llama : do not print "offloading layers" message in CPU-only builds (#5416)

* fix f16_sycl cpy call

* rm old logic

* add fp16 build CI

* use macro

* format fix

.devops/main-intel.Dockerfile

@@ -1,8 +1,8 @@
 ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
-ARG UBUNTU_VERSION=22.04
 
-FROM intel/hpckit:$ONEAPI_VERSION as build
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
 
+ARG LLAMA_SYCL_F16=OFF
 RUN apt-get update && \
     apt-get install -y git
 
@@ -10,16 +10,18 @@ WORKDIR /app
 
 COPY . .
 
-# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance
 RUN mkdir build && \
     cd build && \
-    cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \
-    cmake --build . --config Release --target main server
+    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+        echo "LLAMA_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
+    fi && \
+    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
+    cmake --build . --config Release --target main
 
-FROM ubuntu:$UBUNTU_VERSION as runtime
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
 
 COPY --from=build /app/build/bin/main /main
-COPY --from=build /app/build/bin/server /server
 
 ENV LC_ALL=C.utf8
 

.devops/main-vulkan.Dockerfile

@@ -0,0 +1,29 @@
+ARG UBUNTU_VERSION=jammy
+
+FROM ubuntu:$UBUNTU_VERSION as build
+
+# Install build tools
+RUN apt update && apt install -y git build-essential cmake wget
+
+# Install Vulkan SDK
+RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
+    apt update -y && \
+    apt-get install -y vulkan-sdk
+
+# Build it
+WORKDIR /app
+COPY . .
+RUN mkdir build && \
+    cd build && \
+    cmake .. -DLLAMA_VULKAN=1 && \
+    cmake --build . --config Release --target main
+
+# Clean up
+WORKDIR /
+RUN cp /app/build/bin/main /main && \
+    rm -rf /app
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/main" ]

.devops/nix/package.nix

@@ -13,18 +13,22 @@
   cudaPackages,
   darwin,
   rocmPackages,
+  vulkan-headers,
+  vulkan-loader,
   clblast,
   useBlas ? builtins.all (x: !x) [
     useCuda
     useMetalKit
     useOpenCL
     useRocm
+    useVulkan
   ],
   useCuda ? config.cudaSupport,
   useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL,
   useMpi ? false, # Increases the runtime closure size by ~700M
   useOpenCL ? false,
   useRocm ? config.rocmSupport,
+  useVulkan ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
 }@inputs:
 
@@ -48,7 +52,8 @@ let
     ++ lib.optionals useMetalKit [ "MetalKit" ]
     ++ lib.optionals useMpi [ "MPI" ]
     ++ lib.optionals useOpenCL [ "OpenCL" ]
-    ++ lib.optionals useRocm [ "ROCm" ];
+    ++ lib.optionals useRocm [ "ROCm" ]
+    ++ lib.optionals useVulkan [ "Vulkan" ];
 
   pnameSuffix =
     strings.optionalString (suffices != [ ])
@@ -108,6 +113,11 @@ let
     hipblas
     rocblas
   ];
+
+  vulkanBuildInputs = [
+    vulkan-headers
+    vulkan-loader
+  ];
 in
 
 effectiveStdenv.mkDerivation (
@@ -164,7 +174,8 @@ effectiveStdenv.mkDerivation (
       ++ optionals useCuda cudaBuildInputs
       ++ optionals useMpi [ mpi ]
       ++ optionals useOpenCL [ clblast ]
-      ++ optionals useRocm rocmBuildInputs;
+      ++ optionals useRocm rocmBuildInputs
+      ++ optionals useVulkan vulkanBuildInputs;
 
     cmakeFlags =
       [
@@ -178,6 +189,7 @@ effectiveStdenv.mkDerivation (
         (cmakeBool "LLAMA_HIPBLAS" useRocm)
         (cmakeBool "LLAMA_METAL" useMetalKit)
         (cmakeBool "LLAMA_MPI" useMpi)
+        (cmakeBool "LLAMA_VULKAN" useVulkan)
       ]
       ++ optionals useCuda [
         (
@@ -218,6 +230,7 @@ effectiveStdenv.mkDerivation (
         useMpi
         useOpenCL
         useRocm
+        useVulkan
         ;
 
       shell = mkShell {
@@ -242,11 +255,11 @@ effectiveStdenv.mkDerivation (
       # Configurations we don't want even the CI to evaluate. Results in the
       # "unsupported platform" messages. This is mostly a no-op, because
       # cudaPackages would've refused to evaluate anyway.
-      badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin;
+      badPlatforms = optionals (useCuda || useOpenCL || useVulkan) lib.platforms.darwin;
 
       # Configurations that are known to result in build failures. Can be
       # overridden by importing Nixpkgs with `allowBroken = true`.
-      broken = (useMetalKit && !effectiveStdenv.isDarwin);
+      broken = (useMetalKit && !effectiveStdenv.isDarwin) || (useVulkan && effectiveStdenv.isDarwin);
 
       description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}";
       homepage = "https://github.com/ggerganov/llama.cpp/";

.devops/server-intel.Dockerfile

@@ -1,8 +1,8 @@
 ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
-ARG UBUNTU_VERSION=22.04
 
-FROM intel/hpckit:$ONEAPI_VERSION as build
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
 
+ARG LLAMA_SYCL_F16=OFF
 RUN apt-get update && \
     apt-get install -y git
 
@@ -10,13 +10,16 @@ WORKDIR /app
 
 COPY . .
 
-# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance
 RUN mkdir build && \
     cd build && \
-    cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \
-    cmake --build . --config Release --target main server
+    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+        echo "LLAMA_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
+    fi && \
+    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
+    cmake --build . --config Release --target server
 
-FROM ubuntu:$UBUNTU_VERSION as runtime
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
 
 COPY --from=build /app/build/bin/server /server
 

.devops/server-vulkan.Dockerfile

@@ -0,0 +1,29 @@
+ARG UBUNTU_VERSION=jammy
+
+FROM ubuntu:$UBUNTU_VERSION as build
+
+# Install build tools
+RUN apt update && apt install -y git build-essential cmake wget
+
+# Install Vulkan SDK
+RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
+    apt update -y && \
+    apt-get install -y vulkan-sdk
+
+# Build it
+WORKDIR /app
+COPY . .
+RUN mkdir build && \
+    cd build && \
+    cmake .. -DLLAMA_VULKAN=1 && \
+    cmake --build . --config Release --target server
+
+# Clean up
+WORKDIR /
+RUN cp /app/build/bin/server /server && \
+    rm -rf /app
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/server" ]

.github/workflows/build.yml

@@ -184,6 +184,47 @@ jobs:
           cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
           cmake --build . --config Release -j $(nproc)
 
+  ubuntu-22-cmake-sycl-fp16:
+    runs-on: ubuntu-22.04
+
+    continue-on-error: true
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: add oneAPI to apt
+        shell: bash
+        run: |
+          cd /tmp
+          wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+          sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+          rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+          sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
+
+      - name: install oneAPI dpcpp compiler
+        shell: bash
+        run: |
+          sudo apt update
+          sudo apt install intel-oneapi-compiler-dpcpp-cpp
+
+      - name: install oneAPI MKL library
+        shell: bash
+        run: |
+          sudo apt install intel-oneapi-mkl-devel
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v3
+
+      - name: Build
+        id: cmake_build
+        run: |
+          source /opt/intel/oneapi/setvars.sh
+          mkdir build
+          cd build
+          cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON ..
+          cmake --build . --config Release -j $(nproc)
+
   # TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
   #       how to debug it.
   #       ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124

CMakeLists.txt

@@ -79,7 +79,7 @@ if (NOT MSVC)
 endif()
 
 if (WIN32)
-    option(LLAMA_WIN_VER                     "llama: Windows Version"                           0x602)
+    set(LLAMA_WIN_VER "0x602" CACHE STRING "llama: Windows Version")
 endif()
 
 # 3rd party libs
@@ -100,6 +100,10 @@ option(LLAMA_HIPBLAS                         "llama: use hipBLAS"
 option(LLAMA_HIP_UMA                         "llama: use HIP unified memory architecture"       OFF)
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
 option(LLAMA_VULKAN                          "llama: use Vulkan"                                OFF)
+option(LLAMA_VULKAN_CHECK_RESULTS            "llama: run Vulkan op checks"                      OFF)
+option(LLAMA_VULKAN_DEBUG                    "llama: enable Vulkan debug output"                OFF)
+option(LLAMA_VULKAN_VALIDATE                 "llama: enable Vulkan validation"                  OFF)
+option(LLAMA_VULKAN_RUN_TESTS                "llama: run Vulkan tests"                          OFF)
 option(LLAMA_METAL                           "llama: use Metal"                                 ${LLAMA_METAL_DEFAULT})
 option(LLAMA_METAL_NDEBUG                    "llama: disable Metal debugging"                   OFF)
 option(LLAMA_METAL_SHADER_DEBUG              "llama: compile Metal with -fno-fast-math"         OFF)
@@ -431,6 +435,22 @@ if (LLAMA_VULKAN)
 
         add_compile_definitions(GGML_USE_VULKAN)
 
+        if (LLAMA_VULKAN_CHECK_RESULTS)
+            target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_CHECK_RESULTS)
+        endif()
+
+        if (LLAMA_VULKAN_DEBUG)
+            target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_DEBUG)
+        endif()
+
+        if (LLAMA_VULKAN_VALIDATE)
+            target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_VALIDATE)
+        endif()
+
+        if (LLAMA_VULKAN_RUN_TESTS)
+            target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_RUN_TESTS)
+        endif()
+
         set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-vulkan)
     else()
         message(WARNING "Vulkan not found")
@@ -789,9 +809,9 @@ if (LLAMA_CCACHE)
     if (LLAMA_CCACHE_FOUND)
         set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
         set(ENV{CCACHE_SLOPPINESS} time_macros)
-        message(STATUS "Using ccache")
+        message(STATUS "ccache found, compilation results will be cached. Disable with LLAMA_CCACHE=OFF.")
     else()
-        message(STATUS "Warning: ccache not found - consider installing it or use LLAMA_CCACHE=OFF")
+        message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF")
     endif ()
 endif()
 
@@ -830,7 +850,9 @@ endif()
 
 set(ARCH_FLAGS "")
 
-if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64"))
+if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
+    (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+     CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))
     message(STATUS "ARM detected")
     if (MSVC)
         add_compile_definitions(__ARM_NEON)
@@ -856,7 +878,9 @@ if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATC
             list(APPEND ARCH_FLAGS -mno-unaligned-access)
         endif()
     endif()
-elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" )
+elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
+        (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+         CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$"))
     message(STATUS "x86 detected")
     if (MSVC)
         # instruction set detection for MSVC only

Makefile

@@ -109,8 +109,21 @@ MK_NVCCFLAGS  += -O3
 else
 MK_CFLAGS     += -O3
 MK_CXXFLAGS   += -O3
+MK_NVCCFLAGS  += -O3
 endif
 
+ifndef LLAMA_NO_CCACHE
+CCACHE := $(shell which ccache)
+ifdef CCACHE
+export CCACHE_SLOPPINESS = time_macros
+$(info I ccache found, compilation results will be cached. Disable with LLAMA_NO_CCACHE.)
+CC    := $(CCACHE) $(CC)
+CXX   := $(CCACHE) $(CXX)
+else
+$(info I ccache not found. Consider installing it for faster compilation.)
+endif # CCACHE
+endif # LLAMA_NO_CCACHE
+
 # clock_gettime came in POSIX.1b (1993)
 # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
 # posix_memalign came in POSIX.1-2001 / SUSv3
@@ -365,7 +378,7 @@ ifdef LLAMA_CUBLAS
 	MK_CPPFLAGS  += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include
 	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib
 	OBJS         += ggml-cuda.o
-	MK_NVCCFLAGS  = -use_fast_math
+	MK_NVCCFLAGS += -use_fast_math
 ifndef JETSON_EOL_MODULE_DETECT
 	MK_NVCCFLAGS += --forward-unknown-to-host-compiler
 endif # JETSON_EOL_MODULE_DETECT
@@ -373,9 +386,9 @@ ifdef LLAMA_DEBUG
 	MK_NVCCFLAGS += -lineinfo
 endif # LLAMA_DEBUG
 ifdef LLAMA_CUDA_NVCC
-	NVCC = $(LLAMA_CUDA_NVCC)
+	NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC)
 else
-	NVCC = nvcc
+	NVCC = $(CCACHE) nvcc
 endif #LLAMA_CUDA_NVCC
 ifdef CUDA_DOCKER_ARCH
 	MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
@@ -457,6 +470,18 @@ ifdef LLAMA_VULKAN_CHECK_RESULTS
 	MK_CPPFLAGS  += -DGGML_VULKAN_CHECK_RESULTS
 endif
 
+ifdef LLAMA_VULKAN_DEBUG
+	MK_CPPFLAGS  += -DGGML_VULKAN_DEBUG
+endif
+
+ifdef LLAMA_VULKAN_VALIDATE
+	MK_CPPFLAGS  += -DGGML_VULKAN_VALIDATE
+endif
+
+ifdef LLAMA_VULKAN_RUN_TESTS
+	MK_CPPFLAGS  += -DGGML_VULKAN_RUN_TESTS
+endif
+
 ggml-vulkan.o: ggml-vulkan.cpp ggml-vulkan.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 endif # LLAMA_VULKAN
@@ -470,7 +495,7 @@ ifdef LLAMA_HIPBLAS
 		ROCM_PATH	?= /opt/rocm
 		GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
 	endif
-	HIPCC                   ?= $(ROCM_PATH)/bin/hipcc
+	HIPCC                   ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc
 	LLAMA_CUDA_DMMV_X       ?= 32
 	LLAMA_CUDA_MMV_Y        ?= 1
 	LLAMA_CUDA_KQUANTS_ITER ?= 2
@@ -540,8 +565,11 @@ $(info I CFLAGS:    $(CFLAGS))
 $(info I CXXFLAGS:  $(CXXFLAGS))
 $(info I NVCCFLAGS: $(NVCCFLAGS))
 $(info I LDFLAGS:   $(LDFLAGS))
-$(info I CC:        $(shell $(CC) --version | head -n 1))
-$(info I CXX:       $(shell $(CXX) --version | head -n 1))
+$(info I CC:        $(shell $(CC)   --version | head -n 1))
+$(info I CXX:       $(shell $(CXX)  --version | head -n 1))
+ifdef LLAMA_CUBLAS
+$(info I NVCC:      $(shell $(NVCC) --version | tail -n 1))
+endif # LLAMA_CUBLAS
 $(info )
 
 #
@@ -591,97 +619,135 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS)
 
 clean:
 	rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
+	find examples pocs -type f -name "*.o" -delete
 
 #
 # Examples
 #
 
+# $< is the first prerequisite, i.e. the source file.
+# Explicitly compile this to an object file so that it can be cached with ccache.
+# The source file is then filtered out from $^ (the list of all prerequisites) and the object file is added instead.
+
+# Helper function that replaces .c, .cpp, and .cu file endings with .o:
+GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1))))
+
 main: examples/main/main.cpp                                  ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 	@echo
 	@echo '====  Run ./main -h for help.  ===='
 	@echo
 
 infill: examples/infill/infill.cpp                            ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 simple: examples/simple/simple.cpp                            ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tokenize: examples/tokenize/tokenize.cpp                      ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 batched: examples/batched/batched.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 batched-bench: examples/batched-bench/batched-bench.cpp       build-info.o ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 quantize: examples/quantize/quantize.cpp                      build-info.o ggml.o llama.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 quantize-stats: examples/quantize-stats/quantize-stats.cpp    build-info.o ggml.o llama.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 perplexity: examples/perplexity/perplexity.cpp                ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 imatrix: examples/imatrix/imatrix.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 embedding: examples/embedding/embedding.cpp                   ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual
+	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
 gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
 
 llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp  -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual
+	$(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS)
 
 baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 ifeq ($(UNAME_S),Darwin)
 swift: examples/batched.swift
@@ -689,7 +755,7 @@ swift: examples/batched.swift
 endif
 
 common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh
-	@sh scripts/build-info.sh $(CC) > $@.tmp
+	@sh scripts/build-info.sh "$(CC)" > $@.tmp
 	@if ! cmp -s $@.tmp $@; then \
 		mv $@.tmp $@; \
 	else \
@@ -706,7 +772,8 @@ build-info.o: common/build-info.cpp
 tests: $(TEST_TARGETS)
 
 benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 run-benchmark-matmult: benchmark-matmult
 	./$@
@@ -714,58 +781,76 @@ run-benchmark-matmult: benchmark-matmult
 .PHONY: run-benchmark-matmult swift
 
 vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-c.o: tests/test-c.c llama.h
 	$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@
 
 tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

README-sycl.md

@@ -1,22 +1,15 @@
 # llama.cpp for SYCL
 
-[Background](#background)
-
-[OS](#os)
-
-[Intel GPU](#intel-gpu)
-
-[Linux](#linux)
-
-[Windows](#windows)
-
-[Environment Variable](#environment-variable)
-
-[Known Issue](#known-issue)
-
-[Q&A](#q&a)
-
-[Todo](#todo)
+- [Background](#background)
+- [OS](#os)
+- [Intel GPU](#intel-gpu)
+- [Docker](#docker)
+- [Linux](#linux)
+- [Windows](#windows)
+- [Environment Variable](#environment-variable)
+- [Known Issue](#known-issue)
+- [Q&A](#q&a)
+- [Todo](#todo)
 
 ## Background
 
@@ -36,7 +29,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
 
 |OS|Status|Verified|
 |-|-|-|
-|Linux|Support|Ubuntu 22.04|
+|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39|
 |Windows|Support|Windows 11|
 
 
@@ -50,7 +43,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
 |Intel Data Center Flex Series| Support| Flex 170|
 |Intel Arc Series| Support| Arc 770, 730M|
 |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake|
-|Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7|
+|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7|
 
 Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use.
 
@@ -64,6 +57,38 @@ For iGPU, please make sure the shared memory from host memory is enough. For lla
 
 For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+.
 
+## Docker
+
+Note:
+- Only docker on Linux is tested. Docker on WSL may not work.
+- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that)
+
+### Build the image
+
+You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
+
+
+```sh
+# For F16:
+#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
+
+# Or, for F32:
+docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile .
+
+# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example
+```
+
+### Run
+
+```sh
+# Firstly, find all the DRI cards:
+ls -la /dev/dri
+# Then, pick the card that you want to use.
+
+# For example with "/dev/dri/card1"
+docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+```
+
 ## Linux
 
 ### Setup Environment
@@ -76,7 +101,7 @@ Note: for iGPU, please install the client GPU driver.
 
 b. Add user to group: video, render.
 
-```
+```sh
 sudo usermod -aG render username
 sudo usermod -aG video username
 ```
@@ -85,7 +110,7 @@ Note: re-login to enable it.
 
 c. Check
 
-```
+```sh
 sudo apt install clinfo
 sudo clinfo -l
 ```
@@ -103,7 +128,6 @@ Platform #0: Intel(R) OpenCL HD Graphics
 
 2. Install Intel® oneAPI Base toolkit.
 
-
 a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
 
 Recommend to install to default folder: **/opt/intel/oneapi**.
@@ -112,7 +136,7 @@ Following guide use the default folder as example. If you use other folder, plea
 
 b. Check
 
-```
+```sh
 source /opt/intel/oneapi/setvars.sh
 
 sycl-ls
@@ -131,21 +155,25 @@ Output (example):
 
 2. Build locally:
 
-```
+Note:
+- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
+- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
+
+```sh
 mkdir -p build
 cd build
 source /opt/intel/oneapi/setvars.sh
 
-#for FP16
-#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference
+# For FP16:
+#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
 
-#for FP32
+# Or, for FP32:
 cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
-#build example/main only
+# Build example/main only
 #cmake --build . --config Release --target main
 
-#build all binary
+# Or, build all binary
 cmake --build . --config Release -v
 
 cd ..
@@ -153,14 +181,10 @@ cd ..
 
 or
 
-```
+```sh
 ./examples/sycl/build.sh
 ```
 
-Note:
-
-- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
-
 ### Run
 
 1. Put model file to folder **models**
@@ -177,10 +201,10 @@ source /opt/intel/oneapi/setvars.sh
 
 Run without parameter:
 
-```
+```sh
 ./build/bin/ls-sycl-device
 
-or
+# or running the "main" executable and look at the output log:
 
 ./build/bin/main
 ```
@@ -209,13 +233,13 @@ found 4 SYCL devices:
 
 Set device ID = 0 by **GGML_SYCL_DEVICE=0**
 
-```
+```sh
 GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
 ```
 or run by script:
 
-```
-./examples/sycl/run-llama2.sh
+```sh
+./examples/sycl/run_llama2.sh
 ```
 
 Note:
@@ -287,15 +311,13 @@ Output (example):
 
 a. Download & install cmake for Windows: https://cmake.org/download/
 
-b. Download & install make for Windows provided by mingw-w64
+b. Download & install mingw-w64 make for Windows provided by w64devkit
 
-- Download binary package for Windows in https://github.com/niXman/mingw-builds-binaries/releases.
+- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
 
-  Like [x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z](https://github.com/niXman/mingw-builds-binaries/releases/download/13.2.0-rt_v11-rev1/x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z).
+- Extract `w64devkit` on your pc.
 
-- Unzip the binary package. In the **bin** sub-folder and rename **xxx-make.exe** to **make.exe**.
-
-- Add the **bin** folder path in the Windows system PATH environment.
+- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`.
 
 ### Build locally:
 

README.md

@@ -6,7 +6,7 @@
 
 [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
 
-Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
+Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
 
 ### Hot topics
 
@@ -33,17 +33,14 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
         <li><a href="#get-the-code">Get the Code</a></li>
         <li><a href="#build">Build</a></li>
         <li><a href="#blas-build">BLAS Build</a></li>
-        <li><a href="#prepare-data--run">Prepare Data & Run</a></li>
+        <li><a href="#prepare-and-quantize">Prepare and Quantize</a></li>
+        <li><a href="#run-the-quantized-model">Run the quantized model</a></li>
         <li><a href="#memorydisk-requirements">Memory/Disk Requirements</a></li>
         <li><a href="#quantization">Quantization</a></li>
         <li><a href="#interactive-mode">Interactive mode</a></li>
         <li><a href="#constrained-output-with-grammars">Constrained output with grammars</a></li>
-        <li><a href="#instruction-mode-with-alpaca">Instruction mode with Alpaca</a></li>
-        <li><a href="#using-openllama">Using OpenLLaMA</a></li>
-        <li><a href="#using-gpt4all">Using GPT4All</a></li>
-        <li><a href="#using-pygmalion-7b--metharme-7b">Using Pygmalion 7B & Metharme 7B</a></li>
-        <li><a href="#obtaining-the-facebook-llama-original-model-and-stanford-alpaca-model-data">Obtaining the Facebook LLaMA original model and Stanford Alpaca model data</a></li>
-        <li><a href="#verifying-the-model-files">Verifying the model files</a></li>
+        <li><a href="#instruct-mode">Instruct mode</a></li>
+        <li><a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a></li>
         <li><a href="#seminal-papers-and-background-on-the-models">Seminal papers and background on the models</a></li>
         <li><a href="#perplexity-measuring-model-quality">Perplexity (measuring model quality)</a></li>
         <li><a href="#android">Android</a></li>
@@ -58,18 +55,20 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 ## Description
 
-The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quantization on a MacBook
+The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
+variety of hardware - locally and in the cloud.
 
-- Plain C/C++ implementation without dependencies
-- Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
+- Plain C/C++ implementation without any dependencies
+- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
 - AVX, AVX2 and AVX512 support for x86 architectures
-- Mixed F16 / F32 precision
-- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support
-- CUDA, Metal, OpenCL, SYCL GPU backend support
+- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
+- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)
+- Vulkan, SYCL, and (partial) OpenCL backend support
+- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
 
-The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022).
-Since then, the project has improved significantly thanks to many contributions. This project is mainly for educational purposes and serves
-as the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library.
+Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has
+improved significantly thanks to many contributions. It is the main playground for developing new features for the
+[ggml](https://github.com/ggerganov/ggml) library.
 
 **Supported platforms:**
 
@@ -77,44 +76,46 @@ as the main playground for developing new features for the [ggml](https://github
 - [X] Linux
 - [X] Windows (via CMake)
 - [X] Docker
+- [X] FreeBSD
 
 **Supported models:**
 
+Typically finetunes of the base models below are supported as well.
+
 - [X] LLaMA 🦙
 - [x] LLaMA 2 🦙🦙
+- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
 - [X] Falcon
-- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
-- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
-- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894)
 - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
-- [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)
-- [X] [Pygmalion/Metharme](#using-pygmalion-7b--metharme-7b)
-- [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
 - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
 - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
 - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187)
-- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
 - [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410)
 - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
 - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
 - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
-- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586)
+- [X] [StableLM models](https://huggingface.co/stabilityai)
 - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
 - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
-- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
 - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
+- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
 - [x] [GPT-2](https://huggingface.co/gpt2)
+- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
+- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
+- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
 
 **Multimodal models:**
 
-- [x] [Llava 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e)
-- [x] [Bakllava](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
+- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e)
+- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
 - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
 - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
 - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
+- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
 
 
 **Bindings:**
@@ -136,19 +137,30 @@ as the main playground for developing new features for the [ggml](https://github
 
 **UI:**
 
-- [nat/openplayground](https://github.com/nat/openplayground)
-- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
-- [withcatai/catai](https://github.com/withcatai/catai)
-- [semperai/amica](https://github.com/semperai/amica)
-- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
-- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
+Unless otherwise noted these projects are open-source with permissive licensing:
+
 - [iohub/collama](https://github.com/iohub/coLLaMA)
+- [janhq/jan](https://github.com/janhq/jan) (AGPL)
+- [nat/openplayground](https://github.com/nat/openplayground)
+- [Faraday](https://faraday.dev/) (proprietary)
+- [LMStudio](https://lmstudio.ai/) (proprietary)
+- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
+- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile)
+- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all)
+- [ollama/ollama](https://github.com/ollama/ollama)
+- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL)
+- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
+- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
+- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
+- [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
+- [semperai/amica](https://github.com/semperai/amica)
+- [withcatai/catai](https://github.com/withcatai/catai)
 
 ---
 
 Here is a typical run using LLaMA v2 13B on M2 Ultra:
 
-```java
+```
 $ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
 I llama.cpp build info:
 I UNAME_S:  Darwin
@@ -232,7 +244,7 @@ https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8
 
 ## Usage
 
-Here are the end-to-end binary build and model conversion steps for the LLaMA-7B model.
+Here are the end-to-end binary build and model conversion steps for most supported models.
 
 ### Get the Code
 
@@ -393,28 +405,28 @@ Building the program with BLAS support may lead to some performance improvements
 
   Check [BLIS.md](docs/BLIS.md) for more information.
 
+- #### SYCL
+  SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
+
+  llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
+
+  For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md).
+
 - #### Intel oneMKL
+  Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md).
+
   - Using manual oneAPI installation:
     By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
       ```bash
       mkdir build
       cd build
-      source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-runtime docker image, only required for manual installation
+      source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit docker image, only required for manual installation
       cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
       cmake --build . --config Release
       ```
 
   - 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-runtime](https://hub.docker.com/r/intel/oneapi-runtime)
-
-      ```bash
-      mkdir build
-      cd build
-      cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
-      cmake --build . --config Release
-      ```
-
-  Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni.
+    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.
 
@@ -601,43 +613,87 @@ Building the program with BLAS support may lead to some performance improvements
 
   You can get a list of platforms and devices from the `clinfo -l` command, etc.
 
-- #### SYCL
+- #### Vulkan
 
-  SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
+  **With docker**:
 
-  llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
+  You don't need to install Vulkan SDK. It will be installed inside the container.
 
-  For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md).
+  ```sh
+  # Build the image
+  docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile .
 
+  # Then, use it:
+  docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+  ```
 
-### Prepare Data & Run
+  **Without docker**:
+
+  Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html)
+
+  For example, on Ubuntu 22.04 (jammy), use the command below:
+
+  ```bash
+  wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
+  wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
+  apt update -y
+  apt-get install -y vulkan-sdk
+  # To verify the installation, use the command below:
+  vulkaninfo
+  ```
+
+  Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
+
+  Then, build llama.cpp using the cmake command below:
+
+  ```bash
+  mkdir -p build
+  cd build
+  cmake .. -DLLAMA_VULKAN=1
+  cmake --build . --config Release
+  # Test the output binary (with "-ngl 33" to offload all layers to GPU)
+  ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
+
+  # You should see in the output, ggml_vulkan detected your GPU. For example:
+  # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
+  ```
+
+### Prepare and Quantize
+
+To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
 
 ```bash
-# obtain the original LLaMA model weights and place them in ./models
+# obtain the official LLaMA model weights and place them in ./models
 ls ./models
-65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
+llama-2-7b tokenizer_checklist.chk tokenizer.model
 # [Optional] for models using BPE tokenizers
 ls ./models
-65B 30B 13B 7B vocab.json
+<folder containing weights and tokenizer json> vocab.json
+# [Optional] for PyTorch .bin models like Mistral-7B
+ls ./models
+<folder containing weights and tokenizer json>
 
 # install Python dependencies
 python3 -m pip install -r requirements.txt
 
-# convert the 7B model to ggml FP16 format
-python3 convert.py models/7B/
+# convert the model to ggml FP16 format
+python3 convert.py models/mymodel/
 
 # [Optional] for models using BPE tokenizers
-python convert.py models/7B/ --vocabtype bpe
+python convert.py models/mymodel/ --vocab-type bpe
 
-# quantize the model to 4-bits (using q4_0 method)
-./quantize ./models/7B/ggml-model-f16.gguf ./models/7B/ggml-model-q4_0.gguf q4_0
+# quantize the model to 4-bits (using Q4_K_M method)
+./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
 
-# update the gguf filetype to current if older version is unsupported by another application
-./quantize ./models/7B/ggml-model-q4_0.gguf ./models/7B/ggml-model-q4_0-v2.gguf COPY
+# update the gguf filetype to current version if older version is now unsupported
+./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY
+```
 
+### Run the quantized model
 
-# run the inference
-./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
+```bash
+# start inference on a gguf model
+./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128
 ```
 
 When running the larger models, make sure you have enough disk space to store all the intermediate files.
@@ -658,7 +714,7 @@ From the unzipped folder, open a terminal/cmd window here and place a pre-conver
 
 As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
 
-| Model | Original size | Quantized size (4-bit) |
+| Model | Original size | Quantized size (Q4_0) |
 |------:|--------------:|-----------------------:|
 |    7B |         13 GB |                 3.9 GB |
 |   13B |         24 GB |                 7.8 GB |
@@ -685,9 +741,21 @@ Several quantization methods are supported. They differ in the resulting model d
 |   13B | bits/weight  |   16.0 |    4.5 |    5.0 |    5.5 |    6.0 |    8.5 |
 
 - [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
-- recent k-quants improvements
+- recent k-quants improvements and new i-quants
   - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
   - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
+  - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
+  - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
+  - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
+  - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
+  - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
+  - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
+  - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
+  - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
+  - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
+  - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
+  - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
+  - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
 
 ### Perplexity (measuring model quality)
 
@@ -762,9 +830,9 @@ The `grammars/` folder contains a handful of sample grammars. To write your own,
 
 For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one.
 
-### Instruction mode with Alpaca
+### Instruct mode
 
-1. First, download the `ggml` Alpaca model into the `./models` folder
+1. First, download and place the `ggml` model into the `./models` folder
 2. Run the `main` tool like this:
 
 ```
@@ -790,50 +858,6 @@ cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
 >
 ```
 
-### Using [OpenLLaMA](https://github.com/openlm-research/open_llama)
-
-OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. It uses the same architecture and is a drop-in replacement for the original LLaMA weights.
-
-- Download the [3B](https://huggingface.co/openlm-research/open_llama_3b), [7B](https://huggingface.co/openlm-research/open_llama_7b), or [13B](https://huggingface.co/openlm-research/open_llama_13b) model from Hugging Face.
-- Convert the model to ggml FP16 format using `python convert.py <path to OpenLLaMA directory>`
-
-### Using [GPT4All](https://github.com/nomic-ai/gpt4all)
-
-*Note: these instructions are likely obsoleted by the GGUF update*
-
-- Obtain the `tokenizer.model` file from LLaMA model and put it to `models`
-- Obtain the `added_tokens.json` file from Alpaca model and put it to `models`
-- Obtain the `gpt4all-lora-quantized.bin` file from GPT4All model and put it to `models/gpt4all-7B`
-- It is distributed in the old `ggml` format which is now obsoleted
-- You have to convert it to the new format using `convert.py`:
-
-```bash
-python3 convert.py models/gpt4all-7B/gpt4all-lora-quantized.bin
-```
-
-- You can now use the newly generated `models/gpt4all-7B/ggml-model-q4_0.bin` model in exactly the same way as all other models
-
-- The newer GPT4All-J model is not yet supported!
-
-### Using Pygmalion 7B & Metharme 7B
-
-- Obtain the [LLaMA weights](#obtaining-the-facebook-llama-original-model-and-stanford-alpaca-model-data)
-- Obtain the [Pygmalion 7B](https://huggingface.co/PygmalionAI/pygmalion-7b/) or [Metharme 7B](https://huggingface.co/PygmalionAI/metharme-7b) XOR encoded weights
-- Convert the LLaMA model with [the latest HF convert script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)
-- Merge the XOR files with the converted LLaMA weights by running the [xor_codec](https://huggingface.co/PygmalionAI/pygmalion-7b/blob/main/xor_codec.py) script
-- Convert to `ggml` format using the `convert.py` script in this repo:
-```bash
-python3 convert.py pygmalion-7b/ --outtype q4_1
-```
-> The Pygmalion 7B & Metharme 7B weights are saved in [bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) precision. If you wish to convert to `ggml` without quantizating, please specify the `--outtype` as `f32` instead of `f16`.
-
-
-### Obtaining the Facebook LLaMA original model and Stanford Alpaca model data
-
-- **Under no circumstances should IPFS, magnet links, or any other links to model downloads be shared anywhere in this repository, including in issues, discussions, or pull requests. They will be immediately deleted.**
-- The LLaMA models are officially distributed by Facebook and will **never** be provided through this repository.
-- Refer to [Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data.
-
 ### Obtaining and using the Facebook LLaMA 2 model
 
 - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data.
@@ -845,20 +869,6 @@ python3 convert.py pygmalion-7b/ --outtype q4_1
   - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF)
   - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF)
 
-### Verifying the model files
-
-Please verify the [sha256 checksums](SHA256SUMS) of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files.
-- The following python script will verify if you have all possible latest files in your self-installed `./models` subdirectory:
-
-```bash
-# run the verification script
-./scripts/verify-checksum-models.py
-```
-
-- On linux or macOS it is also possible to run the following commands to verify if you have all possible latest files in your self-installed `./models` subdirectory:
-    - On Linux: `sha256sum --ignore-missing -c SHA256SUMS`
-    - on macOS: `shasum -a 256 --ignore-missing -c SHA256SUMS`
-
 ### Seminal papers and background on the models
 
 If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:

SHA256SUMS

@@ -1,40 +0,0 @@
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-9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347  models/tokenizer.model

common/common.cpp

@@ -46,6 +46,10 @@
 #define GGML_USE_CUBLAS_SYCL
 #endif
 
+#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
+#define GGML_USE_CUBLAS_SYCL_VULKAN
+#endif
+
 int32_t get_num_physical_cores() {
 #ifdef __linux__
     // enumerate the set of thread siblings, num entries is num cores
@@ -399,6 +403,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             sparams.penalty_present = std::stof(argv[i]);
+        } else if (arg == "--dynatemp-range") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.dynatemp_range = std::stof(argv[i]);
+        } else if (arg == "--dynatemp-exp") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.dynatemp_exponent = std::stof(argv[i]);
         } else if (arg == "--mirostat") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -515,7 +531,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                 invalid_param = true;
                 break;
             }
-            params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
+            params.lora_adapter.emplace_back(argv[i], 1.0f);
             params.use_mmap = false;
         } else if (arg == "--lora-scaled") {
             if (++i >= argc) {
@@ -527,7 +543,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                 invalid_param = true;
                 break;
             }
-            params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
+            params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
             params.use_mmap = false;
         } else if (arg == "--lora-base") {
             if (++i >= argc) {
@@ -648,8 +664,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                     params.tensor_split[i] = 0.0f;
                 }
             }
-#ifndef GGML_USE_CUBLAS_SYCL
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting a tensor split has no effect.\n");
+#ifndef GGML_USE_CUBLAS_SYCL_VULKAN
+            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n");
 #endif // GGML_USE_CUBLAS_SYCL
         } else if (arg == "--no-mmap") {
             params.use_mmap = false;
@@ -664,7 +680,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                 invalid_param = true;
                 break;
             }
-            params.antiprompt.push_back(argv[i]);
+            params.antiprompt.emplace_back(argv[i]);
         } else if (arg == "-ld" || arg == "--logdir") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -880,7 +896,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
     }
 
     if (!params.kv_overrides.empty()) {
-        params.kv_overrides.emplace_back(llama_model_kv_override());
+        params.kv_overrides.emplace_back();
         params.kv_overrides.back().key[0] = 0;
     }
 
@@ -942,6 +958,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --repeat-penalty N    penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat);
     printf("  --presence-penalty N  repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present);
     printf("  --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq);
+    printf("  --dynatemp-range N    dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range);
+    printf("  --dynatemp-exp N      dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent);
     printf("  --mirostat N          use Mirostat sampling.\n");
     printf("                        Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
     printf("                        (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat);

common/common.h

@@ -75,8 +75,7 @@ struct gpt_params {
     float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
     float   yarn_beta_slow        = 1.0f;  // YaRN high correction dim
     int32_t yarn_orig_ctx         = 0;     // YaRN original context length
-    int8_t  rope_scaling_type     = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
-                                                                    //       pinging @cebtenzzre
+    int32_t rope_scaling_type     = LLAMA_ROPE_SCALING_UNSPECIFIED;
 
     // // sampling parameters
     struct llama_sampling_params sparams;

common/sampling.cpp

@@ -132,7 +132,7 @@ static void sampler_queue(
     const float         temp              = params.temp;
     const float         dynatemp_range    = params.dynatemp_range;
     const float         dynatemp_exponent = params.dynatemp_exponent;
-    const int32_t       top_k             = params.top_k <= 0 ? n_vocab : params.top_k;
+    const int32_t       top_k             = params.top_k;
     const float         top_p             = params.top_p;
     const float         min_p             = params.min_p;
     const float         tfs_z             = params.tfs_z;

convert-hf-to-gguf.py

@@ -22,6 +22,8 @@ if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
 
+from convert import HfVocab
+
 
 # check for any of the given keys in the dictionary and return the value of the first key found
 def get_key_opts(d, keys):
@@ -205,6 +207,8 @@ class Model:
             return OrionModel
         if model_architecture == "InternLM2ForCausalLM":
             return InternLM2Model
+        if model_architecture == "MiniCPMForCausalLM":
+            return MiniCPMModel
         return Model
 
     def _is_model_safetensors(self) -> bool:
@@ -258,6 +262,8 @@ class Model:
             return gguf.MODEL_ARCH.ORION
         if arch == "InternLM2ForCausalLM":
             return gguf.MODEL_ARCH.INTERNLM2
+        if arch == "MiniCPMForCausalLM":
+            return gguf.MODEL_ARCH.MINICPM
 
         raise NotImplementedError(f'Architecture "{arch}" not supported!')
 
@@ -402,6 +408,31 @@ class Model:
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         special_vocab.add_to_gguf(self.gguf_writer)
 
+    def _set_vocab_hf(self):
+        path = self.dir_model
+        added_tokens_path = self.dir_model
+        vocab = HfVocab(
+            path, added_tokens_path if added_tokens_path.exists() else None
+        )
+        tokens = []
+        scores = []
+        toktypes = []
+
+        for text, score, toktype in vocab.all_tokens():
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        assert len(tokens) == vocab.vocab_size
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
 
 class GPTNeoXModel(Model):
     def set_gguf_parameters(self):
@@ -1041,6 +1072,83 @@ class MixtralModel(Model):
         self._set_vocab_sentencepiece()
 
 
+class MiniCPMModel(Model):
+    def set_gguf_parameters(self):
+        block_count = self.hparams["num_hidden_layers"]
+        self.gguf_writer.add_name("MiniCPM")
+        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def set_vocab(self):
+        self._set_vocab_hf()
+
+    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
+        if n_kv_head is not None and n_head != n_kv_head:
+            n_head //= n_kv_head
+
+        return (
+            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+            .swapaxes(1, 2)
+            .reshape(weights.shape)
+        )
+
+    def write_tensors(self):
+        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        n_head = self.hparams.get("num_attention_heads")
+        n_kv_head = self.hparams.get("num_key_value_heads")
+        for name, data_torch in self.get_tensors():
+            # we don't need these
+            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
+                continue
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            # HF models permute some of the tensors, so we need to undo that
+            if name.endswith(("q_proj.weight")):
+                data_torch = self._reverse_hf_permute(data_torch, n_head, n_head)
+            if name.endswith(("k_proj.weight")):
+                data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head)
+
+            data = data_torch.squeeze().numpy()
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+
 class QwenModel(Model):
     @staticmethod
     def token_bytes_to_string(b):
@@ -1138,7 +1246,7 @@ class GPT2Model(Model):
 
         for name, data_torch in self.get_tensors():
             # we don't need these
-            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias")):
+            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")):
                 continue
 
             if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
@@ -1416,8 +1524,32 @@ class InternLM2Model(Model):
         self.gguf_writer.add_add_space_prefix(add_prefix)
 
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        old_eos = special_vocab.special_token_ids["eos"]
+        if "chat" in os.path.basename(self.dir_model.absolute()):
+            # For the chat model, we replace the eos with '<|im_end|>'.
+            special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
+            print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
+in chat mode so that the conversation can end normally.")
+
         special_vocab.add_to_gguf(self.gguf_writer)
 
+    def _try_get_sft_eos(self, tokenizer):
+        unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]')
+        im_end_list = tokenizer.encode('<|im_end|>')
+        assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1)
+        if len(unused_145_list) == 1:
+            eos_token = unused_145_list[0]
+        if len(im_end_list) == 1:
+            eos_token = im_end_list[0]
+        return eos_token
+
+    def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
     def set_gguf_parameters(self):
         self.gguf_writer.add_name("InternLM2")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
@@ -1486,8 +1618,9 @@ class InternLM2Model(Model):
                 qkv = data_torch
                 qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
                 q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
-                q = rearrange(q, " o g n i ->  o (g n i)").T
-                k = rearrange(k, " o g n i ->  o (g n i)").T
+                # The model weights of q and k equire additional reshape.
+                q = self._hf_permute_qk(rearrange(q, " o g n i ->  o (g n i)").T, num_heads, num_heads)
+                k = self._hf_permute_qk(rearrange(k, " o g n i ->  o (g n i)").T, num_heads, num_kv_heads)
                 v = rearrange(v, " o g n i ->  o (g n i)").T
                 self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q)
                 self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k)

convert.py

@@ -334,9 +334,9 @@ class Params:
 class BpeVocab:
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
         self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
-        try:
+        if isinstance(self.bpe_tokenizer.get('model'), dict):
             self.vocab = self.bpe_tokenizer["model"]["vocab"]
-        except KeyError:
+        else:
             self.vocab = self.bpe_tokenizer
         added_tokens: dict[str, int]
         if fname_added_tokens is not None:
@@ -515,10 +515,14 @@ class HfVocab:
 
             # Yield token text, score, and type
             yield token_text, self.get_token_score(token_id), self.get_token_type(
-                token_id, self.special_ids  # Reuse already stored special IDs
+                token_id, token_text, self.special_ids  # Reuse already stored special IDs
             )
 
-    def get_token_type(self, token_id: int, special_ids: set[int]) -> gguf.TokenType:
+    def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
+        # Special case for byte tokens
+        if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
+            return gguf.TokenType.BYTE
+
         # Determine token type based on whether it's a special token
         return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
 
@@ -530,7 +534,7 @@ class HfVocab:
     def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
         for text in self.added_tokens_list:
             if text in self.specials:
-                toktype = self.get_token_type(self.specials[text], self.special_ids)
+                toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
                 score = self.get_token_score(self.specials[text])
             else:
                 toktype = gguf.TokenType.USER_DEFINED

examples/imatrix/imatrix.cpp

@@ -36,6 +36,8 @@ public:
     void set_parameters(StatParams&& params) { m_params = std::move(params); }
     bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
     void save_imatrix() const;
+    bool load_imatrix(const char * file_name, bool add);
+    static bool load_imatrix(const char * file_name, std::unordered_map<std::string, Stats>& imatrix);
 private:
     std::unordered_map<std::string, Stats> m_stats;
     StatParams                             m_params;
@@ -189,6 +191,57 @@ void IMatrixCollector::save_imatrix(const char * fname) const {
     }
 }
 
+bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map<std::string, Stats>& imatrix_data) {
+    std::ifstream in(imatrix_file, std::ios::binary);
+    if (!in) {
+        printf("%s: failed to open %s\n",__func__,imatrix_file);
+        return false;
+    }
+    int n_entries;
+    in.read((char*)&n_entries, sizeof(n_entries));
+    if (in.fail() || n_entries < 1) {
+        printf("%s: no data in file %s\n", __func__, imatrix_file);
+        return false;
+    }
+    for (int i = 0; i < n_entries; ++i) {
+        int len; in.read((char *)&len, sizeof(len));
+        std::vector<char> name_as_vec(len+1);
+        in.read((char *)name_as_vec.data(), len);
+        if (in.fail()) {
+            printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file);
+            return false;
+        }
+        name_as_vec[len] = 0;
+        std::string name{name_as_vec.data()};
+        auto& e = imatrix_data[std::move(name)];
+        int ncall;
+        in.read((char*)&ncall, sizeof(ncall));
+        int nval;
+        in.read((char *)&nval, sizeof(nval));
+        if (in.fail() || nval < 1) {
+            printf("%s: failed reading number of values for entry %d\n",__func__,i);
+            imatrix_data = {};
+            return false;
+        }
+        e.values.resize(nval);
+        in.read((char*)e.values.data(), nval*sizeof(float));
+        if (in.fail()) {
+            printf("%s: failed reading data for entry %d\n",__func__,i);
+            imatrix_data = {};
+            return false;
+        }
+        e.ncall = ncall;
+    }
+    return true;
+}
+
+bool IMatrixCollector::load_imatrix(const char * file_name, bool add) {
+    if (!add) {
+        m_stats.clear();
+    }
+    return load_imatrix(file_name, m_stats);
+}
+
 static IMatrixCollector g_collector;
 
 static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
@@ -269,7 +322,7 @@ static void process_logits(
     }
 }
 
-static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl) {
+static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
 
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
     const int n_ctx = llama_n_ctx(ctx);
@@ -282,6 +335,15 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
     auto tim2 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
 
+    if (from_chunk > 0) {
+        if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) {
+            fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk);
+            return false;
+        }
+        fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx);
+        tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx);
+    }
+
     if (int(tokens.size()) < 2*n_ctx) {
         fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx,
                 n_ctx);
@@ -402,7 +464,10 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
 int main(int argc, char ** argv) {
 
     StatParams sparams;
+    std::string prev_result_file;
+    std::string combine_files;
     bool compute_ppl = true;
+    int  from_chunk  = 0;
     std::vector<char*> args;
     args.push_back(argv[0]);
     int iarg = 1;
@@ -423,6 +488,13 @@ int main(int argc, char ** argv) {
             compute_ppl = false;
         } else if (arg == "--keep-imatrix") {
             sparams.keep_every = std::stoi(argv[++iarg]);
+        } else if (arg == "--continue-from") {
+            prev_result_file = argv[++iarg];
+        } else if (arg == "--combine") {
+            combine_files = argv[++iarg];
+        }
+        else if (arg == "--from-chunk") {
+            from_chunk = std::stoi(argv[++iarg]);
         } else {
             args.push_back(argv[iarg]);
         }
@@ -436,14 +508,50 @@ int main(int argc, char ** argv) {
         }
     }
 
+    g_collector.set_parameters(std::move(sparams));
+
+    if (!combine_files.empty()) {
+        std::vector<std::string> files;
+        size_t pos = 0;
+        while (true) {
+            auto new_pos = combine_files.find(',', pos);
+            if (new_pos != std::string::npos) {
+                files.emplace_back(combine_files.substr(pos, new_pos - pos));
+                pos = new_pos + 1;
+            } else {
+                files.emplace_back(combine_files.substr(pos));
+                break;
+            }
+        }
+        if (files.size() < 2) {
+            fprintf(stderr, "You must provide at least two comma separated files to use --combine\n");
+            return 1;
+        }
+        printf("Combining the following %d files\n", int(files.size()));
+        for (auto& file : files) {
+            printf("    %s\n", file.c_str());
+            if (!g_collector.load_imatrix(file.c_str(), true)) {
+                fprintf(stderr, "Failed to load %s\n", file.c_str());
+                return 1;
+            }
+        }
+        g_collector.save_imatrix();
+        return 0;
+    }
+
+    if (!prev_result_file.empty()) {
+        if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) {
+            fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str());
+            return 1;
+        }
+    }
+
     gpt_params params;
     params.n_batch = 512;
     if (!gpt_params_parse(args.size(), args.data(), params)) {
         return 1;
     }
 
-    g_collector.set_parameters(std::move(sparams));
-
     params.logits_all = true;
     params.n_batch = std::min(params.n_batch, params.n_ctx);
 
@@ -495,7 +603,7 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "%s\n", get_system_info(params).c_str());
     }
 
-    bool OK = compute_imatrix(ctx, params, compute_ppl);
+    bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk);
     if (!OK) {
         return 1;
     }

examples/llama-bench/llama-bench.cpp

@@ -948,46 +948,46 @@ struct markdown_printer : public printer {
 
     void print_header(const cmd_params & params) override {
         // select fields to print
-        fields.push_back("model");
-        fields.push_back("size");
-        fields.push_back("params");
-        fields.push_back("backend");
+        fields.emplace_back("model");
+        fields.emplace_back("size");
+        fields.emplace_back("params");
+        fields.emplace_back("backend");
         bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS";
         if (!is_cpu_backend) {
-            fields.push_back("n_gpu_layers");
+            fields.emplace_back("n_gpu_layers");
         }
         if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
-            fields.push_back("n_threads");
+            fields.emplace_back("n_threads");
         }
         if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
-            fields.push_back("n_batch");
+            fields.emplace_back("n_batch");
         }
         if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
-            fields.push_back("type_k");
+            fields.emplace_back("type_k");
         }
         if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) {
-            fields.push_back("type_v");
+            fields.emplace_back("type_v");
         }
         if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
-            fields.push_back("main_gpu");
+            fields.emplace_back("main_gpu");
         }
         if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) {
-            fields.push_back("split_mode");
+            fields.emplace_back("split_mode");
         }
         if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
-            fields.push_back("mul_mat_q");
+            fields.emplace_back("mul_mat_q");
         }
         if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
-            fields.push_back("no_kv_offload");
+            fields.emplace_back("no_kv_offload");
         }
         if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
-            fields.push_back("tensor_split");
+            fields.emplace_back("tensor_split");
         }
         if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) {
-            fields.push_back("use_mmap");
+            fields.emplace_back("use_mmap");
         }
-        fields.push_back("test");
-        fields.push_back("t/s");
+        fields.emplace_back("test");
+        fields.emplace_back("t/s");
 
         fprintf(fout, "|");
         for (const auto & field : fields) {

examples/llava/README.md

@@ -14,14 +14,14 @@ Build with cmake or run `make llava-cli` to build it.
 After building, run: `./llava-cli` to see the usage. For example:
 
 ```sh
-./llava-cli -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
+./llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
 ```
 
 **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
 
 ## Model conversion
 
-- Clone `llava-v15-7b`` and `clip-vit-large-patch14-336`` locally:
+- Clone `llava-v15-7b` and `clip-vit-large-patch14-336` locally:
 
 ```sh
 git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
@@ -38,7 +38,7 @@ python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
 3. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF:
 
 ```sh
-python ./examples/llava/convert-image-encoder-to-gguf -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
+python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
 ```
 
 4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:

examples/llava/llava-cli.cpp

@@ -34,7 +34,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
 
 static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
     std::string              str2     = str;
-    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos);
+    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true);
     eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
     return true;
 }
@@ -152,20 +152,8 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
     size_t image_pos = prompt.find("<image>");
     if (image_pos != std::string::npos) {
         // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
-
         system_prompt = prompt.substr(0, image_pos);
         user_prompt = prompt.substr(image_pos + std::string("<image>").length());
-        // We replace \n with actual newlines in user_prompt, just in case -e was not used in templating string
-        size_t pos = 0;
-        while ((pos = user_prompt.find("\\n", pos)) != std::string::npos) {
-            user_prompt.replace(pos, 2, "\n");
-            pos += 1; // Advance past the replaced newline
-        }
-        while ((pos = system_prompt.find("\\n", pos)) != std::string::npos) {
-            system_prompt.replace(pos, 2, "\n");
-            pos += 1; // Advance past the replaced newline
-        }
-
         printf("system_prompt: %s\n", system_prompt.c_str());
         printf("user_prompt: %s\n", user_prompt.c_str());
     } else {

examples/main/main.cpp

@@ -352,12 +352,12 @@ int main(int argc, char ** argv) {
     // in instruct mode, we inject a prefix and a suffix to each input by the user
     if (params.instruct) {
         params.interactive_first = true;
-        params.antiprompt.push_back("### Instruction:\n\n");
+        params.antiprompt.emplace_back("### Instruction:\n\n");
     }
     // similar for chatml mode
     else if (params.chatml) {
         params.interactive_first = true;
-        params.antiprompt.push_back("<|im_start|>user\n");
+        params.antiprompt.emplace_back("<|im_start|>user\n");
     }
 
     // enable interactive mode if interactive start is specified

examples/perplexity/perplexity.cpp

@@ -457,14 +457,14 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
 
     std::ofstream logits_stream;
     if (!params.logits_file.empty()) {
-        logits_stream.open(params.logits_file.c_str());
+        logits_stream.open(params.logits_file.c_str(), std::ios::binary);
         if (!logits_stream.is_open()) {
             fprintf(stderr, "%s: failed to open %s for writing\n", __func__, params.logits_file.c_str());
             return {};
         }
         fprintf(stderr, "%s: saving all logits to %s\n", __func__, params.logits_file.c_str());
         logits_stream.write("_logits_", 8);
-        logits_stream.write((const char *)&n_ctx, sizeof(n_ctx));
+        logits_stream.write(reinterpret_cast<const char *>(&n_ctx), sizeof(n_ctx));
     }
 
     auto tim1 = std::chrono::high_resolution_clock::now();
@@ -881,7 +881,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
             size_t li = hs_cur.common_prefix;
             for (int s = 0; s < 4; ++s) {
                 for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
-                    eval_pairs.push_back(std::make_pair(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]));
+                    eval_pairs.emplace_back(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]);
                 }
                 ++li;
             }
@@ -1159,13 +1159,13 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
             const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
             size_t li = n_base1 - 1;
             for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
-                eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[0][j+1]));
+                eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[0][j+1]);
             }
             const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
             const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
             li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1;
             for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
-                eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[1][j+1]));
+                eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[1][j+1]);
             }
         }
         compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
@@ -1524,7 +1524,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
             size_t li = cur_task.common_prefix;
             for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
                 for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
-                    eval_pairs.push_back(std::make_pair(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]));
+                    eval_pairs.emplace_back(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]);
                 }
                 ++li;
             }

examples/quantize-stats/quantize-stats.cpp

@@ -257,13 +257,13 @@ int main(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            params.include_layers.push_back(argv[i]);
+            params.include_layers.emplace_back(argv[i]);
         } else if (arg == "-L" || arg == "--exclude-layer") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.exclude_layers.push_back(argv[i]);
+            params.exclude_layers.emplace_back(argv[i]);
         } else if (arg == "-t" || arg == "--type") {
             if (++i >= argc) {
                 invalid_param = true;

examples/quantize/quantize.cpp

@@ -208,13 +208,13 @@ int main(int argc, char ** argv) {
             }
         } else if (strcmp(argv[arg_idx], "--include-weights") == 0) {
             if (arg_idx < argc-1) {
-                included_weights.push_back(argv[++arg_idx]);
+                included_weights.emplace_back(argv[++arg_idx]);
             } else {
                 usage(argv[0]);
             }
         } else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) {
             if (arg_idx < argc-1) {
-                excluded_weights.push_back(argv[++arg_idx]);
+                excluded_weights.emplace_back(argv[++arg_idx]);
             } else {
                 usage(argv[0]);
             }

examples/server/README.md

@@ -137,6 +137,10 @@ node index.js
 
     `temperature`: Adjust the randomness of the generated text (default: 0.8).
 
+    `dynatemp_range`: Dynamic temperature range (default: 0.0, 0.0 = disabled).
+
+    `dynatemp_exponent`: Dynamic temperature exponent (default: 1.0).
+
     `top_k`: Limit the next token selection to the K most probable tokens (default: 40).
 
     `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).
@@ -264,7 +268,23 @@ Notice that each `probs` is an array of length `n_probs`.
 
     It also accepts all the options of `/completion` except `stream` and `prompt`.
 
-- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
+- **GET** `/props`: Return current server settings.
+
+### Result JSON
+
+```json
+{
+  "assistant_name": "",
+  "user_name": "",
+  "default_generation_settings": { ... },
+  "total_slots": 1
+}
+```
+
+- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
+- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
+- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
+- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
 
 - **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
 

examples/server/completion.js.hpp

@@ -236,214 +236,250 @@ unsigned char completion_js[] = {
   0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x70, 0x61, 0x72, 0x73, 0x65, 0x28,
   0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x65, 0x72, 0x72, 0x6f, 0x72,
   0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
-  0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x65,
-  0x72, 0x72, 0x6f, 0x72, 0x28, 0x60, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e,
-  0x63, 0x70, 0x70, 0x20, 0x65, 0x72, 0x72, 0x6f, 0x72, 0x3a, 0x20, 0x24,
-  0x7b, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x65, 0x72, 0x72, 0x6f,
-  0x72, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x7d, 0x60, 0x29,
-  0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
-  0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a,
+  0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c,
+  0x74, 0x2e, 0x65, 0x72, 0x72, 0x6f, 0x72, 0x2e, 0x63, 0x6f, 0x6e, 0x74,
+  0x65, 0x6e, 0x74, 0x2e, 0x69, 0x6e, 0x63, 0x6c, 0x75, 0x64, 0x65, 0x73,
+  0x28, 0x27, 0x73, 0x6c, 0x6f, 0x74, 0x20, 0x75, 0x6e, 0x61, 0x76, 0x61,
+  0x69, 0x6c, 0x61, 0x62, 0x6c, 0x65, 0x27, 0x29, 0x29, 0x20, 0x7b, 0x0a,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x2f, 0x2f, 0x20, 0x54, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x61,
+  0x6e, 0x20, 0x65, 0x72, 0x72, 0x6f, 0x72, 0x20, 0x74, 0x6f, 0x20, 0x62,
+  0x65, 0x20, 0x63, 0x61, 0x75, 0x67, 0x68, 0x74, 0x20, 0x62, 0x79, 0x20,
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 };
-unsigned int completion_js_len = 5346;
+unsigned int completion_js_len = 5782;

examples/server/public/completion.js

@@ -195,7 +195,8 @@ export const llamaComplete = async (params, controller, callback) => {
 // Get the model info from the server. This is useful for getting the context window and so on.
 export const llamaModelInfo = async () => {
   if (!generation_settings) {
-    generation_settings = await fetch("/model.json").then(r => r.json());
+    const props = await fetch("/props").then(r => r.json());
+    generation_settings = props.default_generation_settings;
   }
   return generation_settings;
 }

examples/server/server.cpp

@@ -334,6 +334,7 @@ struct llama_server_context
 
     // slots / clients
     std::vector<llama_client_slot> slots;
+    json default_generation_settings_for_props;
 
     llama_server_queue queue_tasks;
     llama_server_response queue_results;
@@ -430,6 +431,9 @@ struct llama_server_context
             slots.push_back(slot);
         }
 
+        default_generation_settings_for_props = get_formated_generation(slots.front());
+        default_generation_settings_for_props["seed"] = -1;
+
         batch = llama_batch_init(n_ctx, 0, params.n_parallel);
 
         // empty system prompt
@@ -520,27 +524,29 @@ struct llama_server_context
             slot->oaicompat_model = "";
         }
 
-        slot->params.stream           = json_value(data, "stream",            false);
-        slot->params.cache_prompt     = json_value(data, "cache_prompt",      false);
-        slot->params.n_predict        = json_value(data, "n_predict",         default_params.n_predict);
-        slot->sparams.top_k           = json_value(data, "top_k",             default_sparams.top_k);
-        slot->sparams.top_p           = json_value(data, "top_p",             default_sparams.top_p);
-        slot->sparams.min_p           = json_value(data, "min_p",             default_sparams.min_p);
-        slot->sparams.tfs_z           = json_value(data, "tfs_z",             default_sparams.tfs_z);
-        slot->sparams.typical_p       = json_value(data, "typical_p",         default_sparams.typical_p);
-        slot->sparams.temp            = json_value(data, "temperature",       default_sparams.temp);
-        slot->sparams.penalty_last_n  = json_value(data, "repeat_last_n",     default_sparams.penalty_last_n);
-        slot->sparams.penalty_repeat  = json_value(data, "repeat_penalty",    default_sparams.penalty_repeat);
-        slot->sparams.penalty_freq    = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
-        slot->sparams.penalty_present = json_value(data, "presence_penalty",  default_sparams.penalty_present);
-        slot->sparams.mirostat        = json_value(data, "mirostat",          default_sparams.mirostat);
-        slot->sparams.mirostat_tau    = json_value(data, "mirostat_tau",      default_sparams.mirostat_tau);
-        slot->sparams.mirostat_eta    = json_value(data, "mirostat_eta",      default_sparams.mirostat_eta);
-        slot->sparams.penalize_nl     = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
-        slot->params.n_keep           = json_value(data, "n_keep",            slot->params.n_keep);
-        slot->params.seed             = json_value(data, "seed",              default_params.seed);
-        slot->sparams.grammar         = json_value(data, "grammar",           default_sparams.grammar);
-        slot->sparams.n_probs         = json_value(data, "n_probs",           default_sparams.n_probs);
+        slot->params.stream             = json_value(data, "stream",            false);
+        slot->params.cache_prompt       = json_value(data, "cache_prompt",      false);
+        slot->params.n_predict          = json_value(data, "n_predict",         default_params.n_predict);
+        slot->sparams.top_k             = json_value(data, "top_k",             default_sparams.top_k);
+        slot->sparams.top_p             = json_value(data, "top_p",             default_sparams.top_p);
+        slot->sparams.min_p             = json_value(data, "min_p",             default_sparams.min_p);
+        slot->sparams.tfs_z             = json_value(data, "tfs_z",             default_sparams.tfs_z);
+        slot->sparams.typical_p         = json_value(data, "typical_p",         default_sparams.typical_p);
+        slot->sparams.temp              = json_value(data, "temperature",       default_sparams.temp);
+        slot->sparams.dynatemp_range    = json_value(data, "dynatemp_range",    default_sparams.dynatemp_range);
+        slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
+        slot->sparams.penalty_last_n    = json_value(data, "repeat_last_n",     default_sparams.penalty_last_n);
+        slot->sparams.penalty_repeat    = json_value(data, "repeat_penalty",    default_sparams.penalty_repeat);
+        slot->sparams.penalty_freq      = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
+        slot->sparams.penalty_present   = json_value(data, "presence_penalty",  default_sparams.penalty_present);
+        slot->sparams.mirostat          = json_value(data, "mirostat",          default_sparams.mirostat);
+        slot->sparams.mirostat_tau      = json_value(data, "mirostat_tau",      default_sparams.mirostat_tau);
+        slot->sparams.mirostat_eta      = json_value(data, "mirostat_eta",      default_sparams.mirostat_eta);
+        slot->sparams.penalize_nl       = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
+        slot->params.n_keep             = json_value(data, "n_keep",            slot->params.n_keep);
+        slot->params.seed               = json_value(data, "seed",              default_params.seed);
+        slot->sparams.grammar           = json_value(data, "grammar",           default_sparams.grammar);
+        slot->sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
 
         // infill
         if (data.count("input_prefix") != 0)
@@ -983,11 +989,6 @@ struct llama_server_context
         queue_results.send(res);
     }
 
-    json get_model_props()
-    {
-        return get_formated_generation(slots[0]);
-    }
-
     json get_formated_generation(llama_client_slot &slot)
     {
         const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
@@ -998,6 +999,8 @@ struct llama_server_context
             {"model",             params.model_alias},
             {"seed",              slot.params.seed},
             {"temperature",       slot.sparams.temp},
+            {"dynatemp_range",    slot.sparams.dynatemp_range},
+            {"dynatemp_exponent", slot.sparams.dynatemp_exponent},
             {"top_k",             slot.sparams.top_k},
             {"top_p",             slot.sparams.top_p},
             {"min_p",             slot.sparams.min_p},
@@ -1159,13 +1162,30 @@ struct llama_server_context
         task.multitask_id = multitask_id;
 
         // when a completion task's prompt array is not a singleton, we split it into multiple requests
-        if (task.data.count("prompt") && task.data.at("prompt").size() > 1)
-        {
-            split_multiprompt_task(task_id, task);
-        }
-
         // otherwise, it's a single-prompt task, we actually queue it
-        queue_tasks.post(task);
+        // if there's numbers in the prompt array it will be treated as an array of tokens
+        if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
+            bool numbers = false;
+            for (const auto& e : task.data.at("prompt")) {
+                if (e.is_number()) {
+                    numbers = true;
+                    break;
+                }
+            }
+
+            // NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
+            // it will completely stall the server. I don't know where the bug for this is.
+            //
+            // if there are numbers, it needs to be treated like a single prompt,
+            // queue_tasks handles a mix of strings and numbers just fine.
+            if (numbers) {
+                queue_tasks.post(task);
+            } else {
+                split_multiprompt_task(task_id, task);
+            }
+        } else {
+            queue_tasks.post(task);
+        }
     }
 
     // for multiple images processing
@@ -1247,7 +1267,10 @@ struct llama_server_context
     void split_multiprompt_task(int multitask_id, task_server& multiprompt_task)
     {
         int prompt_count = multiprompt_task.data.at("prompt").size();
-        assert(prompt_count > 1);
+        if (prompt_count <= 1) {
+            send_error(multiprompt_task, "error while handling multiple prompts");
+            return;
+        }
 
         // generate all the ID for subtask
         std::vector<int> subtask_ids(prompt_count);
@@ -1884,7 +1907,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 invalid_param = true;
                 break;
             }
-            sparams.api_keys.push_back(argv[i]);
+            sparams.api_keys.emplace_back(argv[i]);
         }
         else if (arg == "--api-key-file")
         {
@@ -2160,7 +2183,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 invalid_param = true;
                 break;
             }
-            params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
+            params.lora_adapter.emplace_back(argv[i], 1.0f);
             params.use_mmap = false;
         }
         else if (arg == "--lora-scaled")
@@ -2176,7 +2199,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 invalid_param = true;
                 break;
             }
-            params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
+            params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
             params.use_mmap = false;
         }
         else if (arg == "--lora-base")
@@ -2318,7 +2341,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
         }
     }
     if (!params.kv_overrides.empty()) {
-        params.kv_overrides.emplace_back(llama_model_kv_override());
+        params.kv_overrides.emplace_back();
         params.kv_overrides.back().key[0] = 0;
     }
 
@@ -2614,7 +2637,9 @@ int main(int argc, char **argv)
                 res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
                 json data = {
                     { "user_name",      llama.name_user.c_str() },
-                    { "assistant_name", llama.name_assistant.c_str() }
+                    { "assistant_name", llama.name_assistant.c_str() },
+                    { "default_generation_settings", llama.default_generation_settings_for_props },
+                    { "total_slots",    llama.params.n_parallel }
                 };
                 res.set_content(data.dump(), "application/json; charset=utf-8");
             });
@@ -2865,12 +2890,6 @@ int main(int argc, char **argv)
                 }
             });
 
-    svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
-            {
-                const json data = llama.get_model_props();
-                return res.set_content(data.dump(), "application/json; charset=utf-8");
-            });
-
     svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
                 { return res.set_content("", "application/json; charset=utf-8"); });
 

flake.lock

@@ -5,11 +5,11 @@
         "nixpkgs-lib": "nixpkgs-lib"
       },
       "locked": {
-        "lastModified": 1704982712,
-        "narHash": "sha256-2Ptt+9h8dczgle2Oo6z5ni5rt/uLMG47UFTR1ry/wgg=",
+        "lastModified": 1706830856,
+        "narHash": "sha256-a0NYyp+h9hlb7ddVz4LUn1vT/PLwqfrWYcHMvFB1xYg=",
         "owner": "hercules-ci",
         "repo": "flake-parts",
-        "rev": "07f6395285469419cf9d078f59b5b49993198c00",
+        "rev": "b253292d9c0a5ead9bc98c4e9a26c6312e27d69f",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1706191920,
-        "narHash": "sha256-eLihrZAPZX0R6RyM5fYAWeKVNuQPYjAkCUBr+JNvtdE=",
+        "lastModified": 1706732774,
+        "narHash": "sha256-hqJlyJk4MRpcItGYMF+3uHe8HvxNETWvlGtLuVpqLU0=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "ae5c332cbb5827f6b1f02572496b141021de335f",
+        "rev": "b8b232ae7b8b144397fdb12d20f592e5e7c1a64d",
         "type": "github"
       },
       "original": {
@@ -37,11 +37,11 @@
     "nixpkgs-lib": {
       "locked": {
         "dir": "lib",
-        "lastModified": 1703961334,
-        "narHash": "sha256-M1mV/Cq+pgjk0rt6VxoyyD+O8cOUiai8t9Q6Yyq4noY=",
+        "lastModified": 1706550542,
+        "narHash": "sha256-UcsnCG6wx++23yeER4Hg18CXWbgNpqNXcHIo5/1Y+hc=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "b0d36bd0a420ecee3bc916c91886caca87c894e9",
+        "rev": "97b17f32362e475016f942bbdfda4a4a72a8a652",
         "type": "github"
       },
       "original": {

flake.nix

@@ -157,6 +157,7 @@
 
                 mpi-cpu = config.packages.default.override { useMpi = true; };
                 mpi-cuda = config.packages.default.override { useMpi = true; };
+                vulkan = config.packages.default.override { useVulkan = true; };
               }
               // lib.optionalAttrs (system == "x86_64-linux") {
                 rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp;

ggml-cuda.cu

@@ -5310,41 +5310,50 @@ template <bool need_check> static __global__ void
 #endif // __CUDA_ARCH__ >= CC_VOLTA
 }
 
-template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
-static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) {
+template <int ncols_y_template, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
+static __global__ void mul_mat_vec_q(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par, const int nrows_dst) {
+
+    const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par;
+
     const int row = blockIdx.x*blockDim.y + threadIdx.y;
 
-    if (row >= nrows) {
+    if (row >= nrows_x) {
         return;
     }
 
-    const int blocks_per_row = ncols / qk;
+    const int blocks_per_row_x = ncols_x / qk;
+    const int blocks_per_col_y = nrows_y / QK8_1;
     const int blocks_per_warp = vdr * WARP_SIZE / qi;
 
 // partial sum for each thread
-    float tmp = 0.0f;
+    float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {0.0f};
 
     const block_q_t  * x = (const block_q_t  *) vx;
     const block_q8_1 * y = (const block_q8_1 *) vy;
 
-    for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row; i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
+    for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row_x; i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row_x + i; // x block index
 
         const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
 
         const int iqs  = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
 
-        tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
+#pragma unroll
+        for (int j = 0; j < ncols_y; ++j) {
+            tmp[j] += vec_dot_q_cuda(&x[ibx], &y[j*blocks_per_col_y + iby], iqs);
+        }
     }
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
+    for (int j = 0; j < ncols_y; ++j) {
+        tmp[j] = warp_reduce_sum(tmp[j]);
 
-    if (threadIdx.x == 0) {
-        dst[row] = tmp;
+        if (threadIdx.x == 0) {
+            dst[j*nrows_dst + row] = tmp[j];
+        }
     }
 }
 
@@ -6816,121 +6825,56 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK4_0 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
+template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot>
+static void mul_mat_vec_q_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK4_1 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
+    GGML_ASSERT(ncols_x % qk == 0);
+    GGML_ASSERT(ncols_y <= 4);
 
-static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK5_0 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK5_1 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK8_0 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+    switch (ncols_y) {
+        case 1:
+            mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
+            break;
+        case 2:
+            mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
+            break;
+        case 3:
+            mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
+            break;
+        case 4:
+            mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
+            break;
+        // case 5:
+        //     mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
+        //         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
+        //     break;
+        // case 6:
+        //     mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
+        //         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
+        //     break;
+        // case 7:
+        //     mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
+        //         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
+        //     break;
+        // case 8:
+        //     mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
+        //         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
+        //     break;
+        default:
+            GGML_ASSERT(false);
+            // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot>
+            //     <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst);
+            break;
+    }
 }
 
 static void ggml_mul_mat_q4_0_q8_1_cuda(
@@ -8447,7 +8391,7 @@ static void ggml_cuda_op_mul_mat_q(
     CUDA_CHECK(cudaGetDevice(&id));
 
     // the main device has a larger memory buffer to hold the results from all GPUs
-    // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
+    // nrows_dst == nrows of the matrix that the kernel writes into
     const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
 
     switch (src0->type) {
@@ -8578,50 +8522,73 @@ static void ggml_cuda_op_mul_mat_vec_q(
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
     const int64_t src1_padded_row_size, cudaStream_t stream) {
 
-    GGML_ASSERT(ggml_nrows(src1) == 1);
-
     const int64_t ne00 = src0->ne[0];
     const int64_t row_diff = row_high - row_low;
 
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the kernel writes into
+    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
+
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
-            mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_Q4_1:
-            mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK4_1, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_Q5_0:
-            mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_Q5_1:
-            mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_Q8_0:
-            mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_Q2_K:
-            mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_Q3_K:
-            mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_Q4_K:
-            mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_Q5_K:
-            mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_Q6_K:
-            mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_IQ2_XXS:
-            mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_IQ2_XS:
-            mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         case GGML_TYPE_IQ3_XXS:
-            mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q_cuda<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
+                (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         default:
             GGML_ASSERT(false);
@@ -9945,17 +9912,18 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
 #ifdef GGML_CUDA_FORCE_DMMV
             const bool use_mul_mat_vec_q = false;
 #else
-            const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1;
+            const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type);
 #endif // GGML_CUDA_FORCE_DMMV
 
             if (use_mul_mat_vec_q) {
-                // NOTE: this kernel does not support ggml_nrows(src1) > 1
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
             } else {
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
             }
         } else {
-            if (use_mul_mat_q) {
+            if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32) {
+                ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
+            } else if (use_mul_mat_q) {
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
             } else {
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);

ggml-impl.h

@@ -19,6 +19,7 @@ extern "C" {
 // fall back to the _Static_assert C11 keyword.
 // if C99 - static_assert is noop
 // ref: https://stackoverflow.com/a/53923785/4039976
+#ifndef __cplusplus
 #ifndef static_assert
 #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
 #define static_assert(cond, msg) _Static_assert(cond, msg)
@@ -26,6 +27,7 @@ extern "C" {
 #define static_assert(cond, msg) struct global_scope_noop_trick
 #endif
 #endif
+#endif
 
 // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))

ggml-quants.c

@@ -2381,19 +2381,20 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
 
     uint8_t L[QK_K];
     uint8_t Laux[32];
+    uint8_t Ls[QK_K/32];
+    uint8_t Lm[QK_K/32];
     float   weights[32];
-    float mins[QK_K/32];
-    float scales[QK_K/32];
+    float   sw[QK_K/32];
+    float   mins[QK_K/32];
+    float   scales[QK_K/32];
 
     for (int i = 0; i < nb; i++) {
 
         float sum_x2 = 0;
         for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
-        float sigma2 = sum_x2/QK_K;
+        float sigma2 = 2*sum_x2/QK_K;
         float av_x = sqrtf(sigma2);
 
-        float max_scale = 0; // as we are deducting the min, scales are always positive
-        float max_min = 0;
         for (int j = 0; j < QK_K/32; ++j) {
             if (quant_weights) {
                 const float * qw = quant_weights + QK_K*i + 32*j;
@@ -2401,25 +2402,17 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
             } else {
                 for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
             }
+            float sumw = 0;
+            for (int l = 0; l < 32; ++l) sumw += weights[l];
+            sw[j] = sumw;
             scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
-          //scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false);
-            float scale = scales[j];
-            if (scale > max_scale) {
-                max_scale = scale;
-            }
-            float min = mins[j];
-            if (min > max_min) {
-                max_min = min;
-            }
         }
 
-        float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
-        float inv_min   = max_min   > 0 ? 63.f/max_min   : 0.f;
+        float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
+        float m_block = make_qp_quants(QK_K/32, 63, mins,   Lm, sw);
         for (int j = 0; j < QK_K/32; ++j) {
-            uint8_t ls = nearest_int(inv_scale*scales[j]);
-            uint8_t lm = nearest_int(inv_min*mins[j]);
-            ls = MIN(63, ls);
-            lm = MIN(63, lm);
+            uint8_t ls = Ls[j];
+            uint8_t lm = Lm[j];
             if (j < 4) {
                 y[i].scales[j] = ls;
                 y[i].scales[j+4] = lm;
@@ -2429,8 +2422,8 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
                 y[i].scales[j-0] |= ((lm >> 4) << 6);
             }
         }
-        y[i].d = GGML_FP32_TO_FP16(max_scale/63.f);
-        y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f);
+        y[i].d = GGML_FP32_TO_FP16(d_block);
+        y[i].dmin = GGML_FP32_TO_FP16(m_block);
 
         uint8_t sc, m;
         for (int j = 0; j < QK_K/32; ++j) {
@@ -2688,20 +2681,21 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
     const int nb = n_per_row / QK_K;
 
     uint8_t L[QK_K];
-    float mins[QK_K/32];
-    float scales[QK_K/32];
-    float weights[32];
     uint8_t Laux[32];
+    uint8_t Ls[QK_K/32];
+    uint8_t Lm[QK_K/32];
+    float   mins[QK_K/32];
+    float   scales[QK_K/32];
+    float   sw[QK_K/32];
+    float   weights[32];
 
     for (int i = 0; i < nb; i++) {
 
         float sum_x2 = 0;
         for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
-        float sigma2 = sum_x2/QK_K;
+        float sigma2 = 2*sum_x2/QK_K;
         float av_x = sqrtf(sigma2);
 
-        float max_scale = 0; // as we are deducting the min, scales are always positive
-        float max_min = 0;
         for (int j = 0; j < QK_K/32; ++j) {
             if (quant_weights) {
                 const float * qw = quant_weights + QK_K*i + 32*j;
@@ -2709,22 +2703,19 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
             } else {
                 for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
             }
+            float sumw = 0;
+            for (int l = 0; l < 32; ++l) sumw += weights[l];
+            sw[j] = sumw;
+
             scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
-            float scale = scales[j];
-            if (scale > max_scale) {
-                max_scale = scale;
-            }
-            float min = mins[j];
-            if (min > max_min) {
-                max_min = min;
-            }
         }
 
-        float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
-        float inv_min   = max_min   > 0 ? 63.f/max_min   : 0.f;
+        float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
+        float m_block = make_qp_quants(QK_K/32, 63, mins,   Lm, sw);
+
         for (int j = 0; j < QK_K/32; ++j) {
-            uint8_t ls = nearest_int(inv_scale*scales[j]);
-            uint8_t lm = nearest_int(inv_min*mins[j]);
+            uint8_t ls = Ls[j];
+            uint8_t lm = Lm[j];
             ls = MIN(63, ls);
             lm = MIN(63, lm);
             if (j < 4) {
@@ -2736,8 +2727,8 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
                 y[i].scales[j-0] |= ((lm >> 4) << 6);
             }
         }
-        y[i].d = GGML_FP32_TO_FP16(max_scale/63.f);
-        y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f);
+        y[i].d = GGML_FP32_TO_FP16(d_block);
+        y[i].dmin = GGML_FP32_TO_FP16(m_block);
 
         uint8_t sc, m;
         for (int j = 0; j < QK_K/32; ++j) {
@@ -9048,8 +9039,6 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
     int8_t L[32];
     int8_t Laux[32];
     float  waux[32];
-    bool   is_on_grid[4];
-    bool   is_on_grid_aux[4];
     uint8_t block_signs[4];
     uint32_t q2[2*(QK_K/32)];
 
@@ -9099,10 +9088,11 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
                 memset(L, 0, 32);
                 continue;
             }
+            float scale = make_qp_quants(32, kMaxQ+1, xval, (uint8_t*)L, weight);
+            float eff_max = scale*kMaxQ;
             float best = 0;
-            float scale = max/(2*kMaxQ-1);
-            for (int is = -9; is <= 9; ++is) {
-                float id = (2*kMaxQ-1+is*0.1f)/max;
+            for (int is = -6; is <= 6; ++is) {
+                float id = (2*kMaxQ-1+is*0.1f)/eff_max;
                 float this_scale = 1/id;
                 for (int k = 0; k < 4; ++k) {
                     for (int i = 0; i < 8; ++i) {
@@ -9112,9 +9102,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
                     uint16_t u = 0;
                     for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i);
                     int grid_index = kmap_q2xs[u];
-                    is_on_grid_aux[k] = true;
                     if (grid_index < 0) {
-                        is_on_grid_aux[k] = false;
                         const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
                         grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k);
                     }
@@ -9128,16 +9116,12 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
                 }
                 if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
                     scale = sumqx/sumq2; best = scale*sumqx;
-                    for (int i = 0; i < 32; ++i) L[i] = Laux[i];
-                    for (int k = 0; k <  4; ++k) is_on_grid[k] = is_on_grid_aux[k];
+                    memcpy(L, Laux, 32);
                 }
             }
-            int n_not_ongrid = 0;
-            for (int k = 0; k < 4; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
-            if (n_not_ongrid > 0 && scale > 0) {
+            if (scale > 0) {
                 float id = 1/scale;
                 for (int k = 0; k < 4; ++k) {
-                    if (is_on_grid[k]) continue;
                     uint16_t u = 0;
                     for (int i = 0; i < 8; ++i) {
                         int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
@@ -9193,49 +9177,10 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
         float d = max_scale/31;
         y[ibl].d = GGML_FP32_TO_FP16(d);
         float id = 1/d;
-        float sumqx = 0, sumq2 = 0;
         for (int ib = 0; ib < QK_K/32; ++ib) {
             int l = nearest_int(0.5f*(id*scales[ib]-1));
             l = MAX(0, MIN(15, l));
             q2[2*ib+1] |= ((uint32_t)l << 28);
-            const float * xb = xbl + 32*ib;
-            const float * qw = quant_weights + QK_K*ibl + 32*ib;
-            for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
-            const uint8_t * aux8 = (const uint8_t *)(q2 + 2*ib);
-            const float db = d * (1 + 2*l);
-            uint32_t u = 0;
-            for (int k = 0; k < 4; ++k) {
-                const int8_t * signs = keven_signs_q2xs + 8*((q2[2*ib+1] >> 7*k) & 127);
-                const float * xk = xb + 8*k;
-                const float * wk = weight + 8*k;
-                const uint8_t * grid = (const uint8_t *)(kgrid_q2xs + aux8[k]);
-                float best_mse = 0; int best_index = aux8[k];
-                for (int j = 0; j < 8; ++j) {
-                    float diff = db * grid[j] * signs[j] - xk[j];
-                    best_mse += wk[j] * diff * diff;
-                }
-                for (int idx = 0; idx < 256; ++idx) {
-                    grid = (const uint8_t *)(kgrid_q2xs + idx);
-                    float mse = 0;
-                    for (int j = 0; j < 8; ++j) {
-                        float diff = db * grid[j] * signs[j] - xk[j];
-                        mse += wk[j] * diff * diff;
-                    }
-                    if (mse < best_mse) {
-                        best_mse = mse; best_index = idx;
-                    }
-                }
-                u |= (best_index << 8*k);
-                grid = (const uint8_t *)(kgrid_q2xs + best_index);
-                //grid = (const uint8_t *)(kgrid_q2xs + aux8[k]);
-                for (int j = 0; j < 8; ++j) {
-                    float q = db * grid[j] * signs[j];
-                    sumqx += wk[j] * q * xk[j];
-                    sumq2 += wk[j] * q * q;
-                }
-            }
-            q2[2*ib] = u;
-            if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2);
         }
         memcpy(y[ibl].qs, q2, QK_K/4);
     }

ggml-quants.h

@@ -191,70 +191,74 @@ typedef struct {
 } block_iq3_xxs;
 static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
 
+#ifdef __cplusplus
+extern "C" {
+#endif
+
 // Quantization
-void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k);
-void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k);
-void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k);
-void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k);
-void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k);
-void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k);
+void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int k);
+void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int k);
+void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int k);
+void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int k);
+void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int k);
+void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int k);
 
-void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
-void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
-void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
-void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
-void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
-void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
-void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k);
+void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int k);
+void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int k);
+void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int k);
+void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k);
+void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k);
+void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k);
+void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k);
 
-void quantize_row_q4_0(const float * restrict x, void * restrict y, int k);
-void quantize_row_q4_1(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_0(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_1(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_0(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_1(const float * restrict x, void * restrict y, int k);
+void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
 
-void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_iq3_xxs(const float * restrict x, void * restrict y, int k);
+void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
 
 // Dequantization
-void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k);
-void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k);
-void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k);
-void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k);
-void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k);
-//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k);
+void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
 
-void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
-void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
-void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
-void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);
-void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);
-void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
-void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k);
-void dequantize_row_iq2_xs (const block_iq2_xs  * restrict x, float * restrict y, int k);
-void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k);
+void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq2_xs (const block_iq2_xs  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
 
 // Dot product
-void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
 
-void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
+void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy);
 
 //
 // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
@@ -276,3 +280,8 @@ void iq2xs_init_impl(int grid_size);
 void iq2xs_free_impl(int grid_size);
 void iq3xs_init_impl(int grid_size);
 void iq3xs_free_impl(int grid_size);
+
+#ifdef __cplusplus
+}
+#endif
+

ggml-sycl.cpp

@@ -1366,6 +1366,7 @@ namespace dpct
             }
 #else
             return q.memcpy(to_ptr, from_ptr, size, dep_events);
+            GGML_UNUSED(direction);
 #endif // DPCT_USM_LEVEL_NONE
         }
 
@@ -1667,7 +1668,7 @@ namespace dpct
             using Ty = typename DataType<T>::T2;
             Ty s_h;
             if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only)
-                detail::dpct_memcpy(q, (void *)&s_h, (void *)s, sizeof(T), device_to_host)
+                detail::dpct_memcpy(q, (void *)&s_h, (const void *)s, sizeof(T), device_to_host)
                     .wait();
             else
                 s_h = *reinterpret_cast<const Ty *>(s);
@@ -1691,6 +1692,20 @@ namespace dpct
                               int ldb, const void *beta, void *c, int ldc)
         {
 #ifndef __INTEL_MKL__
+            GGML_UNUSED(q);
+            GGML_UNUSED(a_trans);
+            GGML_UNUSED(b_trans);
+            GGML_UNUSED(m);
+            GGML_UNUSED(n);
+            GGML_UNUSED(k);
+            GGML_UNUSED(alpha);
+            GGML_UNUSED(a);
+            GGML_UNUSED(lda);
+            GGML_UNUSED(b);
+            GGML_UNUSED(ldb);
+            GGML_UNUSED(beta);
+            GGML_UNUSED(c);
+            GGML_UNUSED(ldc);
             throw std::runtime_error("The oneAPI Math Kernel Library (oneMKL) Interfaces "
                                      "Project does not support this API.");
 #else
@@ -1830,7 +1845,7 @@ namespace dpct
 
     template <typename T>
     T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask,
-                               int logical_sub_group_size = 32)
+                               unsigned int logical_sub_group_size = 32)
     {
         unsigned int id = g.get_local_linear_id();
         unsigned int start_index =
@@ -2160,6 +2175,7 @@ namespace dpct
         }
 #else
         return q.memcpy(to_ptr, from_ptr, size, dep_events);
+        GGML_UNUSED(direction);
 #endif // DPCT_USM_LEVEL_NONE
     }
 
@@ -3302,7 +3318,7 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo
     std::ofstream logfile;
     logfile.open(filename);
     // printf("local buf element %d\n", total_elements);
-    for(int i=0; i<total_elements; i++){
+    for(size_t i=0; i<total_elements; i++){
         if((i+1)%20 ==0) logfile <<std::endl;
         else logfile << local_buf[i] <<" ";
     }
@@ -3396,6 +3412,7 @@ static __dpct_inline__ float warp_reduce_max(float x,
 
 static __dpct_inline__ float op_repeat(const float a, const float b) {
     return b;
+    GGML_UNUSED(a);
 }
 
 static __dpct_inline__ float op_add(const float a, const float b) {
@@ -7676,6 +7693,13 @@ static void cpy_1_f16_f16(const char * cxi, char * cdsti) {
     *dsti = *xi;
 }
 
+static void cpy_1_f16_f32(const char * cxi, char * cdsti) {
+    const sycl::half *xi = (const sycl::half *)cxi;
+    float *dsti = (float *)cdsti;
+
+    *dsti = *xi;
+}
+
 static void cpy_1_i16_i16(const char * cxi, char * cdsti) {
     const int16_t *xi = (const int16_t *)cxi;
     int16_t *dsti = (int16_t *)cdsti;
@@ -7692,9 +7716,9 @@ static void cpy_1_i32_i32(const char * cxi, char * cdsti) {
 
 template <cpy_kernel_t cpy_1>
 static void cpy_f32_f16(const char * cx, char * cdst, const int ne,
-                                   const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-                                   const int ne10, const int ne11, const int nb10, const int nb11, const int nb12,
-                                   const sycl::nd_item<3> &item_ct1) {
+                        const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                        const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                        const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
     const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
                   item_ct1.get_local_id(2);
 
@@ -7704,15 +7728,17 @@ static void cpy_f32_f16(const char * cx, char * cdst, const int ne,
 
     // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
     // then combine those indices with the corresponding byte offsets to get the total offsets
-    const int i02 = i / (ne00*ne01);
-    const int i01 = (i - i02*ne01*ne00) / ne00;
-    const int i00 = i - i02*ne01*ne00 - i01*ne00;
-    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02;
+    const int i03 = i/(ne00 * ne01 * ne02);
+    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
 
-    const int i12 = i / (ne10*ne11);
-    const int i11 = (i - i12*ne10*ne11) / ne10;
-    const int i10 = i - i12*ne10*ne11 - i11*ne10;
-    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12;
+    const int i13 = i/(ne10 * ne11 * ne12);
+    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
 
     cpy_1(cx + x_offset, cdst + dst_offset);
 }
@@ -7806,9 +7832,9 @@ static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
 
 template <cpy_kernel_t cpy_blck, int qk>
 static void cpy_f32_q(const char * cx, char * cdst, const int ne,
-                                 const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-                                 const int ne10, const int ne11, const int nb10, const int nb11, const int nb12,
-                                 const sycl::nd_item<3> &item_ct1) {
+                      const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                      const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                      const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
     const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
                    item_ct1.get_local_id(2)) *
                   qk;
@@ -7817,15 +7843,17 @@ static void cpy_f32_q(const char * cx, char * cdst, const int ne,
         return;
     }
 
-    const int i02 = i / (ne00*ne01);
-    const int i01 = (i - i02*ne01*ne00) / ne00;
-    const int i00 = (i - i02*ne01*ne00 - i01*ne00);
-    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02;
+    const int i03 = i/(ne00 * ne01 * ne02);
+    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
 
-    const int i12 = i / (ne10*ne11);
-    const int i11 = (i - i12*ne10*ne11) / ne10;
-    const int i10 = (i - i12*ne10*ne11 - i11*ne10)/qk;
-    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12;
+    const int i13 = i/(ne10 * ne11 * ne12);
+    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
 
     cpy_blck(cx + x_offset, cdst + dst_offset);
 }
@@ -8230,7 +8258,8 @@ static void clamp_f32(const float * x, float * dst, const float min, const float
     dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
 }
 
-static void im2col_f32_f16(const float *x, sycl::half *dst, int offset_delta,
+template <typename T>
+static void im2col_kernel(const float *x, T *dst, int offset_delta,
                            int IW, int IH, int OW, int KW, int KH,
                            int pelements, int CHW, int s0, int s1, int p0,
                            int p1, int d0, int d1,
@@ -10581,10 +10610,12 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl(
 
 static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
                                   const int ne00, const int ne01,
-                                  const int nb00, const int nb01,
-                                  const int nb02, const int ne10,
-                                  const int ne11, const int nb10,
-                                  const int nb11, const int nb12,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
                                   dpct::queue_ptr stream) {
 
     const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@@ -10597,8 +10628,8 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
                                   sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_f32_f32>(cx, cdst, ne, ne00, ne01, nb00, nb01,
-                                           nb02, ne10, ne11, nb10, nb11, nb12,
+                cpy_f32_f16<cpy_1_f32_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                            item_ct1);
             });
     }
@@ -10606,10 +10637,12 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
 
 static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne,
                                   const int ne00, const int ne01,
-                                  const int nb00, const int nb01,
-                                  const int nb02, const int ne10,
-                                  const int ne11, const int nb10,
-                                  const int nb11, const int nb12,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
                                   dpct::queue_ptr stream) {
 
     const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@@ -10622,8 +10655,8 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne,
                                   sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_f32_f16>(cx, cdst, ne, ne00, ne01, nb00, nb01,
-                                           nb02, ne10, ne11, nb10, nb11, nb12,
+                cpy_f32_f16<cpy_1_f32_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                            item_ct1);
             });
     }
@@ -10631,10 +10664,12 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne,
 
 static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne,
                                    const int ne00, const int ne01,
-                                   const int nb00, const int nb01,
-                                   const int nb02, const int ne10,
-                                   const int ne11, const int nb10,
-                                   const int nb11, const int nb12,
+                                   const int ne02, const int nb00,
+                                   const int nb01, const int nb02,
+                                   const int nb03, const int ne10,
+                                   const int ne11, const int ne12,
+                                   const int nb10, const int nb11,
+                                   const int nb12, const int nb13,
                                    dpct::queue_ptr stream) {
 
     GGML_ASSERT(ne % QK8_0 == 0);
@@ -10643,17 +10678,20 @@ static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne,
                                            sycl::range<3>(1, 1, 1)),
                          [=](sycl::nd_item<3> item_ct1) {
                              cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>(
-                                 cx, cdst, ne, ne00, ne01, nb00, nb01, nb02,
-                                 ne10, ne11, nb10, nb11, nb12, item_ct1);
+                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                 item_ct1);
                          });
 }
 
 static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne,
                                    const int ne00, const int ne01,
-                                   const int nb00, const int nb01,
-                                   const int nb02, const int ne10,
-                                   const int ne11, const int nb10,
-                                   const int nb11, const int nb12,
+                                   const int ne02, const int nb00,
+                                   const int nb01, const int nb02,
+                                   const int nb03, const int ne10,
+                                   const int ne11, const int ne12,
+                                   const int nb10, const int nb11,
+                                   const int nb12, const int nb13,
                                    dpct::queue_ptr stream) {
 
     GGML_ASSERT(ne % QK4_0 == 0);
@@ -10662,17 +10700,20 @@ static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne,
                                            sycl::range<3>(1, 1, 1)),
                          [=](sycl::nd_item<3> item_ct1) {
                              cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>(
-                                 cx, cdst, ne, ne00, ne01, nb00, nb01, nb02,
-                                 ne10, ne11, nb10, nb11, nb12, item_ct1);
+                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                 item_ct1);
                          });
 }
 
 static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne,
                                    const int ne00, const int ne01,
-                                   const int nb00, const int nb01,
-                                   const int nb02, const int ne10,
-                                   const int ne11, const int nb10,
-                                   const int nb11, const int nb12,
+                                   const int ne02, const int nb00,
+                                   const int nb01, const int nb02,
+                                   const int nb03, const int ne10,
+                                   const int ne11, const int ne12,
+                                   const int nb10, const int nb11,
+                                   const int nb12, const int nb13,
                                    dpct::queue_ptr stream) {
 
     GGML_ASSERT(ne % QK4_1 == 0);
@@ -10681,17 +10722,20 @@ static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne,
                                            sycl::range<3>(1, 1, 1)),
                          [=](sycl::nd_item<3> item_ct1) {
                              cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>(
-                                 cx, cdst, ne, ne00, ne01, nb00, nb01, nb02,
-                                 ne10, ne11, nb10, nb11, nb12, item_ct1);
+                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                 item_ct1);
                          });
 }
 
 static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne,
                                   const int ne00, const int ne01,
-                                  const int nb00, const int nb01,
-                                  const int nb02, const int ne10,
-                                  const int ne11, const int nb10,
-                                  const int nb11, const int nb12,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
                                   dpct::queue_ptr stream) {
 
     const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@@ -10704,8 +10748,8 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne,
                                   sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_f16_f16>(cx, cdst, ne, ne00, ne01, nb00, nb01,
-                                           nb02, ne10, ne11, nb10, nb11, nb12,
+                cpy_f32_f16<cpy_1_f16_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                            item_ct1);
             });
     }
@@ -10713,10 +10757,12 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne,
 
 static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne,
                                   const int ne00, const int ne01,
-                                  const int nb00, const int nb01,
-                                  const int nb02, const int ne10,
-                                  const int ne11, const int nb10,
-                                  const int nb11, const int nb12,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
                                   dpct::queue_ptr stream) {
 
     const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@@ -10729,8 +10775,8 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne,
                                   sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_i16_i16>(cx, cdst, ne, ne00, ne01, nb00, nb01,
-                                           nb02, ne10, ne11, nb10, nb11, nb12,
+                cpy_f32_f16<cpy_1_i16_i16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                            item_ct1);
             });
     }
@@ -10738,10 +10784,12 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne,
 
 static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne,
                                   const int ne00, const int ne01,
-                                  const int nb00, const int nb01,
-                                  const int nb02, const int ne10,
-                                  const int ne11, const int nb10,
-                                  const int nb11, const int nb12,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
                                   dpct::queue_ptr stream) {
 
     const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
@@ -10754,8 +10802,8 @@ static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne,
                                   sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_i32_i32>(cx, cdst, ne, ne00, ne01, nb00, nb01,
-                                           nb02, ne10, ne11, nb10, nb11, nb12,
+                cpy_f32_f16<cpy_1_i32_i32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                            item_ct1);
             });
     }
@@ -11002,7 +11050,8 @@ static void soft_max_f32_sycl(const float *x, const float *y, float *dst,
     });
 }
 
-static void im2col_f32_f16_sycl(const float *x, sycl::half *dst, int IW, int IH,
+template <typename T>
+static void im2col_sycl(const float *x, T *dst, int IW, int IH,
                                 int OW, int OH, int KW, int KH, int IC,
                                 int offset_delta, int s0, int s1, int p0,
                                 int p1, int d0, int d1,
@@ -11019,7 +11068,7 @@ static void im2col_f32_f16_sycl(const float *x, sycl::half *dst, int IW, int IH,
                                   sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
-                im2col_f32_f16(x, dst, offset_delta, IW, IH, OW, KW, KH,
+                im2col_kernel(x, dst, offset_delta, IW, IH, OW, KW, KH,
                                parallel_elements, (IC * KH * KW), s0, s1, p0,
                                p1, d0, d1, item_ct1);
             });
@@ -11156,10 +11205,10 @@ DPCT1082:64: Migration of CUmemGenericAllocationHandle type is not supported.
 //     g_sycl_pool_handles[GGML_SYCL_MAX_DEVICES];
 static dpct::device_ptr g_sycl_pool_addr[GGML_SYCL_MAX_DEVICES] = {0};
 static size_t g_sycl_pool_used[GGML_SYCL_MAX_DEVICES] = {0};
-static const size_t SYCL_POOL_VMM_MAX_SIZE = 1ull << 36; // 64 GB
 
 static void *ggml_sycl_pool_malloc_vmm(size_t size, size_t *actual_size) try {
-
+    GGML_UNUSED(size);
+    GGML_UNUSED(actual_size);
     return NULL;
 }
 catch (sycl::exception const &exc) {
@@ -11349,9 +11398,8 @@ void ggml_init_sycl() try {
             if(id!=user_device_id) continue;
 
             device_inx++;
-            int device_vmm = 0;
 
-            g_device_caps[device_inx].vmm = !!device_vmm;
+            g_device_caps[device_inx].vmm = 0;
             g_device_caps[device_inx].device_id = id;
             g_sycl_device_id2index[id].index = device_inx;
 
@@ -11359,18 +11407,12 @@ void ggml_init_sycl() try {
             SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
                 prop, dpct::dev_mgr::instance().get_device(id))));
 
-            // fprintf(stderr,
-            //         "  Device %d: %s, compute capability %d.%d, VMM: %s\n", id,
-            //         prop.get_name(), prop.get_major_version(),
-            //         prop.get_minor_version(), device_vmm ? "yes" : "no");
-
             g_tensor_split[device_inx] = total_vram;
             total_vram += prop.get_global_mem_size();
 
             g_device_caps[device_inx].cc =
                 100 * prop.get_major_version() + 10 * prop.get_minor_version();
 
-            // printf("g_device_caps[%d].cc=%d\n", device_inx, g_device_caps[device_inx].cc);
         }
         device_inx = -1;
         for (int id = 0; id < g_all_sycl_device_count; ++id) {
@@ -12106,7 +12148,8 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec(
     const int64_t src1_ncols, const int64_t src1_padded_row_size,
     const dpct::queue_ptr &stream) {
 
-    const int64_t ne00 = src0->ne[0];
+    GGML_TENSOR_BINARY_OP_LOCALS
+
     const int64_t row_diff = row_high - row_low;
 
     // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
@@ -12125,8 +12168,9 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec(
         } else {
             src1_dfloat = src1_dfloat_a.alloc(ne00);
             ggml_cpy_f32_f16_sycl((const char *)src1_ddf_i, (char *)src1_dfloat,
-                                  ne00, ne00, 1, sizeof(float), 0, 0, ne00, 1,
-                                  sizeof(sycl::half), 0, 0, stream);
+                                  ne00, ne00, ne01, ne02, nb00, nb01, nb02,
+                                  nb03, ne10, ne11, ne12, nb10, nb11, nb12,
+                                  nb13, stream);
         }
     }
 #else
@@ -12206,7 +12250,6 @@ inline void ggml_sycl_op_mul_mat_sycl(
     // ldc == nrows of the matrix that cuBLAS writes into
     int ldc = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff;
 
-    const int compute_capability = g_device_caps[id].cc;
 #ifdef GGML_SYCL_F16
     bool use_fp16 = true;  // TODO(Yu) SYCL capability check
 #else
@@ -12415,7 +12458,7 @@ inline void ggml_sycl_op_im2col(const ggml_tensor *src0,
 
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
 
     const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
     const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
@@ -12438,8 +12481,11 @@ inline void ggml_sycl_op_im2col(const ggml_tensor *src0,
 
     const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
 
-    im2col_f32_f16_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH,
-                        IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
+    if (dst->type == GGML_TYPE_F16) {
+        im2col_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
+    } else {
+        im2col_sycl(src1_dd, (float *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
+    }
 
     (void) src0;
     (void) src0_dd;
@@ -12691,7 +12737,7 @@ static void ggml_sycl_set_peer_access(const int n_tokens) {
                 continue;
             }
 
-            int can_access_peer;
+            // int can_access_peer;
             // SYCL_CHECK(syclDeviceCanAccessPeer(&can_access_peer, id, id_other));
             // if (can_access_peer) {
             //     if (enable_peer_access) {
@@ -12716,7 +12762,6 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
     const int64_t ne03 = src0->ne[3];
-    const int64_t nrows0 = ggml_nrows(src0);
 
     const int64_t ne10 = src1->ne[0];
     const int64_t ne11 = src1->ne[1];
@@ -13812,13 +13857,6 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
         src1_row_extra.data_device[g_main_device_index] = src1_contiguous.get();
         dst_row_extra.data_device[g_main_device_index]  =  dst_contiguous.get();
 
-        const dpct::memcpy_direction src1_kind =
-            src1->backend == GGML_BACKEND_CPU ? dpct::host_to_device
-                                              : dpct::device_to_device;
-        const dpct::memcpy_direction dst_kind = dst->backend == GGML_BACKEND_CPU
-                                                    ? dpct::device_to_host
-                                                    : dpct::device_to_device;
-
         for (int32_t row_id = 0; row_id < n_as; ++row_id) {
             const struct ggml_tensor * src0_row = dst->src[row_id + 2];
 
@@ -13904,19 +13942,23 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
 
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
-    GGML_ASSERT(src0->ne[3] == 1);
+    const int64_t ne02 = src0->ne[2];
+
 
     const int64_t nb00 = src0->nb[0];
     const int64_t nb01 = src0->nb[1];
     const int64_t nb02 = src0->nb[2];
+    const int64_t nb03 = src0->nb[3];
 
     const int64_t ne10 = src1->ne[0];
     const int64_t ne11 = src1->ne[1];
-    GGML_ASSERT(src1->ne[3] == 1);
+    const int64_t ne12 = src1->ne[2];
+
 
     const int64_t nb10 = src1->nb[0];
     const int64_t nb11 = src1->nb[1];
     const int64_t nb12 = src1->nb[2];
+    const int64_t nb13 = src1->nb[3];
 
     SYCL_CHECK(ggml_sycl_set_device(g_main_device));
     dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0];
@@ -13928,21 +13970,21 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
     char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index];
 
     if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
-        ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
+        ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
+        ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
-        ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
+        ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
-        ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
+        ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
-        ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
+        ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
+        ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) {
-        ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
+        ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) {
-        ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
+        ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
     } else {
         fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
                 ggml_type_name(src0->type), ggml_type_name(src1->type));

ggml-vulkan.h

@@ -8,24 +8,29 @@ extern "C" {
 #endif
 
 #define GGML_VK_NAME "Vulkan"
+#define GGML_VK_MAX_DEVICES 16
 
-GGML_API void ggml_vk_init(void);
+GGML_API void ggml_vk_init_cpu_assist(void);
 
-GGML_API void ggml_vk_preallocate_buffers_graph(struct ggml_tensor * node);
-GGML_API void ggml_vk_preallocate_buffers(void);
-GGML_API void ggml_vk_build_graph(struct ggml_tensor * node, bool last_node);
-GGML_API bool ggml_vk_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
+GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node);
+GGML_API void ggml_vk_preallocate_buffers_cpu_assist(void);
+GGML_API void ggml_vk_build_graph_cpu_assist(struct ggml_tensor * node, bool last_node);
+GGML_API bool ggml_vk_compute_forward_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
 #ifdef GGML_VULKAN_CHECK_RESULTS
-void ggml_vk_check_results_1(struct ggml_compute_params * params, struct ggml_tensor * tensor);
+void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
 #endif
-GGML_API void ggml_vk_graph_cleanup(void);
+GGML_API void ggml_vk_graph_cleanup_cpu_assist(void);
+GGML_API void ggml_vk_free_cpu_assist(void);
 
 // backend API
-GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(void);
+GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num);
 
 GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend);
+GGML_API GGML_CALL int  ggml_backend_vk_get_device_count(void);
+GGML_API GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size);
+GGML_API GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total);
 
-GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(void);
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
 // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);
 

ggml.c

@@ -2343,7 +2343,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
 #elif defined(GGML_USE_CLBLAST)
         ggml_cl_init();
 #elif defined(GGML_USE_VULKAN)
-        ggml_vk_init();
+        ggml_vk_init_cpu_assist();
 #elif defined(GGML_USE_SYCL)
         ggml_init_sycl();
 #endif
@@ -2470,7 +2470,8 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
     size_t max_size = 0;
 
     for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) {
-        max_size = MAX(max_size, ggml_nbytes(tensor));
+        size_t bytes = ggml_nbytes(tensor);
+        max_size = MAX(max_size, bytes);
     }
 
     return max_size;
@@ -11887,8 +11888,10 @@ GGML_CALL void ggml_rope_yarn_corr_dims(
     int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
 ) {
     // start and end correction dims
-    dims[0] = MAX(0,         floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base)));
-    dims[1] = MIN(n_dims - 1, ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base)));
+    float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base));
+    float end   =  ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base));
+    dims[0] = MAX(0, start);
+    dims[1] = MIN(n_dims - 1, end);
 }
 
 static void ggml_compute_forward_rope_f32(
@@ -14847,10 +14850,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
     GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU);
     GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU);
 #elif defined(GGML_USE_VULKAN)
-    const bool skip_cpu = ggml_vk_compute_forward(params, tensor);
+    const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor);
 #ifdef GGML_VULKAN_CHECK_RESULTS
     if (skip_cpu) {
-        ggml_vk_check_results_1(params, tensor);
+        ggml_vk_check_results_1_cpu_assist(params, tensor);
     }
 #endif
     if (skip_cpu) {
@@ -17266,12 +17269,12 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
 
 #ifdef GGML_USE_VULKAN
     for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]);
+        ggml_vk_preallocate_buffers_graph_cpu_assist(cgraph->nodes[i]);
     }
-    ggml_vk_preallocate_buffers();
+    ggml_vk_preallocate_buffers_cpu_assist();
 
     for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1);
+        ggml_vk_build_graph_cpu_assist(cgraph->nodes[i], i == cgraph->n_nodes - 1);
     }
 #endif
 
@@ -17327,7 +17330,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
     }
 
 #ifdef GGML_USE_VULKAN
-    ggml_vk_graph_cleanup();
+    ggml_vk_graph_cleanup_cpu_assist();
 #endif
 
     // performance stats (graph)

ggml_vk_generate_shaders.py

@@ -157,19 +157,10 @@ struct block_q6_K
 
 # Dequant functions
 shader_f16_dequant_func = """
-#define DEQUANT_FUNC f16vec2 v = f16vec2(data_a[ib + 0], data_a[ib + 1]);
-"""
-shader_f16_dequant_func_compat = """
 #define DEQUANT_FUNC vec2 v = vec2(data_a[ib + 0], data_a[ib + 1]);
 """
 
 shader_q4_0_dequant_func = """
-#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \
-const uint8_t vui = data_a[ib].qs[iqs]; \
-f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \
-v = (v - 8.0hf)*d;
-"""
-shader_q4_0_dequant_func_compat = """
 #define DEQUANT_FUNC const float d = float(data_a[ib].d); \
 const uint vui = uint(data_a[ib].qs[iqs]); \
 vec2 v = vec2(vui & 0xF, vui >> 4); \
@@ -177,13 +168,6 @@ v = (v - 8.0f)*d;
 """
 
 shader_q4_1_dequant_func = """
-#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \
-const float16_t m = data_a[ib].m; \
-const uint8_t vui = data_a[ib].qs[iqs]; \
-f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \
-v = v*d + m;
-"""
-shader_q4_1_dequant_func_compat = """
 #define DEQUANT_FUNC const float d = float(data_a[ib].d); \
 const float m = float(data_a[ib].m); \
 const uint vui = uint(data_a[ib].qs[iqs]); \
@@ -192,14 +176,6 @@ v = v*d + m;
 """
 
 shader_q5_0_dequant_func = """
-#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \
-const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \
-const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \
-const uint8_t vui = data_a[ib].qs[iqs]; \
-f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \
-v = (v - 16.0hf) * d;
-"""
-shader_q5_0_dequant_func_compat = """
 #define DEQUANT_FUNC const float d = float(data_a[ib].d); \
 const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \
 const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \
@@ -209,14 +185,6 @@ v = (v - 16.0f) * d;
 """
 
 shader_q5_1_dequant_func = """
-#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \
-const float16_t m = data_a[ib].m; \
-const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \
-const uint8_t vui = data_a[ib].qs[iqs]; \
-f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \
-v = v*d + m;
-"""
-shader_q5_1_dequant_func_compat = """
 #define DEQUANT_FUNC const float d = float(data_a[ib].d); \
 const float m = float(data_a[ib].m); \
 const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \
@@ -226,11 +194,6 @@ v = v*d + m;
 """
 
 shader_q8_0_dequant_func = """
-#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \
-f16vec2 v = f16vec2(data_a[ib].qs[iqs], data_a[ib].qs[iqs + 1]); \
-v = v * d;
-"""
-shader_q8_0_dequant_func_compat = """
 #define DEQUANT_FUNC const float d = float(data_a[ib].d); \
 vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])); \
 v = v * d;
@@ -2110,7 +2073,7 @@ lock = asyncio.Lock()
 shader_fnames = []
 
 
-async def string_to_spv(name, code, defines, fp16):
+async def string_to_spv(name, code, defines, fp16=True):
     f = NamedTemporaryFile(mode="w", delete=False)
     f.write(code)
     f.flush()
@@ -2200,64 +2163,6 @@ async def main():
         tasks.append(string_to_spv("matmul_f16_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
         tasks.append(string_to_spv("matmul_f16_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
 
-        # Build dequant shaders
-        tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {}, fp16))
-
-        for i in range(0, VK_NUM_TYPES):
-            stream.clear()
-
-            stream.extend((dequant_head, shader_int8_ext, shader_float_type))
-
-            if i == GGML_TYPE_F16:
-                stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, dequant_body))
-            elif i == GGML_TYPE_Q4_0:
-                stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, dequant_body))
-            elif i == GGML_TYPE_Q4_1:
-                stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, dequant_body))
-            elif i == GGML_TYPE_Q5_0:
-                stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, dequant_body))
-            elif i == GGML_TYPE_Q5_1:
-                stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, dequant_body))
-            elif i == GGML_TYPE_Q8_0:
-                stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, dequant_body))
-            elif i == GGML_TYPE_Q2_K:
-                stream.extend((shader_q2_K_defines, dequant_q2_K_body))
-            elif i == GGML_TYPE_Q3_K:
-                stream.extend((shader_q3_K_defines, dequant_q3_K_body))
-            elif i == GGML_TYPE_Q4_K:
-                stream.extend((shader_q4_K_defines, dequant_q4_K_body))
-            elif i == GGML_TYPE_Q5_K:
-                stream.extend((shader_q5_K_defines, dequant_q5_K_body))
-            elif i == GGML_TYPE_Q6_K:
-                stream.extend((shader_q6_K_defines, dequant_q6_K_body))
-            else:
-                continue
-
-            tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"}, fp16))
-
-        # get_rows
-        for i in range(0, VK_NUM_TYPES):
-            stream.clear()
-            stream.extend((generic_head, shader_int8_ext, shader_float_type))
-
-            if i == GGML_TYPE_F16:
-                stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, get_rows_body))
-            elif i == GGML_TYPE_Q4_0:
-                stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, get_rows_body))
-            elif i == GGML_TYPE_Q4_1:
-                stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, get_rows_body))
-            elif i == GGML_TYPE_Q5_0:
-                stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, get_rows_body))
-            elif i == GGML_TYPE_Q5_1:
-                stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, get_rows_body))
-            elif i == GGML_TYPE_Q8_0:
-                stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, get_rows_body))
-            else:
-                continue
-
-            tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"}, fp16))
-            tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"}, fp16))
-
     # Shaders where precision is needed, so no fp16 version
 
     # mul mat vec
@@ -2266,17 +2171,17 @@ async def main():
         stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32))
 
         if i == GGML_TYPE_F16:
-            stream.extend((shader_f16_defines, shader_f16_dequant_func_compat, mul_mat_vec_body))
+            stream.extend((shader_f16_defines, shader_f16_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q4_0:
-            stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat, mul_mat_vec_body))
+            stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q4_1:
-            stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat, mul_mat_vec_body))
+            stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q5_0:
-            stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat, mul_mat_vec_body))
+            stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q5_1:
-            stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat, mul_mat_vec_body))
+            stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q8_0:
-            stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat, mul_mat_vec_body))
+            stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, mul_mat_vec_body))
         elif i == GGML_TYPE_Q2_K:
             stream.extend((shader_q2_K_defines, mul_mat_vec_q2_K_body))
         elif i == GGML_TYPE_Q3_K:
@@ -2290,43 +2195,101 @@ async def main():
         else:
             continue
 
-        tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}, fp16))
+        tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}))
 
-    tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True))
-    tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True))
+    # Dequant shaders
+    for i in range(0, VK_NUM_TYPES):
+        stream.clear()
+
+        stream.extend((dequant_head, shader_int8_ext, shader_f32))
+
+        if i == GGML_TYPE_F16:
+            stream.extend((shader_f16_defines,  shader_f16_dequant_func,  dequant_body))
+        elif i == GGML_TYPE_Q4_0:
+            stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, dequant_body))
+        elif i == GGML_TYPE_Q4_1:
+            stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, dequant_body))
+        elif i == GGML_TYPE_Q5_0:
+            stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, dequant_body))
+        elif i == GGML_TYPE_Q5_1:
+            stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, dequant_body))
+        elif i == GGML_TYPE_Q8_0:
+            stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, dequant_body))
+        elif i == GGML_TYPE_Q2_K:
+            stream.extend((shader_q2_K_defines, dequant_q2_K_body))
+        elif i == GGML_TYPE_Q3_K:
+            stream.extend((shader_q3_K_defines, dequant_q3_K_body))
+        elif i == GGML_TYPE_Q4_K:
+            stream.extend((shader_q4_K_defines, dequant_q4_K_body))
+        elif i == GGML_TYPE_Q5_K:
+            stream.extend((shader_q5_K_defines, dequant_q5_K_body))
+        elif i == GGML_TYPE_Q6_K:
+            stream.extend((shader_q6_K_defines, dequant_q6_K_body))
+        else:
+            continue
+
+        tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"}))
+
+    tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {}))
+
+    # get_rows
+    for i in range(0, VK_NUM_TYPES):
+        stream.clear()
+        stream.extend((generic_head, shader_int8_ext, shader_f32))
+
+        if i == GGML_TYPE_F16:
+            stream.extend((shader_f16_defines,  shader_f16_dequant_func,  get_rows_body))
+        elif i == GGML_TYPE_Q4_0:
+            stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, get_rows_body))
+        elif i == GGML_TYPE_Q4_1:
+            stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, get_rows_body))
+        elif i == GGML_TYPE_Q5_0:
+            stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, get_rows_body))
+        elif i == GGML_TYPE_Q5_1:
+            stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, get_rows_body))
+        elif i == GGML_TYPE_Q8_0:
+            stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, get_rows_body))
+        else:
+            continue
+
+        tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"}))
+        tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"}))
+
+    tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
 
     # Norms
-    tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
-    tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
+    tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
-    tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}, True))
-    tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True))
+    tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}))
+    tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
 
-    tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True))
+    tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {}, True))
-    tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True))
+    tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {}))
+    tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
+    tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
+    tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
+    tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
-    tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
-    tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
+    tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True))
+    tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True))
+    tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}, True))
-    tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True))
+    tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
 
-    tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}, True))
-    tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True))
+    tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
 
     await asyncio.gather(*tasks)
 

gguf-py/gguf/constants.py

@@ -104,6 +104,7 @@ class MODEL_ARCH(IntEnum):
     CODESHELL = auto()
     ORION     = auto()
     INTERNLM2  = auto()
+    MINICPM   = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -156,6 +157,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.CODESHELL:      "codeshell",
     MODEL_ARCH.ORION:          "orion",
     MODEL_ARCH.INTERNLM2:      "internlm2",
+    MODEL_ARCH.MINICPM:        "minicpm",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -464,6 +466,25 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.MINICPM: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
     # TODO
 }
 

llama.cpp

@@ -205,10 +205,11 @@ enum llm_arch {
     LLM_ARCH_CODESHELL,
     LLM_ARCH_ORION,
     LLM_ARCH_INTERNLM2,
+    LLM_ARCH_MINICPM,
     LLM_ARCH_UNKNOWN,
 };
 
-static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
+static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_LLAMA,           "llama"     },
     { LLM_ARCH_FALCON,          "falcon"    },
     { LLM_ARCH_GPT2,            "gpt2"      },
@@ -228,6 +229,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
     { LLM_ARCH_CODESHELL,       "codeshell" },
     { LLM_ARCH_ORION,           "orion"     },
     { LLM_ARCH_INTERNLM2,       "internlm2" },
+    { LLM_ARCH_MINICPM,         "minicpm"   },
 };
 
 enum llm_kv {
@@ -285,7 +287,7 @@ enum llm_kv {
     LLM_KV_TOKENIZER_RWKV,
 };
 
-static std::map<llm_kv, std::string> LLM_KV_NAMES = {
+static std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
     { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
     { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
@@ -346,7 +348,7 @@ struct LLM_KV {
     llm_arch arch;
 
     std::string operator()(llm_kv kv) const {
-        return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
+        return ::format(LLM_KV_NAMES[kv], LLM_ARCH_NAMES[arch]);
     }
 };
 
@@ -690,6 +692,29 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_MINICPM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -747,13 +772,13 @@ struct LLM_TN {
 // gguf helpers
 //
 
-static std::map<int8_t, std::string> LLAMA_ROPE_SCALING_TYPES = {
+static std::map<int32_t, const char *> LLAMA_ROPE_SCALING_TYPES = {
     { LLAMA_ROPE_SCALING_NONE,   "none"   },
     { LLAMA_ROPE_SCALING_LINEAR, "linear" },
     { LLAMA_ROPE_SCALING_YARN,   "yarn"   },
 };
 
-static int8_t llama_rope_scaling_type_from_string(const std::string & name) {
+static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
     for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
         if (kv.second == name) {
             return kv.first;
@@ -1330,7 +1355,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
 #elif defined(GGML_USE_CUBLAS)
     buft = ggml_backend_cuda_buffer_type(gpu);
 #elif defined(GGML_USE_VULKAN)
-    buft = ggml_backend_vk_buffer_type();
+    buft = ggml_backend_vk_buffer_type(gpu);
 #elif defined(GGML_USE_SYCL)
     buft = ggml_backend_sycl_buffer_type(gpu);
 #elif defined(GGML_USE_CLBLAST)
@@ -1367,6 +1392,33 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g
     GGML_UNUSED(tensor_split);
 }
 
+static size_t llama_get_device_count() {
+#if defined(GGML_USE_CUBLAS)
+    return ggml_backend_cuda_get_device_count();
+#elif defined(GGML_USE_VULKAN)
+    return ggml_backend_vk_get_device_count();
+#else
+    return 1;
+#endif
+}
+
+static size_t llama_get_device_memory(int device) {
+#if defined(GGML_USE_CUBLAS)
+    size_t total;
+    size_t free;
+    ggml_backend_cuda_get_device_memory(device, &total, &free);
+    return free;
+#elif defined(GGML_USE_VULKAN)
+    size_t total;
+    size_t free;
+    ggml_backend_vk_get_device_memory(device, &total, &free);
+    return free;
+#else
+    return 1;
+    GGML_UNUSED(device);
+#endif
+}
+
 //
 // globals
 //
@@ -1390,6 +1442,7 @@ enum e_model {
     MODEL_UNKNOWN,
     MODEL_0_5B,
     MODEL_1B,
+    MODEL_2B,
     MODEL_3B,
     MODEL_4B,
     MODEL_7B,
@@ -1415,6 +1468,7 @@ static const size_t GiB = 1024*MiB;
 
 struct llama_hparams {
     bool     vocab_only;
+    bool     rope_finetuned;
     uint32_t n_vocab;
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
@@ -1434,8 +1488,7 @@ struct llama_hparams {
     float    rope_freq_base_train;
     float    rope_freq_scale_train;
     uint32_t n_yarn_orig_ctx;
-    int8_t   rope_scaling_type_train : 3;
-    bool     rope_finetuned : 1;
+    int32_t  rope_scaling_type_train;
 
     float f_clamp_kqv;
     float f_max_alibi_bias;
@@ -1737,6 +1790,10 @@ struct llama_context {
             ggml_backend_free(backend);
         }
 
+#ifdef GGML_USE_VULKAN
+        ggml_vk_free_cpu_assist();
+#endif
+
         ggml_backend_buffer_free(buf_input);
         ggml_free(ctx_input);
     }
@@ -2701,7 +2758,7 @@ struct llama_model_loader {
 // load LLaMA models
 //
 
-static std::string llama_model_arch_name(llm_arch arch) {
+static const char * llama_model_arch_name(llm_arch arch) {
     auto it = LLM_ARCH_NAMES.find(arch);
     if (it == LLM_ARCH_NAMES.end()) {
         return "unknown";
@@ -2748,6 +2805,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
 static const char * llama_model_type_name(e_model type) {
     switch (type) {
         case MODEL_1B:     return "1B";
+        case MODEL_2B:     return "2B";
         case MODEL_3B:     return "3B";
         case MODEL_7B:     return "7B";
         case MODEL_8B:     return "8B";
@@ -2887,6 +2945,15 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_MINICPM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 40: model.type = e_model::MODEL_2B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_FALCON:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -3310,11 +3377,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     const auto & hparams = model.hparams;
     const auto & vocab   = model.vocab;
 
-    const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
+    const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
 
     // hparams
     LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
-    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch).c_str());
+    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch));
     LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, llama_model_vocab_type_name(vocab.type));
     LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
     LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
@@ -3336,7 +3403,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
     LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
     LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
-    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type.c_str());
+    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
     LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
     LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
     LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
@@ -3402,22 +3469,18 @@ static bool llm_load_tensors(
         model.buft_layer[i] = llama_default_buffer_type_cpu(true);
     }
 
-#ifdef GGML_USE_CUBLAS
     if (split_mode == LLAMA_SPLIT_LAYER) {
         // calculate the split points
-        int device_count = ggml_backend_cuda_get_device_count();
+        int device_count = llama_get_device_count();
         bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
-        float splits[GGML_CUDA_MAX_DEVICES];
+        std::vector<float> splits(device_count);
         if (all_zero) {
             // default split, by free memory
             for (int i = 0; i < device_count; ++i) {
-                size_t total;
-                size_t free;
-                ggml_backend_cuda_get_device_memory(i, &total, &free);
-                splits[i] = free;
+                splits[i] = llama_get_device_memory(i);
             }
         } else {
-            std::copy(tensor_split, tensor_split + device_count, splits);
+            std::copy(tensor_split, tensor_split + device_count, splits.begin());
         }
 
         // sum and normalize the splits to get the split points
@@ -3433,19 +3496,17 @@ static bool llm_load_tensors(
         // assign the repeating layers to the devices according to the splits
         int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
         for (int64_t i = i_gpu_start; i < n_layer; ++i) {
-            int layer_gpu = std::upper_bound(splits, splits + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits;
+            int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
             model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
         }
         // assign the output layer
         if (n_gpu_layers > n_layer) {
-            int layer_gpu = std::upper_bound(splits, splits + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits;
+            int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
             model.buft_output = llama_default_buffer_type_offload(layer_gpu);
         } else {
             model.buft_output = llama_default_buffer_type_cpu(true);
         }
-    } else
-#endif
-    {
+    } else {
         ggml_backend_buffer_type_t split_buft;
         if (split_mode == LLAMA_SPLIT_ROW) {
             split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
@@ -3524,13 +3585,16 @@ static bool llm_load_tensors(
         switch (model.arch) {
             case LLM_ARCH_LLAMA:
             case LLM_ARCH_REFACT:
+            case LLM_ARCH_MINICPM:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
                     // output
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                        if (model.arch != LLM_ARCH_MINICPM){
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                        }
                     }
 
                     for (int i = 0; i < n_layer; ++i) {
@@ -4145,8 +4209,7 @@ static bool llm_load_tensors(
         ctx_bufs.emplace_back(ctx, buf);
     }
 
-    // print memory requirements
-    {
+    if (llama_supports_gpu_offload()) {
         const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
 
         LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
@@ -4158,10 +4221,11 @@ static bool llm_load_tensors(
         const int max_offloadable_layers       = hparams.n_layer + 1;
 
         LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
+    }
 
-        for (ggml_backend_buffer_t buf : model.bufs) {
-            LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
-        }
+    // print memory requirements
+    for (ggml_backend_buffer_t buf : model.bufs) {
+        LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
     }
 
     // populate tensors_by_name
@@ -6781,6 +6845,153 @@ struct llm_build_context {
         return gf;
     }
 
+    // ref: https://arxiv.org/abs/2203.03466
+    //      https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
+    // based on the original build_llama() function
+    struct ggml_cgraph * build_minicpm() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        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);
+
+        const int64_t n_embd = hparams.n_embd;
+        //TODO: if the model varies, these parameters need to be read from the model
+        const int64_t n_embd_base = 256;
+        const float scale_embd  = 12.0f;
+        const float scale_depth = 1.4f;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
+        cb(inpL, "inp_embd", -1);
+
+        // scale the input embeddings
+        inpL = ggml_scale(ctx0, inpL, scale_embd);
+        cb(inpL, "inp_scaled", -1);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
+        cb(inp_pos, "inp_pos", -1);
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
+        cb(KQ_mask, "KQ_mask", -1);
+
+        // shift the entire K-cache if needed
+        if (do_rope_shift) {
+            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
+        }
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
+                    hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+                cb(cur, "kqv_out", il);
+            }
+
+            // scale_res - scale the hidden states for residual connection
+            const float scale_res = scale_depth/sqrtf(float(n_layer));
+            cur = ggml_scale(ctx0, cur, scale_res);
+            cb(cur, "hidden_scaled", -1);
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            // scale the hidden states for residual connection
+            cur = ggml_scale(ctx0, cur, scale_res);
+            cb(cur, "hidden_scaled_ffn", -1);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head scaling
+        const float scale_lmhead = float(n_embd_base)/float(n_embd);
+        cur = ggml_scale(ctx0, cur, scale_lmhead);
+        cb(cur, "lmhead_scaling", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
 };
 
 static struct ggml_cgraph * llama_build_graph(
@@ -6943,6 +7154,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_internlm2();
             } break;
+        case LLM_ARCH_MINICPM:
+            {
+                result = llm.build_minicpm();
+            } break;
         default:
             GGML_ASSERT(false);
     }
@@ -8373,6 +8588,10 @@ void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * can
 
     const int64_t t_start_sample_us = ggml_time_us();
 
+    if (k <= 0) {
+        k = candidates->size;
+    }
+
     k = std::max(k, (int) min_keep);
     k = std::min(k, (int) candidates->size);
 
@@ -9456,8 +9675,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
             new_type = GGML_TYPE_Q4_K;
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && qs.model.hparams.n_gqa() >= 4) {
-            new_type = GGML_TYPE_Q4_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_Q3_K : GGML_TYPE_IQ3_XXS;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
             new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
@@ -9496,9 +9715,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) {
             if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
         }
-        //else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-        //    if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K;
-        //}
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
+            new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
             new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
                      : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
@@ -10295,6 +10514,8 @@ size_t llama_max_devices(void) {
     return GGML_CUDA_MAX_DEVICES;
 #elif defined(GGML_USE_SYCL)
     return GGML_SYCL_MAX_DEVICES;
+#elif defined(GGML_USE_VULKAN)
+    return GGML_VK_MAX_DEVICES;
 #else
     return 1;
 #endif
@@ -10502,13 +10723,15 @@ struct llama_context * llama_new_context_with_model(
         }
 #elif defined(GGML_USE_VULKAN)
         if (model->n_gpu_layers > 0) {
-            ggml_backend_t backend = ggml_backend_vk_init();
-            if (backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
-                llama_free(ctx);
-                return nullptr;
+            for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
+                ggml_backend_t backend = ggml_backend_vk_init(device);
+                if (backend == nullptr) {
+                    LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
             }
-            ctx->backends.push_back(backend);
         }
 #elif defined(GGML_USE_SYCL)
         if (model->n_gpu_layers > 0) {
@@ -10735,7 +10958,7 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3
 
 int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
     return snprintf(buf, buf_size, "%s %s %s",
-            llama_model_arch_name(model->arch).c_str(),
+            llama_model_arch_name(model->arch),
             llama_model_type_name(model->type),
             llama_model_ftype_name(model->ftype).c_str());
 }

llama.h

@@ -213,7 +213,7 @@ extern "C" {
         uint32_t n_batch;           // prompt processing maximum batch size
         uint32_t n_threads;         // number of threads to use for generation
         uint32_t n_threads_batch;   // number of threads to use for batch processing
-        int8_t   rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
+        int32_t  rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
         float    rope_freq_base;   // RoPE base frequency, 0 = from model

scripts/server-llm.sh

@@ -14,16 +14,17 @@
 # - Might be unstable!
 #
 # Usage:
-#   ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]
+#   ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]
 #
-#   --port:       port number, default is 8888
-#   --repo:       path to a repo containing GGUF model files
-#   --wtype:      weights type (f16, q8_0, q4_0, q4_1), default is user-input
-#   --backend:    cpu, cuda, metal, opencl, depends on the OS
-#   --gpu-id:     gpu id, default is 0
-#   --n-parallel: number of parallel requests, default is 8
-#   --n-kv:       KV cache size, default is 4096
-#   --verbose:    verbose output
+#   --port:            port number, default is 8888
+#   --repo:            path to a repo containing GGUF model files
+#   --wtype:           weights type (f16, q8_0, q4_0, q4_1), default is user-input
+#   --backend:         cpu, cuda, metal, opencl, depends on the OS
+#   --gpu-id:          gpu id, default is 0
+#   --n-parallel:      number of parallel requests, default is 8
+#   --n-kv:            KV cache size, default is 4096
+#   --verbose:         verbose output
+#   --non-interactive: run without asking a permission to run
 #
 # Example:
 #
@@ -47,6 +48,7 @@ if ! command -v make &> /dev/null; then
 fi
 
 # parse arguments
+is_interactive=1
 port=8888
 repo=""
 wtype=""
@@ -66,15 +68,16 @@ verbose=0
 
 function print_usage {
     printf "Usage:\n"
-    printf "  ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n"
-    printf "  --port:       port number, default is 8888\n"
-    printf "  --repo:       path to a repo containing GGUF model files\n"
-    printf "  --wtype:      weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
-    printf "  --backend:    cpu, cuda, metal, opencl, depends on the OS\n"
-    printf "  --gpu-id:     gpu id, default is 0\n"
-    printf "  --n-parallel: number of parallel requests, default is 8\n"
-    printf "  --n-kv:       KV cache size, default is 4096\n"
-    printf "  --verbose:    verbose output\n\n"
+    printf "  ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]\n\n"
+    printf "  --port:             port number, default is 8888\n"
+    printf "  --repo:             path to a repo containing GGUF model files\n"
+    printf "  --wtype:            weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
+    printf "  --backend:          cpu, cuda, metal, opencl, depends on the OS\n"
+    printf "  --gpu-id:           gpu id, default is 0\n"
+    printf "  --n-parallel:       number of parallel requests, default is 8\n"
+    printf "  --n-kv:             KV cache size, default is 4096\n"
+    printf "  --verbose:          verbose output\n\n"
+    printf "  --non-interactive:  run without asking a permission to run\n"
     printf "Example:\n\n"
     printf '  bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n'
 }
@@ -82,6 +85,10 @@ function print_usage {
 while [[ $# -gt 0 ]]; do
     key="$1"
     case $key in
+        --non-interactive)
+            is_interactive=0
+            shift
+            ;;
         --port)
             port="$2"
             shift
@@ -141,6 +148,28 @@ for wt in "${wtypes[@]}"; do
     wfiles+=("")
 done
 
+# map wtype input to index
+if [[ ! -z "$wtype" ]]; then
+    iw=-1
+    is=0
+    for wt in "${wtypes[@]}"; do
+        # uppercase
+        uwt=$(echo "$wt" | tr '[:lower:]' '[:upper:]')
+        if [[ "$uwt" == "$wtype" ]]; then
+            iw=$is
+            break
+        fi
+        is=$((is+1))
+    done
+
+    if [[ $iw -eq -1 ]]; then
+        printf "[-] Invalid weight type: %s\n" "$wtype"
+        exit 1
+    fi
+
+    wtype="$iw"
+fi
+
 # sample repos
 repos=(
     "https://huggingface.co/TheBloke/Llama-2-7B-GGUF"
@@ -154,31 +183,32 @@ repos=(
     "https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GGUF"
     "https://huggingface.co/TheBloke/CausalLM-7B-GGUF"
 )
+if [ $is_interactive -eq 1 ]; then
+    printf "\n"
+    printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n"
+    printf "    Based on the options that follow, the script might download a model file\n"
+    printf "    from the internet, which can be a few GBs in size. The script will also\n"
+    printf "    build the latest llama.cpp source code from GitHub, which can be unstable.\n"
+    printf "\n"
+    printf "    Upon success, an HTTP server will be started and it will serve the selected\n"
+    printf "    model using llama.cpp for demonstration purposes.\n"
+    printf "\n"
+    printf "    Please note:\n"
+    printf "\n"
+    printf "    - All new data will be stored in the current folder\n"
+    printf "    - The server will be listening on all network interfaces\n"
+    printf "    - The server will run with default settings which are not always optimal\n"
+    printf "    - Do not judge the quality of a model based on the results from this script\n"
+    printf "    - Do not use this script to benchmark llama.cpp\n"
+    printf "    - Do not use this script in production\n"
+    printf "    - This script is only for demonstration purposes\n"
+    printf "\n"
+    printf "    If you don't know what you are doing, please press Ctrl-C to abort now\n"
+    printf "\n"
+    printf "    Press Enter to continue ...\n\n"
 
-printf "\n"
-printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n"
-printf "    Based on the options that follow, the script might download a model file\n"
-printf "    from the internet, which can be a few GBs in size. The script will also\n"
-printf "    build the latest llama.cpp source code from GitHub, which can be unstable.\n"
-printf "\n"
-printf "    Upon success, an HTTP server will be started and it will serve the selected\n"
-printf "    model using llama.cpp for demonstration purposes.\n"
-printf "\n"
-printf "    Please note:\n"
-printf "\n"
-printf "    - All new data will be stored in the current folder\n"
-printf "    - The server will be listening on all network interfaces\n"
-printf "    - The server will run with default settings which are not always optimal\n"
-printf "    - Do not judge the quality of a model based on the results from this script\n"
-printf "    - Do not use this script to benchmark llama.cpp\n"
-printf "    - Do not use this script in production\n"
-printf "    - This script is only for demonstration purposes\n"
-printf "\n"
-printf "    If you don't know what you are doing, please press Ctrl-C to abort now\n"
-printf "\n"
-printf "    Press Enter to continue ...\n\n"
-
-read
+    read
+fi
 
 if [[ -z "$repo" ]]; then
     printf "[+] No repo provided from the command line\n"
@@ -252,8 +282,10 @@ for file in $model_files; do
     printf "    %2d) %s %s\n" $iw "$have" "$file"
 done
 
+wfile="${wfiles[$wtype]}"
+
 # ask for weights type until provided and available
-while [[ -z "$wtype" ]]; do
+while [[ -z "$wfile" ]]; do
     printf "\n"
     read -p "[+] Select weight type: " wtype
     wfile="${wfiles[$wtype]}"

tests/.gitignore

@@ -1,3 +1,3 @@
 *
 !*.*
-test-c.o
+*.o

tests/test-llama-grammar.cpp

@@ -105,7 +105,7 @@ int main()
 
     for (auto rule : expected_rules)
     {
-        parsed_grammar.rules.push_back({});
+        parsed_grammar.rules.emplace_back();
         for (auto element : rule)
         {
             parsed_grammar.rules.back().push_back(element);

tests/test-sampling.cpp

@@ -235,6 +235,8 @@ int main(void) {
 
     test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1);
     test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3);
+    test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4);
+    test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0);
 
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0);
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f);