(arg1=1)]<|tool_call_end|>") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + // Tool call with string argument + tst.test("<|tool_call_start|>[get_time(city=\"XYZCITY\")]<|tool_call_end|>") + .tools({ get_time_tool }) + .expect(message_with_tool_calls("get_time", "{\"city\":\"XYZCITY\"}")) + .run(); + + // Tool call with reasoning (enable_thinking=true) + tst.test("I'm\nthinking<|tool_call_start|>[special_function(arg1=1)]<|tool_call_end|>") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call_thoughts) + .run(); + + // Multiple tool calls (parallel) + tst.test("<|tool_call_start|>[special_function(arg1=1), special_function_with_opt(arg1=1, arg2=2)]<|tool_call_end|>") + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} }, + }) + .run(); + + // Tool call with reasoning and content + tst.test("I need to call a function" + "Let me check the time.<|tool_call_start|>[get_time(city=\"Paris\")]<|tool_call_end|>") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ get_time_tool }) + .expect(message_with_reasoning_content_and_multiple_tool_calls( + "I need to call a function", "Let me check the time.", { { "get_time", "{\"city\":\"Paris\"}" } } + )) + .run(); + + // Python tool with multiline code in string + tst.test("<|tool_call_start|>[python(code=\"def hello():\\n print('hey')\")]<|tool_call_end|>") + .tools({ python_tool }) + .expect_tool_calls({ + { "python", R"#({"code": "def hello():\\n print('hey')"})#", "" } + }) + .run(); + + // Partial tool call (streaming) + tst.test("<|tool_call_start|>[special_function(arg1=") + .tools({ special_function_tool }) + .is_partial(true) + .expect(simple_assist_msg("", "", "special_function", "{\"arg1\": ")) + .run(); + + // Tool call with empty arguments + tst.test("<|tool_call_start|>[empty_args()]<|tool_call_end|>") + .tools({ empty_args_tool }) + .expect(simple_assist_msg("", "", "empty_args", "{}")) + .run(); + } + + // Apertus-8B-Instruct tests - FUNC_NAME_AS_KEY format + // Format: <|tools_prefix|>[{"function_name": {...arguments...}}]<|tools_suffix|> + { + auto tst = peg_tester("models/templates/Apertus-8B-Instruct.jinja", detailed_debug); + tst.test("<|tools_prefix|>[{\"special_function\": {\"arg1\": 1}}]<|tools_suffix|>") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // MiniMax-M2 tests - XML invoke format with parameter tags + // Format: value + { + auto tst = peg_tester("models/templates/MiniMax-M2.jinja", detailed_debug); + tst.test( + "\n\n1\n\n") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // NVIDIA-Nemotron-Nano-v2 tests - ... format + // Format: [{"name": "func", "arguments": {...}}] + { + auto tst = peg_tester("models/templates/NVIDIA-Nemotron-Nano-v2.jinja", detailed_debug); + tst.test("[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // CohereForAI-c4ai-command-r7b (uses START_RESPONSE/END_RESPONSE, START_THINKING/END_THINKING, START_ACTION/END_ACTION) + { + auto tst = peg_tester("models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja", detailed_debug); + tst.test("<|START_RESPONSE|>Hello, world!\nWhat's up?<|END_RESPONSE|>").expect(message_assist).run(); + tst.test( + "<|START_THINKING|>I'm\nthinking<|END_THINKING|>" + "<|START_ACTION|>[\n" + " {\"tool_call_id\": \"0\", \"tool_name\": \"special_function\", \"parameters\": {\"arg1\": 1}}\n" + "]<|END_ACTION|>") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect(message_assist_thoughts_call_idx) + .run(); + } + // CohereForAI-c4ai-command-r-plus (uses markdown code block format) + { + auto tst = peg_tester("models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja", detailed_debug); + tst.test("<|CHATBOT_TOKEN|>Hello, world!\nWhat's up?<|END_OF_TURN_TOKEN|>").expect(message_assist).run(); + // Tool calls: Action: followed by JSON code block + tst.test( + "Action:\n" + "```json\n" + "[{\"tool_name\": \"special_function\", \"parameters\": {\"arg1\": 1}}]\n" + "```") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // mistralai-Mistral-Nemo-Instruct-2407.jinja + { + auto tst = peg_tester("models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test("[TOOL_CALLS][{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}, \"id\": \"123456789\"}]") + .tools({ special_function_tool }) + .expect(message_assist_call_id) + .run(); + } + { + auto tst = peg_tester("models/templates/meetkai-functionary-medium-v3.1.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test("{\"arg1\": 1}") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + // Functionary v3.2 - recipient-based format: >>>recipient\n{content} + { + auto tst = peg_tester("models/templates/meetkai-functionary-medium-v3.2.jinja", detailed_debug); + tst.test(">>>all\nHello, world!\nWhat's up?").expect(message_assist).run(); + tst.test(">>>special_function\n{\"arg1\": 1}") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // FireFunction + { + auto tst = peg_tester("models/templates/fireworks-ai-llama-3-firefunction-v2.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test(" functools[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // DeepSeek R1 Distill Llama 8B - reasoning tests only (forced open thinking) + // Note: Template uses forced-open mode (prompt ends with ), so input shouldn't include opening tag + { + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?") + .enable_thinking(true) // Forced open + .expect(message_assist) + .run(); + tst.test("I'm\nthinkingHello, world!\nWhat's up?") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(message_assist_thoughts) + .run(); + } + // llama-cpp DeepSeek R1 template (always forced-open thinking) + { + auto tst = peg_tester("models/templates/llama-cpp-deepseek-r1.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test("I'm\nthinkingHello, world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(message_assist_thoughts) + .run(); + tst.test( + "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n" + "```json\n{\"arg1\": 1}```<｜tool▁call▁end｜><｜tool▁calls▁end｜>") + .tools({ special_function_tool }) + .parallel_tool_calls(true) + .expect(message_assist_call) + .run(); + } + // DeepSeek R1 Distill Qwen 32B - reasoning tests only (forced open thinking) + // Note: Template uses forced-open mode (prompt ends with ), so input shouldn't include opening tag + { + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").enable_thinking(true).expect(message_assist).run(); + tst.test("I'm\nthinkingHello, world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(message_assist_thoughts) + .run(); + tst.test( + "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n" + "```json\n{\"arg1\": 1}```<｜tool▁call▁end｜><｜tool▁calls▁end｜>") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // MiMo-VL / Hermes 3 / Qwen 2.5 (Common JSON format) + for (const auto & path : + { "models/templates/MiMo-VL.jinja", "models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja", + "models/templates/Qwen-Qwen2.5-7B-Instruct.jinja" }) { + auto tst = peg_tester(path, detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test("\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}\n") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // Apriel 1.5 + { + auto tst = peg_tester("models/templates/unsloth-Apriel-1.5.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test("[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // Apriel 1.6 Thinker (reasoning-only support) + { + auto tst = peg_tester("models/templates/Apriel-1.6-15b-Thinker-fixed.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + + // Implicit reasoning start (forced open) + tst.test("I'm\nthinking\n[BEGIN FINAL RESPONSE]\nHello, world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect(message_assist_thoughts) + .run(); + + // Reasoning + Tool calls + tst.test( + "I'm\nthinking\n[BEGIN FINAL RESPONSE]\n[{\"name\": \"special_function\", \"arguments\": " + "{\"arg1\": 1}}]") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call_thoughts) + .run(); + } + + // Mistral Small 3.2 - FUNC_BRACKET_TAG format: [TOOL_CALLS]func_name[CALL_ID]id[ARGS]{...} + { + auto tst = peg_tester("models/templates/Mistral-Small-3.2-24B-Instruct-2506.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test("[TOOL_CALLS]special_function[CALL_ID]123456789[ARGS]{\"arg1\": 1}") + .tools({ special_function_tool }) + .expect(message_assist_call_id) + .run(); + } + // Devstral + { + auto tst = peg_tester("models/templates/unsloth-mistral-Devstral-Small-2507.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test("[TOOL_CALLS]special_function[ARGS]{\"arg1\": 1}") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + tst.test("Hello, world!\nWhat's up?[TOOL_CALLS]special_function[ARGS]{\"arg1\": 1}") + .tools({ special_function_tool }) + .expect(message_assist_call_content) + .run(); + } + + { + // Llama 3.1 + auto tst = peg_tester("models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").tools({ special_function_tool }).expect(message_assist).run(); + } + + { + // Llama 3.2 + auto tst = peg_tester("models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").tools({ special_function_tool }).expect(message_assist).run(); + } + + { + // Llama 3.3 + auto tst = peg_tester("models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").tools({ python_tool }).expect(message_assist).run(); + } + + // GPT-OSS format tests + { + auto tst = peg_tester("models/templates/openai-gpt-oss-120b.jinja", detailed_debug); + + // Basic content only - final channel + tst.test("<|channel|>final<|message|>Hello, world!\nWhat's up?").expect(message_assist).run(); + + // Basic content only - commentary channel + tst.test("<|channel|>commentary<|message|>Hello, world!\nWhat's up?").expect(message_assist).run(); + + // Analysis channel (reasoning) with final channel (content) + tst.test( + "<|channel|>analysis<|message|>I'm\nthinking<|end|>\n<|channel|>final<|message|>Hello, world!\nWhat's " + "up?") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect(message_assist_thoughts) + .run(); + + // Analysis channel only (partial) - still works when reasoning format is set + tst.test("<|channel|>analysis<|message|>I'm\nthinking") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .is_partial(true) + .expect_reasoning("I'm\nthinking") + .run(); + + // Reasoning format none - reasoning stays in content + tst.test( + "<|channel|>analysis<|message|>I'm\nthinking<|end|>\n<|channel|>final<|message|>Hello, world!\nWhat's " + "up?") + .reasoning_format(COMMON_REASONING_FORMAT_NONE) + .expect_content( + "<|channel|>analysis<|message|>I'm\nthinking<|end|>Hello, world!\nWhat's up?") + .run(); + + // Tool call with recipient in role header: " to=functions.NAME<|channel|>analysis<|message|>JSON" + tst.test(" to=functions.special_function<|channel|>analysis<|message|>{\"arg1\": 1}") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + // Tool call with recipient in channel header: "<|channel|>analysis to=functions.NAME<|message|>JSON" + tst.test("<|channel|>analysis to=functions.special_function<|message|>{\"arg1\": 1}") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + // Tool call with constraint: " to=functions.NAME<|channel|>analysis <|constrain|>json<|message|>JSON" + tst.test(" to=functions.special_function<|channel|>analysis <|constrain|>json<|message|>{\"arg1\": 1}") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + // Tool call in commentary channel (channel header variant) + tst.test("<|channel|>commentary to=functions.special_function<|message|>{\"arg1\": 1}") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + // Tool call with reasoning + content (analysis first, then tool call) + tst.test( + "<|channel|>analysis<|message|>I'm\nthinking<|end|>\n" + "<|start|>assistant to=functions.special_function<|channel|>analysis<|message|>{\"arg1\": 1}") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call_thoughts) + .run(); + + // Tool calling with extra channel before + tst.test( + "<|channel|>analysis<|message|>I'm\nthinking<|end|><|start|>assistant<|channel|>commentary" + " to=functions.special_function <|message|>{\"arg1\": 1}") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call_thoughts) + .run(); + + // Reasoning after final channel + // Tool calling after final channel + tst.test( + "<|channel|>final<|message|><|end|>" + "<|start|>assistant<|channel|>analysis<|message|>Thinking about edit..." + ) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect_reasoning("Thinking about edit...") + .expect_content("") + .run(); + + // Tool calling after final channel + tst.test( + "<|channel|>final<|message|><|end|>" + "<|start|>assistant<|channel|>analysis<|message|>Thinking about edit...<|end|>" + "<|start|>assistant<|channel|>commentary to=functions.edit <|constrain|>json" + "<|message|>{\"oldString\": \"if (part < railCount - 1) {\", \"newString\": \"if (part < 4) {\", \"replaceAll\": false}" + ) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ + { + /* .name = */ "edit", + /* .description = */ "Edit a file", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "oldString": { + "type": "string", + "description": "Old string to replace." + }, + "newString": { + "type": "string", + "description": "New replacement string." + }, + "replaceAll": { + "type": "boolean", + "description": "Whether to replace all occurences." + } + }, + "required": ["oldString", "newString"] + })", + } + }) + .expect_reasoning("Thinking about edit...") + .expect_tool_calls({ + { "edit", R"({"oldString": "if (part < railCount - 1) {", "newString": "if (part < 4) {", "replaceAll": false})", {} } + }) + .run(); + + // Parallel tool calls + tst.test( + " to=functions.special_function<|channel|>analysis<|message|>{\"arg1\": 1}\n" + "<|start|>assistant to=functions.special_function_with_opt<|channel|>analysis<|message|>{\"arg1\": 1, " + "\"arg2\": 2}") + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} }, + }) + .run(); + } + + { + auto tst = peg_tester("models/templates/StepFun3.5-Flash.jinja", detailed_debug); + tst.test("I was thinking\nNow I'm not."). + enable_thinking(true). + reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK). + expect_reasoning("I was thinking"). + expect_content("Now I'm not.") + .run(); + + // Test that numeric-looking string values are coerced to strings per the schema + tst.test( + "Let me call the magic tool\n" + "\n" + "\n" + "\n" + "\nfooBar\n\n" + "\n5123123\n\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ magic_tool }) + .expect_reasoning("Let me call the magic tool") + .expect_tool_calls({ + { "magic", R"({"name": "fooBar", "ref": "5123123"})", {} }, + }) + .run(); + + // Test that numeric values are correctly interpreted as numbers when schema calls for number + tst.test( + "Let me call the special function\n" + "\n" + "\n" + "\n" + "\n42555916\n\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect_reasoning("Let me call the special function") + .expect_tool_calls({ + { "special_function", R"({"arg1": 42555916})", {} }, + }) + .run(); + + tst.test( + "Let me call the special function with opt\n" + "\n" + "\n" + "\n" + "\n42555916\n\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool_with_optional_param }) + .expect_reasoning("Let me call the special function with opt") + .expect_tool_calls({ + { "special_function_with_opt", R"({"arg1": 42555916})", {} }, + }) + .run(); + + tst.test( + "Let me call the magic_int function\n" + "\n" + "\n" + "\n" + "\n42555916\n\n" + "\nbaz\n\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ magic_int_tool }) + .expect_reasoning("Let me call the magic_int function") + .expect_tool_calls({ + { "magic_int", R"({"ref": 42555916, "name": "baz"})", {} }, + }) + .run(); + + tst.test( + "Call string_param with empty text\n" + "\n" + "\n" + "\n" + "\n\n\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ string_param_tool }) + .expect_reasoning("Call string_param with empty text") + .expect_tool_calls({ + { "string_param", R"({"text": ""})", {} }, + }) + .run(); + + tst.test( + "Test simple quoted unquoted\n" + "\n" + "\n" + "\n" + "\n\"foo\"\n\n" + "\nfoo\n\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ quoted_unquoted_tool }) + .expect_reasoning("Test simple quoted unquoted") + .expect_tool_calls({ + { "quoted_unquoted", R"({"quoted": "\"foo\"", "unquoted": "foo"})", {} }, + }) + .run(); + + tst.test( + "Test complex quoted unquoted\n" + "\n" + "\n" + "\n" + "\n\"printf(\\\"foo\\\");\"\n\n" + "\nprintf(\"foo\");\n\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ quoted_unquoted_tool }) + .expect_reasoning("Test complex quoted unquoted") + .expect_tool_calls({ + { "quoted_unquoted", R"({ "quoted" : "\"printf(\\\"foo\\\");\"", "unquoted": "printf(\"foo\");" })", {} } + }) + .run(); + + tst.test( + "Test negative number\n" + "\n" + "\n" + "\n" + "\n-14\n\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ magic_int_tool }) + .expect_reasoning("Test negative number") + .expect_tool_calls({ + { "magic_int", R"({ "ref" : -14 })", {} } + }) + .run(); + + tst.test( + "Test decimal number\n" + "\n" + "\n" + "\n" + "\n3.14\n\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ amount_tool }) + .expect_reasoning("Test decimal number") + .expect_tool_calls({ + { "amount", R"({ "orig" : 3.14 })", {} } + }) + .run(); + + tst.test( + "Test imaginary number\n" + "\n" + "\n" + "\n" + "\n" + "{ \"real\": 3.14, \"imaginary\": 2.71 }\n" + "\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ imaginary_number_tool }) + .expect_reasoning("Test imaginary number") + .expect_tool_calls({ + { "imaginary_number", R"({ "number" : {"real":3.14,"imaginary":2.71 } })", {} } + }) + .run(); + + } + + // GigaChat V3 + { + auto tst = peg_tester("models/templates/GigaChat3-10B-A1.8B.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test("<|message_sep|>\n\nfunction call<|role_sep|>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + tst.test( + "Hello, world!\nWhat's up?" + "<|message_sep|>\n\nfunction call<|role_sep|>\n{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}" + ) + .tools({ special_function_tool }) + .expect(message_assist_call_content) + .run(); + } + + // GigaChat V3.1 + { + auto tst = peg_tester("models/templates/GigaChat3.1-10B-A1.8B.jinja", detailed_debug); + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + tst.test("<|function_call|>{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + tst.test( + "Hello, world!\nWhat's up?" + "<|function_call|>{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}" + ) + .tools({ special_function_tool }) + .expect(message_assist_call_content) + .run(); + } +} + +// Test the developer role to system workaround with a simple mock template +static void test_developer_role_to_system_workaround() { + LOG_DBG("%s\n", __func__); + + // Simple mock template that supports system role + const std::string mock_template = + "{%- for message in messages -%}\n" + " {{- '<|' + message.role + '|>' + message.content + '<|end|>' -}}\n" + "{%- endfor -%}\n" + "{%- if add_generation_prompt -%}\n" + " {{- '<|assistant|>' -}}\n" + "{%- endif -%}"; + + auto tmpls = common_chat_templates_ptr(common_chat_templates_init(/* model= */ nullptr, mock_template)); + + // Test case 1: Developer message - should be changed to system + // After simplification we only test this case + { + common_chat_templates_inputs inputs; + common_chat_msg developer_msg; + developer_msg.role = "developer"; + developer_msg.content = "You are a helpful developer assistant."; + inputs.messages = { developer_msg }; + inputs.add_generation_prompt = false; + + auto params = common_chat_templates_apply(tmpls.get(), inputs); + + // The developer role should have been changed to system + if (params.prompt.find("<|developer|>") != std::string::npos) { + throw std::runtime_error("Test failed: developer role was not changed to system"); + } + if (params.prompt.find("<|system|>You are a helpful developer assistant.<|end|>") == std::string::npos) { + throw std::runtime_error("Test failed: system message not found in output"); + } + LOG_ERR("Test 1 passed: developer role changed to system\n"); } } static void test_msg_diffs_compute() { - printf("[%s]\n", __func__); + LOG_DBG("%s\n", __func__); { common_chat_msg msg1; @@ -3759,9 +2852,7 @@ static void test_msg_diffs_compute() { common_chat_msg_diff diff; diff.content_delta = "Hello, world!"; - assert_equals( - {diff}, - common_chat_msg_diff::compute_diffs(msg1, msg2)); + assert_equals({ diff }, common_chat_msg_diff::compute_diffs(msg1, msg2)); } { common_chat_msg msg1; @@ -3773,37 +2864,35 @@ static void test_msg_diffs_compute() { common_chat_msg_diff diff; diff.content_delta = " world!"; - assert_equals( - {diff}, - common_chat_msg_diff::compute_diffs(msg1, msg2)); + assert_equals({ diff }, common_chat_msg_diff::compute_diffs(msg1, msg2)); } { common_chat_msg msg0; common_chat_msg msg1; - msg1.tool_calls = { { "special_function", "{\"ar", /* .id = */ "123" } }; + msg1.tool_calls = { + { "special_function", "{\"ar", /* .id = */ "123" } + }; common_chat_msg msg2; - msg2.tool_calls = { { "special_function", "{\"arg1\": 1}", /* .id = */ "123" } }; + msg2.tool_calls = { + { "special_function", "{\"arg1\": 1}", /* .id = */ "123" } + }; common_chat_msg_diff diff01; - diff01.tool_call_index = 0; - diff01.tool_call_delta.name = "special_function"; - diff01.tool_call_delta.id = "123"; + diff01.tool_call_index = 0; + diff01.tool_call_delta.name = "special_function"; + diff01.tool_call_delta.id = "123"; diff01.tool_call_delta.arguments = "{\"ar"; - assert_equals( - {diff01}, - common_chat_msg_diff::compute_diffs(msg0, msg1)); + assert_equals({ diff01 }, common_chat_msg_diff::compute_diffs(msg0, msg1)); common_chat_msg_diff diff12; - diff12.tool_call_index = 0; + diff12.tool_call_index = 0; // Note: neither id nor name change here. diff12.tool_call_delta.arguments = "g1\": 1}"; - assert_equals( - {diff12}, - common_chat_msg_diff::compute_diffs(msg1, msg2)); + assert_equals({ diff12 }, common_chat_msg_diff::compute_diffs(msg1, msg2)); } { common_chat_msg msg0; @@ -3815,68 +2904,82 @@ static void test_msg_diffs_compute() { }; common_chat_msg_diff diff1; - diff1.tool_call_index = 0; - diff1.tool_call_delta.name = "f1"; - diff1.tool_call_delta.id = "123"; + diff1.tool_call_index = 0; + diff1.tool_call_delta.name = "f1"; + diff1.tool_call_delta.id = "123"; diff1.tool_call_delta.arguments = "{\"arg1\": 1}"; common_chat_msg_diff diff2; - diff2.tool_call_index = 1; - diff2.tool_call_delta.name = "f2"; - diff2.tool_call_delta.id = "222"; + diff2.tool_call_index = 1; + diff2.tool_call_delta.name = "f2"; + diff2.tool_call_delta.id = "222"; diff2.tool_call_delta.arguments = "{\"arg2\": 2}"; - assert_equals( - {diff1, diff2}, - common_chat_msg_diff::compute_diffs(msg0, msg2)); + assert_equals({ diff1, diff2 }, common_chat_msg_diff::compute_diffs(msg0, msg2)); } } int main(int argc, char ** argv) { - common_log_set_verbosity_thold(999); + bool detailed_debug = false; + bool only_run_filtered = false; - // try { -#ifndef _WIN32 - if (argc > 1) { - common_chat_templates_inputs inputs; - common_chat_msg msg; - msg.role = "user"; - msg.content = "Hey"; - inputs.messages = {msg}; - inputs.tools = { special_function_tool }; - - std::cout << "| Template | Format |\n"; - std::cout << "|----------|--------|\n"; - - for (int i = 1; i < argc; i++) { - try { - std::string path = argv[i]; - if (path.rfind(".jinja") != path.size() - 6) { - std::cerr << "Skipping non-jinja file: " << path << '\n'; - continue; - } - auto tmpls = read_templates(path); - auto parts = string_split(path, "/"); - auto name = parts[parts.size() - 1]; - auto format = common_chat_format_name(common_chat_templates_apply(tmpls.get(), inputs).format); - std::cout << "| " << name << " | " << format << " |\n"; - } catch (const std::exception & e) { - std::cerr << "Failed to process " << argv[i] << ": " << e.what() << '\n'; - } - } - } else -#endif - { - test_msg_diffs_compute(); - test_msgs_oaicompat_json_conversion(); - test_tools_oaicompat_json_conversion(); - test_template_output_parsers(); - test_template_output_peg_parsers(); - std::cout << "\n[chat] All tests passed!" << '\n'; + // Check for --template flag + for (int i = 1; i < argc; i++) { + std::string arg = argv[i]; + if (arg == "--template" && i + 1 < argc) { + g_template_filter = argv[++i]; + // Only run PEG parser tests with the filter + only_run_filtered = true; } + if (arg == "--detailed") { + detailed_debug = true; + common_log_set_verbosity_thold(999); + } + } + + if (only_run_filtered) { + test_template_output_peg_parsers(detailed_debug); + std::cout << "\n[chat] All template tests passed!" << '\n'; return 0; - // } catch (const std::exception & e) { - // std::cerr << "Error: " << e.what() << '\n'; - // return 1; - // } + } + +#ifndef _WIN32 + if (argc > 1) { + common_chat_templates_inputs inputs; + common_chat_msg msg; + msg.role = "user"; + msg.content = "Hey"; + inputs.messages = { msg }; + inputs.tools = { special_function_tool }; + + std::cout << "| Template | Format |\n"; + std::cout << "|----------|--------|\n"; + + for (int i = 1; i < argc; i++) { + try { + std::string path = argv[i]; + if (path.rfind(".jinja") != path.size() - 6) { + std::cerr << "Skipping non-jinja file: " << path << '\n'; + continue; + } + auto tmpls = read_templates(path); + auto parts = string_split(path, "/"); + const auto & name = parts[parts.size() - 1]; + const auto * format = common_chat_format_name(common_chat_templates_apply(tmpls.get(), inputs).format); + std::cout << "| " << name << " | " << format << " |\n"; + } catch (const std::exception & e) { + std::cerr << "Failed to process " << argv[i] << ": " << e.what() << '\n'; + } + } + } else +#endif + { + test_msg_diffs_compute(); + test_msgs_oaicompat_json_conversion(); + test_tools_oaicompat_json_conversion(); + test_developer_role_to_system_workaround(); + test_template_output_peg_parsers(detailed_debug); + std::cout << "\n[chat] All tests passed!" << '\n'; + } + return 0; } diff --git a/tests/test-gguf-model-data.cpp b/tests/test-gguf-model-data.cpp new file mode 100644 index 0000000000..cc0174961d --- /dev/null +++ b/tests/test-gguf-model-data.cpp @@ -0,0 +1,121 @@ +#include "gguf-model-data.h" + +#include + +#define TEST_ASSERT(cond, msg) \ + do { \ + if (!(cond)) { \ + fprintf(stderr, "FAIL: %s (line %d): %s\n", #cond, __LINE__, msg); \ + return 1; \ + } \ + } while (0) + +int main() { + fprintf(stderr, "=== test-gguf-model-data ===\n"); + + // Fetch Qwen3-0.6B Q8_0 metadata + auto result = gguf_fetch_model_meta("ggml-org/Qwen3-0.6B-GGUF", "Q8_0"); + + if (!result.has_value()) { + fprintf(stderr, "SKIP: could not fetch model metadata (no network or HTTP disabled)\n"); + return 0; + } + + const auto & model = result.value(); + + fprintf(stderr, "Architecture: %s\n", model.architecture.c_str()); + fprintf(stderr, "n_embd: %u\n", model.n_embd); + fprintf(stderr, "n_ff: %u\n", model.n_ff); + fprintf(stderr, "n_vocab: %u\n", model.n_vocab); + fprintf(stderr, "n_layer: %u\n", model.n_layer); + fprintf(stderr, "n_head: %u\n", model.n_head); + fprintf(stderr, "n_head_kv: %u\n", model.n_head_kv); + fprintf(stderr, "n_expert: %u\n", model.n_expert); + fprintf(stderr, "n_embd_head_k: %u\n", model.n_embd_head_k); + fprintf(stderr, "n_embd_head_v: %u\n", model.n_embd_head_v); + fprintf(stderr, "tensors: %zu\n", model.tensors.size()); + + // Verify architecture + TEST_ASSERT(model.architecture == "qwen3", "expected architecture 'qwen3'"); + + // Verify key dimensions (Qwen3-0.6B) + TEST_ASSERT(model.n_layer == 28, "expected n_layer == 28"); + TEST_ASSERT(model.n_embd == 1024, "expected n_embd == 1024"); + TEST_ASSERT(model.n_head == 16, "expected n_head == 16"); + TEST_ASSERT(model.n_head_kv == 8, "expected n_head_kv == 8"); + TEST_ASSERT(model.n_expert == 0, "expected n_expert == 0 (not MoE)"); + TEST_ASSERT(model.n_vocab == 151936, "expected n_vocab == 151936"); + + // Verify tensor count + TEST_ASSERT(model.tensors.size() == 311, "expected tensor count == 311"); + + // Verify known tensor names exist + bool found_attn_q = false; + bool found_token_embd = false; + bool found_output_norm = false; + for (const auto & t : model.tensors) { + if (t.name == "blk.0.attn_q.weight") { + found_attn_q = true; + } + if (t.name == "token_embd.weight") { + found_token_embd = true; + } + if (t.name == "output_norm.weight") { + found_output_norm = true; + } + } + TEST_ASSERT(found_attn_q, "expected tensor 'blk.0.attn_q.weight'"); + TEST_ASSERT(found_token_embd, "expected tensor 'token_embd.weight'"); + TEST_ASSERT(found_output_norm, "expected tensor 'output_norm.weight'"); + + // Verify token_embd.weight shape + for (const auto & t : model.tensors) { + if (t.name == "token_embd.weight") { + TEST_ASSERT(t.ne[0] == 1024, "expected token_embd.weight ne[0] == 1024"); + TEST_ASSERT(t.n_dims == 2, "expected token_embd.weight to be 2D"); + break; + } + } + + // Test that second call uses cache (just call again, it should work) + auto result2 = gguf_fetch_model_meta("ggml-org/Qwen3-0.6B-GGUF", "Q8_0"); + TEST_ASSERT(result2.has_value(), "cached fetch should succeed"); + TEST_ASSERT(result2->tensors.size() == model.tensors.size(), "cached result should match"); + + // Test a split MoE model without specifying quant (should default to Q8_0) + auto result3 = gguf_fetch_model_meta("ggml-org/GLM-4.6V-GGUF"); + if (!result3.has_value()) { + fprintf(stderr, "SKIP: could not fetch GLM-4.6V metadata (no network?)\n"); + return 0; + } + const auto & model3 = result3.value(); + + fprintf(stderr, "Architecture: %s\n", model3.architecture.c_str()); + fprintf(stderr, "n_embd: %u\n", model3.n_embd); + fprintf(stderr, "n_ff: %u\n", model3.n_ff); + fprintf(stderr, "n_vocab: %u\n", model3.n_vocab); + fprintf(stderr, "n_layer: %u\n", model3.n_layer); + fprintf(stderr, "n_head: %u\n", model3.n_head); + fprintf(stderr, "n_head_kv: %u\n", model3.n_head_kv); + fprintf(stderr, "n_expert: %u\n", model3.n_expert); + fprintf(stderr, "n_embd_head_k: %u\n", model3.n_embd_head_k); + fprintf(stderr, "n_embd_head_v: %u\n", model3.n_embd_head_v); + fprintf(stderr, "tensors: %zu\n", model3.tensors.size()); + + // Verify architecture + TEST_ASSERT(model3.architecture == "glm4moe", "expected architecture 'glm4moe'"); + + // Verify key dimensions (GLM-4.6V) + TEST_ASSERT(model3.n_layer == 46, "expected n_layer == 46"); + TEST_ASSERT(model3.n_embd == 4096, "expected n_embd == 4096"); + TEST_ASSERT(model3.n_head == 96, "expected n_head == 96"); + TEST_ASSERT(model3.n_head_kv == 8, "expected n_head_kv == 8"); + TEST_ASSERT(model3.n_expert == 128, "expected n_expert == 128 (MoE)"); + TEST_ASSERT(model3.n_vocab == 151552, "expected n_vocab == 151552"); + + // Verify tensor count + TEST_ASSERT(model3.tensors.size() == 780, "expected tensor count == 780"); + + fprintf(stderr, "=== ALL TESTS PASSED ===\n"); + return 0; +} diff --git a/tests/test-gguf.cpp b/tests/test-gguf.cpp index 84b7f3bc49..8ebd16ba82 100644 --- a/tests/test-gguf.cpp +++ b/tests/test-gguf.cpp @@ -48,6 +48,7 @@ enum handcrafted_file_type { HANDCRAFTED_DATA_NOT_ENOUGH_DATA = 10 + offset_has_data, HANDCRAFTED_DATA_BAD_ALIGN = 15 + offset_has_data, HANDCRAFTED_DATA_INCONSISTENT_ALIGN = 20 + offset_has_data, + HANDCRAFTED_DATA_MEM_SIZE_OVERFLOW = 30 + offset_has_data, HANDCRAFTED_DATA_SUCCESS = 800 + offset_has_data, HANDCRAFTED_DATA_CUSTOM_ALIGN = 810 + offset_has_data, }; @@ -84,6 +85,7 @@ static std::string handcrafted_file_type_name(const enum handcrafted_file_type h case HANDCRAFTED_DATA_NOT_ENOUGH_DATA: return "DATA_NOT_ENOUGH_DATA"; case HANDCRAFTED_DATA_BAD_ALIGN: return "DATA_BAD_ALIGN"; case HANDCRAFTED_DATA_INCONSISTENT_ALIGN: return "DATA_INCONSISTENT_ALIGN"; + case HANDCRAFTED_DATA_MEM_SIZE_OVERFLOW: return "DATA_MEM_SIZE_OVERFLOW"; case HANDCRAFTED_DATA_SUCCESS: return "DATA_SUCCESS"; case HANDCRAFTED_DATA_CUSTOM_ALIGN: return "DATA_CUSTOM_ALIGN"; } @@ -196,6 +198,13 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft tensor_configs = get_tensor_configs(rng); } + if (hft == HANDCRAFTED_DATA_MEM_SIZE_OVERFLOW) { + tensor_configs.resize(2); + + tensor_configs[0] = { GGML_TYPE_I8, { 0x7FFFFFFFFFFFFFC0, 1, 1, 1 } }; + tensor_configs[1] = { GGML_TYPE_I8, { 0x7FFFFFFFFFFFFFC0, 1, 1, 1 } }; + } + if (hft == HANDCRAFTED_HEADER_BAD_N_TENSORS) { const uint64_t n_tensors = -1; helper_write(file, n_tensors); @@ -397,7 +406,8 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft for (uint32_t i = 1; i < n_dims; ++i) { ne *= shape[i]; } - offset += GGML_PAD(ggml_row_size(type, ne), alignment); + + offset += GGML_PAD(ggml_row_size(type, ne), (uint64_t) alignment); } while (ftell(file) % alignment != 0) { @@ -411,6 +421,9 @@ static FILE * get_handcrafted_file(const unsigned int seed, const enum handcraft if (hft == HANDCRAFTED_DATA_NOT_ENOUGH_DATA) { nbytes -= 1; } + if (hft == HANDCRAFTED_DATA_MEM_SIZE_OVERFLOW) { + nbytes = 32; + } for (uint64_t i = 0; i < nbytes; ++i) { const uint8_t random_byte = i % 256; helper_write(file, random_byte); @@ -704,6 +717,7 @@ static std::pair test_handcrafted_file(const unsigned int seed) { HANDCRAFTED_DATA_NOT_ENOUGH_DATA, HANDCRAFTED_DATA_BAD_ALIGN, HANDCRAFTED_DATA_INCONSISTENT_ALIGN, + HANDCRAFTED_DATA_MEM_SIZE_OVERFLOW, HANDCRAFTED_DATA_SUCCESS, HANDCRAFTED_DATA_CUSTOM_ALIGN, }; diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp index 7aa7e58a5c..526470a224 100644 --- a/tests/test-grammar-integration.cpp +++ b/tests/test-grammar-integration.cpp @@ -15,8 +15,12 @@ using json = nlohmann::ordered_json; +static llama_grammar * build_grammar_with_root(const std::string & grammar_str, const char * grammar_root) { + return llama_grammar_init_impl(nullptr, grammar_str.c_str(), grammar_root, false, nullptr, 0, nullptr, 0); +} + static llama_grammar * build_grammar(const std::string & grammar_str) { - return llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0); + return build_grammar_with_root(grammar_str, "root"); } static bool test_build_grammar_fails(const std::string & grammar_str) { @@ -860,6 +864,36 @@ static void test_failure_left_recursion() { fprintf(stderr, " ✅︎ Passed\n"); } +static void test_failure_missing_root_symbol() { + fprintf(stderr, "⚫ Testing missing root symbol:\n"); + + const std::string grammar_str = R"""( + root ::= "foobar" + )"""; + + llama_grammar * failure_result = build_grammar_with_root(grammar_str, "nonexistent"); + assert(failure_result == nullptr); + + fprintf(stderr, " ✅︎ Passed\n"); +} + +static void test_custom_root_symbol_check() { + fprintf(stderr, "⚫ Testing custom root symbol check:\n"); + + const std::string custom_root_grammar_str = R"""( + foobar ::= "foobar" + )"""; + + llama_grammar * failure_result = build_grammar_with_root(custom_root_grammar_str, "root"); + assert(failure_result == nullptr); + + llama_grammar * success_result = build_grammar_with_root(custom_root_grammar_str, "foobar"); + assert(success_result != nullptr); + llama_grammar_free_impl(success_result); + + fprintf(stderr, " ✅︎ Passed\n"); +} + static void test_json_schema() { // Note that this is similar to the regular grammar tests, // but we convert each json schema to a grammar before parsing. @@ -1433,6 +1467,8 @@ int main() { test_failure_missing_root(); test_failure_missing_reference(); test_failure_left_recursion(); + test_failure_missing_root_symbol(); + test_custom_root_symbol_check(); test_json_schema(); fprintf(stdout, "All tests passed.\n"); return 0; diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index a8e9ff33a4..ac697c4d24 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -569,6 +569,55 @@ static void test_all(const std::string & lang, std::function +#include +#include +#include +#include +#include +#include +#include +#include + +// normalized mean squared error = mse(a, b) / mse(a, 0) +static double nmse(const std::vector & a, const std::vector & b) { + GGML_ASSERT(a.size() == b.size()); + double mse_a_b = 0.0; + double mse_a_0 = 0.0; + + for (size_t i = 0; i < a.size(); i++) { + float a_i = a[i]; + float b_i = b[i]; + + mse_a_b += (a_i - b_i) * (a_i - b_i); + mse_a_0 += a_i * a_i; + } + + return mse_a_b / mse_a_0; +} + +static void set_tensor_data(struct ggml_tensor * tensor, void * userdata) { + std::hash hasher; + std::mt19937 gen(hasher(tensor->name) + *(const size_t *) userdata); + std::normal_distribution dis(0.0f, 1.0e-2f); + + const int64_t ne = ggml_nelements(tensor); + if (tensor->type == GGML_TYPE_F32) { + std::vector tmp(ne); + for (int64_t i = 0; i < ne; i++) { + tmp[i] = dis(gen); + } + ggml_backend_tensor_set(tensor, tmp.data(), 0, ggml_nbytes(tensor)); + } else if (tensor->type == GGML_TYPE_F16) { + std::vector tmp(ne); + for (int64_t i = 0; i < ne; i++) { + tmp[i] = ggml_fp32_to_fp16(dis(gen)); + } + ggml_backend_tensor_set(tensor, tmp.data(), 0, ggml_nbytes(tensor)); + } else { + GGML_ABORT("fatal error"); + } +} + +static void usage(char ** argv) { + printf("Usage: %s [-a/--arch arch] [-s/--seed seed] [-v/--verbose]\n", argv[0]); +} + +static std::vector get_tokens(const uint32_t n_tokens, const uint32_t n_vocab, const size_t seed){ + std::mt19937 gen(seed); + std::uniform_int_distribution<> dis(0, n_vocab - 1); + std::vector ret; + ret.reserve(n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + ret.push_back(dis(gen)); + } + return ret; +} + +static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { + gguf_context_ptr ret(gguf_init_empty()); + llama_model_saver ms(arch, ret.get()); + const uint32_t n_ctx = 128; + + uint32_t n_vocab = 128; + uint32_t n_embd = 256; + uint32_t n_head = 2; + uint32_t n_ff = 384; + uint32_t n_layer = 2; + if (arch == LLM_ARCH_LLAMA4) { + n_layer = 4; // hparams.n_no_rope_layer_step is hard-coded to 4 + } else if (arch == LLM_ARCH_GEMMA3N) { + n_embd = 64; + n_head = 1; + n_ff = 96; + } else if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_KIMI_LINEAR) { + n_embd = 128; + n_head = 1; + n_ff = 192; + } else if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) { + n_layer = 3; + } else if (arch == LLM_ARCH_CHAMELEON) { + n_vocab = 10240; + } else if (arch == LLM_ARCH_GEMMA3N) { + n_layer = 22; // hparams.n_layer_kv_from_start = 20 is hardcoded + } + + const uint32_t n_embd_head = n_embd / n_head; + + ms.add_kv(LLM_KV_GENERAL_ARCHITECTURE, llm_arch_name(arch)); + ms.add_kv(LLM_KV_VOCAB_SIZE, n_vocab); + ms.add_kv(LLM_KV_CONTEXT_LENGTH, n_ctx); + ms.add_kv(LLM_KV_EMBEDDING_LENGTH, n_embd); + ms.add_kv(LLM_KV_FEATURES_LENGTH, n_embd); + ms.add_kv(LLM_KV_BLOCK_COUNT, n_layer); + ms.add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT, uint32_t(1)); + + if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) { + std::vector n_ff_per_layer; + n_ff_per_layer.reserve(n_layer); + for (uint32_t il = 0; il < n_layer; il++) { + n_ff_per_layer.push_back(il <= 1 ? 0 : n_ff); + } + ms.add_kv(LLM_KV_FEED_FORWARD_LENGTH, n_ff_per_layer); + } else { + ms.add_kv(LLM_KV_FEED_FORWARD_LENGTH, n_ff); + } + + ms.add_kv(LLM_KV_USE_PARALLEL_RESIDUAL, false); + ms.add_kv(LLM_KV_LOGIT_SCALE, 1.0f); + ms.add_kv(LLM_KV_TIME_MIX_EXTRA_DIM, uint32_t(64)); + ms.add_kv(LLM_KV_TIME_DECAY_EXTRA_DIM, uint32_t(128)); + ms.add_kv(LLM_KV_FULL_ATTENTION_INTERVAL, uint32_t(2)); + + if (arch == LLM_ARCH_PLAMO2 || arch == LLM_ARCH_JAMBA || arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE || + arch == LLM_ARCH_GRANITE_HYBRID || arch == LLM_ARCH_LFM2 || arch == LLM_ARCH_LFM2MOE || arch == LLM_ARCH_KIMI_LINEAR) { + GGML_ASSERT(n_layer >= 2); + std::vector n_head_per_layer; + n_head_per_layer.reserve(n_layer); + for (uint32_t il = 0; il < n_layer; il++) { + n_head_per_layer.push_back(il == 1 ? 0 : n_head); + } + ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head_per_layer); + ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_per_layer); + } else { + ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head); + ms.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head); + } + + ms.add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, 8.0f); + if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_KIMI_LINEAR) { + ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH, uint32_t(576)); + ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH, uint32_t(512)); + ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, uint32_t(64)); + ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA, uint32_t(192)); + ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, uint32_t(128)); + } + ms.add_kv(LLM_KV_ATTENTION_CLAMP_KQV, 1.0f); + ms.add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, 1e-5f); + ms.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f); + ms.add_kv(LLM_KV_ATTENTION_GROUPNORM_EPS, 1e-5f); + ms.add_kv(LLM_KV_ATTENTION_GROUPNORM_GROUPS, uint32_t(8)); + ms.add_kv(LLM_KV_ATTENTION_Q_LORA_RANK, uint32_t(512)); + ms.add_kv(LLM_KV_ATTENTION_KV_LORA_RANK, uint32_t(512)); + ms.add_kv(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, uint32_t(8)); + ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW, n_ctx/8); + + if (arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35) { + std::vector pattern; + pattern.reserve(n_layer); + for (uint32_t il = 0; il < n_layer; il++) { + pattern.push_back(il % 2); + } + ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, pattern); + } else { + ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(2)); + } + + ms.add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, uint32_t(1)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, uint32_t(64)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, uint32_t(8)); + ms.add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS, std::vector({n_embd_head/4, n_embd_head/4, n_embd_head/4, n_embd_head/4})); + ms.add_kv(LLM_KV_TOKENIZER_MODEL, "no_vocab"); + // ms.add_kv(LLM_KV_DENSE_2_FEAT_OUT, n_embd); + // ms.add_kv(LLM_KV_DENSE_3_FEAT_IN, n_embd); + + if (moe) { + ms.add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, n_ff); + ms.add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, uint32_t(2)); + ms.add_kv(LLM_KV_EXPERT_COUNT, uint32_t(2)); + ms.add_kv(LLM_KV_EXPERT_USED_COUNT, uint32_t(1)); + ms.add_kv(LLM_KV_EXPERT_SHARED_COUNT, uint32_t(1)); + ms.add_kv(LLM_KV_EXPERT_GATING_FUNC, uint32_t(2)); // sigmoid + ms.add_kv(LLM_KV_EXPERT_GROUP_SCALE, 1.0f); + ms.add_kv(LLM_KV_EXPERTS_PER_GROUP, uint32_t(1)); + } + + ms.add_kv(LLM_KV_POSNET_EMBEDDING_LENGTH, n_embd); + ms.add_kv(LLM_KV_POSNET_BLOCK_COUNT, n_layer); + ms.add_kv(LLM_KV_CONVNEXT_EMBEDDING_LENGTH, n_embd); + ms.add_kv(LLM_KV_CONVNEXT_BLOCK_COUNT, n_layer); + ms.add_kv(LLM_KV_XIELU_ALPHA_N, 1.0f); + ms.add_kv(LLM_KV_XIELU_ALPHA_P, 1.0f); + ms.add_kv(LLM_KV_XIELU_BETA, 1.0f); + ms.add_kv(LLM_KV_XIELU_EPS, 1.0e-7f); + ms.add_kv(LLM_KV_SSM_INNER_SIZE, arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE ? 64 : 2*n_embd); + ms.add_kv(LLM_KV_SSM_CONV_KERNEL, uint32_t(4)); + ms.add_kv(LLM_KV_SSM_STATE_SIZE, uint32_t(32)); + ms.add_kv(LLM_KV_SSM_TIME_STEP_RANK, n_head); + ms.add_kv(LLM_KV_SSM_GROUP_COUNT, arch == LLM_ARCH_PLAMO2 ? 0 : uint32_t(2)); + ms.add_kv(LLM_KV_KDA_HEAD_DIM, uint32_t(128)); + ms.add_kv(LLM_KV_WKV_HEAD_SIZE, n_embd/n_head); + ms.add_kv(LLM_KV_SHORTCONV_L_CACHE, uint32_t(3)); + + for (uint32_t il = 0; il < n_layer; il++) { + ggml_tensor t; + memset(&t, 0, sizeof(ggml_tensor)); + t.type = GGML_TYPE_F16; + ggml_format_name(&t, "conv%" PRIu32 "d.weight", il); + gguf_add_tensor(ms.gguf_ctx, &t); + ggml_format_name(&t, "posnet.%" PRIu32 ".conv1.weight", il); + gguf_add_tensor(ms.gguf_ctx, &t); + ggml_format_name(&t, "posnet.%" PRIu32 ".conv2.weight", il); + gguf_add_tensor(ms.gguf_ctx, &t); + ggml_format_name(&t, "convnext.%" PRIu32 ".dw.weight", il); + gguf_add_tensor(ms.gguf_ctx, &t); + } + return ret; +} + +static std::pair get_model_and_ctx( + struct gguf_context * gguf_ctx, const size_t seed, const std::vector & devs) { + llama_model_params model_params = llama_model_default_params(); + std::vector devs_copy = devs; + devs_copy.push_back(nullptr); + model_params.devices = devs_copy.data(); + + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.n_ctx = 0; + ctx_params.n_threads = 4; + ctx_params.n_threads_batch = 4; + + size_t tmp = seed; + llama_model_ptr model(llama_model_init_from_user(gguf_ctx, set_tensor_data, &tmp, model_params)); + if (!model) { + throw std::runtime_error("failed to create llama model"); + } + llama_context_ptr lctx(llama_init_from_model(model.get(), ctx_params)); + if (!lctx) { + throw std::runtime_error("failed to create llama context"); + } + return std::make_pair(std::move(model), std::move(lctx)); +} + +static std::vector get_logits( + llama_model * model, llama_context * lctx, const std::vector & tokens, bool encode = false) { + const uint32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model)); + const uint32_t n_ctx = llama_n_ctx(lctx); + const uint32_t n_tokens = tokens.size(); + llama_batch batch = llama_batch_init(n_ctx, 0, 1); + GGML_ASSERT(n_tokens <= n_ctx); + for (uint32_t pos = 0; pos < n_tokens; pos++) { + common_batch_add(batch, tokens[pos], pos, {0}, true); + } + batch.n_tokens = n_tokens; + if (encode) { + if (llama_encode(lctx, batch)) { + llama_batch_free(batch); + throw std::runtime_error("failed to encode batch"); + } + } + if (llama_decode(lctx, batch)) { + llama_batch_free(batch); + throw std::runtime_error("failed to decode batch"); + } + + std::vector ret; + ret.reserve(n_tokens*n_vocab); + for (uint32_t i = 0; i < n_tokens; i++) { + const float * logits_ith = llama_get_logits_ith(lctx, i); + for (uint32_t j = 0; j < n_vocab; j++) { + ret.push_back(logits_ith[j]); + } + } + llama_batch_free(batch); + return ret; +} + +static bool moe_mandatory(const llm_arch arch) { + switch (arch) { + case LLM_ARCH_LLAMA4: + case LLM_ARCH_GROK: + case LLM_ARCH_QWEN2MOE: + case LLM_ARCH_QWEN3MOE: + case LLM_ARCH_QWEN3NEXT: + case LLM_ARCH_QWEN3VLMOE: + case LLM_ARCH_QWEN35MOE: + case LLM_ARCH_PHIMOE: + case LLM_ARCH_DBRX: + case LLM_ARCH_OLMOE: + case LLM_ARCH_ARCTIC: + case LLM_ARCH_DEEPSEEK: + case LLM_ARCH_DEEPSEEK2: + case LLM_ARCH_GLM4_MOE: + case LLM_ARCH_GLM_DSA: + case LLM_ARCH_EXAONE_MOE: + case LLM_ARCH_BAILINGMOE: + case LLM_ARCH_BAILINGMOE2: + case LLM_ARCH_DOTS1: + case LLM_ARCH_AFMOE: + case LLM_ARCH_ERNIE4_5: + case LLM_ARCH_ERNIE4_5_MOE: + case LLM_ARCH_HUNYUAN_MOE: + case LLM_ARCH_OPENAI_MOE: + case LLM_ARCH_LFM2MOE: + case LLM_ARCH_SMALLTHINKER: + case LLM_ARCH_LLADA_MOE: + case LLM_ARCH_GROVEMOE: + case LLM_ARCH_MINIMAX_M2: + case LLM_ARCH_RND1: + case LLM_ARCH_PADDLEOCR: + case LLM_ARCH_MIMO2: + case LLM_ARCH_KIMI_LINEAR: + case LLM_ARCH_STEP35: + return true; + default: + return false; + } +} + +static bool moe_implemented(const llm_arch arch) { + if (moe_mandatory(arch)) { + return true; + } + switch (arch) { + case LLM_ARCH_LLAMA: + case LLM_ARCH_REFACT: + case LLM_ARCH_MINICPM: + case LLM_ARCH_GRANITE: + case LLM_ARCH_GRANITE_MOE: + case LLM_ARCH_MISTRAL3: + case LLM_ARCH_LLAMA_EMBED: + return true; + default: + return false; + } +} + +static int save_models(const llm_arch target_arch, const size_t seed, const ggml_log_level log_level, const std::string & dir) { + GGML_ABORT("llama_model_save_to_file is broken"); + struct user_data_t { + struct { + ggml_log_callback callback; + void * user_data; + } original_logger; + ggml_log_level min_level; // prints below this log level go to debug log + }; + user_data_t ud; + llama_log_get(&ud.original_logger.callback, &ud.original_logger.user_data); + ud.min_level = log_level; + + llama_log_set([](ggml_log_level level, const char * text, void * user_data) { + const user_data_t * ud = (const user_data_t *) user_data; + const ggml_log_level level_eff = level >= ud->min_level ? level : GGML_LOG_LEVEL_DEBUG; + ud->original_logger.callback(level_eff, text, ud->original_logger.user_data); + }, &ud); + + for (const llm_arch & arch : llm_arch_all()) { + if (target_arch != LLM_ARCH_UNKNOWN && arch != target_arch) { + continue; + } + if (arch == LLM_ARCH_CLIP || arch == LLM_ARCH_GPTJ || arch == LLM_ARCH_UNKNOWN) { + continue; // These models don't have usable implementations. + } + for (bool moe : {false, true}) { + if (moe && !moe_implemented(arch)) { + continue; + } + if (!moe && moe_mandatory(arch)) { + continue; + } + gguf_context_ptr gguf_ctx = get_gguf_ctx(arch, moe); + auto model_and_ctx = get_model_and_ctx(gguf_ctx.get(), seed, {}); + const std::string path = dir + "/" + llm_arch_name(arch) + (moe ? "-moe.gguf" : "-dense.gguf"); + LOG_INF("%s: Saving %s model (%s) to %s...\n", __func__, llm_arch_name(arch), moe ? "MoE" : "dense", path.c_str()); + llama_model_save_to_file(model_and_ctx.first.get(), path.c_str()); + } + } + llama_log_set(ud.original_logger.callback, ud.original_logger.user_data); + return 0; +} + +static int test_backends(const llm_arch target_arch, const size_t seed, const ggml_log_level log_level) { + struct user_data_t { + struct { + ggml_log_callback callback; + void * user_data; + } original_logger; + ggml_log_level min_level; // prints below this log level go to debug log + }; + user_data_t ud; + llama_log_get(&ud.original_logger.callback, &ud.original_logger.user_data); + ud.min_level = log_level; + + llama_log_set([](ggml_log_level level, const char * text, void * user_data) { + const user_data_t * ud = (const user_data_t *) user_data; + const ggml_log_level level_eff = level >= ud->min_level ? level : GGML_LOG_LEVEL_DEBUG; + ud->original_logger.callback(level_eff, text, ud->original_logger.user_data); + }, &ud); + + const std::vector tokens = get_tokens(128, 128, seed); + + bool all_ok = true; + common_log_flush(common_log_main()); + printf("|%15s|%30s|%6s|%8s|%6s|\n", "Model arch.", "Device", "Config", "NMSE", "Status"); + printf("|---------------|------------------------------|------|--------|------|\n"); + for (const llm_arch & arch : llm_arch_all()) { + if (target_arch != LLM_ARCH_UNKNOWN && arch != target_arch) { + continue; + } + if (arch == LLM_ARCH_CLIP || arch == LLM_ARCH_GPTJ || arch == LLM_ARCH_UNKNOWN) { + continue; // These models don't have usable implementations. + } + if (arch == LLM_ARCH_WAVTOKENIZER_DEC) { + continue; // FIXME CUDA backend crashes. + } + if (arch == LLM_ARCH_LLAMA_EMBED || arch == LLM_ARCH_GEMMA_EMBEDDING || arch == LLM_ARCH_T5ENCODER) { + continue; // FIXME Embedding (?) models produce inconsistent results. + } + if (arch == LLM_ARCH_RWKV6 || arch == LLM_ARCH_RWKV6QWEN2 || arch == LLM_ARCH_RWKV7 || arch == LLM_ARCH_ARWKV7) { + continue; // FIXME RWKV models hang indefinitely. + } + if (arch == LLM_ARCH_BERT || arch == LLM_ARCH_MODERN_BERT || arch == LLM_ARCH_NOMIC_BERT || arch == LLM_ARCH_NOMIC_BERT_MOE || + arch == LLM_ARCH_NEO_BERT || arch == LLM_ARCH_JINA_BERT_V2 || arch == LLM_ARCH_JINA_BERT_V3 || arch == LLM_ARCH_EUROBERT) { + continue; // TODO vocab + } + if (arch == LLM_ARCH_PLM) { + continue; // TODO tensor shapes + } + + // FIXME some models are segfaulting with WebGPU: +#ifdef GGML_USE_WEBGPU + if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE || arch == LLM_ARCH_KIMI_LINEAR) { + continue; + } +#endif // GGML_USE_WEBGPU + + const bool encode = arch == LLM_ARCH_T5; + for (bool moe : {false, true}) { + if (moe && !moe_implemented(arch)) { + continue; + } + if (!moe && moe_mandatory(arch)) { + continue; + } + gguf_context_ptr gguf_ctx = get_gguf_ctx(arch, moe); + auto model_and_ctx_cpu = get_model_and_ctx(gguf_ctx.get(), seed, {}); + const std::vector logits_cpu = get_logits(model_and_ctx_cpu.first.get(), model_and_ctx_cpu.second.get(), tokens, encode); + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) { + continue; + } + auto model_and_ctx_dev = get_model_and_ctx(gguf_ctx.get(), seed, {dev}); + const std::vector logits_dev = get_logits(model_and_ctx_dev.first.get(), model_and_ctx_dev.second.get(), tokens, encode); + const double nmse_val = nmse(logits_cpu, logits_dev); + const bool ok = nmse_val <= 1e-4; + all_ok = all_ok && ok; + char nmse_str[10]; + snprintf(nmse_str, sizeof(nmse_str), "%.2e", nmse_val); + printf("|%15s|%30s|%6s|%8s|%17s|\n", llm_arch_name(arch), ggml_backend_dev_description(dev), + moe ? "MoE" : "Dense", nmse_str, ok ? "\033[1;32mOK\033[0m" : "\033[1;31mFAIL\033[0m"); + } + } + } + llama_log_set(ud.original_logger.callback, ud.original_logger.user_data); + return all_ok ? 0 : 1; +} + +int main(int argc, char ** argv) { + // FIXME these tests are disabled in the CI for macOS-latest-cmake-arm64 because they are segfaulting + common_init(); + std::random_device rd; + + llm_arch arch = LLM_ARCH_UNKNOWN; + size_t seed = rd(); + ggml_log_level log_level = GGML_LOG_LEVEL_ERROR; + std::string out; + + for (int i = 1; i < argc; i++) { + if (strcmp(argv[i], "-a") == 0 || strcmp(argv[i], "--arch") == 0) { + if (i + 1 < argc) { + const std::string arch_name = argv[++i]; + arch = llm_arch_from_string(arch_name); + if (arch == LLM_ARCH_UNKNOWN) { + LOG_ERR("%s: unkown LLM architecture: %s\n", __func__, arch_name.c_str()); + return 1; + } + } else { + usage(argv); + return 1; + } + } + if (strcmp(argv[i], "-s") == 0 || strcmp(argv[i], "--seed") == 0) { + if (i + 1 < argc) { + seed = std::stoull(argv[++i]); + } else { + usage(argv); + return 1; + } + } + if (strcmp(argv[i], "-v") == 0 || strcmp(argv[i], "--verbose") == 0) { + log_level = GGML_LOG_LEVEL_INFO; + continue; + } + if (strcmp(argv[i], "-o") == 0 || strcmp(argv[i], "--out") == 0) { + if (i + 1 < argc) { + out = argv[++i]; + } else { + usage(argv); + return 1; + } + } + } + + try { + if (!out.empty()) { + return save_models(arch, seed, log_level, out); + } + return test_backends(arch, seed, log_level); + } catch (const std::exception & err) { + fprintf(stderr, "encountered runtime error: %s\n", err.what()); + return -1; + } +} diff --git a/tests/test-peg-parser.cpp b/tests/test-peg-parser.cpp index 220745d029..7d22d77612 100644 --- a/tests/test-peg-parser.cpp +++ b/tests/test-peg-parser.cpp @@ -20,6 +20,7 @@ int main(int argc, char *argv[]) { t.test("json", test_json_parser); t.test("gbnf", test_gbnf_generation); t.test("serialization", test_json_serialization); + t.test("python-dict", test_python_dict_parser); return t.summary(); } diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index 037c0582bb..a8fb192623 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -20,8 +20,10 @@ constexpr float MAX_QUANTIZATION_TOTAL_ERROR_TERNARY = 0.01f; constexpr float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f; constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f; constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS = 0.0050f; +constexpr float MAX_QUANTIZATION_TOTAL_ERROR_FP4 = 0.0030f; constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f; constexpr float MAX_DOT_PRODUCT_ERROR_LOWBIT = 0.04f; +constexpr float MAX_DOT_PRODUCT_ERROR_FP4 = 0.03f; constexpr float MAX_DOT_PRODUCT_ERROR_TERNARY = 0.15f; static const char* RESULT_STR[] = {"ok", "FAILED"}; @@ -149,7 +151,8 @@ int main(int argc, char * argv[]) { type == GGML_TYPE_IQ2_S ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS : type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : type == GGML_TYPE_IQ3_S ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : - type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR; + type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : + type == GGML_TYPE_NVFP4 ? MAX_QUANTIZATION_TOTAL_ERROR_FP4 : MAX_QUANTIZATION_TOTAL_ERROR; failed = !(total_error < max_quantization_error); num_failed += failed; if (failed || verbose) { @@ -169,6 +172,8 @@ int main(int argc, char * argv[]) { ? MAX_DOT_PRODUCT_ERROR_LOWBIT : type == GGML_TYPE_TQ1_0 || type == GGML_TYPE_TQ2_0 ? MAX_DOT_PRODUCT_ERROR_TERNARY + : type == GGML_TYPE_NVFP4 + ? MAX_DOT_PRODUCT_ERROR_FP4 : MAX_DOT_PRODUCT_ERROR; failed = !(vec_dot_error < max_allowed_error); num_failed += failed; diff --git a/tests/test-reasoning-budget.cpp b/tests/test-reasoning-budget.cpp new file mode 100644 index 0000000000..ab540a8463 --- /dev/null +++ b/tests/test-reasoning-budget.cpp @@ -0,0 +1,238 @@ +#include "reasoning-budget.h" +#include "unicode.h" + +#include "llama.h" +#include "ggml.h" + +#ifdef NDEBUG +#undef NDEBUG +#endif + +#include +#include +#include +#include +#include + +// Reasoning budget sampler test helper +// These tests use nullptr vocab which safely falls back to treating all tokens as complete +// (The UTF-8 boundary detection logic is tested separately in test_utf8_boundary_detection) +static void test_reasoning_budget( + const char * test_name, + const std::vector & sequence, + const std::vector & start_tokens, + const std::vector & end_tokens, + const std::vector & forced_tokens, + int32_t budget, + common_reasoning_budget_state initial_state, + size_t expected_force_start, // token index where forcing should start (SIZE_MAX = never) + size_t expected_force_end // token index where forcing should end (after this, no more forcing) +) { + // Find the maximum token ID to ensure our vocab covers all tokens + llama_token max_token = 0; + for (auto t : sequence) max_token = std::max(max_token, t); + for (auto t : start_tokens) max_token = std::max(max_token, t); + for (auto t : end_tokens) max_token = std::max(max_token, t); + for (auto t : forced_tokens) max_token = std::max(max_token, t); + + // Create a minimal sampler with mock vocabulary + // For this test, we use nullptr as vocab since we're testing state transitions + // The UTF-8 boundary check will treat all tokens as complete (safe fallback) + auto * sampler = common_reasoning_budget_init( + nullptr, // vocab - not used for basic state machine tests + start_tokens, + end_tokens, + forced_tokens, + budget, + initial_state + ); + + // Create a test token data array for checking forcing behavior + // Vocab size must be large enough to include all tokens (start, end, forced, sequence) + std::vector cur; + const size_t n_vocab = (size_t)max_token + 1; + for (size_t i = 0; i < n_vocab; i++) { + cur.emplace_back(llama_token_data{(llama_token)i, logf((float)(i+1)), 0.0f}); + } + llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false }; + + size_t actual_force_start = SIZE_MAX; + size_t actual_force_end = SIZE_MAX; + + // Feed the sequence and track when forcing occurs + for (size_t i = 0; i < sequence.size(); i++) { + llama_sampler_accept(sampler, sequence[i]); + + // Check if we're in forcing state by applying and seeing if logits are modified + cur_p.selected = -1; + for (size_t j = 0; j < cur.size(); j++) { + cur[j].logit = logf((float)(j+1)); // reset logits + } + + llama_sampler_apply(sampler, &cur_p); + + // Check if forcing is active (all logits except one should be -INFINITY) + size_t finite_count = 0; + llama_token finite_token = -1; + for (size_t j = 0; j < cur.size(); j++) { + if (std::isfinite(cur[j].logit)) { + finite_count++; + finite_token = cur[j].id; + } + } + + fprintf(stderr, " i=%zu: token=%d, finite_count=%zu, finite_token=%d\n", i, (int)sequence[i], finite_count, (int)finite_token); + + if (finite_count == 1) { + if (actual_force_start == SIZE_MAX) { + actual_force_start = i; + } + actual_force_end = i; + } else if (actual_force_start != SIZE_MAX && actual_force_end != SIZE_MAX) { + // Forcing stopped + break; + } + } + + llama_sampler_free(sampler); + + // Verify forcing occurred at expected positions + if (expected_force_start == SIZE_MAX) { + if (actual_force_start != SIZE_MAX) { + fprintf(stderr, "Test '%s' FAILED: Expected no forcing, but forcing occurred at %zu\n", test_name, actual_force_start); + GGML_ASSERT(false && "Expected no forcing, but forcing occurred"); + } + } else { + if (actual_force_start == SIZE_MAX) { + fprintf(stderr, "Test '%s' FAILED: Expected forcing but none occurred\n", test_name); + GGML_ASSERT(false && "Expected forcing but none occurred"); + } + if (actual_force_start != expected_force_start) { + fprintf(stderr, "Test '%s' FAILED: Forcing started at %zu, expected %zu\n", test_name, actual_force_start, expected_force_start); + GGML_ASSERT(false && "Forcing started at wrong position"); + } + } + + if (expected_force_end != SIZE_MAX) { + if (actual_force_end < expected_force_end) { + fprintf(stderr, "Test '%s' FAILED: Forcing ended at %zu, expected >= %zu\n", test_name, actual_force_end, expected_force_end); + GGML_ASSERT(false && "Forcing ended too early"); + } + } + + fprintf(stderr, " Test '%s' passed (force_start=%zu, force_end=%zu)\n", test_name, actual_force_start, actual_force_end); + (void)sequence; +} + +// UTF-8 boundary detection unit test +// Tests common_utf8_is_complete() from reasoning-budget.h +static void test_utf8_boundary_detection() { + // Complete sequences + GGML_ASSERT(common_utf8_is_complete("hello")); + GGML_ASSERT(common_utf8_is_complete("")); + GGML_ASSERT(common_utf8_is_complete("\xC2\xA0")); // complete 2-byte UTF-8 (U+00A0) + GGML_ASSERT(common_utf8_is_complete("\xE2\x80\x9C")); // complete 3-byte UTF-8 (left double quote) + GGML_ASSERT(common_utf8_is_complete("\xF0\x9F\x98\x80")); // complete 4-byte UTF-8 (emoji) + GGML_ASSERT(common_utf8_is_complete("abc\xC3\xA9")); // ASCII + complete 2-byte + + // Incomplete sequences + GGML_ASSERT(!common_utf8_is_complete(std::string("\xC2", 1))); // 2-byte start, missing continuation + GGML_ASSERT(!common_utf8_is_complete(std::string("\xE2\x80", 2))); // 3-byte start + 1 cont, missing 1 + GGML_ASSERT(!common_utf8_is_complete(std::string("\xE2", 1))); // 3-byte start, missing 2 + GGML_ASSERT(!common_utf8_is_complete(std::string("\xF0\x9F\x98", 3))); // 4-byte start + 2 cont, missing 1 + GGML_ASSERT(!common_utf8_is_complete(std::string("\xF0\x9F", 2))); // 4-byte start + 1 cont, missing 2 + GGML_ASSERT(!common_utf8_is_complete(std::string("\xF0", 1))); // 4-byte start, missing 3 + GGML_ASSERT(!common_utf8_is_complete(std::string("\x80", 1))); // orphan continuation byte + + // Mixed: ASCII followed by start of multi-byte + GGML_ASSERT(!common_utf8_is_complete(std::string("hello\xC3", 6))); // ASCII + incomplete 2-byte + GGML_ASSERT(common_utf8_is_complete(std::string("hello\xC3\xA9", 7))); // ASCII + complete 2-byte +} + +int main(void) { + // Reasoning budget sampler tests + printf("Testing reasoning budget sampler... "); + + // Test 1: Basic budget with start/end tokens - no forcing (natural end before budget exhausted) + { + const std::vector start = {100}; // start token + const std::vector end = {101}; // end token + const std::vector forced = {102}; // forced token (not used in this test) + const std::vector sequence = {100, 50, 51, 101, 52}; // start, two tokens, end, one more + + test_reasoning_budget("natural end before budget exhausted", sequence, start, end, forced, + 5, // budget of 5 tokens + REASONING_BUDGET_IDLE, + SIZE_MAX, SIZE_MAX); // no forcing expected (natural end) + } + + // Test 2: Budget exhausted, forcing should occur + // Flow: i=0 accept(100)->COUNTING, i=1 accept(50)->remaining=1, i=2 accept(51)->remaining=0->FORCING + // Forcing is active at i=2 and i=3 (when apply() is called while in FORCING state) + // At i=4, force_pos becomes 2 which equals forced_tokens.size(), so state becomes DONE + { + const std::vector start = {100}; + const std::vector end = {101}; + const std::vector forced = {102, 101}; // forced message + end + const std::vector sequence = {100, 50, 51, 52, 53}; // start + 4 tokens (budget=2) + + test_reasoning_budget("budget exhausted forcing", sequence, start, end, forced, + 2, // budget of 2 tokens + REASONING_BUDGET_IDLE, + 2, // forcing starts at i=2 (after accept(51) depletes budget, apply() forces) + 3); // forcing continues through i=3 (at i=4 state becomes DONE) + } + + // Test 3: Activate immediately with budget=0, forcing should start right away + // Flow: Since no start token in sequence, state stays IDLE (no start/end configured means passthrough) + // This test needs start token to be in the sequence or use activate_immediately with start token present + { + const std::vector start = {100}; + const std::vector end = {101}; + const std::vector forced = {102, 101}; + const std::vector sequence = {100, 50, 51, 52}; // start token first, then 3 tokens + + test_reasoning_budget("activate immediately budget=0", sequence, start, end, forced, + 0, // budget of 0 tokens + REASONING_BUDGET_COUNTING, // starts counting, promoted to FORCING since budget=0 + 0, // forcing starts at i=0 (after accept(100), budget=0 goes straight to FORCING) + 1); // forcing continues through i=1 (at i=2 state becomes DONE) + } + + // Test 4: No start/end tokens configured - passthrough (no forcing) + { + const std::vector start = {}; + const std::vector end = {}; + const std::vector forced = {102}; + const std::vector sequence = {50, 51, 52, 53}; + + test_reasoning_budget("no start/end configured", sequence, start, end, forced, + 2, // budget + REASONING_BUDGET_IDLE, + SIZE_MAX, SIZE_MAX); // no forcing (no start/end configured) + } + + // Test 5: Activate immediately with budget > 0, count down then force + // Flow: i=0 accept(50)->remaining=1, i=1 accept(51)->remaining=0->FORCING + // So forcing starts at i=1 (apply after accept sees FORCING with force_pos=0) + { + const std::vector start = {100}; + const std::vector end = {101}; + const std::vector forced = {102, 101}; + const std::vector sequence = {50, 51, 52, 53}; + + test_reasoning_budget("activate immediately with budget", sequence, start, end, forced, + 2, // budget of 2 tokens + REASONING_BUDGET_COUNTING, + 1, // forcing starts at i=1 (after 2 accepts deplete budget) + 2); // forcing continues through i=2 + } + + printf("OK (5 tests passed)\n"); + + printf("Testing UTF-8 boundary detection... "); + test_utf8_boundary_detection(); + printf("OK\n"); + + return 0; +} diff --git a/tests/test-tokenizer-0.sh b/tests/test-tokenizer-0.sh index 7ef009dc90..7024b00afe 100755 --- a/tests/test-tokenizer-0.sh +++ b/tests/test-tokenizer-0.sh @@ -13,7 +13,12 @@ fi name=$1 input=$2 -make -j tests/test-tokenizer-0 +# Build using CMake if binary doesn't exist +if [ ! -f ./build/bin/test-tokenizer-0 ]; then + printf "Building test-tokenizer-0 with CMake...\n" + cmake -B build -DLLAMA_BUILD_TESTS=ON + cmake --build build --target test-tokenizer-0 -j +fi printf "Testing %s on %s ...\n" $name $input @@ -23,7 +28,7 @@ printf "Tokenizing using (py) Python AutoTokenizer ...\n" python3 ./tests/test-tokenizer-0.py ./models/tokenizers/$name --fname-tok $input > /tmp/test-tokenizer-0-$name-py.log 2>&1 printf "Tokenizing using (cpp) llama.cpp ...\n" -./tests/test-tokenizer-0 ./models/ggml-vocab-$name.gguf $input > /tmp/test-tokenizer-0-$name-cpp.log 2>&1 +./build/bin/test-tokenizer-0 ./models/ggml-vocab-$name.gguf $input > /tmp/test-tokenizer-0-$name-cpp.log 2>&1 cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in" cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in" diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt index 518f8b9ae7..b433c91d85 100644 --- a/tools/CMakeLists.txt +++ b/tools/CMakeLists.txt @@ -26,6 +26,7 @@ else() add_subdirectory(server) endif() add_subdirectory(tokenize) + add_subdirectory(parser) add_subdirectory(tts) add_subdirectory(mtmd) if (GGML_RPC) @@ -37,4 +38,5 @@ else() add_subdirectory(export-lora) endif() add_subdirectory(fit-params) + add_subdirectory(results) endif() diff --git a/tools/batched-bench/batched-bench.cpp b/tools/batched-bench/batched-bench.cpp index 0f627c5ff6..224f0e1f1c 100644 --- a/tools/batched-bench/batched-bench.cpp +++ b/tools/batched-bench/batched-bench.cpp @@ -4,6 +4,7 @@ #include "llama.h" #include +#include #include #include #include @@ -15,6 +16,8 @@ static void print_usage(int, char ** argv) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) { diff --git a/tools/cli/README.md b/tools/cli/README.md index 4a15cbad9d..22d3fc87e9 100644 --- a/tools/cli/README.md +++ b/tools/cli/README.md @@ -57,8 +57,8 @@ | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | | `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--mmap, --no-mmap` | whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | -| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. Takes precedence over --mmap (default: enabled)
(env: LLAMA_ARG_DIO) | +| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | +| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)
(env: LLAMA_ARG_DIO) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -109,14 +109,14 @@ | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | | `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | -| `--temp N` | temperature (default: 0.80) | +| `--temp, --temperature N` | temperature (default: 0.80) | | `--top-k N` | top-k sampling (default: 40, 0 = disabled)
(env: LLAMA_ARG_TOP_K) | | `--top-p N` | top-p sampling (default: 0.95, 1.0 = disabled) | | `--min-p N` | min-p sampling (default: 0.05, 0.0 = disabled) | -| `--top-nsigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) | +| `--top-nsigma, --top-n-sigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) | | `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) | | `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) | -| `--typical N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) | +| `--typical, --typical-p N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) | | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) | | `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) | diff --git a/tools/cli/cli.cpp b/tools/cli/cli.cpp index e57bf52e36..7c4342d6bf 100644 --- a/tools/cli/cli.cpp +++ b/tools/cli/cli.cpp @@ -1,3 +1,4 @@ +#include "chat.h" #include "common.h" #include "arg.h" #include "console.h" @@ -6,7 +7,10 @@ #include "server-context.h" #include "server-task.h" +#include #include +#include +#include #include #include #include @@ -53,6 +57,8 @@ struct cli_context { std::vector input_files; task_params defaults; bool verbose_prompt; + int reasoning_budget = -1; + std::string reasoning_budget_message; // thread for showing "loading" animation std::atomic loading_show; @@ -69,6 +75,8 @@ struct cli_context { // defaults.return_progress = true; // TODO: show progress verbose_prompt = params.verbose_prompt; + reasoning_budget = params.reasoning_budget; + reasoning_budget_message = params.reasoning_budget_message; } std::string generate_completion(result_timings & out_timings) { @@ -91,6 +99,24 @@ struct cli_context { task.params.chat_parser_params.parser.load(chat_params.parser); } + // reasoning budget sampler + if (reasoning_budget >= 0 && !chat_params.thinking_end_tag.empty()) { + const llama_vocab * vocab = llama_model_get_vocab( + llama_get_model(ctx_server.get_llama_context())); + + task.params.sampling.reasoning_budget_tokens = reasoning_budget; + task.params.sampling.reasoning_budget_activate_immediately = chat_params.thinking_forced_open; + + if (!chat_params.thinking_start_tag.empty()) { + task.params.sampling.reasoning_budget_start = + common_tokenize(vocab, chat_params.thinking_start_tag, false, true); + } + task.params.sampling.reasoning_budget_end = + common_tokenize(vocab, chat_params.thinking_end_tag, false, true); + task.params.sampling.reasoning_budget_forced = + common_tokenize(vocab, reasoning_budget_message + chat_params.thinking_end_tag, false, true); + } + rd.post_task({std::move(task)}); } @@ -188,13 +214,130 @@ struct cli_context { inputs.use_jinja = chat_params.use_jinja; inputs.parallel_tool_calls = false; inputs.add_generation_prompt = true; - inputs.enable_thinking = chat_params.enable_thinking; + inputs.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; + inputs.enable_thinking = chat_params.enable_thinking ? common_chat_templates_support_enable_thinking(chat_params.tmpls.get()) : false; // Apply chat template to the list of messages return common_chat_templates_apply(chat_params.tmpls.get(), inputs); } }; +// TODO?: Make this reusable, enums, docs +static const std::array cmds = { + "/audio ", + "/clear", + "/exit", + "/image ", + "/read ", + "/regen", +}; + +static std::vector> auto_completion_callback(std::string_view line, size_t cursor_byte_pos) { + std::vector> matches; + std::string cmd; + + if (line.length() > 1 && line[0] == '/' && !std::any_of(cmds.begin(), cmds.end(), [line](const std::string & prefix) { + return string_starts_with(line, prefix); + })) { + auto it = cmds.begin(); + + while ((it = std::find_if(it, cmds.end(), [line](const std::string & cmd_line) { + return string_starts_with(cmd_line, line); + })) != cmds.end()) { + matches.emplace_back(*it, (*it).length()); + ++it; + } + } else { + auto it = std::find_if(cmds.begin(), cmds.end(), [line](const std::string & prefix) { + return prefix.back() == ' ' && string_starts_with(line, prefix); + }); + + if (it != cmds.end()) { + cmd = *it; + } + } + + if (!cmd.empty() && line.length() >= cmd.length() && cursor_byte_pos >= cmd.length()) { + const std::string path_prefix = std::string(line.substr(cmd.length(), cursor_byte_pos - cmd.length())); + const std::string path_postfix = std::string(line.substr(cursor_byte_pos)); + auto cur_dir = std::filesystem::current_path(); + std::string cur_dir_str = cur_dir.string(); + std::string expanded_prefix = path_prefix; + +#if !defined(_WIN32) + if (string_starts_with(path_prefix, "~")) { + const char * home = std::getenv("HOME"); + if (home && home[0]) { + expanded_prefix = std::string(home) + path_prefix.substr(1); + } + } + if (string_starts_with(expanded_prefix, "/")) { +#else + if (std::isalpha(expanded_prefix[0]) && expanded_prefix.find(':') == 1) { +#endif + cur_dir = std::filesystem::path(expanded_prefix).parent_path(); + cur_dir_str = ""; + } else if (!path_prefix.empty()) { + cur_dir /= std::filesystem::path(path_prefix).parent_path(); + } + + std::error_code ec; + for (const auto & entry : std::filesystem::directory_iterator(cur_dir, ec)) { + if (ec) { + break; + } + if (!entry.exists(ec)) { + ec.clear(); + continue; + } + + const std::string path_full = entry.path().string(); + std::string path_entry = !cur_dir_str.empty() && string_starts_with(path_full, cur_dir_str) ? path_full.substr(cur_dir_str.length() + 1) : path_full; + + if (entry.is_directory(ec)) { + path_entry.push_back(std::filesystem::path::preferred_separator); + } + + if (expanded_prefix.empty() || string_starts_with(path_entry, expanded_prefix)) { + std::string updated_line = cmd + path_entry; + matches.emplace_back(updated_line + path_postfix, updated_line.length()); + } + + if (ec) { + ec.clear(); + } + } + + if (matches.empty()) { + std::string updated_line = cmd + path_prefix; + matches.emplace_back(updated_line + path_postfix, updated_line.length()); + } + + // Add the longest common prefix + if (!expanded_prefix.empty() && matches.size() > 1) { + const std::string_view match0(matches[0].first); + const std::string_view match1(matches[1].first); + auto it = std::mismatch(match0.begin(), match0.end(), match1.begin(), match1.end()); + size_t len = it.first - match0.begin(); + + for (size_t i = 2; i < matches.size(); ++i) { + const std::string_view matchi(matches[i].first); + auto cmp = std::mismatch(match0.begin(), match0.end(), matchi.begin(), matchi.end()); + len = std::min(len, static_cast(cmp.first - match0.begin())); + } + + std::string updated_line = std::string(match0.substr(0, len)); + matches.emplace_back(updated_line + path_postfix, updated_line.length()); + } + + std::sort(matches.begin(), matches.end(), [](const auto & a, const auto & b) { + return a.first.compare(0, a.second, b.first, 0, b.second) < 0; + }); + } + + return matches; +} + int main(int argc, char ** argv) { common_params params; @@ -223,6 +366,7 @@ int main(int argc, char ** argv) { atexit([]() { console::cleanup(); }); console::set_display(DISPLAY_TYPE_RESET); + console::set_completion_callback(auto_completion_callback); #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) struct sigaction sigint_action; @@ -262,12 +406,15 @@ int main(int argc, char ** argv) { modalities += ", audio"; } - if (!params.system_prompt.empty()) { - ctx_cli.messages.push_back({ - {"role", "system"}, - {"content", params.system_prompt} - }); - } + auto add_system_prompt = [&]() { + if (!params.system_prompt.empty()) { + ctx_cli.messages.push_back({ + {"role", "system"}, + {"content", params.system_prompt} + }); + } + }; + add_system_prompt(); console::log("\n"); console::log("%s\n", LLAMA_ASCII_LOGO); @@ -357,6 +504,8 @@ int main(int argc, char ** argv) { } } else if (string_starts_with(buffer, "/clear")) { ctx_cli.messages.clear(); + add_system_prompt(); + ctx_cli.input_files.clear(); console::log("Chat history cleared.\n"); continue; diff --git a/tools/completion/README.md b/tools/completion/README.md index 3ca3e68454..f868c2c7d7 100644 --- a/tools/completion/README.md +++ b/tools/completion/README.md @@ -140,8 +140,8 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | | `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--mmap, --no-mmap` | whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | -| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. Takes precedence over --mmap (default: enabled)
(env: LLAMA_ARG_DIO) | +| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | +| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)
(env: LLAMA_ARG_DIO) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -192,14 +192,14 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | | `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | -| `--temp N` | temperature (default: 0.80) | +| `--temp, --temperature N` | temperature (default: 0.80) | | `--top-k N` | top-k sampling (default: 40, 0 = disabled)
(env: LLAMA_ARG_TOP_K) | | `--top-p N` | top-p sampling (default: 0.95, 1.0 = disabled) | | `--min-p N` | min-p sampling (default: 0.05, 0.0 = disabled) | -| `--top-nsigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) | +| `--top-nsigma, --top-n-sigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) | | `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) | | `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) | -| `--typical N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) | +| `--typical, --typical-p N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) | | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) | | `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) | @@ -480,7 +480,7 @@ Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0` Exclude Top Choices (XTC) is a unique sampler that is designed to remove top tokens from consideration and avoid more obvious and repetitive outputs. With a chance of `xtc-probability` it searches for tokens with probabilities of `xtc-threshold` and above, then removes all such tokens except the least probable one. -By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models. +By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repetition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models. Being experimental and unique, XTC is disabled by default. The recommended combination of samplers is Min-P followed by XTC on its default settings: `--sampling-seq mx --min-p 0.02 --xtc-probability 0.5`. @@ -531,7 +531,7 @@ These options help improve the performance and memory usage of the LLaMA models. ### NUMA support -- `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilitizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes. +- `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes. - `--numa isolate`: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node. - `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitrary core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus. diff --git a/tools/completion/completion.cpp b/tools/completion/completion.cpp index aed2c0e38f..2e0f087184 100644 --- a/tools/completion/completion.cpp +++ b/tools/completion/completion.cpp @@ -6,6 +6,7 @@ #include "llama.h" #include "chat.h" +#include #include #include #include @@ -84,6 +85,8 @@ static void sigint_handler(int signo) { #endif int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; g_params = ¶ms; @@ -376,7 +379,7 @@ int main(int argc, char ** argv) { // remove any "future" tokens that we might have inherited from the previous session if (session_tokens.size() > n_match) { if (!llama_memory_seq_rm(mem, -1, n_match, -1)) { - LOG_WRN("%s: unable to resuse common prefix (for example, when the memory is recurrent)\n", __func__); + LOG_WRN("%s: unable to reuse common prefix (for example, when the memory is recurrent)\n", __func__); llama_memory_clear(mem, true); session_tokens.clear(); n_match = 0; diff --git a/tools/cvector-generator/cvector-generator.cpp b/tools/cvector-generator/cvector-generator.cpp index 3ba7c52950..dcce0e9841 100644 --- a/tools/cvector-generator/cvector-generator.cpp +++ b/tools/cvector-generator/cvector-generator.cpp @@ -7,6 +7,8 @@ #include "pca.hpp" #include "mean.hpp" +#include + #ifdef GGML_USE_CUDA #include "ggml-cuda.h" #endif @@ -108,7 +110,7 @@ struct callback_data { auto diff_filtered = filter_nonzero_rows(v_pos[il]); v_diff_filtered.push_back(diff_filtered); } - return v_diff_filtered; // for convinient, we return the result std::vector + return v_diff_filtered; // for convenient, we return the result std::vector } // delete zero rows from a given 2D tensor @@ -392,6 +394,8 @@ static int prepare_entries(common_params & params, train_context & ctx_train) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; params.out_file = "control_vector.gguf"; diff --git a/tools/export-lora/export-lora.cpp b/tools/export-lora/export-lora.cpp index 41f426208f..50774c59ba 100644 --- a/tools/export-lora/export-lora.cpp +++ b/tools/export-lora/export-lora.cpp @@ -5,6 +5,7 @@ #include "arg.h" #include "common.h" +#include #include #include #include @@ -411,6 +412,8 @@ static void print_usage(int, char ** argv) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; params.out_file = "ggml-lora-merged-f16.gguf"; diff --git a/tools/gguf-split/gguf-split.cpp b/tools/gguf-split/gguf-split.cpp index 30e771564e..f99f0299b9 100644 --- a/tools/gguf-split/gguf-split.cpp +++ b/tools/gguf-split/gguf-split.cpp @@ -6,6 +6,7 @@ #include #include #include +#include #include #include #include @@ -567,6 +568,8 @@ static void gguf_merge(const split_params & split_params) { } int main(int argc, const char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + split_params params; split_params_parse(argc, argv, params); diff --git a/tools/imatrix/README.md b/tools/imatrix/README.md index 4505cb4ce8..4cbe4fd0cf 100644 --- a/tools/imatrix/README.md +++ b/tools/imatrix/README.md @@ -95,4 +95,4 @@ Weighted averages of Σ(Act²), ZD Score and CosSim are also calculated. #### Important note on the computed Statistics When using these statistics, please note that they are computed on the squared activations, **not on the actual (raw) activations**. -Whilst the results are still useful, they're less realiable than using the raw values, and in the case of the cosine similarity, could be misleading if the tensor contains opposite vectors. +Whilst the results are still useful, they're less reliable than using the raw values, and in the case of the cosine similarity, could be misleading if the tensor contains opposite vectors. diff --git a/tools/imatrix/imatrix.cpp b/tools/imatrix/imatrix.cpp index 669de55ddb..bbedb159cd 100644 --- a/tools/imatrix/imatrix.cpp +++ b/tools/imatrix/imatrix.cpp @@ -6,6 +6,7 @@ #include #include +#include #include #include #include @@ -912,7 +913,9 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c const bool add_bos = llama_vocab_get_add_bos(vocab); - GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); + if (llama_pooling_type(ctx) != LLAMA_POOLING_TYPE_LAST) { + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); + } auto tim1 = std::chrono::high_resolution_clock::now(); LOG_INF("%s: tokenizing the input ..\n", __func__); @@ -1189,6 +1192,8 @@ static bool show_statistics(const common_params & params) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); + common_params params; params.out_file = "imatrix.gguf"; diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp index 7da6c3957c..b0f1d6b936 100644 --- a/tools/llama-bench/llama-bench.cpp +++ b/tools/llama-bench/llama-bench.cpp @@ -20,6 +20,7 @@ #include #include "common.h" +#include "download.h" #include "ggml.h" #include "llama.h" @@ -312,6 +313,9 @@ static std::vector parse_int_range(const std::string & s) { struct cmd_params { std::vector model; + std::vector hf_repo; + std::vector hf_file; + std::string hf_token; std::vector n_prompt; std::vector n_gen; std::vector> n_pg; @@ -351,6 +355,9 @@ struct cmd_params { static const cmd_params cmd_params_defaults = { /* model */ { "models/7B/ggml-model-q4_0.gguf" }, + /* hf_repo */ {}, + /* hf_file */ {}, + /* hf_token */ "", /* n_prompt */ { 512 }, /* n_gen */ { 128 }, /* n_pg */ {}, @@ -372,7 +379,7 @@ static const cmd_params cmd_params_defaults = { /* devices */ { {} }, /* tensor_split */ { std::vector(llama_max_devices(), 0.0f) }, /* tensor_buft_overrides*/ { std::vector{ { nullptr, nullptr } } }, - /* use_mmap */ { false }, + /* use_mmap */ { true }, /* use_direct_io */ { false }, /* embeddings */ { false }, /* no_op_offload */ { false }, @@ -393,74 +400,57 @@ static void print_usage(int /* argc */, char ** argv) { printf("\n"); printf("options:\n"); printf(" -h, --help\n"); - printf(" --numa numa mode (default: disabled)\n"); - printf(" -r, --repetitions number of times to repeat each test (default: %d)\n", - cmd_params_defaults.reps); - printf(" --prio <-1|0|1|2|3> process/thread priority (default: %d)\n", - cmd_params_defaults.prio); - printf(" --delay <0...N> (seconds) delay between each test (default: %d)\n", - cmd_params_defaults.delay); - printf(" -o, --output output format printed to stdout (default: %s)\n", - output_format_str(cmd_params_defaults.output_format)); - printf(" -oe, --output-err output format printed to stderr (default: %s)\n", - output_format_str(cmd_params_defaults.output_format_stderr)); - printf(" --list-devices list available devices and exit\n"); - printf(" -v, --verbose verbose output\n"); - printf(" --progress print test progress indicators\n"); - printf(" --no-warmup skip warmup runs before benchmarking\n"); + printf(" --numa numa mode (default: disabled)\n"); + printf(" -r, --repetitions number of times to repeat each test (default: %d)\n", cmd_params_defaults.reps); + printf(" --prio <-1|0|1|2|3> process/thread priority (default: %d)\n", cmd_params_defaults.prio); + printf(" --delay <0...N> (seconds) delay between each test (default: %d)\n", cmd_params_defaults.delay); + printf(" -o, --output output format printed to stdout (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); + printf(" -oe, --output-err output format printed to stderr (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr)); + printf(" --list-devices list available devices and exit\n"); + printf(" -v, --verbose verbose output\n"); + printf(" --progress print test progress indicators\n"); + printf(" --no-warmup skip warmup runs before benchmarking\n"); if (llama_supports_rpc()) { - printf(" -rpc, --rpc register RPC devices (comma separated)\n"); + printf(" -rpc, --rpc register RPC devices (comma separated)\n"); } printf("\n"); printf("test parameters:\n"); - printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - printf(" -p, --n-prompt (default: %s)\n", - join(cmd_params_defaults.n_prompt, ",").c_str()); - printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - printf(" -pg (default: %s)\n", - join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); - printf(" -d, --n-depth (default: %s)\n", - join(cmd_params_defaults.n_depth, ",").c_str()); - printf(" -b, --batch-size (default: %s)\n", - join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ub, --ubatch-size (default: %s)\n", - join(cmd_params_defaults.n_ubatch, ",").c_str()); - printf(" -ctk, --cache-type-k (default: %s)\n", - join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv, --cache-type-v (default: %s)\n", - join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); - printf(" -t, --threads (default: %s)\n", - join(cmd_params_defaults.n_threads, ",").c_str()); - printf(" -C, --cpu-mask (default: %s)\n", - join(cmd_params_defaults.cpu_mask, ",").c_str()); - printf(" --cpu-strict <0|1> (default: %s)\n", - join(cmd_params_defaults.cpu_strict, ",").c_str()); - printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); - printf(" -ngl, --n-gpu-layers (default: %s)\n", - join(cmd_params_defaults.n_gpu_layers, ",").c_str()); - printf(" -ncmoe, --n-cpu-moe (default: %s)\n", - join(cmd_params_defaults.n_cpu_moe, ",").c_str()); - printf(" -sm, --split-mode (default: %s)\n", - join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); - printf(" -mg, --main-gpu (default: %s)\n", - join(cmd_params_defaults.main_gpu, ",").c_str()); - printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", - join(cmd_params_defaults.no_kv_offload, ",").c_str()); - printf(" -fa, --flash-attn <0|1> (default: %s)\n", - join(cmd_params_defaults.flash_attn, ",").c_str()); - printf(" -dev, --device (default: auto)\n"); - printf(" -mmp, --mmap <0|1> (default: %s)\n", - join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" -dio, --direct-io <0|1> (default: %s)\n", - join(cmd_params_defaults.use_direct_io, ",").c_str()); - printf(" -embd, --embeddings <0|1> (default: %s)\n", - join(cmd_params_defaults.embeddings, ",").c_str()); - printf(" -ts, --tensor-split (default: 0)\n"); + printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); + printf(" -hf, -hfr, --hf-repo /[:quant] Hugging Face model repository; quant is optional, case-insensitive\n"); + printf(" default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"); + printf(" example: unsloth/phi-4-GGUF:Q4_K_M\n"); + printf(" (default: unused)\n"); + printf(" -hff, --hf-file Hugging Face model file. If specified, it will override the quant in --hf-repo\n"); + printf(" (default: unused)\n"); + printf(" -hft, --hf-token Hugging Face access token\n"); + printf(" (default: value from HF_TOKEN environment variable)\n"); + printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); + printf(" -d, --n-depth (default: %s)\n", join(cmd_params_defaults.n_depth, ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" -ub, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); + printf(" -ctk, --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -C, --cpu-mask (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str()); + printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str()); + printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); + printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -ncmoe, --n-cpu-moe (default: %s)\n", join(cmd_params_defaults.n_cpu_moe, ",").c_str()); + printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); + printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str()); + printf(" -dev, --device (default: auto)\n"); + printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); + printf(" -dio, --direct-io <0|1> (default: %s)\n", join(cmd_params_defaults.use_direct_io, ",").c_str()); + printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); + printf(" -ts, --tensor-split (default: 0)\n"); printf(" -ot --override-tensor =;...\n"); - printf(" (default: disabled)\n"); - printf(" -nopo, --no-op-offload <0|1> (default: 0)\n"); - printf(" --no-host <0|1> (default: %s)\n", - join(cmd_params_defaults.no_host, ",").c_str()); + printf(" (default: disabled)\n"); + printf(" -nopo, --no-op-offload <0|1> (default: 0)\n"); + printf(" --no-host <0|1> (default: %s)\n", join(cmd_params_defaults.no_host, ",").c_str()); printf("\n"); printf( "Multiple values can be given for each parameter by separating them with ','\n" @@ -514,6 +504,10 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { params.progress = cmd_params_defaults.progress; params.no_warmup = cmd_params_defaults.no_warmup; + if (const char * env = getenv("HF_TOKEN")) { + params.hf_token = env; + } + for (int i = 1; i < argc; i++) { arg = argv[i]; if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { @@ -531,6 +525,26 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = string_split(argv[i], split_delim); params.model.insert(params.model.end(), p.begin(), p.end()); + } else if (arg == "-hf" || arg == "-hfr" || arg == "--hf-repo") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.hf_repo.insert(params.hf_repo.end(), p.begin(), p.end()); + } else if (arg == "-hff" || arg == "--hf-file") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.hf_file.insert(params.hf_file.end(), p.begin(), p.end()); + } else if (arg == "-hft" || arg == "--hf-token") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.hf_token = argv[i]; } else if (arg == "-p" || arg == "--n-prompt") { if (++i >= argc) { invalid_param = true; @@ -961,6 +975,44 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { exit(1); } + if (!params.hf_repo.empty()) { + for (size_t i = 0; i < params.hf_repo.size(); i++) { + common_params_model model; + + // step 1: no `-hff` provided, we auto-detect based on the `-hf` flag + if (params.hf_file.empty() || params.hf_file[i].empty()) { + auto auto_detected = common_get_hf_file(params.hf_repo[i], params.hf_token, false); + if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) { + exit(1); + } + + model.name = params.hf_repo[i]; + model.hf_repo = auto_detected.repo; + model.hf_file = auto_detected.ggufFile; + } else { + model.hf_file = params.hf_file[i]; + } + + // step 2: construct the model cache path + std::string clean_fname = model.hf_repo + "_" + model.hf_file; + string_replace_all(clean_fname, "\\", "_"); + string_replace_all(clean_fname, "/", "_"); + model.path = fs_get_cache_file(clean_fname); + + // step 3: download the model if not exists + std::string model_endpoint = get_model_endpoint(); + model.url = model_endpoint + model.hf_repo + "/resolve/main/" + model.hf_file; + + bool ok = common_download_model(model, params.hf_token, false); + if (!ok) { + fprintf(stderr, "error: failed to download model from %s\n", model.url.c_str()); + exit(1); + } + + params.model.push_back(model.path); + } + } + // set defaults if (params.model.empty()) { params.model = cmd_params_defaults.model; @@ -2034,8 +2086,9 @@ static std::unique_ptr create_printer(output_formats format) { } int main(int argc, char ** argv) { + std::setlocale(LC_NUMERIC, "C"); // try to set locale for unicode characters in markdown - setlocale(LC_CTYPE, ".UTF-8"); + std::setlocale(LC_CTYPE, ".UTF-8"); #if !defined(NDEBUG) fprintf(stderr, "warning: asserts enabled, performance may be affected\n"); diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index 3be3c27e87..a39de8c928 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -62,6 +62,10 @@ set_target_properties(mtmd PROPERTIES PUBLIC_HEADER "${MTMD_PUBLIC_HEADERS}") +set_target_properties(mtmd + PROPERTIES + PRIVATE_HEADER debug/mtmd-debug.h) + install(TARGETS mtmd LIBRARY PUBLIC_HEADER) if (NOT MSVC) @@ -96,3 +100,9 @@ if(LLAMA_TOOLS_INSTALL) endif() target_link_libraries (${TARGET} PRIVATE common mtmd Threads::Threads) target_compile_features(${TARGET} PRIVATE cxx_std_17) + +# mtmd-debug tool +add_executable(llama-mtmd-debug debug/mtmd-debug.cpp) +set_target_properties(llama-mtmd-debug PROPERTIES OUTPUT_NAME llama-mtmd-debug) +target_link_libraries(llama-mtmd-debug PRIVATE common mtmd Threads::Threads) +target_compile_features(llama-mtmd-debug PRIVATE cxx_std_17) diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index a30c32ed42..3eb66f9145 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -68,7 +68,7 @@ #define TN_POS_EMBD "%s.position_embd.weight" #define TN_CLASS_EMBD "v.class_embd" -#define TN_PATCH_EMBD "v.patch_embd.weight" // not rename tensor with ".0" postfix for backwrad compat +#define TN_PATCH_EMBD "v.patch_embd.weight" // not rename tensor with ".0" postfix for backward compat #define TN_PATCH_EMBD_1 "v.patch_embd.weight.1" #define TN_PATCH_BIAS "v.patch_embd.bias" #define TN_NORM_EMBD "v.norm_embd.%s" @@ -216,6 +216,7 @@ enum projector_type { PROJECTOR_TYPE_GEMMA3, PROJECTOR_TYPE_GEMMA3NV, PROJECTOR_TYPE_GEMMA3NA, + PROJECTOR_TYPE_PHI4, PROJECTOR_TYPE_IDEFICS3, PROJECTOR_TYPE_PIXTRAL, PROJECTOR_TYPE_QWEN25VL, @@ -253,6 +254,7 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_GEMMA3, "gemma3"}, { PROJECTOR_TYPE_GEMMA3NV, "gemma3nv"}, { PROJECTOR_TYPE_GEMMA3NA, "gemma3na"}, + { PROJECTOR_TYPE_PHI4, "phi4"}, { PROJECTOR_TYPE_IDEFICS3, "idefics3"}, { PROJECTOR_TYPE_PIXTRAL, "pixtral"}, { PROJECTOR_TYPE_ULTRAVOX, "ultravox"}, @@ -577,10 +579,9 @@ static void print_tensor_data(ggml_tensor * t, uint8_t * data, int64_t n) { } } -void clip_debug_encode(clip_ctx * ctx, int h, int w, float fill_value); - // // API used internally with mtmd // projector_type clip_get_projector_type(const struct clip_ctx * ctx); +void clip_set_debug_output_embeddings(struct clip_ctx * ctx, bool debug); diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h index e0eb9b32c8..eeb8da58e0 100644 --- a/tools/mtmd/clip-model.h +++ b/tools/mtmd/clip-model.h @@ -46,7 +46,7 @@ struct clip_hparams { float image_std[3]; // for models using dynamic image size, we need to have a smaller image size to warmup - // otherwise, user will get OOM everytime they load the model + // otherwise, user will get OOM every time they load the model int32_t warmup_image_size = 0; int32_t warmup_audio_size = 3000; @@ -221,7 +221,7 @@ struct clip_model { // embeddings ggml_tensor * class_embedding = nullptr; ggml_tensor * patch_embeddings_0 = nullptr; - ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) + ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temporal dimension (Qwen2VL) ggml_tensor * patch_bias = nullptr; ggml_tensor * position_embeddings = nullptr; ggml_tensor * norm_embd_w = nullptr; diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 607d4b8373..3d6cf6fd84 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -159,6 +159,8 @@ struct clip_ctx { clip_flash_attn_type flash_attn_type = CLIP_FLASH_ATTN_TYPE_AUTO; bool is_allocated = false; + bool debug_output_embeddings = false; + clip_ctx(clip_context_params & ctx_params) { flash_attn_type = ctx_params.flash_attn_type; backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr); @@ -205,6 +207,8 @@ struct clip_ctx { if (ctx_params.cb_eval != nullptr) { ggml_backend_sched_set_eval_callback(sched.get(), ctx_params.cb_eval, ctx_params.cb_eval_user_data); } + + debug_output_embeddings = std::getenv("MTMD_DEBUG_EMBEDDINGS") != nullptr; } ~clip_ctx() { @@ -792,6 +796,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 case PROJECTOR_TYPE_IDEFICS3: case PROJECTOR_TYPE_LFM2: case PROJECTOR_TYPE_JANUS_PRO: + case PROJECTOR_TYPE_PHI4: { builder = std::make_unique(ctx, img); } break; @@ -1144,6 +1149,13 @@ struct clip_model_loader { // ref: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B/blob/main/processor_config.json hparams.set_limit_image_tokens(64, 256); } break; + case PROJECTOR_TYPE_PHI4: + { + hparams.n_merge = 1; + get_u32(KEY_IMAGE_MIN_PIXELS, hparams.image_min_pixels); + get_u32(KEY_IMAGE_MAX_PIXELS, hparams.image_max_pixels); + hparams.set_warmup_n_tokens(16*16); + } break; case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_LIGHTONOCR: { @@ -1841,6 +1853,13 @@ struct clip_model_loader { model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight")); model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias")); } break; + case PROJECTOR_TYPE_PHI4: + { + model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); + model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias")); + model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); + model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias")); + } break; case PROJECTOR_TYPE_LFM2A: { for (int i : {0, 2, 3, 5, 6}) { @@ -2178,8 +2197,6 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params // TODO: we don't support audio for Gemma 3N, but GGUF contains audio tensors // we can remove this check when we implement audio support for Gemma 3N skip_audio = ctx_vision->model.proj_type == PROJECTOR_TYPE_GEMMA3NV; - - // clip_debug_encode(ctx_vision, 24*14, 24*14, 0.5f); } if (loader.has_audio && !skip_audio) { @@ -2287,7 +2304,7 @@ static void normalize_image_u8_to_f32(const clip_image_u8 & src, clip_image_f32 } } -// set of tools to manupulate images +// set of tools to manipulate images // in the future, we can have HW acceleration by allowing this struct to access 3rd party lib like imagick or opencv struct img_tool { enum resize_algo { @@ -3157,6 +3174,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str res_imgs->entries.push_back(std::move(img_f32)); } break; + case PROJECTOR_TYPE_PHI4: case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_LIGHTONOCR: { @@ -3383,6 +3401,7 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im case PROJECTOR_TYPE_MLP: case PROJECTOR_TYPE_MLP_NORM: case PROJECTOR_TYPE_JANUS_PRO: + case PROJECTOR_TYPE_PHI4: { // do nothing } break; @@ -3884,6 +3903,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima case PROJECTOR_TYPE_VOXTRAL: case PROJECTOR_TYPE_MUSIC_FLAMINGO: case PROJECTOR_TYPE_JANUS_PRO: + case PROJECTOR_TYPE_PHI4: case PROJECTOR_TYPE_COGVLM: { // do nothing @@ -3963,7 +3983,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } // Debug: dump final embeddings if MTMD_DEBUG_EMBEDDINGS is set - if (std::getenv("MTMD_DEBUG_EMBEDDINGS") != nullptr) { + if (ctx->debug_output_embeddings) { const int64_t n_embd = embeddings->ne[0]; const int64_t n_tokens = embeddings->ne[1]; std::vector emb_data(n_embd * n_tokens); @@ -4013,6 +4033,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { case PROJECTOR_TYPE_LDPV2: return ctx->model.mm_model_peg_0_b->ne[0]; case PROJECTOR_TYPE_MLP: + case PROJECTOR_TYPE_PHI4: case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_LIGHTONOCR: return ctx->model.mm_2_w->ne[1]; @@ -4141,14 +4162,7 @@ const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx) { // // API for debugging // -void clip_debug_encode(clip_ctx * ctx, int h, int w, float fill_value) { - clip_image_f32 img; - img.nx = w; - img.ny = h; - img.buf.resize(h * w * 3); - for (int i = 0; i < h * w * 3; i++) { - img.buf[i] = static_cast(fill_value); - } - clip_image_encode(ctx, 1, &img, nullptr); - GGML_ASSERT(img.buf.empty() && "expected, always stop here"); + +void clip_set_debug_output_embeddings(clip_ctx * ctx, bool enable) { + ctx->debug_output_embeddings = enable; } diff --git a/tools/mtmd/debug/mtmd-debug.cpp b/tools/mtmd/debug/mtmd-debug.cpp new file mode 100644 index 0000000000..d42806ec3f --- /dev/null +++ b/tools/mtmd/debug/mtmd-debug.cpp @@ -0,0 +1,229 @@ +#include "mtmd-debug.h" + +#include "arg.h" +#include "debug.h" +#include "log.h" +#include "common.h" +#include "llama.h" +#include "ggml.h" +#include "mtmd.h" +#include "mtmd-helper.h" + +#include +#include +#include +#include +#include + +// INTERNAL TOOL FOR DEBUGGING PURPOSES ONLY +// NOT INTENDED FOR PUBLIC USE + +static void show_additional_info(int /*argc*/, char ** argv) { + LOG( + "Internal debugging tool for mtmd; See mtmd-debug.md for the pytorch equivalent code\n" + "Note: we repurpose some args from other examples, they will have different meaning here\n" + "\n" + "Usage: %s -m --mmproj -p -n --image --audio