Compare commits

...

4 Commits
b8587 ... b8591

Author SHA1 Message Date
Adrien Gallouët
26dac845cc vendor : update BoringSSL to 0.20260327.0 (#21211)
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
2026-03-31 09:21:54 +02:00
Galunid
5ce013cd7e common : Disable backend sampling if reasoning budget is enabled (#21209) 2026-03-31 10:14:01 +03:00
shaofeiqi
08f21453ae opencl: add q4_K gemm and gemv kernels for Adreno (#20919)
* opencl: add q4_K gemm and gemv kernels for Adreno

* opencl: fix whitespace

* opencl: add workarounds for compiler bugs on older devices

* opencl: handle fp16 denorm on X Elite

* opencl: fix kernel build error

* opencl: fix whitespace

* opencl: make q4_K cvt kernels signature consistent

---------

Co-authored-by: Li He <lih@qti.qualcomm.com>
2026-03-30 12:19:16 -07:00
Seungmin Kim
84ae8434d0 CI : Enable CUDA and Vulkan ARM64 runners and fix CI/CD (#21122)
* CI: Enable CUDA and Vulkan ARM64 runners and fix CI/CD

Co-authored-by: Ts-sound <44093942+Ts-sound@users.noreply.github.com>

* Obtain source tag name from git tag

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Ts-sound <44093942+Ts-sound@users.noreply.github.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
2026-03-30 20:24:37 +02:00
17 changed files with 1320 additions and 208 deletions

View File

@@ -36,7 +36,7 @@ RUN mkdir -p /app/full \
FROM ubuntu:$UBUNTU_VERSION AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt-get install -y libgomp1 curl \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \

View File

@@ -1,6 +1,6 @@
ARG UBUNTU_VERSION=24.04
# This needs to generally match the container host's environment.
ARG CUDA_VERSION=13.1.0
ARG CUDA_VERSION=13.1.1
# Target the CUDA build image
ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
@@ -12,7 +12,9 @@ FROM ${BASE_CUDA_DEV_CONTAINER} AS build
ARG CUDA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y build-essential cmake python3 python3-pip git libssl-dev libgomp1
apt-get install -y gcc-14 g++-14 build-essential cmake python3 python3-pip git libssl-dev libgomp1
ENV CC=gcc-14 CXX=g++-14 CUDAHOSTCXX=g++-14
WORKDIR /app
@@ -39,7 +41,7 @@ RUN mkdir -p /app/full \
FROM ${BASE_CUDA_RUN_CONTAINER} AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt-get install -y libgomp1 curl \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \

View File

@@ -1,6 +1,6 @@
ARG UBUNTU_VERSION=22.04
ARG UBUNTU_VERSION=24.04
# This needs to generally match the container host's environment.
ARG CUDA_VERSION=12.4.0
ARG CUDA_VERSION=12.8.1
# Target the CUDA build image
ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
@@ -12,7 +12,9 @@ FROM ${BASE_CUDA_DEV_CONTAINER} AS build
ARG CUDA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y build-essential cmake python3 python3-pip git libssl-dev libgomp1
apt-get install -y gcc-14 g++-14 build-essential cmake python3 python3-pip git libssl-dev libgomp1
ENV CC=gcc-14 CXX=g++-14 CUDAHOSTCXX=g++-14
WORKDIR /app
@@ -39,7 +41,7 @@ RUN mkdir -p /app/full \
FROM ${BASE_CUDA_RUN_CONTAINER} AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt-get install -y libgomp1 curl \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
@@ -60,7 +62,8 @@ RUN apt-get update \
git \
python3 \
python3-pip \
&& pip install --upgrade pip setuptools wheel \
python3-wheel \
&& pip install --break-system-packages --upgrade setuptools \
&& pip install --break-system-packages -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \

View File

@@ -51,7 +51,7 @@ RUN mkdir /tmp/neo/ && cd /tmp/neo/ \
&& dpkg --install *.deb
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt-get install -y libgomp1 curl \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \

View File

@@ -46,7 +46,7 @@ RUN mkdir -p /app/full \
FROM ${BASE_MUSA_RUN_CONTAINER} AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt-get install -y libgomp1 curl \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \

View File

@@ -78,7 +78,7 @@ ARG http_proxy
ARG https_proxy
RUN apt-get update \
&& apt-get install -y libgomp1 libtbb12 curl\
&& apt-get install -y libgomp1 libtbb12 curl \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \

View File

@@ -58,7 +58,7 @@ RUN mkdir -p /app/full \
FROM ${BASE_ROCM_DEV_CONTAINER} AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt-get install -y libgomp1 curl \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
@@ -79,7 +79,7 @@ RUN apt-get update \
git \
python3-pip \
python3 \
python3-wheel\
python3-wheel \
&& pip install --break-system-packages --upgrade setuptools \
&& pip install --break-system-packages -r requirements.txt \
&& apt autoremove -y \

View File

@@ -49,17 +49,20 @@ COPY --from=build /app/full /app
WORKDIR /app
ENV PATH="/root/.venv/bin:/root/.local/bin:${PATH}"
# Flag for compatibility with pip
ARG UV_INDEX_STRATEGY="unsafe-best-match"
RUN apt-get update \
&& apt-get install -y \
build-essential \
curl \
git \
python3.13 \
python3.13-dev \
python3-pip \
python3-wheel \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.13 100 \
&& pip install --break-system-packages --upgrade setuptools \
&& pip install --break-system-packages -r requirements.txt \
ca-certificates \
&& curl -LsSf https://astral.sh/uv/install.sh | sh \
&& uv python install 3.13 \
&& uv venv --python 3.13 /root/.venv \
&& uv pip install --python /root/.venv/bin/python -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \

View File

@@ -25,184 +25,13 @@ permissions:
packages: write
jobs:
push_to_registry:
name: Push Docker image to Docker Hub
runs-on: ${{ matrix.config.runs_on }}
env:
COMMIT_SHA: ${{ github.sha }}
strategy:
fail-fast: false
matrix:
config:
# Multi-stage build
- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/arm64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
- { tag: "cuda cuda12", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true, runs_on: "ubuntu-24.04", cuda_version: "12.4.0", ubuntu_version: "22.04" }
- { tag: "cuda13", dockerfile: ".devops/cuda-new.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true, runs_on: "ubuntu-24.04", cuda_version: "13.1.0", ubuntu_version: "24.04" }
- { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true, runs_on: "ubuntu-24.04" }
- { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true, runs_on: "ubuntu-24.04" }
- { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
- { tag: "s390x", dockerfile: ".devops/s390x.Dockerfile", platforms: "linux/s390x", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04-s390x" }
- { tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true, runs_on: "ubuntu-24.04" }
- { tag: "openvino", dockerfile: ".devops/openvino.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
steps:
- name: Check out the repo
uses: actions/checkout@v6
with:
fetch-depth: 0 # preserve git history, so we can determine the build number
- name: Set up QEMU
if: ${{ matrix.config.tag != 's390x' }}
uses: docker/setup-qemu-action@c7c53464625b32c7a7e944ae62b3e17d2b600130 # v3
with:
image: tonistiigi/binfmt:qemu-v10.2.1
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
- name: Log in to Docker Hub
uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Determine source tag name
id: srctag
uses: ./.github/actions/get-tag-name
env:
BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
- name: Determine image tag name
id: tag
shell: bash
run: |
REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}" # to lower case
REPO_NAME="${{ github.event.repository.name }}"
PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:"
# list all tags possible
tags="${{ matrix.config.tag }}"
for tag in $tags; do
if [[ "$tag" == "cpu" ]]; then
TYPE=""
else
TYPE="-$tag"
fi
CACHETAGS="${PREFIX}buildcache${TYPE}"
FULLTAGS="${FULLTAGS:+$FULLTAGS,}${PREFIX}full${TYPE},${PREFIX}full${TYPE}-${{ steps.srctag.outputs.name }}"
LIGHTTAGS="${LIGHTTAGS:+$LIGHTTAGS,}${PREFIX}light${TYPE},${PREFIX}light${TYPE}-${{ steps.srctag.outputs.name }}"
SERVERTAGS="${SERVERTAGS:+$SERVERTAGS,}${PREFIX}server${TYPE},${PREFIX}server${TYPE}-${{ steps.srctag.outputs.name }}"
done
echo "cache_output_tags=$CACHETAGS" >> $GITHUB_OUTPUT
echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT
echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT
echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT
echo "cache_output_tags=$CACHETAGS" # print out for debugging
echo "full_output_tags=$FULLTAGS" # print out for debugging
echo "light_output_tags=$LIGHTTAGS" # print out for debugging
echo "server_output_tags=$SERVERTAGS" # print out for debugging
env:
GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
- name: Free Disk Space (Ubuntu)
if: ${{ matrix.config.free_disk_space == true }}
uses: ggml-org/free-disk-space@v1.3.1
with:
# this might remove tools that are actually needed,
# if set to "true" but frees about 6 GB
tool-cache: false
# all of these default to true, but feel free to set to
# "false" if necessary for your workflow
android: true
dotnet: true
haskell: true
large-packages: true
docker-images: true
swap-storage: true
- name: Build and push Full Docker image (tagged + versioned)
if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }}
uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
with:
context: .
push: true
platforms: ${{ matrix.config.platforms }}
# tag list is generated from step above
tags: ${{ steps.tag.outputs.full_output_tags }}
file: ${{ matrix.config.dockerfile }}
target: full
provenance: false
build-args: |
${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
# using github experimental cache
#cache-from: type=gha
#cache-to: type=gha,mode=max
# return to this if the experimental github cache is having issues
#cache-to: type=local,dest=/tmp/.buildx-cache
#cache-from: type=local,src=/tmp/.buildx-cache
# using registry cache (no storage limit)
cache-from: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }}
cache-to: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }},mode=max
- name: Build and push Light Docker image (tagged + versioned)
if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
with:
context: .
push: true
platforms: ${{ matrix.config.platforms }}
# tag list is generated from step above
tags: ${{ steps.tag.outputs.light_output_tags }}
file: ${{ matrix.config.dockerfile }}
target: light
provenance: false
build-args: |
${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
# using github experimental cache
#cache-from: type=gha
#cache-to: type=gha,mode=max
# return to this if the experimental github cache is having issues
#cache-to: type=local,dest=/tmp/.buildx-cache
#cache-from: type=local,src=/tmp/.buildx-cache
# using registry cache (no storage limit)
cache-from: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }}
cache-to: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }},mode=max
- name: Build and push Server Docker image (tagged + versioned)
if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
with:
context: .
push: true
platforms: ${{ matrix.config.platforms }}
# tag list is generated from step above
tags: ${{ steps.tag.outputs.server_output_tags }}
file: ${{ matrix.config.dockerfile }}
target: server
provenance: false
build-args: |
${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
# using github experimental cache
#cache-from: type=gha
#cache-to: type=gha,mode=max
# return to this if the experimental github cache is having issues
#cache-to: type=local,dest=/tmp/.buildx-cache
#cache-from: type=local,src=/tmp/.buildx-cache
# using registry cache (no storage limit)
cache-from: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }}
cache-to: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }},mode=max
create_tag:
name: Create and push git tag
runs-on: ubuntu-22.04
runs-on: ubuntu-slim
permissions:
contents: write
outputs:
source_tag: ${{ steps.srctag.outputs.name }}
steps:
- name: Clone
@@ -223,3 +52,391 @@ jobs:
run: |
git tag ${{ steps.srctag.outputs.name }} || exit 0
git push origin ${{ steps.srctag.outputs.name }} || exit 0
prepare_matrices:
name: Prepare Docker matrices
runs-on: ubuntu-24.04
outputs:
build_matrix: ${{ steps.matrices.outputs.build_matrix }}
merge_matrix: ${{ steps.matrices.outputs.merge_matrix }}
steps:
- name: Generate build and merge matrices
id: matrices
shell: bash
run: |
set -euo pipefail
# Keep all build targets in one place and derive merge targets from it.
cat > build-matrix.json <<'JSON'
[
{ "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },
{ "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-arm" },
{ "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x" },
{ "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
{ "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
{ "tag": "cuda13", "dockerfile": ".devops/cuda-new.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
{ "tag": "cuda13", "dockerfile": ".devops/cuda-new.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
{ "tag": "musa", "dockerfile": ".devops/musa.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
{ "tag": "intel", "dockerfile": ".devops/intel.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
{ "tag": "vulkan", "dockerfile": ".devops/vulkan.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },
{ "tag": "vulkan", "dockerfile": ".devops/vulkan.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-arm" },
{ "tag": "rocm", "dockerfile": ".devops/rocm.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
{ "tag": "openvino", "dockerfile": ".devops/openvino.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" }
]
JSON
BUILD_MATRIX="$(jq -c . build-matrix.json)"
MERGE_MATRIX="$(jq -c '
reduce .[] as $entry ({}; .[$entry.tag] |= (
. // {
tag: $entry.tag,
arches: [],
full: false,
light: false,
server: false
}
| .full = (.full or ($entry.full // false))
| .light = (.light or ($entry.light // false))
| .server = (.server or ($entry.server // false))
| .arches += [($entry.platforms | sub("^linux/"; ""))]
))
# Backward compatibility: s390x tags are aliases of cpu for the linux/s390x platform.
| if (has("cpu") and (((.cpu.arches // []) | index("s390x")) != null)) then
. + {
s390x: {
tag: "s390x",
arches: ["s390x"],
full: .cpu.full,
light: .cpu.light,
server: .cpu.server
}
}
else
.
end
| [.[] | .arches = (.arches | unique | sort | join(" "))]
' build-matrix.json)"
echo "build_matrix=$BUILD_MATRIX" >> "$GITHUB_OUTPUT"
echo "merge_matrix=$MERGE_MATRIX" >> "$GITHUB_OUTPUT"
push_to_registry:
name: Push Docker image to Docker Registry
needs: [prepare_matrices, create_tag]
runs-on: ${{ matrix.config.runs_on }}
strategy:
fail-fast: false
matrix:
config: ${{ fromJSON(needs.prepare_matrices.outputs.build_matrix) }}
steps:
- name: Check out the repo
uses: actions/checkout@v6
with:
fetch-depth: 0
ref: ${{ needs.create_tag.outputs.source_tag }}
- name: Set up QEMU
if: ${{ contains(matrix.config.platforms, 'linux/amd64') }}
uses: docker/setup-qemu-action@ce360397dd3f832beb865e1373c09c0e9f86d70a # v4
with:
image: tonistiigi/binfmt:qemu-v10.2.1
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@4d04d5d9486b7bd6fa91e7baf45bbb4f8b9deedd # v4
- name: Log in to Docker Registry
uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Determine image metadata
id: meta
shell: bash
run: |
set -euo pipefail
REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}" # to lower case
REPO_NAME="${{ github.event.repository.name }}"
IMAGE_REPO="ghcr.io/${REPO_OWNER}/${REPO_NAME}"
PREFIX="${IMAGE_REPO}:"
PLATFORM="${{ matrix.config.platforms }}"
ARCH_SUFFIX="${PLATFORM#linux/}"
# list all tags possible
tags="${{ matrix.config.tag }}"
for tag in $tags; do
if [[ "$tag" == "cpu" ]]; then
TYPE=""
else
TYPE="-$tag"
fi
CACHETAG="${PREFIX}buildcache${TYPE}-${ARCH_SUFFIX}"
done
SAFE_TAGS="$(echo "$tags" | tr ' ' '_')"
echo "image_repo=$IMAGE_REPO" >> $GITHUB_OUTPUT
echo "arch_suffix=$ARCH_SUFFIX" >> $GITHUB_OUTPUT
echo "cache_output_tag=$CACHETAG" >> $GITHUB_OUTPUT
echo "digest_artifact_suffix=${SAFE_TAGS}-${ARCH_SUFFIX}" >> $GITHUB_OUTPUT
echo "cache_output_tag=$CACHETAG" # print out for debugging
env:
GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
- name: Free Disk Space (Ubuntu)
if: ${{ matrix.config.free_disk_space == true }}
uses: ggml-org/free-disk-space@v1.3.1
with:
# this might remove tools that are actually needed,
# if set to "true" but frees about 6 GB
tool-cache: false
# all of these default to true, but feel free to set to
# "false" if necessary for your workflow
android: true
dotnet: true
haskell: true
large-packages: true
docker-images: true
swap-storage: true
- name: Build and push Full Docker image by digest
id: build_full
if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }}
uses: docker/build-push-action@d08e5c354a6adb9ed34480a06d141179aa583294 # v7
with:
context: .
platforms: ${{ matrix.config.platforms }}
outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true
file: ${{ matrix.config.dockerfile }}
target: full
provenance: false
build-args: |
${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
# using github experimental cache
#cache-from: type=gha
#cache-to: type=gha,mode=max
# return to this if the experimental github cache is having issues
#cache-to: type=local,dest=/tmp/.buildx-cache
#cache-from: type=local,src=/tmp/.buildx-cache
# using registry cache (no storage limit)
cache-from: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }}
cache-to: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }},mode=max
- name: Build and push Light Docker image by digest
id: build_light
if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
uses: docker/build-push-action@d08e5c354a6adb9ed34480a06d141179aa583294 # v7
with:
context: .
platforms: ${{ matrix.config.platforms }}
outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true
file: ${{ matrix.config.dockerfile }}
target: light
provenance: false
build-args: |
${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
# using github experimental cache
#cache-from: type=gha
#cache-to: type=gha,mode=max
# return to this if the experimental github cache is having issues
#cache-to: type=local,dest=/tmp/.buildx-cache
#cache-from: type=local,src=/tmp/.buildx-cache
# using registry cache (no storage limit)
cache-from: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }}
cache-to: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }},mode=max
- name: Build and push Server Docker image by digest
id: build_server
if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
uses: docker/build-push-action@d08e5c354a6adb9ed34480a06d141179aa583294 # v7
with:
context: .
platforms: ${{ matrix.config.platforms }}
outputs: type=image,name=${{ steps.meta.outputs.image_repo }},push-by-digest=true,name-canonical=true,push=true
file: ${{ matrix.config.dockerfile }}
target: server
provenance: false
build-args: |
${{ matrix.config.ubuntu_version && format('UBUNTU_VERSION={0}', matrix.config.ubuntu_version) || '' }}
${{ matrix.config.cuda_version && format('CUDA_VERSION={0}', matrix.config.cuda_version) || '' }}
# using github experimental cache
#cache-from: type=gha
#cache-to: type=gha,mode=max
# return to this if the experimental github cache is having issues
#cache-to: type=local,dest=/tmp/.buildx-cache
#cache-from: type=local,src=/tmp/.buildx-cache
# using registry cache (no storage limit)
cache-from: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }}
cache-to: type=registry,ref=${{ steps.meta.outputs.cache_output_tag }},mode=max
- name: Export digest metadata
shell: bash
run: |
set -euo pipefail
TAGS="${{ matrix.config.tag }}"
ARCH_SUFFIX="${{ steps.meta.outputs.arch_suffix }}"
DIGEST_FILE="/tmp/digests/${{ steps.meta.outputs.digest_artifact_suffix }}.tsv"
mkdir -p /tmp/digests
add_digest_rows() {
local image_type="$1"
local digest="$2"
if [[ -z "$digest" ]]; then
echo "Missing digest for image_type=${image_type}" >&2
exit 1
fi
for tag in $TAGS; do
printf '%s\t%s\t%s\t%s\n' "$tag" "$ARCH_SUFFIX" "$image_type" "$digest" >> "$DIGEST_FILE"
done
}
if [[ "${{ matrix.config.full }}" == "true" ]]; then
add_digest_rows "full" "${{ steps.build_full.outputs.digest }}"
fi
if [[ "${{ matrix.config.light }}" == "true" ]]; then
add_digest_rows "light" "${{ steps.build_light.outputs.digest }}"
fi
if [[ "${{ matrix.config.server }}" == "true" ]]; then
add_digest_rows "server" "${{ steps.build_server.outputs.digest }}"
fi
- name: Upload digest metadata
uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7
with:
name: digests-${{ steps.meta.outputs.digest_artifact_suffix }}
path: /tmp/digests/${{ steps.meta.outputs.digest_artifact_suffix }}.tsv
if-no-files-found: error
merge_arch_tags:
name: Create shared tags from digests
needs: [prepare_matrices, push_to_registry, create_tag]
runs-on: ubuntu-24.04
strategy:
fail-fast: false
matrix:
config: ${{ fromJSON(needs.prepare_matrices.outputs.merge_matrix) }}
steps:
- name: Check out the repo
uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Download digest metadata
uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8
with:
pattern: digests-*
path: /tmp/digests
merge-multiple: true
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@4d04d5d9486b7bd6fa91e7baf45bbb4f8b9deedd # v4
- name: Log in to Docker Registry
uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Create tags from digests
shell: bash
run: |
set -euo pipefail
REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}" # to lower case
REPO_NAME="${{ github.event.repository.name }}"
IMAGE_REPO="ghcr.io/${REPO_OWNER}/${REPO_NAME}"
PREFIX="${IMAGE_REPO}:"
SRC_TAG="${{ needs.create_tag.outputs.source_tag }}"
TAGS="${{ matrix.config.tag }}"
ARCHES="${{ matrix.config.arches }}"
DIGEST_GLOB="/tmp/digests/*.tsv"
if ! ls ${DIGEST_GLOB} >/dev/null 2>&1; then
echo "No digest metadata found in /tmp/digests" >&2
exit 1
fi
if [[ -z "$SRC_TAG" ]]; then
echo "Missing source tag from create_tag" >&2
exit 1
fi
find_digest() {
local tag_name="$1"
local arch="$2"
local image_type="$3"
local digest
digest="$(awk -F '\t' -v t="$tag_name" -v a="$arch" -v i="$image_type" '$1 == t && $2 == a && $3 == i { print $4; exit }' ${DIGEST_GLOB})"
# Backward compatibility: s390x tags are aliases of cpu for the linux/s390x platform.
if [[ -z "$digest" && "$tag_name" == "s390x" && "$arch" == "s390x" ]]; then
digest="$(awk -F '\t' -v t="cpu" -v a="$arch" -v i="$image_type" '$1 == t && $2 == a && $3 == i { print $4; exit }' ${DIGEST_GLOB})"
fi
if [[ -z "$digest" ]]; then
echo "Missing digest for tag=${tag_name} arch=${arch} image_type=${image_type}" >&2
exit 1
fi
echo "$digest"
}
create_manifest_tags() {
local image_type="$1"
local tag_name="$2"
local suffix="$3"
local merged_tag="${PREFIX}${image_type}${suffix}"
local merged_versioned_tag="${merged_tag}-${SRC_TAG}"
local refs=()
for arch in $ARCHES; do
local digest
digest="$(find_digest "$tag_name" "$arch" "$image_type")"
refs+=("${IMAGE_REPO}@${digest}")
done
echo "Creating ${merged_tag} from ${refs[*]}"
docker buildx imagetools create --tag "${merged_tag}" "${refs[@]}"
echo "Creating ${merged_versioned_tag} from ${refs[*]}"
docker buildx imagetools create --tag "${merged_versioned_tag}" "${refs[@]}"
}
for tag in $TAGS; do
if [[ "$tag" == "cpu" ]]; then
TYPE=""
else
TYPE="-$tag"
fi
if [[ "${{ matrix.config.full }}" == "true" ]]; then
create_manifest_tags "full" "$tag" "$TYPE"
fi
if [[ "${{ matrix.config.light }}" == "true" ]]; then
create_manifest_tags "light" "$tag" "$TYPE"
fi
if [[ "${{ matrix.config.server }}" == "true" ]]; then
create_manifest_tags "server" "$tag" "$TYPE"
fi
done
env:
GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'

View File

@@ -383,6 +383,12 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
params.backend_sampling = false;
}
if (rbudget && params.backend_sampling) {
LOG_WRN("%s: backend sampling is not compatible with reasoning budget, disabling\n", __func__);
params.backend_sampling = false;
}
auto * result = new common_sampler {
/* .params = */ params,
/* .grmr = */ grmr,

View File

@@ -13,24 +13,30 @@ We have three Docker images available for this project:
Additionally, there the following images, similar to the above:
- `ghcr.io/ggml-org/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA 12 support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:full-cuda13`: Same as `full` but compiled with CUDA 13 support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA 12 support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:light-cuda13`: Same as `light` but compiled with CUDA 13 support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA 12 support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:server-cuda13`: Same as `server` but compiled with CUDA 13 support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:full-intel`: Same as `full` but compiled with SYCL support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:light-intel`: Same as `light` but compiled with SYCL support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:server-intel`: Same as `server` but compiled with SYCL support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:full-vulkan`: Same as `full` but compiled with Vulkan support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:light-vulkan`: Same as `light` but compiled with Vulkan support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:server-vulkan`: Same as `server` but compiled with Vulkan support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:full-vulkan`: Same as `full` but compiled with Vulkan support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:light-vulkan`: Same as `light` but compiled with Vulkan support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:server-vulkan`: Same as `server` but compiled with Vulkan support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggml-org/llama.cpp:full-openvino`: Same as `full` but compiled with OpenVino support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:light-openvino`: Same as `light` but compiled with OpenVino support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:server-openvino`: Same as `server` but compiled with OpenVino support. (platforms: `linux/amd64`)
- `ghcr.io/ggml-org/llama.cpp:full-s390x`: Identical to `full`, an alias for the `s390x` platform. (platforms: `linux/s390x`)
- `ghcr.io/ggml-org/llama.cpp:light-s390x`: Identical to `light`, an alias for the `s390x` platform. (platforms: `linux/s390x`)
- `ghcr.io/ggml-org/llama.cpp:server-s390x`: Identical to `server`, an alias for the `s390x` platform. (platforms: `linux/s390x`)
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now).
@@ -82,7 +88,7 @@ You may want to pass in some different `ARGS`, depending on the CUDA environment
The defaults are:
- `CUDA_VERSION` set to `12.4.0`
- `CUDA_VERSION` set to `12.8.1`
- `CUDA_DOCKER_ARCH` set to the cmake build default, which includes all the supported architectures
The resulting images, are essentially the same as the non-CUDA images:

View File

@@ -114,6 +114,8 @@ set(GGML_OPENCL_KERNELS
gemv_noshuffle_q4_1_f32
gemm_noshuffle_q4_1_f32
gemv_noshuffle_general_q8_0_f32
gemv_noshuffle_q4_k_f32
gemm_noshuffle_q4_k_f32
gemv_noshuffle_q6_k_f32
gemm_noshuffle_q6_k_f32
mul

View File

@@ -538,6 +538,8 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_restore_block_q4_0_noshuffle;
cl_kernel kernel_convert_block_q4_1_noshuffle;
cl_kernel kernel_restore_block_q4_1_noshuffle;
cl_kernel kernel_convert_block_q4_K_noshuffle;
cl_kernel kernel_restore_block_q4_K_noshuffle;
cl_kernel kernel_convert_block_q4_K, kernel_restore_block_q4_K;
cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K;
cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
@@ -720,6 +722,8 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_gemm_noshuffle_q4_1_f32;
cl_kernel kernel_mul_mm_q8_0_f32_8x4;
cl_kernel CL_mul_mat_vec_q8_0_f32;
cl_kernel kernel_gemv_noshuffle_q4_k_f32;
cl_kernel kernel_gemm_noshuffle_q4_k_f32;
cl_kernel kernel_gemv_noshuffle_q6_K_f32;
cl_kernel kernel_gemm_noshuffle_q6_K_f32;
#endif // GGML_OPENCL_USE_ADRENO_KERNELS
@@ -932,6 +936,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
CL_CHECK((backend_ctx->kernel_restore_block_q8_0_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0_trans", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q4_K = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_K", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q4_K = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_K", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q4_K_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_K_noshuffle", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q4_K_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_K_noshuffle", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q6_K = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q6_K = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q6_K_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K_noshuffle", &err), err));
@@ -2619,6 +2625,45 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// gemm_noshuffle_q4_k_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "gemm_noshuffle_q4_k_f32.cl.h"
};
#else
const std::string kernel_src = read_file("gemm_noshuffle_q4_k_f32.cl");
#endif
cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_f32", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// gemv_noshuffle_q4_k_f32
{
std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
" -cl-mad-enable ";
if (backend_ctx->has_vector_subgroup_broadcast) {
CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
}
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "gemv_noshuffle_q4_k_f32.cl.h"
};
#else
const std::string kernel_src = read_file("gemv_noshuffle_q4_k_f32.cl");
#endif
cl_program prog = build_program_from_source(
backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_k_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_k_f32", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
std::string CL_moe_compile_opts = std::string("-cl-std=") + opencl_c_std +
" -cl-mad-enable "
" -cl-fast-relaxed-math";
@@ -5060,12 +5105,25 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
cl_kernel kernel = backend_ctx->kernel_convert_block_q4_K;
if (use_adreno_kernels(backend_ctx, tensor)) {
kernel = backend_ctx->kernel_convert_block_q4_K_noshuffle;
}
#else
cl_kernel kernel = backend_ctx->kernel_convert_block_q4_K;
#endif
cl_uchar mask_0F = 0x0F;
cl_uchar mask_F0 = 0xF0;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->s));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra->dm));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_0F));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_uchar), &mask_F0));
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {64, 1, 1};
@@ -5076,6 +5134,20 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
CL_CHECK(clReleaseMemObject(data_device));
tensor->extra = extra;
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
if (use_adreno_kernels(backend_ctx, tensor)) {
int M = tensor->ne[1];
int K = tensor->ne[0];
GGML_ASSERT(K % 32 == 0);
// Transpose q, d, dm as ushort
transpose_2d_as_16b(backend_ctx, extra->q, extra->q, size_q, K/4, M);
transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/256, M);
transpose_2d_as_16b(backend_ctx, extra->dm, extra->dm, size_dm, K/256, M);
}
#endif // GGML_OPENCL_USE_ADRENO_KERNELS
return;
}
if (tensor->type == GGML_TYPE_Q6_K) {
@@ -5516,12 +5588,60 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
ggml_nbytes(tensor), NULL, &err);
CL_CHECK(err);
cl_uchar mask_0F = 0x0F;
cl_uchar mask_F0 = 0xF0;
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
if (use_adreno_kernels(backend_ctx, tensor)) {
int M = tensor->ne[1];
int K = tensor->ne[0];
size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
size_t size_dm = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
static ggml_cl_buffer buf_trans_q;
static ggml_cl_buffer buf_trans_d;
static ggml_cl_buffer buf_trans_dm;
buf_trans_q.allocate(backend_ctx->context, size_q);
buf_trans_d.allocate(backend_ctx->context, size_d);
buf_trans_dm.allocate(backend_ctx->context, size_dm);
// Transpose q, d, dm back
transpose_2d_as_16b(backend_ctx, extra->q, buf_trans_q.buffer, size_q, M, K/4);
transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/256);
transpose_2d_as_16b(backend_ctx, extra->dm, buf_trans_dm.buffer, size_dm, M, K/256);
cl_kernel kernel = backend_ctx->kernel_restore_block_q4_K_noshuffle;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &buf_trans_q.buffer));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->s));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &buf_trans_d.buffer));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &buf_trans_dm.buffer));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_0F));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_uchar), &mask_F0));
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {1, 1, 1};
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
global_work_size, local_work_size, 0, NULL, NULL));
CL_CHECK(clEnqueueReadBuffer(queue, data_device, CL_TRUE, offset,
size, data, 0, NULL, NULL));
CL_CHECK(clReleaseMemObject(data_device));
return;
}
#endif // GGML_OPENCL_USE_ADRENO_KERNELS
cl_kernel kernel = backend_ctx->kernel_restore_block_q4_K;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->s));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->dm));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_0F));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_uchar), &mask_F0));
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {1, 1, 1};
@@ -9688,6 +9808,192 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t
#endif
}
static void ggml_cl_mul_mat_q4_k_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
GGML_ASSERT(src1);
GGML_ASSERT(src1->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
ggml_tensor_extra_cl_q4_K * extra0_q4_k = (ggml_tensor_extra_cl_q4_K *)src0->extra;
cl_ulong offset1 = extra1->offset + src1->view_offs;
cl_ulong offsetd = extrad->offset + dst->view_offs;
const int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne1 = dst->ne[1];
GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
cl_context context = backend_ctx->context;
cl_kernel kernel;
cl_int err;
cl_image_format img_fmt;
cl_image_desc img_desc;
cl_buffer_region region;
int M = ne01;
int N = ne1;
int K = ne00;
cl_uchar mask_d6 = 0x3F;
cl_uchar mask_d4 = 0x0F;
cl_uchar mask_hi2 = 0xC0;
if (ne1 == 1) {
cl_mem q_img = nullptr;
cl_mem b_sub_buf = nullptr;
cl_mem b_img = nullptr;
// image for q
img_fmt = { CL_R, CL_UNSIGNED_INT32};
memset(&img_desc, 0, sizeof(img_desc));
img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
img_desc.image_width = M * K / 2 / 4;
img_desc.buffer = extra0_q4_k->q;
CL_CHECK((q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
// subbuffer for activations
region.origin = offset1;
region.size = K * N * sizeof(float);
CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
// image for activations
img_fmt = {CL_RGBA, CL_FLOAT};
memset(&img_desc, 0, sizeof(img_desc));
img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
img_desc.image_width = K * N / 4;
img_desc.buffer = b_sub_buf;
CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
kernel = backend_ctx->kernel_gemv_noshuffle_q4_k_f32;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_img));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_k->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_k->dm));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra0_q4_k->s));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &b_img));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_uchar), &mask_d6));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_uchar), &mask_d4));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_uchar), &mask_hi2));
size_t local_work_size[3] = {64, 4, 1};
size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, 4, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
CL_CHECK(clReleaseMemObject(q_img));
CL_CHECK(clReleaseMemObject(b_sub_buf));
CL_CHECK(clReleaseMemObject(b_img));
} else {
cl_mem b_sub_buf = nullptr;
cl_mem b_sub_buf_trans = nullptr;
cl_mem b_img = nullptr;
cl_mem b_img_trans = nullptr;
// subbuffer for activations
region.origin = offset1;
region.size = K * N * sizeof(float);
CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
// image for activations
img_fmt = {CL_RGBA, CL_FLOAT};
memset(&img_desc, 0, sizeof(img_desc));
img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
img_desc.image_width = K * N / 4;
img_desc.buffer = b_sub_buf;
CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
// pad N to multiple of 8
int extra_elements = N % 8;
int padding = 0;
if (extra_elements > 0){
padding = 8 - extra_elements;
}
// subbuffer for transposed activations
region.origin = 0;
region.size = K * (N + padding) * sizeof(float)/2;
backend_ctx->prealloc_act_trans.allocate(context, region.size);
CL_CHECK((b_sub_buf_trans = clCreateSubBuffer(backend_ctx->prealloc_act_trans.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
// image for transposed activations
img_fmt = {CL_RGBA, CL_HALF_FLOAT};
memset(&img_desc, 0, sizeof(img_desc));
img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
img_desc.image_width = K * (N + padding) / 4;
img_desc.buffer = b_sub_buf_trans;
CL_CHECK((b_img_trans = clCreateImage(context, 0, &img_fmt, &img_desc, NULL, &err), err));
// transpose activations
int height_B = N/4;
if (height_B == 0) {
height_B = 1;
}
int width_B = K/4;
int padded_height_B = (N + padding)/4;
kernel = backend_ctx->kernel_transpose_32_16;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &b_img));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &b_img_trans));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &height_B));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &width_B));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &padded_height_B));
size_t local_work_size_t[2] = { 1, 16 };
size_t global_work_size_t[2] = { (size_t)width_B, (size_t)padded_height_B };
backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst);
// gemm
kernel = backend_ctx->kernel_gemm_noshuffle_q4_k_f32;
int padded_N = N + padding;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_k->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_k->s));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_k->d));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra0_q4_k->dm));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &b_img_trans));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int), &padded_N));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_int), &ne1));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_uchar), &mask_d6));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_uchar), &mask_d4));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_uchar), &mask_hi2));
size_t global_work_size[3] = {(size_t)CEIL_DIV(ne1, 8), (size_t)CEIL_DIV(ne01, 4), 1};
size_t local_work_size[3] = {1, 128, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
CL_CHECK(clReleaseMemObject(b_sub_buf));
CL_CHECK(clReleaseMemObject(b_sub_buf_trans));
CL_CHECK(clReleaseMemObject(b_img));
CL_CHECK(clReleaseMemObject(b_img_trans));
}
#else
GGML_UNUSED(backend);
GGML_UNUSED(src0);
GGML_UNUSED(src1);
GGML_UNUSED(dst);
#endif
}
static void ggml_cl_mul_mat_q6_K_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
GGML_ASSERT(src0);
@@ -10014,6 +10320,12 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
return;
}
// q4_k x fp32
if (src0t == GGML_TYPE_Q4_K && src1t == GGML_TYPE_F32) {
ggml_cl_mul_mat_q4_k_f32_adreno(backend, src0, src1, dst);
return;
}
// q6_K x fp32
if (src0t == GGML_TYPE_Q6_K && src1t == GGML_TYPE_F32) {
ggml_cl_mul_mat_q6_K_f32_adreno(backend, src0, src1, dst);

View File

@@ -424,13 +424,17 @@ kernel void kernel_restore_block_q8_0_trans(
// Convert the block_q4_K format to 4 separate arrays (AOS -> SOA).
// This kernel does not deshuffle the bits.
// Each thread processes a super block.
// Mask args are just to keep the signature consistent with the no-shuffle
// version and they are not used in this kernel.
//------------------------------------------------------------------------------
kernel void kernel_convert_block_q4_K(
global struct block_q4_K * src0,
global uchar * dst_q,
global uchar * dst_s,
global half * dst_d,
global half * dst_dm
global half * dst_dm,
uchar mask_0F,
uchar mask_F0
) {
global struct block_q4_K * b = (global struct block_q4_K *) src0 + get_global_id(0);
global uchar * q = (global uchar *) dst_q + QK_K/2*get_global_id(0);
@@ -451,12 +455,15 @@ kernel void kernel_convert_block_q4_K(
// Restore block_q4_K from flattened arrays.
// Each thread processes a super block.
// Mask args are just to keep the signature consistent with the no-shuffle ones.
kernel void kernel_restore_block_q4_K(
global uchar * src_q,
global uchar * src_s,
global half * src_d,
global half * src_dm,
global struct block_q4_K * dst
global struct block_q4_K * dst,
uchar mask_0F,
uchar mask_F0
) {
global struct block_q4_K * b = (global struct block_q4_K *) dst + get_global_id(0);
global uchar * q = (global uchar *) src_q + QK_K/2*get_global_id(0);
@@ -475,6 +482,70 @@ kernel void kernel_restore_block_q4_K(
}
}
kernel void kernel_convert_block_q4_K_noshuffle(
global struct block_q4_K * src0,
global uchar * dst_q,
global uchar * dst_s,
global half * dst_d,
global half * dst_dm,
uchar mask_0F,
uchar mask_F0
) {
global struct block_q4_K * b = (global struct block_q4_K *) src0 + get_global_id(0);
global uchar * q = (global uchar *) dst_q + QK_K/2 * get_global_id(0);
global uchar * s = (global uchar *) dst_s + K_SCALE_SIZE * get_global_id(0);
global half * d = (global half *) dst_d + get_global_id(0);
global half * dm = (global half *) dst_dm + get_global_id(0);
*d = b->d;
*dm = b->dm;
for (int i = 0; i < QK_K / 64; ++i) {
for (int j = 0; j < 16; ++j) {
uchar x0 = b->q[i*32 + 2*j];
uchar x1 = b->q[i*32 + 2*j + 1];
q[i*32 + j] = convert_uchar(x0 & mask_0F) | convert_uchar((x1 & mask_0F) << 4);
q[i*32 + j + 16] = convert_uchar((x0 & mask_F0) >> 4) | convert_uchar(x1 & mask_F0);
}
}
for (int i = 0; i < K_SCALE_SIZE; ++i) {
s[i] = b->s[i];
}
}
kernel void kernel_restore_block_q4_K_noshuffle(
global uchar * src_q,
global uchar * src_s,
global half * src_d,
global half * src_dm,
global struct block_q4_K * dst,
uchar mask_0F,
uchar mask_F0
) {
global struct block_q4_K * b = (global struct block_q4_K *) dst + get_global_id(0);
global uchar * q = (global uchar *) src_q + QK_K/2 * get_global_id(0);
global uchar * s = (global uchar *) src_s + K_SCALE_SIZE * get_global_id(0);
global half * d = (global half *) src_d + get_global_id(0);
global half * dm = (global half *) src_dm + get_global_id(0);
b->d = *d;
b->dm = *dm;
for (int i = 0; i < QK_K / 64; ++i) {
for (int j = 0; j < 16; ++j) {
uchar lo = q[i*32 + j];
uchar hi = q[i*32 + j + 16];
b->q[i*32 + 2*j] = convert_uchar((lo & mask_0F) | ((hi & mask_0F) << 4));
b->q[i*32 + 2*j + 1] = convert_uchar(((lo & mask_F0) >> 4) | (hi & mask_F0));
}
}
for (int i = 0; i < K_SCALE_SIZE; ++i) {
b->s[i] = s[i];
}
}
//------------------------------------------------------------------------------
// kernel_convert_block_q6_K
// Convert the block_q6_K format to 3 separate arrays (AOS -> SOA).

View File

@@ -0,0 +1,172 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#ifdef cl_qcom_reqd_sub_group_size
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
#define QK_K 256
#define K_SCALE_SIZE 12
inline void get_scale_min_k4(
int j,
global const uchar * q,
uchar * d,
uchar * m,
uchar mask_d6,
uchar mask_d4,
uchar mask_hi2
) {
if (j < 4) {
*d = q[j] & mask_d6;
*m = q[j+4] & mask_d6;
} else {
*d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2);
*m = ((q[j+4] >> 4) & mask_d4) | ((q[j] & mask_hi2) >> 2);
}
}
#ifdef ADRENO_GPU
REQD_SUBGROUP_SIZE_128
#endif
kernel void kernel_gemm_noshuffle_q4_k_f32(
global const ushort * src0_q,
global const uchar * src0_s,
global const half * src0_d,
global const half * src0_dm,
read_only image1d_buffer_t src1,
global float * dst,
ulong offsetd,
int m,
int n,
int k,
int n_no_padding,
uchar mask_d6,
uchar mask_d4,
uchar mask_hi2
) {
dst = (global float *)((global char *)dst + offsetd);
int n_4 = n >> 2;
int gy = get_global_id(0);
int gx = get_global_id(1);
int gx_2 = gx << 2;
half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0;
half8 B;
half4 dequantized_weights;
int num_blocks_K = k / QK_K;
global const ushort * weight_ptr = src0_q + gx_2;
global const half * d_ptr = src0_d + gx_2;
global const half * dm_ptr = src0_dm + gx_2;
for (int i = 0; i < k; i += 32) {
int sb_idx = i / QK_K;
int sub_idx = (i / 32) % 8;
half4 d = vload4(0, d_ptr + sb_idx * m);
half4 dm = vload4(0, dm_ptr + sb_idx * m);
global const uchar * sc0 = src0_s + (gx_2+0) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE;
global const uchar * sc1 = src0_s + (gx_2+1) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE;
global const uchar * sc2 = src0_s + (gx_2+2) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE;
global const uchar * sc3 = src0_s + (gx_2+3) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE;
uchar sv0, mn0, sv1, mn1, sv2, mn2, sv3, mn3;
get_scale_min_k4(sub_idx, sc0, &sv0, &mn0, mask_d6, mask_d4, mask_hi2);
get_scale_min_k4(sub_idx, sc1, &sv1, &mn1, mask_d6, mask_d4, mask_hi2);
get_scale_min_k4(sub_idx, sc2, &sv2, &mn2, mask_d6, mask_d4, mask_hi2);
get_scale_min_k4(sub_idx, sc3, &sv3, &mn3, mask_d6, mask_d4, mask_hi2);
half4 scale = convert_half4(convert_float4(d) * convert_float4((uchar4)(sv0, sv1, sv2, sv3)));
half4 mval = convert_half4(convert_float4(dm) * convert_float4((uchar4)(mn0, mn1, mn2, mn3)));
for (int l = 0; l < 32; l += 4) {
int ki = i + l;
ushort4 bits4 = vload4(0, weight_ptr + (ki/4) * m);
// j=0
B.s0123 = read_imageh(src1, gy*2 + (ki+0) * n_4);
B.s4567 = read_imageh(src1, gy*2+1 + (ki+0) * n_4);
dequantized_weights.s0 = (bits4.s0 & 0x000F) * scale.s0 - mval.s0;
dequantized_weights.s1 = (bits4.s1 & 0x000F) * scale.s1 - mval.s1;
dequantized_weights.s2 = (bits4.s2 & 0x000F) * scale.s2 - mval.s2;
dequantized_weights.s3 = (bits4.s3 & 0x000F) * scale.s3 - mval.s3;
c0 += B * dequantized_weights.s0;
c1 += B * dequantized_weights.s1;
c2 += B * dequantized_weights.s2;
c3 += B * dequantized_weights.s3;
// j=1
B.s0123 = read_imageh(src1, gy*2 + (ki+1) * n_4);
B.s4567 = read_imageh(src1, gy*2+1 + (ki+1) * n_4);
dequantized_weights.s0 = ((bits4.s0 & 0x00F0) >> 4) * scale.s0 - mval.s0;
dequantized_weights.s1 = ((bits4.s1 & 0x00F0) >> 4) * scale.s1 - mval.s1;
dequantized_weights.s2 = ((bits4.s2 & 0x00F0) >> 4) * scale.s2 - mval.s2;
dequantized_weights.s3 = ((bits4.s3 & 0x00F0) >> 4) * scale.s3 - mval.s3;
c0 += B * dequantized_weights.s0;
c1 += B * dequantized_weights.s1;
c2 += B * dequantized_weights.s2;
c3 += B * dequantized_weights.s3;
// j=2
B.s0123 = read_imageh(src1, gy*2 + (ki+2) * n_4);
B.s4567 = read_imageh(src1, gy*2+1 + (ki+2) * n_4);
dequantized_weights.s0 = ((bits4.s0 & 0x0F00) >> 8) * scale.s0 - mval.s0;
dequantized_weights.s1 = ((bits4.s1 & 0x0F00) >> 8) * scale.s1 - mval.s1;
dequantized_weights.s2 = ((bits4.s2 & 0x0F00) >> 8) * scale.s2 - mval.s2;
dequantized_weights.s3 = ((bits4.s3 & 0x0F00) >> 8) * scale.s3 - mval.s3;
c0 += B * dequantized_weights.s0;
c1 += B * dequantized_weights.s1;
c2 += B * dequantized_weights.s2;
c3 += B * dequantized_weights.s3;
// j=3
B.s0123 = read_imageh(src1, gy*2 + (ki+3) * n_4);
B.s4567 = read_imageh(src1, gy*2+1 + (ki+3) * n_4);
dequantized_weights.s0 = ((bits4.s0 & 0xF000) >> 12) * scale.s0 - mval.s0;
dequantized_weights.s1 = ((bits4.s1 & 0xF000) >> 12) * scale.s1 - mval.s1;
dequantized_weights.s2 = ((bits4.s2 & 0xF000) >> 12) * scale.s2 - mval.s2;
dequantized_weights.s3 = ((bits4.s3 & 0xF000) >> 12) * scale.s3 - mval.s3;
c0 += B * dequantized_weights.s0;
c1 += B * dequantized_weights.s1;
c2 += B * dequantized_weights.s2;
c3 += B * dequantized_weights.s3;
}
}
int idx = (gy<<3)*m + (gx<<2);
if (idx+3 < m*n_no_padding) {
vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx);
idx += m;
}
if (idx+3 < m*n_no_padding) {
vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx);
idx += m;
}
if (idx+3 < m*n_no_padding) {
vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx);
idx += m;
}
if (idx+3 < m*n_no_padding) {
vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx);
idx += m;
}
if (idx+3 < m*n_no_padding) {
vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx);
idx += m;
}
if (idx+3 < m*n_no_padding) {
vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx);
idx += m;
}
if (idx+3 < m*n_no_padding) {
vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx);
idx += m;
}
if (idx+3 < m*n_no_padding) {
vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx);
}
}

View File

@@ -0,0 +1,318 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#ifdef cl_qcom_reqd_sub_group_size
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#endif
#define QK_K 256
#define NSUBGROUPS 4
#define SUBGROUP_SIZE 64
inline void get_scale_min_k4(
int j,
global const uchar * q,
uchar * d,
uchar * m,
uchar mask_d6,
uchar mask_d4,
uchar mask_hi2
) {
if (j < 4) {
*d = q[j] & mask_d6;
*m = q[j+4] & mask_d6;
} else {
*d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2);
*m = ((q[j+4] >> 4) & mask_d4) | ((q[j] & mask_hi2) >> 2);
}
}
#define dequantizeBlockAccum_ns_sgbroadcast_1_hi(total_sums, bits4, scale, minv, y) \
float shared_y; \
shared_y = sub_group_broadcast(y.s0, 0); \
total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s1, 0); \
total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s2, 0); \
total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s3, 0); \
total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s4, 0); \
total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s5, 0); \
total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s6, 0); \
total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s7, 0); \
total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s0, 1); \
total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s1, 1); \
total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s2, 1); \
total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s3, 1); \
total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s4, 1); \
total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s5, 1); \
total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s6, 1); \
total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s7, 1); \
total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
#define dequantizeBlockAccum_ns_sgbroadcast_1_lo(total_sums, bits4, scale, minv, y) \
shared_y = sub_group_broadcast(y.s0, 2); \
total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s1, 2); \
total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s2, 2); \
total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s3, 2); \
total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s4, 2); \
total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s5, 2); \
total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s6, 2); \
total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s7, 2); \
total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s0, 3); \
total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s1, 3); \
total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s2, 3); \
total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s3, 3); \
total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s4, 3); \
total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s5, 3); \
total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s6, 3); \
total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y; \
shared_y = sub_group_broadcast(y.s7, 3); \
total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y; \
total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y; \
#define dequantizeBlockAccum_ns_sgbroadcast_8_hi(total_sums, bits4, scale, minv, y) \
float8 shared_y; \
shared_y = sub_group_broadcast(y, 0); \
total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 - minv.s0) * shared_y.s0; \
total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y.s1; \
total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y.s2; \
total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s3; \
total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 - minv.s0) * shared_y.s4; \
total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y.s5; \
total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y.s6; \
total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s7; \
total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 - minv.s1) * shared_y.s0; \
total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y.s1; \
total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y.s2; \
total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s3; \
total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 - minv.s1) * shared_y.s4; \
total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y.s5; \
total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y.s6; \
total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s7; \
shared_y = sub_group_broadcast(y, 1); \
total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 - minv.s0) * shared_y.s0; \
total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y.s1; \
total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y.s2; \
total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s3; \
total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 - minv.s0) * shared_y.s4; \
total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y.s5; \
total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y.s6; \
total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s7; \
total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 - minv.s1) * shared_y.s0; \
total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y.s1; \
total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y.s2; \
total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s3; \
total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 - minv.s1) * shared_y.s4; \
total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y.s5; \
total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y.s6; \
total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s7; \
#define dequantizeBlockAccum_ns_sgbroadcast_8_lo(total_sums, bits4, scale, minv, y) \
shared_y = sub_group_broadcast(y, 2); \
total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 - minv.s0) * shared_y.s0; \
total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y.s1; \
total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y.s2; \
total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s3; \
total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 - minv.s0) * shared_y.s4; \
total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y.s5; \
total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y.s6; \
total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s7; \
total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 - minv.s1) * shared_y.s0; \
total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y.s1; \
total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y.s2; \
total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s3; \
total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 - minv.s1) * shared_y.s4; \
total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y.s5; \
total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y.s6; \
total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s7; \
shared_y = sub_group_broadcast(y, 3); \
total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 - minv.s0) * shared_y.s0; \
total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y.s1; \
total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y.s2; \
total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s3; \
total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 - minv.s0) * shared_y.s4; \
total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 - minv.s0) * shared_y.s5; \
total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 - minv.s0) * shared_y.s6; \
total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 - minv.s0) * shared_y.s7; \
total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 - minv.s1) * shared_y.s0; \
total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y.s1; \
total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y.s2; \
total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s3; \
total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 - minv.s1) * shared_y.s4; \
total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 - minv.s1) * shared_y.s5; \
total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 - minv.s1) * shared_y.s6; \
total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 - minv.s1) * shared_y.s7; \
#ifdef ADRENO_GPU
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_gemv_noshuffle_q4_k_f32(
read_only image1d_buffer_t src0_q,
global half2 * src0_d,
global half2 * src0_m,
global uchar * src0_s,
read_only image1d_buffer_t src1,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
uchar mask_d6,
uchar mask_d4,
uchar mask_hi2)
{
uint groupId = get_local_id(1);
uint gid = get_global_id(0);
ushort slid = get_sub_group_local_id();
uint K = ne00;
uint M = ne01;
uint LINE_STRIDE_A = M / 2;
uint BLOCK_STRIDE_A = NSUBGROUPS * M;
uint scales_per_row = (K / QK_K) * 12;
private uint4 regA;
private half2 regS;
private half2 regM;
private float8 regB;
private float2 totalSum = (float2)(0.0f);
for (uint k = groupId; k < (K / 32); k += NSUBGROUPS) {
uint sb = k / 8;
uint j = k % 8;
half2 d = src0_d[gid + sb * LINE_STRIDE_A];
half2 dm = src0_m[gid + sb * LINE_STRIDE_A];
global const uchar * sc0 = src0_s + 2 * gid * scales_per_row + sb * 12;
global const uchar * sc1 = src0_s + (2 * gid + 1) * scales_per_row + sb * 12;
uchar sv0, mn0, sv1, mn1;
get_scale_min_k4(j, sc0, &sv0, &mn0, mask_d6, mask_d4, mask_hi2);
get_scale_min_k4(j, sc1, &sv1, &mn1, mask_d6, mask_d4, mask_hi2);
regS = convert_half2(convert_float2(d) * convert_float2((uchar2)(sv0, sv1)));
regM = convert_half2(convert_float2(dm) * convert_float2((uchar2)(mn0, mn1)));
if (slid < 4) {
regB.s0123 = read_imagef(src1, (slid * 2 + k * 8));
regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8));
}
// load half weights for two blocks in consecutive rows
regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x;
regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
#ifdef VECTOR_SUB_GROUP_BROADCAST
dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regM, regB);
#else
dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regM, regB);
#endif // VECTOR_SUB_GROUP_BROADCAST
regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
#ifdef VECTOR_SUB_GROUP_BROADCAST
dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regM, regB);
#else
dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), regS, regM, regB);
#endif // VECTOR_SUB_GROUP_BROADCAST
}
// reduction in local memory, assumes #wave=4
local float2 reduceLM[SUBGROUP_SIZE * 3];
if (groupId == 1) {
reduceLM[SUBGROUP_SIZE * 0 + slid] = totalSum;
}
if (groupId == 2) {
reduceLM[SUBGROUP_SIZE * 1 + slid] = totalSum;
}
if (groupId == 3) {
reduceLM[SUBGROUP_SIZE * 2 + slid] = totalSum;
}
barrier(CLK_LOCAL_MEM_FENCE);
if (groupId == 0) {
totalSum += reduceLM[SUBGROUP_SIZE * 0 + slid];
}
if (groupId == 0) {
totalSum += reduceLM[SUBGROUP_SIZE * 1 + slid];
}
if (groupId == 0) {
totalSum += reduceLM[SUBGROUP_SIZE * 2 + slid];
}
// 2 outputs per fiber in wave 0
if (groupId == 0) {
dst = (global float*)((global char*)dst + offsetd);
vstore2(totalSum, 0, &(dst[gid * 2]));
}
}

View File

@@ -39,7 +39,7 @@ if (LLAMA_BUILD_BORINGSSL)
set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)")
set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository")
set(BORINGSSL_VERSION "0.20260211.0" CACHE STRING "BoringSSL version")
set(BORINGSSL_VERSION "0.20260327.0" CACHE STRING "BoringSSL version")
message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}")