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llama.cpp
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Merge remote-tracking branch 'origin/master' into allozaur/server_tools

This commit is contained in:
Aleksander Grygier 2026-03-27 15:36:37 +01:00
parent 5970f8fffe 48cda24c11
commit 7eeeefa9f6
120 changed files with 4732 additions and 1796 deletions
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2
.devops/cann.Dockerfile
View File

@ -4,7 +4,7 @@
# Define the CANN base image for easier version updates later
ARG CHIP_TYPE=910b
ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-${CHIP_TYPE}-openeuler24.03-py3.11
ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.5.0-${CHIP_TYPE}-openeuler24.03-py3.11
# ==============================================================================
# BUILD STAGE

2
.devops/llama-cli-cann.Dockerfile
View File

@ -1,4 +1,4 @@
ARG ASCEND_VERSION=8.1.RC1.alpha001-910b-openeuler22.03-py3.10
ARG ASCEND_VERSION=8.5.0-910b-openeuler22.03-py3.10
FROM ascendai/cann:$ASCEND_VERSION AS build

85
.github/workflows/build-android.yml vendored
View File

@ -40,13 +40,9 @@ jobs:
steps:
- name: Clone
uses: actions/checkout@v6
# Disabled due to size (400MB) and always 0 cache hits
# - name: ccache
# uses: ggml-org/ccache-action@v1.2.16
# with:
# key: android-build
# evict-old-files: 1d
with:
fetch-depth: 0
lfs: false
- name: Set up JDK
uses: actions/setup-java@v5
@ -55,7 +51,7 @@ jobs:
distribution: zulu
- name: Setup Android SDK
uses: android-actions/setup-android@v3
uses: android-actions/setup-android@9fc6c4e9069bf8d3d10b2204b1fb8f6ef7065407 # v3
with:
log-accepted-android-sdk-licenses: false
@ -66,10 +62,11 @@ jobs:
android-ndk:
runs-on: ubuntu-latest
env:
OPENCL_VERSION: 2025.07.22
container:
image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.3'
defaults:
run:
shell: bash
strategy:
matrix:
include:
@ -82,59 +79,23 @@ jobs:
- name: Clone
id: checkout
uses: actions/checkout@v6
with:
fetch-depth: 0
lfs: false
- name: Install OpenCL Headers and Libs
id: install_opencl
if: ${{ matrix.build == 'arm64-snapdragon' }}
run: |
mkdir opencl
curl -L -o opencl/clhpp.tar.gz https://github.com/KhronosGroup/OpenCL-CLHPP/archive/refs/tags/v${OPENCL_VERSION}.tar.gz
curl -L -o opencl/headers.tar.gz https://github.com/KhronosGroup/OpenCL-Headers/archive/refs/tags/v${OPENCL_VERSION}.tar.gz
curl -L -o opencl/icd-loader.tar.gz https://github.com/KhronosGroup/OpenCL-ICD-Loader/archive/refs/tags/v${OPENCL_VERSION}.tar.gz
tar -xaf opencl/headers.tar.gz -C opencl
tar -xaf opencl/clhpp.tar.gz -C opencl
tar -xaf opencl/icd-loader.tar.gz -C opencl
sudo cp -r opencl/OpenCL-Headers-${OPENCL_VERSION}/CL ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
sudo cp -r opencl/OpenCL-CLHPP-${OPENCL_VERSION}/include/CL/* ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include/CL
cd opencl/OpenCL-ICD-Loader-${OPENCL_VERSION}
cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -DOPENCL_ICD_LOADER_HEADERS_DIR=${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=31 -DANDROID_STL=c++_shared
cmake --build build
sudo cp build/libOpenCL.so ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
rm -rf opencl
- name: Install Hexagon SDK
id: install_hexsdk
if: ${{ matrix.build == 'arm64-snapdragon' }}
env:
HEXSDK_VER: 6.4.0.2
HEXTLS_VER: 19.0.04
run: |
curl -L -o hex-sdk.tar.gz https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v$HEXSDK_VER/hexagon-sdk-v$HEXSDK_VER-amd64-lnx.tar.xz
mkdir hex-sdk
tar -xaf hex-sdk.tar.gz -C hex-sdk
ls -l hex-sdk
sudo mv hex-sdk /opt/hexagon
echo "HEXAGON_SDK_ROOT=/opt/hexagon/$HEXSDK_VER" >> "$GITHUB_ENV"
echo "HEXAGON_TOOLS_ROOT=/opt/hexagon/$HEXSDK_VER/tools/HEXAGON_Tools/$HEXTLS_VER" >> "$GITHUB_ENV"
echo "DEFAULT_HLOS_ARCH=64" >> "$GITHUB_ENV"
echo "DEFAULT_TOOLS_VARIANT=toolv19" >> "$GITHUB_ENV"
echo "DEFAULT_NO_QURT_INC=0" >> "$GITHUB_ENV"
echo "DEFAULT_DSP_ARCH=v73" >> "$GITHUB_ENV"
- name: Update CMake presets
id: update_presets
if: ${{ matrix.build == 'arm64-snapdragon' }}
run: |
cp docs/backend/snapdragon/CMakeUserPresets.json .
- name: Build
id: ndk_build
- name: Build Llama.CPP for Hexagon Android
id: build_llama_cpp_hexagon_android
run: |
if [[ "${{ matrix.build }}" == "arm64-snapdragon" ]]; then
cp docs/backend/snapdragon/CMakeUserPresets.json .
fi
cmake ${{ matrix.defines }} -B build
cmake --build build
cmake --install build --prefix pkg-adb/llama.cpp
- name: Test
id: cmake_test
run: |
echo "FIXME: test on devices"
- name: Upload Llama.CPP Hexagon Android Build Artifact
if: ${{ always() && steps.build_llama_cpp_hexagon_android.outcome == 'success' }}
uses: actions/upload-artifact@v6
with:
name: llama-cpp-android-${{ matrix.build }}
path: pkg-adb/llama.cpp

2
.github/workflows/build-cann.yml vendored
View File

@ -63,7 +63,7 @@ jobs:
- name: Set container image
id: cann-image
run: |
image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.5.0-910b-openeuler24.03-py3.11' || '8.5.0-310p-openeuler24.03-py3.11' }}"
echo "image=${image}" >> "${GITHUB_OUTPUT}"
- name: Pull container image

2
.github/workflows/build-msys.yml vendored
View File

@ -43,7 +43,7 @@ jobs:
# save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Setup ${{ matrix.sys }}
uses: msys2/setup-msys2@v2
uses: msys2/setup-msys2@cafece8e6baf9247cf9b1bf95097b0b983cc558d # v2
with:
update: true
msystem: ${{matrix.sys}}

109
.github/workflows/build-self-hosted.yml vendored
View File

@ -141,60 +141,61 @@ jobs:
# amd-smi static
# GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
ggml-ci-mac-metal:
runs-on: [self-hosted, macOS, ARM64]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v6
- name: Test
id: ggml-ci
run: |
GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
ggml-ci-mac-webgpu:
runs-on: [self-hosted, macOS, ARM64]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v6
- name: Dawn Dependency
id: dawn-depends
run: |
DAWN_VERSION="v2.0.0"
DAWN_OWNER="reeselevine"
DAWN_REPO="dawn"
DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
curl -L -o artifact.zip \
"https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
mkdir dawn
unzip artifact.zip
tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
- name: Test
id: ggml-ci
run: |
GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
ggml-ci-mac-vulkan:
runs-on: [self-hosted, macOS, ARM64]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v6
- name: Test
id: ggml-ci
run: |
vulkaninfo --summary
GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
# TODO: sandbox Mac runners
# ggml-ci-mac-metal:
# runs-on: [self-hosted, macOS, ARM64]
#
# steps:
# - name: Clone
# id: checkout
# uses: actions/checkout@v6
#
# - name: Test
# id: ggml-ci
# run: |
# GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
#
# ggml-ci-mac-webgpu:
# runs-on: [self-hosted, macOS, ARM64]
#
# steps:
# - name: Clone
# id: checkout
# uses: actions/checkout@v6
#
# - name: Dawn Dependency
# id: dawn-depends
# run: |
# DAWN_VERSION="v2.0.0"
# DAWN_OWNER="reeselevine"
# DAWN_REPO="dawn"
# DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
# echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
# curl -L -o artifact.zip \
# "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
# mkdir dawn
# unzip artifact.zip
# tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
#
# - name: Test
# id: ggml-ci
# run: |
# GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
# bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
#
# ggml-ci-mac-vulkan:
# runs-on: [self-hosted, macOS, ARM64]
#
# steps:
# - name: Clone
# id: checkout
# uses: actions/checkout@v6
#
# - name: Test
# id: ggml-ci
# run: |
# vulkaninfo --summary
# GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
ggml-ci-linux-intel-vulkan:
runs-on: [self-hosted, Linux, Intel]

44
.github/workflows/build.yml vendored
View File

@ -87,7 +87,7 @@ jobs:
-DGGML_METAL_EMBED_LIBRARY=OFF \
-DGGML_METAL_SHADER_DEBUG=ON \
-DGGML_RPC=ON
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
leaks -atExit -- ./build/bin/test-thread-safety -hf ggml-org/gemma-3-270m-qat-GGUF -ngl 99 -p "$(printf 'hello %.0s' {1..128})" -n 16 -c 512 -ub 32 -np 2 -t 2 -lv 1
- name: Test
@ -124,7 +124,7 @@ jobs:
-DGGML_METAL=OFF \
-DGGML_RPC=ON \
-DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
id: cmake_test
@ -165,8 +165,8 @@ jobs:
id: cmake_build
run: |
export CMAKE_PREFIX_PATH=dawn
cmake -B build -DGGML_WEBGPU=ON -DGGML_METAL=OFF -DGGML_BLAS=OFF
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
cmake -B build -G "Ninja" -DCMAKE_BUILD_TYPE=Release -DGGML_WEBGPU=ON -DGGML_METAL=OFF -DGGML_BLAS=OFF
time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
id: cmake_test
@ -231,7 +231,7 @@ jobs:
cmake -B build \
-DLLAMA_FATAL_WARNINGS=ON \
-DGGML_RPC=ON
cmake --build build --config Release -j $(nproc)
time cmake --build build --config Release -j $(nproc)
- name: Test
id: cmake_test
@ -274,14 +274,16 @@ jobs:
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential libssl-dev
sudo apt-get install build-essential libssl-dev ninja-build
- name: Build
id: cmake_build
run: |
cmake -B build \
-G "Ninja" \
-DCMAKE_BUILD_TYPE=Release \
-DGGML_RPC=ON
cmake --build build --config Release -j $(nproc)
time cmake --build build --config Release -j $(nproc)
- name: Test
id: cmake_test
@ -300,12 +302,13 @@ jobs:
- name: Dependencies
id: depends
run: |
sudo apt-get install -y glslc libvulkan-dev libssl-dev
sudo apt-get install -y glslc libvulkan-dev libssl-dev ninja-build
- name: Configure
id: cmake_configure
run: |
cmake -B build \
-G "Ninja" \
-DCMAKE_BUILD_TYPE=RelWithDebInfo \
-DGGML_BACKEND_DL=ON \
-DGGML_CPU_ALL_VARIANTS=ON \
@ -314,7 +317,7 @@ jobs:
- name: Build
id: cmake_build
run: |
cmake --build build -j $(nproc)
time cmake --build build -j $(nproc)
ubuntu-24-webgpu:
runs-on: ubuntu-24.04
@ -336,7 +339,8 @@ jobs:
run: |
sudo add-apt-repository -y ppa:kisak/kisak-mesa
sudo apt-get update -y
sudo apt-get install -y build-essential mesa-vulkan-drivers libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libssl-dev
sudo apt-get install -y build-essential mesa-vulkan-drivers \
libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libssl-dev
- name: Get latest Vulkan SDK version
id: vulkan_sdk_version
@ -378,7 +382,7 @@ jobs:
export Dawn_DIR=dawn/lib64/cmake/Dawn
cmake -B build \
-DGGML_WEBGPU=ON
cmake --build build --config Release -j $(nproc)
time cmake --build build --config Release -j $(nproc)
- name: Test
id: cmake_test
@ -415,11 +419,13 @@ jobs:
run: |
source emsdk/emsdk_env.sh
emcmake cmake -B build-wasm \
-G "Ninja" \
-DCMAKE_BUILD_TYPE=Release \
-DGGML_WEBGPU=ON \
-DLLAMA_OPENSSL=OFF \
-DEMDAWNWEBGPU_DIR=emdawnwebgpu_pkg
cmake --build build-wasm --target test-backend-ops -j $(nproc)
time cmake --build build-wasm --config Release --target test-backend-ops -j $(nproc)
ubuntu-22-hip:
runs-on: ubuntu-22.04
@ -479,7 +485,7 @@ jobs:
run: |
cmake -B build -S . \
-DGGML_MUSA=ON
cmake --build build --config Release -j $(nproc)
time cmake --build build --config Release -j $(nproc)
ubuntu-22-sycl:
runs-on: ubuntu-22.04
@ -528,7 +534,7 @@ jobs:
-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx
cmake --build build --config Release -j $(nproc)
time cmake --build build --config Release -j $(nproc)
ubuntu-22-sycl-fp16:
runs-on: ubuntu-22.04
@ -551,7 +557,7 @@ jobs:
shell: bash
run: |
sudo apt update
sudo apt install intel-oneapi-compiler-dpcpp-cpp libssl-dev
sudo apt install intel-oneapi-compiler-dpcpp-cpp libssl-dev ninja-build
- name: install oneAPI MKL library
shell: bash
@ -574,11 +580,13 @@ jobs:
run: |
source /opt/intel/oneapi/setvars.sh
cmake -B build \
-G "Ninja" \
-DCMAKE_BUILD_TYPE=Release \
-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx \
-DGGML_SYCL_F16=ON
cmake --build build --config Release -j $(nproc)
time cmake --build build --config Release -j $(nproc)
ubuntu-24-openvino:
name: ubuntu-24-openvino-${{ matrix.openvino_device }}
@ -648,7 +656,7 @@ jobs:
cmake -B build/ReleaseOV -G Ninja \
-DCMAKE_BUILD_TYPE=Release \
-DGGML_OPENVINO=ON
cmake --build build/ReleaseOV --config Release -j $(nproc)
time cmake --build build/ReleaseOV --config Release -j $(nproc)
- name: Test
id: cmake_test
@ -1039,7 +1047,7 @@ jobs:
-DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
-DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14
cmake --build build --config Release -j $(nproc)
time cmake --build build --config Release -j $(nproc)
- name: Test
id: cmake_test

12
.github/workflows/docker.yml vendored
View File

@ -56,15 +56,15 @@ jobs:
- name: Set up QEMU
if: ${{ matrix.config.tag != 's390x' }}
uses: docker/setup-qemu-action@v3
uses: docker/setup-qemu-action@c7c53464625b32c7a7e944ae62b3e17d2b600130 # v3
with:
image: tonistiigi/binfmt:qemu-v7.0.0-28
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
- name: Log in to Docker Hub
uses: docker/login-action@v3
uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
@ -127,7 +127,7 @@ jobs:
- 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@v6
uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
with:
context: .
push: true
@ -152,7 +152,7 @@ jobs:
- 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@v6
uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
with:
context: .
push: true
@ -177,7 +177,7 @@ jobs:
- 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@v6
uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
with:
context: .
push: true

2
.github/workflows/editorconfig.yml vendored
View File

@ -23,7 +23,7 @@ jobs:
runs-on: ubuntu-slim
steps:
- uses: actions/checkout@v6
- uses: editorconfig-checker/action-editorconfig-checker@v2
- uses: editorconfig-checker/action-editorconfig-checker@840e866d93b8e032123c23bac69dece044d4d84c # v2.2.0
with:
version: v3.0.3
- run: editorconfig-checker

2
.github/workflows/gguf-publish.yml vendored
View File

@ -38,7 +38,7 @@ jobs:
- name: Build package
run: cd gguf-py && poetry build
- name: Publish package
uses: pypa/gh-action-pypi-publish@release/v1
uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1
with:
password: ${{ secrets.PYPI_API_TOKEN }}
packages-dir: gguf-py/dist

6
.github/workflows/hip-quality-check.yml vendored
View File

@ -8,7 +8,8 @@ on:
paths: [
'.github/workflows/hip-quality-check.yml',
'**/*.cu',
'**/*.cuh'
'**/*.cuh',
'scripts/hip/gcn-cdna-vgpr-check.py'
]
pull_request:
@ -16,7 +17,8 @@ on:
paths: [
'.github/workflows/hip-quality-check.yml',
'**/*.cu',
'**/*.cuh'
'**/*.cuh',
'scripts/hip/gcn-cdna-vgpr-check.py'
]
concurrency:

2
.github/workflows/python-lint.yml vendored
View File

@ -31,6 +31,6 @@ jobs:
with:
python-version: "3.11"
- name: flake8 Lint
uses: py-actions/flake8@v2
uses: py-actions/flake8@84ec6726560b6d5bd68f2a5bed83d62b52bb50ba # v2
with:
plugins: "flake8-no-print"

2
.github/workflows/release.yml vendored
View File

@ -907,7 +907,7 @@ jobs:
- name: Set container image
id: cann-image
run: |
image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.5.0-910b-openeuler24.03-py3.11' || '8.5.0-310p-openeuler24.03-py3.11' }}"
echo "image=${image}" >> "${GITHUB_OUTPUT}"
- name: Pull container image

45
ci/run.sh
View File

@ -57,6 +57,13 @@ SRC=`pwd`
CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_OPENSSL=OFF -DGGML_SCHED_NO_REALLOC=ON"
CTEST_EXTRA=""
# Default to use make unless specified for compatibility
CMAKE_GENERATOR="Unix Makefiles"
if [ ! -z "${GG_BUILD_NINJA}" ]; then
CMAKE_GENERATOR="Ninja"
fi
if [ ! -z ${GG_BUILD_METAL} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
fi
@ -242,13 +249,13 @@ function gg_run_ctest_debug {
set -e
# Check cmake, make and ctest are installed
# Check cmake and ctest are installed
gg_check_build_requirements
(time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
(cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time cmake --build . --config Debug -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log
(time ctest --output-on-failure -L main -E "test-opt|test-backend-ops" ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
(time ctest -C Debug --output-on-failure -L main -E "test-opt|test-backend-ops" ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
set +e
}
@ -273,16 +280,16 @@ function gg_run_ctest_release {
set -e
# Check cmake, make and ctest are installed
# Check cmake and ctest are installed
gg_check_build_requirements
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
(cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time cmake --build . --config Release -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log
if [ -z ${GG_BUILD_LOW_PERF} ]; then
(time ctest --output-on-failure -L 'main|python' ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
(time ctest -C Release --output-on-failure -L 'main|python' ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
else
(time ctest --output-on-failure -L main -E test-opt ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
(time ctest -C Release --output-on-failure -L main -E test-opt ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
fi
set +e
@ -340,7 +347,7 @@ function gg_run_ctest_with_model_debug {
cd build-ci-debug
set -e
(LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
(LLAMACPP_TEST_MODELFILE="$model" time ctest -C Debug --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
set +e
cd ..
@ -353,7 +360,7 @@ function gg_run_ctest_with_model_release {
cd build-ci-release
set -e
(LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
(LLAMACPP_TEST_MODELFILE="$model" time ctest -C Release --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
# test memory leaks
#if [[ ! -z ${GG_BUILD_METAL} ]]; then
@ -407,8 +414,8 @@ function gg_run_qwen3_0_6b {
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
(cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time cmake --build . --config Release -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf --outtype f16
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-bf16.gguf --outtype bf16
@ -556,8 +563,8 @@ function gg_run_embd_bge_small {
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
(cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time cmake --build . --config Release -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
@ -601,8 +608,8 @@ function gg_run_rerank_tiny {
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
(cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time cmake --build . --config Release -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
@ -652,10 +659,6 @@ function gg_check_build_requirements {
gg_printf 'cmake not found, please install'
fi
if ! command -v make &> /dev/null; then
gg_printf 'make not found, please install'
fi
if ! command -v ctest &> /dev/null; then
gg_printf 'ctest not found, please install'
fi

9
common/arg.cpp
View File

@ -423,6 +423,9 @@ static bool parse_bool_value(const std::string & value) {
static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
common_params & params = ctx_arg.params;
// setup log directly from params.verbosity: see tools/cli/cli.cpp
common_log_set_verbosity_thold(params.verbosity);
std::unordered_map<std::string, std::pair<common_arg *, bool>> arg_to_options;
for (auto & opt : ctx_arg.options) {
for (const auto & arg : opt.args) {
@ -631,8 +634,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
));
}
common_log_set_verbosity_thold(params.verbosity);
return true;
}
@ -1078,7 +1079,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params) {
params.verbose_prompt = true;
}
));
).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL}));
add_opt(common_arg(
{"--display-prompt"},
{"--no-display-prompt"},
@ -3253,6 +3254,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
"Set verbosity level to infinity (i.e. log all messages, useful for debugging)",
[](common_params & params) {
params.verbosity = INT_MAX;
common_log_set_verbosity_thold(INT_MAX);
}
));
add_opt(common_arg(
@ -3273,6 +3275,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
"(default: %d)\n", params.verbosity),
[](common_params & params, int value) {
params.verbosity = value;
common_log_set_verbosity_thold(value);
}
).set_env("LLAMA_LOG_VERBOSITY"));
add_opt(common_arg(

24
common/download.cpp
View File

@ -454,7 +454,9 @@ static gguf_split_info get_gguf_split_info(const std::string & path) {
std::smatch m;
std::string prefix = path;
string_remove_suffix(prefix, ".gguf");
if (!string_remove_suffix(prefix, ".gguf")) {
return {};
}
int index = 1;
int count = 1;
@ -546,6 +548,20 @@ static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
return best;
}
static bool gguf_filename_is_model(const std::string & filepath) {
if (!string_ends_with(filepath, ".gguf")) {
return false;
}
std::string filename = filepath;
if (auto pos = filename.rfind('/'); pos != std::string::npos) {
filename = filename.substr(pos + 1);
}
return filename.find("mmproj") == std::string::npos &&
filename.find("imatrix") == std::string::npos;
}
static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
const std::string & tag) {
std::vector<std::string> tags;
@ -559,8 +575,7 @@ static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
for (const auto & t : tags) {
std::regex pattern(t + "[.-]", std::regex::icase);
for (const auto & f : files) {
if (string_ends_with(f.path, ".gguf") &&
f.path.find("mmproj") == std::string::npos &&
if (gguf_filename_is_model(f.path) &&
std::regex_search(f.path, pattern)) {
return f;
}
@ -568,8 +583,7 @@ static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
}
for (const auto & f : files) {
if (string_ends_with(f.path, ".gguf") &&
f.path.find("mmproj") == std::string::npos) {
if (gguf_filename_is_model(f.path)) {
return f;
}
}

207
common/hf-cache.cpp
View File

@ -26,6 +26,8 @@ namespace nl = nlohmann;
#include <windows.h>
#else
#define HOME_DIR "HOME"
#include <unistd.h>
#include <pwd.h>
#endif
namespace hf_cache {
@ -38,6 +40,7 @@ static fs::path get_cache_directory() {
const char * var;
fs::path path;
} entries[] = {
{"LLAMA_CACHE", fs::path()},
{"HF_HUB_CACHE", fs::path()},
{"HUGGINGFACE_HUB_CACHE", fs::path()},
{"HF_HOME", fs::path("hub")},
@ -50,6 +53,13 @@ static fs::path get_cache_directory() {
return entry.path.empty() ? base : base / entry.path;
}
}
#ifndef _WIN32
const struct passwd * pw = getpwuid(getuid());
if (pw->pw_dir && *pw->pw_dir) {
return fs::path(pw->pw_dir) / ".cache" / "huggingface" / "hub";
}
#endif
throw std::runtime_error("Failed to determine HF cache directory");
}();
@ -325,9 +335,15 @@ hf_files get_repo_files(const std::string & repo_id,
if (item["lfs"].contains("oid") && item["lfs"]["oid"].is_string()) {
file.oid = item["lfs"]["oid"].get<std::string>();
}
if (item["lfs"].contains("size") && item["lfs"]["size"].is_number()) {
file.size = item["lfs"]["size"].get<size_t>();
}
} else if (item.contains("oid") && item["oid"].is_string()) {
file.oid = item["oid"].get<std::string>();
}
if (file.size == 0 && item.contains("size") && item["size"].is_number()) {
file.size = item["size"].get<size_t>();
}
if (!file.oid.empty() && !is_valid_oid(file.oid)) {
LOG_WRN("%s: skip invalid oid: %s\n", __func__, file.oid.c_str());
@ -487,6 +503,34 @@ std::string finalize_file(const hf_file & file) {
// delete everything after this line, one day
// copied from download.cpp without the tag part
struct gguf_split_info {
std::string prefix; // tag included
int index;
int count;
};
static gguf_split_info get_gguf_split_info(const std::string & path) {
static const std::regex re_split("^(.+)-([0-9]{5})-of-([0-9]{5})$", std::regex::icase);
std::smatch m;
std::string prefix = path;
if (!string_remove_suffix(prefix, ".gguf")) {
return {};
}
int index = 1;
int count = 1;
if (std::regex_match(prefix, m, re_split)) {
index = std::stoi(m[2].str());
count = std::stoi(m[3].str());
prefix = m[1].str();
}
return {std::move(prefix), index, count};
}
static std::pair<std::string, std::string> parse_manifest_name(std::string & filename) {
static const std::regex re(R"(^manifest=([^=]+)=([^=]+)=.*\.json$)");
std::smatch match;
@ -504,25 +548,30 @@ static std::string make_old_cache_filename(const std::string & owner,
return result;
}
static bool migrate_single_file(const fs::path & old_cache,
const std::string & owner,
const std::string & repo,
const nl::json & node,
const hf_files & files) {
struct migrate_file {
std::string path;
std::string sha256;
size_t size;
fs::path old_path;
fs::path etag_path;
const hf_file * file;
};
if (!node.contains("rfilename") ||
!node.contains("lfs") ||
!node["lfs"].contains("sha256")) {
return false;
}
using migrate_files = std::vector<migrate_file>;
std::string path = node["rfilename"];
std::string sha256 = node["lfs"]["sha256"];
static bool collect_file(const fs::path & old_cache,
const std::string & owner,
const std::string & repo,
const std::string & path,
const std::string & sha256,
const hf_files & files,
migrate_files & to_migrate) {
const hf_file * file = nullptr;
const hf_file * file_info = nullptr;
for (const auto & f : files) {
if (f.path == path) {
file_info = &f;
file = &f;
break;
}
}
@ -532,50 +581,104 @@ static bool migrate_single_file(const fs::path & old_cache,
fs::path etag_path = old_path.string() + ".etag";
if (!fs::exists(old_path)) {
if (fs::exists(etag_path)) {
LOG_WRN("%s: %s is orphan, deleting...\n", __func__, etag_path.string().c_str());
fs::remove(etag_path);
if (file && fs::exists(file->final_path)) {
return true;
}
LOG_WRN("%s: %s not found in old cache or HF cache\n", __func__, old_filename.c_str());
return false;
}
bool delete_old_path = false;
if (!file_info) {
LOG_WRN("%s: %s not found in current repo, deleting...\n", __func__, old_filename.c_str());
delete_old_path = true;
} else if (!sha256.empty() && !file_info->oid.empty() && sha256 != file_info->oid) {
LOG_WRN("%s: %s is not up to date (sha256 mismatch), deleting...\n", __func__, old_filename.c_str());
delete_old_path = true;
if (!file) {
LOG_WRN("%s: %s not found in current repo\n", __func__, old_filename.c_str());
return false;
}
std::error_code ec;
if (!sha256.empty() && !file->oid.empty() && sha256 != file->oid) {
LOG_WRN("%s: %s is not up to date (sha256 mismatch)\n", __func__, old_filename.c_str());
return false;
}
if (delete_old_path) {
fs::remove(old_path, ec);
fs::remove(etag_path, ec);
if (file->size > 0) {
size_t size = fs::file_size(old_path);
if (size != file->size) {
LOG_WRN("%s: %s has wrong size %zu (expected %zu)\n", __func__, old_filename.c_str(), size, file->size);
return false;
}
}
to_migrate.push_back({path, sha256, file->size, old_path, etag_path, file});
return true;
}
static bool collect_files(const fs::path & old_cache,
const std::string & owner,
const std::string & repo,
const nl::json & node,
const hf_files & files,
migrate_files & to_migrate) {
if (!node.contains("rfilename") ||
!node.contains("lfs") ||
!node["lfs"].contains("sha256")) {
return true;
}
fs::path new_path(file_info->local_path);
std::string path = node["rfilename"];
std::string sha256 = node["lfs"]["sha256"];
auto split = get_gguf_split_info(path);
if (split.count <= 1) {
return collect_file(old_cache, owner, repo, path, sha256, files, to_migrate);
}
std::vector<std::pair<std::string, std::string>> splits;
for (const auto & f : files) {
auto split_f = get_gguf_split_info(f.path);
if (split_f.count == split.count && split_f.prefix == split.prefix) {
// sadly the manifest only provides the sha256 of the first file (index == 1)
// the rest will be verified using the size...
std::string f_sha256 = (split_f.index == 1) ? sha256 : "";
splits.emplace_back(f.path, f_sha256);
}
}
if ((int)splits.size() != split.count) {
LOG_WRN("%s: expected %d split files but found %d in repo\n", __func__, split.count, (int)splits.size());
return false;
}
for (const auto & [f_path, f_sha256] : splits) {
if (!collect_file(old_cache, owner, repo, f_path, f_sha256, files, to_migrate)) {
return false;
}
}
return true;
}
static bool migrate_file(const migrate_file & file) {
std::error_code ec;
fs::path new_path(file.file->local_path);
fs::create_directories(new_path.parent_path(), ec);
if (!fs::exists(new_path, ec)) {
fs::rename(old_path, new_path, ec);
fs::rename(file.old_path, new_path, ec);
if (ec) {
fs::copy_file(old_path, new_path, ec);
fs::copy_file(file.old_path, new_path, ec);
if (ec) {
LOG_WRN("%s: failed to move/copy %s: %s\n", __func__, old_path.string().c_str(), ec.message().c_str());
LOG_ERR("%s: failed to move/copy %s: %s\n", __func__, file.old_path.string().c_str(), ec.message().c_str());
return false;
}
}
fs::remove(old_path, ec);
fs::remove(file.old_path, ec);
}
fs::remove(etag_path, ec);
std::string filename = finalize_file(*file_info);
LOG_INF("%s: migrated %s -> %s\n", __func__, old_filename.c_str(), filename.c_str());
fs::remove(file.etag_path, ec);
std::string filename = finalize_file(*file.file);
LOG_INF("%s: migrated %s -> %s\n", __func__, file.old_path.filename().string().c_str(), filename.c_str());
return true;
}
@ -624,19 +727,43 @@ void migrate_old_cache_to_hf_cache(const std::string & token, bool offline) {
continue;
}
migrate_files to_migrate;
bool ok = true;
try {
std::ifstream manifest(entry.path());
auto json = nl::json::parse(manifest);
for (const char * key : {"ggufFile", "mmprojFile"}) {
if (json.contains(key)) {
migrate_single_file(old_cache, owner, repo, json[key], files);
if (!collect_files(old_cache, owner, repo, json[key], files, to_migrate)) {
ok = false;
break;
}
}
}
} catch (const std::exception & e) {
LOG_WRN("%s: failed to parse manifest %s: %s\n", __func__, filename.c_str(), e.what());
continue;
}
if (!ok) {
LOG_WRN("%s: migration skipped: one or more files failed validation\n", __func__);
continue;
}
for (const auto & file : to_migrate) {
if (!migrate_file(file)) {
ok = false;
break;
}
}
if (!ok) {
LOG_WRN("%s: migration failed: could not migrate all files\n", __func__);
continue;
}
LOG_INF("%s: migration complete, deleting manifest: %s\n", __func__, entry.path().string().c_str());
fs::remove(entry.path());
}
}

1
common/hf-cache.h
View File

@ -14,6 +14,7 @@ struct hf_file {
std::string final_path;
std::string oid;
std::string repo_id;
size_t size = 0; // only for the migration
};
using hf_files = std::vector<hf_file>;

10
common/jinja/runtime.cpp
View File

@ -667,8 +667,9 @@ value macro_statement::execute_impl(context & ctx) {
if (is_stmt<identifier>(this->args[i])) {
// normal parameter
std::string param_name = cast_stmt<identifier>(this->args[i])->val;
JJ_DEBUG(" Binding parameter '%s' to argument of type %s", param_name.c_str(), args.get_pos(i)->type().c_str());
macro_ctx.set_val(param_name, args.get_pos(i));
value param_value = args.get_kwarg_or_pos(param_name, i);
JJ_DEBUG(" Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
macro_ctx.set_val(param_name, param_value);
} else if (is_stmt<keyword_argument_expression>(this->args[i])) {
// default argument used as normal parameter
auto kwarg = cast_stmt<keyword_argument_expression>(this->args[i]);
@ -676,8 +677,9 @@ value macro_statement::execute_impl(context & ctx) {
throw std::runtime_error("Keyword argument key must be an identifier in macro '" + name + "'");
}
std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
JJ_DEBUG(" Binding parameter '%s' to argument of type %s", param_name.c_str(), args.get_pos(i)->type().c_str());
macro_ctx.set_val(param_name, args.get_pos(i));
value param_value = args.get_kwarg_or_pos(param_name, i);
JJ_DEBUG(" Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
macro_ctx.set_val(param_name, param_value);
} else {
throw std::runtime_error("Invalid parameter type in macro '" + name + "'");
}

302
convert_hf_to_gguf.py
View File

@ -486,7 +486,7 @@ class ModelBase:
elif quant_method == "modelopt":
# Mixed-precision ModelOpt models: NVFP4 tensors are handled by
# _generate_nvfp4_tensors; FP8 tensors have 1D weight_scale and
# are dequantized here. input_scale tensors are unused.
# are dequantized here. k/v scale tensors are unused.
for name in self.model_tensors.keys():
if name.endswith(".weight_scale"):
weight_name = name.removesuffix("_scale")
@ -494,7 +494,7 @@ class ModelBase:
s = self.model_tensors[name]
self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), None)
tensors_to_remove.append(name)
if name.endswith((".input_scale", ".k_scale", ".v_scale")):
if name.endswith((".k_scale", ".v_scale")):
tensors_to_remove.append(name)
elif quant_method is not None:
raise NotImplementedError(f"Quant method is not yet supported: {quant_method!r}")
@ -542,7 +542,6 @@ class ModelBase:
raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
new_name = self.map_tensor_name(name)
# Handle gate/up expert tensor fusion if enabled
@ -607,7 +606,12 @@ class ModelBase:
def _nvfp4_scale2_is_trivial(scale2: Tensor) -> bool:
return scale2.numel() <= 1 and abs(float(scale2.float().sum()) - 1.0) < 1e-6
def _repack_nvfp4(self, new_name: str, weight: Tensor, scale: Tensor, scale2: Tensor):
def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
if "language_model." in name:
name = name.replace("language_model.", "")
new_name = self.map_tensor_name(name)
raw, shape = self._nvfp4_pack(weight, scale)
logger.info(f"Repacked {new_name} with shape {shape} and quantization NVFP4")
self.gguf_writer.add_tensor(new_name, raw, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
@ -619,10 +623,18 @@ class ModelBase:
logger.info(f" + {scale_name} (per-tensor NVFP4 scale2, shape [{scale2_f32.size}])")
self.gguf_writer.add_tensor(scale_name, scale2_f32)
# Emit per-tensor input_scale as a separate F32 tensor when non-trivial
if not self._nvfp4_scale2_is_trivial(input_scale):
input_scale_f32 = input_scale.float().numpy().flatten()
input_scale_name = new_name.replace(".weight", ".input_scale")
logger.info(f" + {input_scale_name} (per-tensor NVFP4 input_scale, shape [{input_scale_f32.size}])")
self.gguf_writer.add_tensor(input_scale_name, input_scale_f32)
def _generate_nvfp4_tensors(self):
# Per-layer expert merging to avoid holding all experts in memory
expert_blocks: dict[tuple[int, str], list[tuple[int, np.ndarray]]] = {}
expert_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
expert_input_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
expert_shapes: dict[tuple[int, str], list[int]] = {}
n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
consumed: list[str] = []
@ -632,6 +644,7 @@ class ModelBase:
continue
scale_name = name.replace(".weight", ".weight_scale")
scale2_name = name.replace(".weight", ".weight_scale_2")
input_scale_name = name.replace(".weight", ".input_scale")
if scale_name not in self.model_tensors:
continue
# Force eager materialization of lazy tensors
@ -643,11 +656,14 @@ class ModelBase:
continue
scale2 = LazyTorchTensor.to_eager(self.model_tensors.get(scale2_name, lambda: torch.tensor(1.0))())
input_scale = LazyTorchTensor.to_eager(self.model_tensors.get(input_scale_name, lambda: torch.tensor(1.0))())
# Mark tensors for removal from model_tensors (already written to gguf)
consumed.extend([name, scale_name])
if scale2_name in self.model_tensors:
consumed.append(scale2_name)
if input_scale_name in self.model_tensors:
consumed.append(input_scale_name)
# Check if this is a per-expert tensor
m = re.search(r'\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight$', name)
@ -663,34 +679,37 @@ class ModelBase:
if key not in expert_blocks:
expert_blocks[key] = []
expert_scales[key] = []
expert_input_scales[key] = []
expert_shapes[key] = shape
expert_blocks[key].append((expert_id, raw.copy()))
# Collect per-expert scale2 (scalar per expert)
expert_scales[key].append((expert_id, float(scale2.float().sum())))
# Collect per-expert input_scale (scalar per expert)
expert_input_scales[key].append((expert_id, float(input_scale.float().sum())))
# Flush when all experts for this (layer, proj) are collected
if n_experts > 0 and len(expert_blocks[key]) >= n_experts:
self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_shapes, bid, proj_type)
self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
else:
new_name = self.map_tensor_name(name)
self._repack_nvfp4(new_name, weight, scale, scale2)
self._repack_nvfp4(name, weight, scale, scale2, input_scale)
# Flush any remaining experts (fallback if n_experts was unknown)
for (bid, proj_type) in list(expert_blocks.keys()):
self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_shapes, bid, proj_type)
self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
# Remove consumed tensors so get_tensors/modify_tensors won't see them
for name in consumed:
self.model_tensors.pop(name, None)
# Remove unused auxiliary tensors (input_scale, k_scale, v_scale)
# Remove any remaining unused auxiliary tensors
for name in list(self.model_tensors.keys()):
if name.endswith((".input_scale", ".k_scale", ".v_scale")):
if name.endswith((".k_scale", ".v_scale")):
del self.model_tensors[name]
def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_shapes, bid, proj_type):
def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type):
experts = expert_blocks.pop(key)
scales = expert_scales.pop(key)
input_scales = expert_input_scales.pop(key)
shape = expert_shapes.pop(key)
experts.sort(key=lambda x: x[0])
@ -708,6 +727,14 @@ class ModelBase:
logger.info(f" + {scale_name} (per-expert NVFP4 scale2, shape [{len(scales)}])")
self.gguf_writer.add_tensor(scale_name, scale_vals)
# Emit per-expert input_scale tensor if any expert has non-trivial input_scale
input_scales.sort(key=lambda x: x[0])
input_scale_vals = np.array([s[1] for s in input_scales], dtype=np.float32)
if not np.allclose(input_scale_vals, 1.0, atol=1e-6):
input_scale_name = new_name.replace(".weight", ".input_scale")
logger.info(f" + {input_scale_name} (per-expert NVFP4 input_scale, shape [{len(input_scales)}])")
self.gguf_writer.add_tensor(input_scale_name, input_scale_vals)
del experts, merged
def prepare_tensors(self):
@ -947,6 +974,9 @@ class ModelBase:
if "thinker_config" in config:
# rename for Qwen2.5-Omni
config["text_config"] = config["thinker_config"]["text_config"]
if "language_config" in config:
# rename for DeepSeekOCR
config["text_config"] = config["language_config"]
if "lfm" in config:
# rename for LFM2-Audio
config["text_config"] = config["lfm"]
@ -1308,6 +1338,9 @@ class TextModel(ModelBase):
if chkhsh == "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df":
# ref: https://huggingface.co/aari1995/German_Semantic_V3
res = "jina-v2-de"
if chkhsh == "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4":
# ref: https://huggingface.co/evilfreelancer/ruGPT3XL
res = "gpt-2"
if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
# ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
res = "llama-bpe"
@ -1503,6 +1536,9 @@ class TextModel(ModelBase):
if chkhsh == "e4d54df1ebc1f2b91acd986c5b51aa50837d5faf7c7398e73c1f9e9ee5d19869":
# ref: https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601
res = "kanana2"
if chkhsh == "862f827721df956049dff5ca81a57f29e575280bc622e290d3bf4e35eca29015":
# ref: https://huggingface.co/codefuse-ai/F2LLM-v2-4B
res = "f2llmv2"
if res is None:
logger.warning("\n")
@ -2071,7 +2107,7 @@ class MmprojModel(ModelBase):
preprocessor_config: dict[str, Any]
global_config: dict[str, Any]
n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers", "vt_num_hidden_layers"]
n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "layers", "encoder_layers", "vt_num_hidden_layers"]
has_vision_encoder: bool = True # by default
has_audio_encoder: bool = False
@ -5005,6 +5041,97 @@ class _LinearAttentionVReorderBase(Qwen3NextModel):
perm[dim], perm[dim + 1] = perm[dim + 1], perm[dim]
return tensor.permute(*perm).contiguous().reshape(*shape)
def _transform_nvfp4_weight(self, name: str, weight: Tensor, scale: Tensor) -> tuple[Tensor, Tensor]:
if not name.endswith((
".linear_attn.in_proj_qkv.weight",
".linear_attn.in_proj_z.weight",
".linear_attn.in_proj_a.weight",
".linear_attn.in_proj_b.weight",
".linear_attn.out_proj.weight",
)):
return weight, scale
num_k_heads = self.hparams["linear_num_key_heads"]
num_v_heads = self.hparams["linear_num_value_heads"]
head_k_dim = self.hparams["linear_key_head_dim"]
head_v_dim = self.hparams["linear_value_head_dim"]
num_v_per_k = num_v_heads // num_k_heads
def unpack_nibbles(qs: Tensor) -> Tensor:
lo = torch.bitwise_and(qs, 0x0F)
hi = torch.bitwise_right_shift(qs, 4)
return torch.stack((lo, hi), dim=-1).reshape(*qs.shape[:-1], qs.shape[-1] * 2)
def pack_nibbles(codes: Tensor) -> Tensor:
codes = codes.reshape(*codes.shape[:-1], codes.shape[-1] // 2, 2)
lo = torch.bitwise_and(codes[..., 0], 0x0F)
hi = torch.bitwise_left_shift(torch.bitwise_and(codes[..., 1], 0x0F), 4)
return torch.bitwise_or(lo, hi).contiguous()
def apply_col_perm(qs: Tensor, scales: Tensor, col_perm: Tensor) -> tuple[Tensor, Tensor]:
assert qs.ndim >= 2
assert scales.ndim >= 2
k = qs.shape[-1] * 2
assert col_perm.numel() == k
assert k % 16 == 0
group_cols = col_perm.reshape(-1, 16)
group_starts = group_cols[:, 0]
expected = group_starts.unsqueeze(1) + torch.arange(16, dtype=col_perm.dtype)
assert torch.equal(group_cols, expected)
assert torch.all(group_starts % 16 == 0)
group_perm = (group_starts // 16).to(dtype=torch.long)
expected_groups = torch.arange(scales.shape[-1], dtype=torch.long)
assert group_perm.numel() == scales.shape[-1]
assert torch.equal(torch.sort(group_perm).values, expected_groups)
codes = unpack_nibbles(qs)
codes = codes.index_select(-1, col_perm.to(device=qs.device, dtype=torch.long))
qs = pack_nibbles(codes)
scales = scales.index_select(-1, group_perm.to(device=scales.device))
return qs, scales
def reorder_rows(qs: Tensor, scales: Tensor, head_dim: int) -> tuple[Tensor, Tensor]:
row_perm = self._reorder_v_heads(
torch.arange(num_v_heads * head_dim, dtype=torch.long).unsqueeze(-1),
0, num_k_heads, num_v_per_k, head_dim,
).squeeze(-1)
return (
qs.index_select(0, row_perm.to(device=qs.device)),
scales.index_select(0, row_perm.to(device=scales.device)),
)
if name.endswith(".linear_attn.in_proj_qkv.weight"):
q_dim = head_k_dim * num_k_heads
k_dim = head_k_dim * num_k_heads
q = weight[:q_dim]
k = weight[q_dim:q_dim + k_dim]
v = weight[q_dim + k_dim:]
q_scale = scale[:q_dim]
k_scale = scale[q_dim:q_dim + k_dim]
v_scale = scale[q_dim + k_dim:]
v, v_scale = reorder_rows(v, v_scale, head_v_dim)
return torch.cat([q, k, v], dim=0), torch.cat([q_scale, k_scale, v_scale], dim=0)
if name.endswith(".linear_attn.in_proj_z.weight"):
weight, scale = reorder_rows(weight, scale, head_v_dim)
elif name.endswith((".linear_attn.in_proj_a.weight", ".linear_attn.in_proj_b.weight")):
weight, scale = reorder_rows(weight, scale, 1)
elif name.endswith(".linear_attn.out_proj.weight"):
col_perm = self._reorder_v_heads(
torch.arange(num_v_heads * head_v_dim, dtype=torch.long).unsqueeze(0),
1, num_k_heads, num_v_per_k, head_v_dim,
).squeeze(0)
weight, scale = apply_col_perm(weight, scale, col_perm)
return weight, scale
def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
weight, scale = self._transform_nvfp4_weight(name, weight, scale)
super()._repack_nvfp4(name, weight, scale, scale2, input_scale)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
num_k_heads = self.hparams.get("linear_num_key_heads", 0)
num_v_heads = self.hparams.get("linear_num_value_heads", 0)
@ -5094,6 +5221,47 @@ class GPT2Model(TextModel):
yield from super().modify_tensors(data_torch, new_name, bid)
@ModelBase.register("RuGPT3XLForCausalLM")
class RuGPT3XLModel(TextModel):
model_arch = gguf.MODEL_ARCH.GPT2
_qkv_parts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# Fuse separate Q, K, V projections into a single QKV tensor
if ".self_attn.q_proj." in name or ".self_attn.k_proj." in name or ".self_attn.v_proj." in name:
suffix = "weight" if name.endswith(".weight") else "bias"
part = "q" if ".q_proj." in name else ("k" if ".k_proj." in name else "v")
key = f"{part}.{suffix}"
assert bid is not None
if self._qkv_parts is None:
self._qkv_parts = [{} for _ in range(self.block_count)]
self._qkv_parts[bid][key] = data_torch
q_key, k_key, v_key = f"q.{suffix}", f"k.{suffix}", f"v.{suffix}"
if all(k in self._qkv_parts[bid] for k in [q_key, k_key, v_key]):
q = self._qkv_parts[bid].pop(q_key)
k = self._qkv_parts[bid].pop(k_key)
v = self._qkv_parts[bid].pop(v_key)
data_torch = torch.cat([q, k, v], dim=0)
name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid, f".{suffix}")
logger.debug(f"Fused Q/K/V {suffix} for layer {bid} -> {name}")
else:
return
yield from super().modify_tensors(data_torch, name, bid)
def prepare_tensors(self):
super().prepare_tensors()
if self._qkv_parts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
parts = [f"({i}){k}" for i, d in enumerate(self._qkv_parts) for k in d.keys()]
if len(parts) > 0:
raise ValueError(f"Unprocessed Q/K/V parts: {parts}")
@ModelBase.register("PhiForCausalLM")
class Phi2Model(TextModel):
model_arch = gguf.MODEL_ARCH.PHI2
@ -6935,6 +7103,70 @@ class ConformerAudioModel(MmprojModel):
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("DeepseekOCRForCausalLM")
class DeepseekOCRVisionModel(MmprojModel):
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DEEPSEEKOCR)
# default values below are taken from HF tranformers code
self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
self.gguf_writer.add_vision_use_gelu(True)
# calculate proj_scale_factor (used by tinygemma3 test model)
image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
n_per_side = int(image_seq_length ** 0.5)
image_size = self.hparams["image_size"]
patch_size = self.hparams["patch_size"]
proj_scale_factor = (image_size // patch_size) // n_per_side
if proj_scale_factor > 0 and proj_scale_factor != 4:
# we only need to write this if it's not the default value
# in this case, we are converting a test model
self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
# @bluebread: there's no window_size in config but just add it here anyway
self.gguf_writer.add_vision_window_size(self.hparams.get("window_size", 14))
# SAM configuration
sam_hparams = hparams['sam']
self.gguf_writer.add_vision_sam_layers_count(sam_hparams['layers'])
self.gguf_writer.add_vision_sam_embedding_length(sam_hparams['width'])
self.gguf_writer.add_vision_sam_head_count(sam_hparams['heads'])
def get_vision_config(self) -> dict[str, Any]:
vision_config: dict[str, Any] | None = self.global_config.get("vision_config")
if not vision_config:
raise ValueError("DeepseekOCR model requires 'vision_config' in the model configuration, but it was not found")
vision_config['sam'] = vision_config['width']['sam_vit_b']
vision_config.update(vision_config['width']['clip-l-14-224'])
vision_config['hidden_size'] = vision_config['width']
vision_config['num_heads'] = vision_config['heads']
vision_config['intermediate_size'] = vision_config['heads'] * 4
return vision_config
def tensor_force_quant(self, name, new_name, bid, n_dims):
if ".embeddings." in name or 'pos_embed' in name:
return gguf.GGMLQuantizationType.F32
if ".rel_pos_h" in name or '.rel_pos_w' in name:
return gguf.GGMLQuantizationType.F32
if ".neck." in name or ".net_" in name:
return gguf.GGMLQuantizationType.F32
return super().tensor_force_quant(name, new_name, bid, n_dims)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# Only process vision-related tensors, skip language model tensors
# Vision components: sam_model, vision_model, projector, image_newline, view_seperator
# Language model components to skip: lm_head, embed_tokens, layers, norm
if name.startswith(("lm_head.", "model.embed_tokens.", "model.layers.", "model.norm.")):
return
if name.endswith("pos_embed") or name.endswith("rel_pos_h") or name.endswith("rel_pos_w"):
name += ".weight"
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Gemma3nForConditionalGeneration")
class Gemma3nVisionAudioModel(ConformerAudioModel):
has_audio_encoder = True
@ -8280,6 +8512,19 @@ class DeepseekV2Model(TextModel):
merge_expert = True
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
hparams: dict = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
self.origin_hf_arch = hparams.get('architectures', [None])[0]
# special handling for Deepseek OCR
if self.origin_hf_arch == "DeepseekOCRForCausalLM":
self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR
self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
self.gguf_writer.add_architecture()
# default jinja template
self.gguf_writer.add_chat_template("{% for m in messages %}{{m['content']}}{% endfor %}")
def set_vocab(self):
try:
self._set_vocab_gpt2()
@ -8335,9 +8580,15 @@ class DeepseekV2Model(TextModel):
raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
def set_gguf_parameters(self):
is_ocr = (self.model_arch == gguf.MODEL_ARCH.DEEPSEEK2OCR)
# note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group)
self.hparams["num_key_value_heads"] = 1
if is_ocr:
self.hparams['rope_theta'] = self.hparams.get('rope_theta', 10000.0)
else:
# note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group)
self.hparams["num_key_value_heads"] = 1
self.hparams['rms_norm_eps'] = self.hparams.get('rms_norm_eps', 1e-6)
super().set_gguf_parameters()
hparams = self.hparams
@ -8351,16 +8602,18 @@ class DeepseekV2Model(TextModel):
# Default: if no MoE, all layers are dense; if MoE, none are dense
first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0
self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
kv_lora_rank = hparams.get("kv_lora_rank", 512)
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
# note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
self.gguf_writer.add_key_length(hparams["kv_lora_rank"] + hparams["qk_rope_head_dim"])
self.gguf_writer.add_value_length(hparams["kv_lora_rank"])
self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
if not is_ocr:
self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
self.gguf_writer.add_key_length(kv_lora_rank + hparams["qk_rope_head_dim"])
self.gguf_writer.add_value_length(kv_lora_rank)
self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
# MoE parameters (required by C++ code for DEEPSEEK2 arch)
# For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length
@ -8392,8 +8645,15 @@ class DeepseekV2Model(TextModel):
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# skip vision tensors and remove "language_model." for Kimi-VL and Kimi-K2.5
if "vision_tower" in name or "multi_modal_projector" in name or "mm_projector" in name:
# skip vision tensors and remove "language_model." for Kimi-VL and Kimi-K2.5, and DeepSeek-OCR
if ("vision_tower" in name
or "multi_modal_projector" in name
or "mm_projector" in name
or "vision_model" in name
or "image_newline" in name
or "model.projector" in name
or "sam_model" in name
or "view_seperator" in name):
return
if name.startswith("siglip2.") or name.startswith("merger."):
return

2
convert_hf_to_gguf_update.py
View File

@ -154,6 +154,7 @@ models = [
{"name": "qwen35", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3.5-9B-Instruct", },
{"name": "joyai-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jdopensource/JoyAI-LLM-Flash", },
{"name": "kanana2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601", },
{"name": "f2llmv2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/codefuse-ai/F2LLM-v2-4B", },
]
# some models are known to be broken upstream, so we will skip them as exceptions
@ -177,6 +178,7 @@ pre_computed_hashes = [
{"name": "grok-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
# jina-v2-de variants
{"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},
{"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/evilfreelancer/ruGPT3XL", "chkhsh": "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4"},
]

132
docs/backend/CANN.md
View File

@ -42,12 +42,22 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
### Ascend NPU
**Verified devices**
You can retrieve your Ascend device IDs using the following command:
| Ascend NPU | Status |
|:-----------------------------:|:-------:|
| Atlas 300T A2 | Support |
| Atlas 300I Duo | Support |
```sh
lspci -n | grep -Eo '19e5:d[0-9a-f]{3}' | cut -d: -f2
```
**Devices**
| Device Id | Product Series | Product Models | Chip Model | Verified Status |
|:---------:|----------------|----------------|:----------:|:---------------:|
| d803 | Atlas A3 Train | | 910C | |
| d803 | Atlas A3 Infer | | 910C | |
| d802 | Atlas A2 Train | | 910B | |
| d802 | Atlas A2 Infer | Atlas 300I A2 | 910B | Support |
| d801 | Atlas Train | | 910 | |
| d500 | Atlas Infer | Atlas 300I Duo | 310P | Support |
*Notes:*
@ -57,6 +67,9 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
## Model Supports
<details>
<summary>Text-only</summary>
| Model Name | FP16 | Q4_0 | Q8_0 |
|:----------------------------|:-----:|:----:|:----:|
| Llama-2 | √ | √ | √ |
@ -118,8 +131,11 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
| Trillion-7B-preview | √ | √ | √ |
| Ling models | √ | √ | √ |
</details>
<details>
<summary>Multimodal</summary>
**Multimodal**
| Model Name | FP16 | Q4_0 | Q8_0 |
|:----------------------------|:-----:|:----:|:----:|
| LLaVA 1.5 models, LLaVA 1.6 models | x | x | x |
@ -134,15 +150,22 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
| GLM-EDGE | √ | √ | √ |
| Qwen2-VL | √ | √ | √ |
</details>
## DataType Supports
| DataType | Status |
|:----------------------:|:-------:|
| FP16 | Support |
| Q8_0 | Support |
| Q4_0 | Support |
| DataType | 910B | 310P |
|:----------------------:|:-------:|:-------:|
| FP16 | Support | Support |
| Q8_0 | Support | Partial |
| Q4_0 | Support | Partial |
| BF16 | Support | |
> **310P note**
> - `Q8_0`: data transform / buffer path is implemented, and `GET_ROWS` is supported, but quantized `MUL_MAT` / `MUL_MAT_ID` are not supported.
> - `Q4_0`: data transform / buffer path is implemented, but quantized `MUL_MAT` / `MUL_MAT_ID` are not supported.
## Docker
@ -160,7 +183,20 @@ npu-smi info
# Select the cards that you want to use, make sure these cards are not used by someone.
# Following using cards of device0.
docker run --name llamacpp --device /dev/davinci0 --device /dev/davinci_manager --device /dev/devmm_svm --device /dev/hisi_hdc -v /usr/local/dcmi:/usr/local/dcmi -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info -v /PATH_TO_YOUR_MODELS/:/app/models -it llama-cpp-cann -m /app/models/MODEL_PATH -ngl 32 -p "Building a website can be done in 10 simple steps:"
docker run --name llamacpp \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /PATH_TO_YOUR_MODELS/:/app/models \
-it llama-cpp-cann \
-m /app/models/MODEL_PATH \
-ngl 32 \
-p "Building a website can be done in 10 simple steps:"
```
*Notes:*
@ -171,69 +207,57 @@ docker run --name llamacpp --device /dev/davinci0 --device /dev/davinci_manager
### I. Setup Environment
1. **Install Ascend Driver and firmware**
1. **Configure Ascend user and group**
```sh
# create driver running user.
sudo groupadd -g HwHiAiUser
sudo groupadd HwHiAiUser
sudo useradd -g HwHiAiUser -d /home/HwHiAiUser -m HwHiAiUser -s /bin/bash
sudo usermod -aG HwHiAiUser $USER
# download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system
# and install driver.
sudo sh Ascend-hdk-910b-npu-driver_x.x.x_linux-{arch}.run --full --install-for-all
```
Once installed, run `npu-smi info` to check whether driver is installed successfully.
2. **Install dependencies**
**Ubuntu/Debian:**
```sh
+-------------------------------------------------------------------------------------------+
| npu-smi 24.1.rc2 Version: 24.1.rc2 |
+----------------------+---------------+----------------------------------------------------+
| NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page)|
| Chip | Bus-Id | AICore(%) Memory-Usage(MB) HBM-Usage(MB) |
+======================+===============+====================================================+
| 2 xxx | OK | 64.4 51 15 / 15 |
| 0 | 0000:01:00.0 | 0 1873 / 15077 0 / 32768 |
+======================+===============+====================================================+
| 5 xxx | OK | 64.0 52 15 / 15 |
| 0 | 0000:81:00.0 | 0 1874 / 15077 0 / 32768 |
+======================+===============+====================================================+
| No running processes found in NPU 2 |
+======================+===============+====================================================+
| No running processes found in NPU 5 |
+======================+===============+====================================================+
sudo apt-get update
sudo apt-get install -y gcc python3 python3-pip linux-headers-$(uname -r)
```
2. **Install Ascend Firmware**
**RHEL/CentOS:**
```sh
# download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system
# and install driver.
sudo sh Ascend-hdk-910b-npu-firmware_x.x.x.x.X.run --full
sudo yum makecache
sudo yum install -y gcc python3 python3-pip kernel-headers-$(uname -r) kernel-devel-$(uname -r)
```
If the following message appears, firmware is installed successfully.
3. **Install CANN (driver + toolkit)**
> The `Ascend-cann` package includes both the driver and toolkit.
> `$ARCH` can be `x86_64` or `aarch64`, `$CHIP` can be `910b` or `310p`.
```sh
Firmware package installed successfully!
wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.5.T63/Ascend-cann_8.5.0_linux-$ARCH.run
sudo bash ./Ascend-cann_8.5.0_linux-$ARCH.run --install
wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.5.T63/Ascend-cann-$CHIP-ops_8.5.0_linux-$ARCH.run
sudo bash ./Ascend-cann-$CHIP-ops_8.5.0_linux-$ARCH.run --install
```
4. **Verify installation**
3. **Install CANN toolkit and kernels**
CANN toolkit and kernels can be obtained from the official [CANN Toolkit](https://www.hiascend.com/zh/developer/download/community/result?module=cann) page.
Please download the corresponding version that satified your system. The minimum version required is 8.0.RC2.alpha002 and here is the install command.
```sh
pip3 install attrs numpy decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions
sh Ascend-cann-toolkit_8.0.RC2.alpha002_linux-aarch64.run --install
sh Ascend-cann-kernels-910b_8.0.RC2.alpha002_linux.run --install
npu-smi info
```
Set Ascend Variables:
If device information is displayed correctly, the driver is functioning properly.
```sh
echo "source ~/Ascend/ascend-toolkit/set_env.sh" >> ~/.bashrc
source ~/.bashrc
# Set environment variables (adjust path if needed)
source /usr/local/Ascend/cann/set_env.sh
python3 -c "import acl; print(acl.get_soc_name())"
```
Upon a successful installation, CANN is enabled for the available ascend devices.
If the command outputs the chip model, the installation was successful.
### II. Build llama.cpp

7
docs/multimodal.md
View File

@ -31,6 +31,13 @@ llama-server -m gemma-3-4b-it-Q4_K_M.gguf --mmproj mmproj-gemma-3-4b-it-Q4_K_M.g
llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload
```
> [!IMPORTANT]
>
> OCR models are trained with specific prompt and input structure, please refer to these discussions for more info:
> - PaddleOCR-VL: https://github.com/ggml-org/llama.cpp/pull/18825
> - GLM-OCR: https://github.com/ggml-org/llama.cpp/pull/19677
> - Deepseek-OCR: https://github.com/ggml-org/llama.cpp/pull/17400
## Pre-quantized models
These are ready-to-use models, most of them come with `Q4_K_M` quantization by default. They can be found at the Hugging Face page of the ggml-org: https://huggingface.co/collections/ggml-org/multimodal-ggufs-68244e01ff1f39e5bebeeedc

4
examples/llama.android/lib/src/main/cpp/ai_chat.cpp
View File

@ -365,13 +365,13 @@ Java_com_arm_aichat_internal_InferenceEngineImpl_processSystemPrompt(
const auto *system_prompt = env->GetStringUTFChars(jsystem_prompt, nullptr);
LOGd("%s: System prompt received: \n%s", __func__, system_prompt);
std::string formatted_system_prompt(system_prompt);
env->ReleaseStringUTFChars(jsystem_prompt, system_prompt);
// Format system prompt if applicable
const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
if (has_chat_template) {
formatted_system_prompt = chat_add_and_format(ROLE_SYSTEM, system_prompt);
}
env->ReleaseStringUTFChars(jsystem_prompt, system_prompt);
// Tokenize system prompt
const auto system_tokens = common_tokenize(g_context, formatted_system_prompt,
@ -414,13 +414,13 @@ Java_com_arm_aichat_internal_InferenceEngineImpl_processUserPrompt(
const auto *const user_prompt = env->GetStringUTFChars(juser_prompt, nullptr);
LOGd("%s: User prompt received: \n%s", __func__, user_prompt);
std::string formatted_user_prompt(user_prompt);
env->ReleaseStringUTFChars(juser_prompt, user_prompt);
// Format user prompt if applicable
const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
if (has_chat_template) {
formatted_user_prompt = chat_add_and_format(ROLE_USER, user_prompt);
}
env->ReleaseStringUTFChars(juser_prompt, user_prompt);
// Decode formatted user prompts
auto user_tokens = common_tokenize(g_context, formatted_user_prompt, has_chat_template, has_chat_template);

2
ggml/include/gguf.h
View File

@ -77,6 +77,7 @@ extern "C" {
};
GGML_API struct gguf_context * gguf_init_empty(void);
GGML_API struct gguf_context * gguf_init_from_file_ptr(FILE * file, struct gguf_init_params params);
GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
//GGML_API struct gguf_context * gguf_init_from_buffer(..);
@ -189,6 +190,7 @@ extern "C" {
//
// write the entire context to a binary file
GGML_API bool gguf_write_to_file_ptr(const struct gguf_context * ctx, FILE * file, bool only_meta);
GGML_API bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
// get the size in bytes of the meta data (header, kv pairs, tensor info) including padding

7
ggml/src/ggml-cpu/CMakeLists.txt
View File

@ -460,6 +460,10 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
endif()
if(NOT GGML_CPU_ALL_VARIANTS)
set(MARCH_STR "rv64gc")
if (GGML_RVV)
string(APPEND MARCH_STR "v")
endif()
if (GGML_RV_ZFH)
string(APPEND MARCH_STR "_zfh")
endif()
@ -467,7 +471,6 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
if (GGML_XTHEADVECTOR)
string(APPEND MARCH_STR "_xtheadvector")
elseif (GGML_RVV)
string(APPEND MARCH_STR "_v")
if (GGML_RV_ZVFH)
string(APPEND MARCH_STR "_zvfh")
endif()
@ -475,12 +478,14 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
string(APPEND MARCH_STR "_zvfbfwma")
endif()
endif()
if (GGML_RV_ZICBOP)
string(APPEND MARCH_STR "_zicbop")
endif()
if (GGML_RV_ZIHINTPAUSE)
string(APPEND MARCH_STR "_zihintpause")
endif()
list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d)
else()
# Begin with the lowest baseline

8
ggml/src/ggml-cpu/ggml-cpu.c
View File

@ -2871,8 +2871,12 @@ struct ggml_cplan ggml_graph_plan(
const int64_t ne11 = node->src[1]->ne[1]; // H
const int64_t ne12 = node->src[1]->ne[2]; // Channels In
cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03;
cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12;
GGML_ASSERT(node->src[0]->type == GGML_TYPE_F16 || node->src[0]->type == GGML_TYPE_F32);
GGML_ASSERT(node->src[1]->type == GGML_TYPE_F32);
cur += ggml_type_size(node->src[0]->type) * ne00 * ne01 * ne02 * ne03;
cur += ggml_type_size(node->src[0]->type) * ne10 * ne11 * ne12;
} break;
case GGML_OP_TOP_K:
{

69
ggml/src/ggml-cpu/ops.cpp
View File

@ -6923,16 +6923,15 @@ void ggml_compute_forward_conv_3d(
ggml_compute_forward_conv_3d_impl(params, src0, src1, dst, src0->type);
}
// ggml_compute_forward_conv_transpose_2d
void ggml_compute_forward_conv_transpose_2d(
const ggml_compute_params * params,
ggml_tensor * dst) {
template <typename kernel_t>
static void ggml_compute_forward_conv_transpose_2d_impl(
const ggml_compute_params * params,
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@ -6943,7 +6942,7 @@ void ggml_compute_forward_conv_transpose_2d(
const int nk = ne00*ne01*ne02*ne03;
GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
GGML_ASSERT(nb00 == ggml_type_size(src0->type));
GGML_ASSERT(nb10 == sizeof(float));
if (ith == 0) {
@ -6951,12 +6950,12 @@ void ggml_compute_forward_conv_transpose_2d(
// permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
{
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
kernel_t * const wdata = (kernel_t *) params->wdata + 0;
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02);
ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03;
const kernel_t * const src = (kernel_t *)((char *) src0->data + i03*nb03 + i02*nb02);
kernel_t * dst_data = wdata + i02*ne01*ne00*ne03;
for (int64_t i01 = 0; i01 < ne01; i01++) {
for (int64_t i00 = 0; i00 < ne00; i00++) {
dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00];
@ -6968,13 +6967,17 @@ void ggml_compute_forward_conv_transpose_2d(
// permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh)
{
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk;
kernel_t * const wdata = (kernel_t *) params->wdata + nk;
for (int i12 = 0; i12 < ne12; i12++) {
for (int i11 = 0; i11 < ne11; i11++) {
const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11);
ggml_fp16_t * dst_data = wdata + i11*ne10*ne12;
kernel_t * dst_data = wdata + i11*ne10*ne12;
for (int i10 = 0; i10 < ne10; i10++) {
dst_data[i10*ne12 + i12] = GGML_CPU_FP32_TO_FP16(src[i10]);
if constexpr (std::is_same_v<kernel_t, ggml_fp16_t>) {
dst_data[i10*ne12 + i12] = GGML_CPU_FP32_TO_FP16(src[i10]);
} else {
dst_data[i10*ne12 + i12] = src[i10];
}
}
}
}
@ -6996,21 +6999,27 @@ void ggml_compute_forward_conv_transpose_2d(
const int ip0 = dp*ith;
const int ip1 = MIN(ip0 + dp, np);
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
ggml_fp16_t * const wdata_src = wdata + nk;
kernel_t * const wdata = (kernel_t *) params->wdata + 0;
kernel_t * const wdata_src = wdata + nk;
for (int i2 = ip0; i2 < ip1; i2++) { // Cout
float * dst_data = (float *)((char *) dst->data + i2*nb2);
ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03;
kernel_t * wdata_kernel = wdata + i2*ne01*ne00*ne03;
for (int i11 = 0; i11 < ne11; i11++) {
for (int i10 = 0; i10 < ne10; i10++) {
const int i1n = i11*ne10*ne12 + i10*ne12;
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
float v = 0;
ggml_vec_dot_f16(ne03, &v, 0,
wdata_src + i1n, 0,
wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
if constexpr (std::is_same_v<kernel_t, ggml_fp16_t>) {
ggml_vec_dot_f16(ne03, &v, 0,
wdata_src + i1n, 0,
wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
} else {
ggml_vec_dot_f32(ne03, &v, 0,
wdata_src + i1n, 0,
wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
}
dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v;
}
}
@ -7019,6 +7028,28 @@ void ggml_compute_forward_conv_transpose_2d(
}
}
void ggml_compute_forward_conv_transpose_2d(
const ggml_compute_params * params,
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F16:
{
ggml_compute_forward_conv_transpose_2d_impl<ggml_fp16_t>(params, dst);
} break;
case GGML_TYPE_F32:
{
ggml_compute_forward_conv_transpose_2d_impl<float>(params, dst);
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
// ggml_compute_forward_conv_2d_dw
struct ggml_conv_2d_dw_params {

23
ggml/src/ggml-cuda/common.cuh
View File

@ -799,6 +799,22 @@ static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
#endif // CUDART_VERSION >= 12050
}
static __device__ __forceinline__ float ggml_cuda_ue4m3_to_fp32(uint8_t x) {
#ifdef FP8_AVAILABLE
const uint32_t bits = x * (x != 0x7F && x != 0xFF); // Convert NaN to 0.0f to match CPU implementation.
#if defined(GGML_USE_HIP) && defined(CDNA3)
// ROCm dose not support fp8 in software on devices with fp8 hardware,
// but CDNA3 supports only e4m3_fnuz (no inf).
const __hip_fp8_e4m3_fnuz xf = *reinterpret_cast<const __hip_fp8_e4m3_fnuz *>(&bits);
#else
const __nv_fp8_e4m3 xf = *reinterpret_cast<const __nv_fp8_e4m3 *>(&bits);
#endif // defined(GGML_USE_HIP) && defined(GGML_USE_HIP)
return static_cast<float>(xf) / 2;
#else
NO_DEVICE_CODE;
#endif // FP8_AVAILABLE
}
__device__ __forceinline__ uint8_t ggml_cuda_float_to_fp4_e2m1(float x, float e) {
const uint8_t sign_bit = (x < 0.0f) << 3;
float ax = fabsf(x) * e;
@ -931,6 +947,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_MXFP4> {
static constexpr int qi = QI_MXFP4;
};
template<>
struct ggml_cuda_type_traits<GGML_TYPE_NVFP4> {
static constexpr int qk = QK_NVFP4;
static constexpr int qr = QR_NVFP4;
static constexpr int qi = QI_NVFP4;
};
template<>
struct ggml_cuda_type_traits<GGML_TYPE_Q2_K> {
static constexpr int qk = QK_K;

64
ggml/src/ggml-cuda/conv2d-transpose.cu
View File

@ -1,12 +1,20 @@
#include <algorithm>
#include "conv2d-transpose.cuh"
#include "ggml.h"
#include "convert.cuh"
__global__ void conv2d_transpose_kernel(const float * __restrict__ input, const half * __restrict__ kernel,
float * __restrict__ output, const int in_w, const int in_h, const int out_w,
const int out_h, const int kernel_w, const int kernel_h, const int stride,
const int c_in, const int c_out, const int batches) {
template <typename kernel_t>
static __global__ void conv2d_transpose_kernel(const float * __restrict__ input,
const kernel_t * __restrict__ kernel,
float * __restrict__ output,
const int in_w,
const int in_h,
const int out_w,
const int out_h,
const int kernel_w,
const int kernel_h,
const int stride,
const int c_in,
const int c_out,
const int batches) {
const int global_idx = blockIdx.x * blockDim.x + threadIdx.x;
const int total_elements = out_w * out_h * c_out * batches;
@ -26,24 +34,32 @@ __global__ void conv2d_transpose_kernel(const float * __restrict__ input, const
for (int c_in_idx = 0; c_in_idx < c_in; c_in_idx++) {
for (int kh = 0; kh < kernel_h; ++kh) {
int in_y = out_y_idx - kh;
if (in_y < 0 || in_y % stride) continue;
if (in_y < 0 || in_y % stride) {
continue;
}
in_y /= stride;
if (in_y >= in_h) continue;
if (in_y >= in_h) {
continue;
}
for (int kw = 0; kw < kernel_w; ++kw) {
int in_x = out_x_idx - kw;
if (in_x < 0 || in_x % stride) continue;
if (in_x < 0 || in_x % stride) {
continue;
}
in_x /= stride;
if (in_x >= in_w) continue;
if (in_x >= in_w) {
continue;
}
const int input_idx = (in_w * in_h * c_in) * n_idx + (in_w * in_h) * c_in_idx + (in_w) *in_y + in_x;
const int kernel_idx =
(kernel_h * kernel_w * c_out) * c_in_idx + (kernel_h * kernel_w) * c_idx + (kernel_w) *kh + kw;
float input_val = input[input_idx];
half kern_val = kernel[kernel_idx];
float input_val = input[input_idx];
kernel_t kern_val = kernel[kernel_idx];
accumulator += input_val * (float) kern_val;
accumulator += input_val * ggml_cuda_cast<float>(kern_val);
}
}
}
@ -56,11 +72,12 @@ void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor
const ggml_tensor * kernel = dst->src[0];
const ggml_tensor * input = dst->src[1];
GGML_ASSERT(kernel->type == GGML_TYPE_F16 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
GGML_ASSERT(input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
const float * input_data = (const float *) input->data;
float * output_data = (float *) dst->data;
const half * kernel_data = (const half *) kernel->data;
const void * kernel_data = kernel->data;
const int input_w = input->ne[0];
const int input_h = input->ne[1];
@ -82,10 +99,17 @@ void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor
GGML_ASSERT(ggml_is_contiguous(kernel));
GGML_ASSERT(ggml_is_contiguous(dst));
const int total = (output_w * output_h * channels_out * batches);
const int total = output_w * output_h * channels_out * batches;
const int blocks = (total + CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE - 1) / CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE;
conv2d_transpose_kernel<<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
input_data, kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w, kernel_h, stride,
channels_in, channels_out, batches);
if (kernel->type == GGML_TYPE_F16) {
conv2d_transpose_kernel<half><<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
input_data, (const half *) kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w,
kernel_h, stride, channels_in, channels_out, batches);
} else {
conv2d_transpose_kernel<float><<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
input_data, (const float *) kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w,
kernel_h, stride, channels_in, channels_out, batches);
}
}

1
ggml/src/ggml-cuda/conv2d-transpose.cuh
View File

@ -1,4 +1,5 @@
#include "common.cuh"
#define CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE 256
void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

43
ggml/src/ggml-cuda/convert.cu
View File

@ -617,6 +617,45 @@ static void dequantize_row_mxfp4_cuda(const void * vx, dst_t * y, const int64_t
dequantize_block_mxfp4<<<nb, 32, 0, stream>>>(vx, y);
}
template <typename dst_t>
static __global__ void dequantize_block_nvfp4(
const void * __restrict__ vx,
dst_t * __restrict__ yy,
const int64_t ne) {
const int64_t i = blockIdx.x;
const int tid = threadIdx.x;
const int64_t base = i * QK_NVFP4;
if (base >= ne) {
return;
}
const block_nvfp4 * x = (const block_nvfp4 *) vx;
const block_nvfp4 & xb = x[i];
const int sub = tid / (QK_NVFP4_SUB / 2);
const int j = tid % (QK_NVFP4_SUB / 2);
const float d = ggml_cuda_ue4m3_to_fp32(xb.d[sub]);
const uint8_t q = xb.qs[sub * (QK_NVFP4_SUB / 2) + j];
const int64_t y0 = base + sub * QK_NVFP4_SUB + j;
const int64_t y1 = y0 + QK_NVFP4_SUB / 2;
yy[y0] = ggml_cuda_cast<dst_t>(d * kvalues_mxfp4[q & 0x0F]);
yy[y1] = ggml_cuda_cast<dst_t>(d * kvalues_mxfp4[q >> 4]);
}
template <typename dst_t>
static void dequantize_row_nvfp4_cuda(
const void * vx,
dst_t * y,
const int64_t k,
cudaStream_t stream) {
GGML_ASSERT(k % QK_NVFP4 == 0);
const int nb = k / QK_NVFP4;
dequantize_block_nvfp4<<<nb, 32, 0, stream>>>(vx, y, k);
}
template <typename src_t, typename dst_t>
static __global__ void convert_unary(
const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01,
@ -715,6 +754,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_MXFP4:
return dequantize_row_mxfp4_cuda;
case GGML_TYPE_NVFP4:
return dequantize_row_nvfp4_cuda;
case GGML_TYPE_F32:
return convert_unary_cont_cuda<float>;
case GGML_TYPE_BF16:
@ -766,6 +807,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_MXFP4:
return dequantize_row_mxfp4_cuda;
case GGML_TYPE_NVFP4:
return dequantize_row_nvfp4_cuda;
case GGML_TYPE_F16:
return convert_unary_cont_cuda<half>;
case GGML_TYPE_BF16:

10
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@ -1297,7 +1297,12 @@ static void ggml_cuda_op_mul_mat_cublas(
const bool supports_bf16 = GGML_CUDA_CC_IS_NVIDIA(cc) || GGML_CUDA_CC_IS_AMD(cc) ||
(GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_QY2);
const bool use_fp16 = (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT;
const bool use_fp16 =
src0->type != GGML_TYPE_NVFP4 &&
(src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
ggml_is_contiguous(src0) &&
row_diff == src0->ne[1] &&
dst->op_params[0] == GGML_PREC_DEFAULT;
if (supports_bf16 && src0->type == GGML_TYPE_BF16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1]) {
ggml_cuda_pool_alloc<nv_bfloat16> src1_as_bf16(ctx.pool(id));
@ -4781,6 +4786,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_MXFP4:
#ifdef FP8_AVAILABLE
case GGML_TYPE_NVFP4:
#endif // FP8_AVAILABLE
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:

8
ggml/src/ggml-cuda/mmvq.cu
View File

@ -15,6 +15,7 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
case GGML_TYPE_Q5_1: return vec_dot_q5_1_q8_1;
case GGML_TYPE_Q8_0: return vec_dot_q8_0_q8_1;
case GGML_TYPE_MXFP4: return vec_dot_mxfp4_q8_1;
case GGML_TYPE_NVFP4: return vec_dot_nvfp4_q8_1;
case GGML_TYPE_Q2_K: return vec_dot_q2_K_q8_1;
case GGML_TYPE_Q3_K: return vec_dot_q3_K_q8_1;
case GGML_TYPE_Q4_K: return vec_dot_q4_K_q8_1;
@ -41,6 +42,7 @@ static constexpr __host__ __device__ int get_vdr_mmvq(ggml_type type) {
case GGML_TYPE_Q5_1: return VDR_Q5_1_Q8_1_MMVQ;
case GGML_TYPE_Q8_0: return VDR_Q8_0_Q8_1_MMVQ;
case GGML_TYPE_MXFP4: return VDR_MXFP4_Q8_1_MMVQ;
case GGML_TYPE_NVFP4: return VDR_NVFP4_Q8_1_MMVQ;
case GGML_TYPE_Q2_K: return VDR_Q2_K_Q8_1_MMVQ;
case GGML_TYPE_Q3_K: return VDR_Q3_K_Q8_1_MMVQ;
case GGML_TYPE_Q4_K: return VDR_Q4_K_Q8_1_MMVQ;
@ -626,6 +628,12 @@ static void mul_mat_vec_q_switch_type(
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_NVFP4:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_NVFP4>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
break;
case GGML_TYPE_Q2_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q2_K>
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,

32
ggml/src/ggml-cuda/vecdotq.cuh
View File

@ -322,6 +322,38 @@ static __device__ __forceinline__ float vec_dot_mxfp4_q8_1(
return d * sumi;
}
#define VDR_NVFP4_Q8_1_MMVQ 4
#define VDR_NVFP4_Q8_1_MMQ 8
static __device__ __forceinline__ float vec_dot_nvfp4_q8_1(
const void * __restrict__ vbq,
const block_q8_1 * __restrict__ bq8_1,
const int32_t & kbx,
const int32_t & iqs) {
const block_nvfp4 * bq4 = (const block_nvfp4 *) vbq + kbx;
float sum = 0.0f;
#pragma unroll
for (int i = 0; i < VDR_NVFP4_Q8_1_MMVQ/2; i++) {
const int32_t iqs0 = iqs + 2*i;
const int32_t iqs1 = iqs0 + 1;
const int32_t is = iqs0 >> 1;
const int2 v0 = get_int_from_table_16(get_int_b4(bq4->qs, iqs0), kvalues_mxfp4);
const int2 v1 = get_int_from_table_16(get_int_b4(bq4->qs, iqs1), kvalues_mxfp4);
const block_q8_1 * bq8 = bq8_1 + (is >> 1);
const int32_t i8 = ((is & 1) << 2);
int sumi = ggml_cuda_dp4a(v0.x, get_int_b4(bq8->qs, i8 + 0), 0);
sumi = ggml_cuda_dp4a(v0.y, get_int_b4(bq8->qs, i8 + 2), sumi);
sumi = ggml_cuda_dp4a(v1.x, get_int_b4(bq8->qs, i8 + 1), sumi);
sumi = ggml_cuda_dp4a(v1.y, get_int_b4(bq8->qs, i8 + 3), sumi);
const float d = ggml_cuda_ue4m3_to_fp32(bq4->d[is]) * __low2float(bq8->ds);
sum += d * float(sumi);
}
return sum;
}
#define VDR_Q2_K_Q8_1_MMVQ 1
#define VDR_Q2_K_Q8_1_MMQ 4

5
ggml/src/ggml-cuda/vendors/cuda.h vendored
View File

@ -6,9 +6,10 @@
#include <cuda_bf16.h>
#include <cuda_fp16.h>
#if CUDART_VERSION >= 12050
#if CUDART_VERSION >= 11080
#include <cuda_fp8.h>
#endif // CUDART_VERSION >= 12050
#define FP8_AVAILABLE
#endif // CUDART_VERSION >= 11080
#if CUDART_VERSION >= 12080
#include <cuda_fp4.h>

6
ggml/src/ggml-cuda/vendors/hip.h vendored
View File

@ -235,6 +235,12 @@
typedef __hip_bfloat16 nv_bfloat16;
typedef __hip_bfloat162 nv_bfloat162;
#if HIP_VERSION >= 60200000
#include <hip/hip_fp8.h>
typedef __hip_fp8_e4m3 __nv_fp8_e4m3;
#define FP8_AVAILABLE
#endif // HIP_VERSION >= 60200000
typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
static __device__ __forceinline__ int __vsubss4(const int a, const int b) {

1
ggml/src/ggml-impl.h
View File

@ -773,6 +773,5 @@ inline bool ggml_check_edges(const struct ggml_cgraph * cgraph,
// expose GGUF internals for test code
GGML_API size_t gguf_type_size(enum gguf_type type);
GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params);
GGML_API void gguf_write_to_buf(const struct gguf_context * ctx, std::vector<int8_t> & buf, bool only_meta);
#endif // __cplusplus

4
ggml/src/ggml-metal/ggml-metal-device.m
View File

@ -690,7 +690,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
" auto tB = B.slice((int)tgid.x, 0); \n"
" \n"
" matmul2d< \n"
" matmul2d_descriptor(8, 8, dynamic_extent), \n"
" matmul2d_descriptor(16, 16, dynamic_extent), \n"
" execution_simdgroups<4>> mm; \n"
" \n"
" auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>(); \n"
@ -740,7 +740,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
" auto tB = B.slice((int)tgid.x, 0); \n"
" \n"
" matmul2d< \n"
" matmul2d_descriptor(8, 8, dynamic_extent), \n"
" matmul2d_descriptor(16, 16, dynamic_extent), \n"
" execution_simdgroups<4>> mm; \n"
" \n"
" auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>(); \n"

26
ggml/src/ggml-opencl/ggml-opencl.cpp
View File

@ -394,6 +394,9 @@ struct ggml_backend_opencl_context {
bool fp16_support;
bool has_vector_subgroup_broadcast;
bool disable_fusion;
bool adreno_has_large_buffer;
bool adreno_use_large_buffer;
ggml_cl_compiler_version adreno_cl_compiler_version;
int adreno_wave_size;
@ -787,6 +790,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
" -cl-mad-enable -cl-unsafe-math-optimizations"
" -cl-finite-math-only -cl-fast-relaxed-math";
if (backend_ctx->adreno_use_large_buffer) {
compile_opts += " -qcom-enable-large-buffer ";
}
GGML_LOG_INFO("ggml_opencl: loading OpenCL kernels");
// add
@ -3020,6 +3027,8 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
// Check if ext_buffer contains cl_khr_fp16
backend_ctx->fp16_support = strstr(ext_buffer, "cl_khr_fp16") != NULL;
GGML_LOG_INFO("ggml_opencl: device FP16 support: %s\n", backend_ctx->fp16_support ? "true" : "false");
// check Adreno large buffer support
backend_ctx->adreno_has_large_buffer = strstr(ext_buffer, "cl_qcom_large_buffer") != NULL;
// fp16 is required
if (!backend_ctx->fp16_support) {
@ -3086,6 +3095,18 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
GGML_LOG_INFO("ggml_opencl: using kernels optimized for Adreno (GGML_OPENCL_USE_ADRENO_KERNELS)\n");
#endif // GGML_OPENCL_USE_ADRENO_KERNELS
// determine whether to use large buffer for Adreno
backend_ctx->adreno_use_large_buffer = getenv("GGML_OPENCL_ADRENO_USE_LARGE_BUFFER") != nullptr &&
backend_ctx->gpu_family == GPU_FAMILY::ADRENO;
if (backend_ctx->adreno_use_large_buffer) {
if (!backend_ctx->adreno_has_large_buffer) {
GGML_LOG_INFO("ggml_opencl: Adreno large buffer requested but not supported by driver, will use regular buffer\n");
backend_ctx->adreno_use_large_buffer = false;
} else {
GGML_LOG_INFO("ggml_opencl: Adreno large buffer enabled\n");
}
}
cl_int err;
// A local ref of cl_context for convenience
@ -5660,6 +5681,11 @@ static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_b
cl_int err;
cl_mem mem = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, size, NULL, &err);
if (err != CL_SUCCESS && backend_ctx->adreno_use_large_buffer) {
cl_mem_properties props[] = { 0x41A6 /* CL_LARGE_BUFFER_QCOM */, 1, 0 };
mem = clCreateBufferWithProperties(backend_ctx->context, props, CL_MEM_READ_WRITE, size, NULL, &err);
}
if (err != CL_SUCCESS) {
GGML_LOG_INFO("%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0);
return nullptr;

11
ggml/src/ggml-rpc/ggml-rpc.cpp
View File

@ -589,8 +589,10 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) {
ggml_backend_buffer_t buffer = tensor->buffer;
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
result.buffer = ctx != nullptr ? ctx->remote_ptr : 0;
result.data = reinterpret_cast<uint64_t>(tensor->data);
} else {
result.buffer = 0;
result.data = 0;
}
for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) {
result.ne[i] = tensor->ne[i];
@ -606,7 +608,6 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) {
}
result.view_src = reinterpret_cast<uint64_t>(tensor->view_src);
result.view_offs = tensor->view_offs;
result.data = reinterpret_cast<uint64_t>(tensor->data);
// Avoid sending uninitialized data over the wire
memset(result.name, 0, sizeof(result.name));
@ -1443,9 +1444,11 @@ ggml_tensor * rpc_server::create_node(uint64_t id,
const rpc_tensor * tensor = it_ptr->second;
struct ggml_tensor * result = deserialize_tensor(ctx, tensor);
if (result == nullptr || result->buffer == nullptr) {
GGML_LOG_ERROR("[%s] invalid tensor: null %s (id=%" PRIu64 ")\n",
__func__, result == nullptr ? "tensor" : "buffer", id);
if (result == nullptr) {
return nullptr;
}
if (result->buffer == nullptr && result->data != nullptr) {
GGML_LOG_ERROR("[%s] invalid data ptr", __func__);
return nullptr;
}
tensor_map[id] = result;

2
ggml/src/ggml-sycl/add-id.cpp
View File

@ -56,7 +56,7 @@ void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
float* dst_d = (float*)dst->data;
const unsigned int max_work_group_size = ggml_sycl_info().max_work_group_sizes[ctx.device];
assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0);
GGML_ASSERT(max_work_group_size % (WARP_SIZE * WARP_SIZE) == 0);
int threads = std::min((unsigned int)ne00, max_work_group_size); // cols

2
ggml/src/ggml.c
View File

@ -4962,6 +4962,7 @@ static struct ggml_tensor * ggml_interpolate_impl(
GGML_ASSERT((mode & 0xFF) < GGML_SCALE_MODE_COUNT);
// TODO: implement antialias for modes other than bilinear
GGML_ASSERT(!(mode & GGML_SCALE_FLAG_ANTIALIAS) || (mode & 0xFF) == GGML_SCALE_MODE_BILINEAR);
GGML_ASSERT(a->type == GGML_TYPE_F32);
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
@ -5307,6 +5308,7 @@ struct ggml_tensor * ggml_flash_attn_ext(
GGML_ASSERT(q->ne[3] == v->ne[3]);
if (mask) {
GGML_ASSERT(mask->type == GGML_TYPE_F16);
GGML_ASSERT(ggml_is_contiguous(mask));
//GGML_ASSERT(ggml_can_repeat_rows(mask, qk));

33
ggml/src/gguf.cpp
View File

@ -394,7 +394,11 @@ bool gguf_read_emplace_helper(const struct gguf_reader & gr, std::vector<struct
return true;
}
struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params) {
struct gguf_context * gguf_init_from_file_ptr(FILE * file, struct gguf_init_params params) {
if (!file) {
return nullptr;
}
const struct gguf_reader gr(file);
struct gguf_context * ctx = new gguf_context;
@ -848,7 +852,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
return nullptr;
}
struct gguf_context * result = gguf_init_from_file_impl(file, params);
struct gguf_context * result = gguf_init_from_file_ptr(file, params);
fclose(file);
return result;
}
@ -1508,6 +1512,19 @@ void gguf_write_to_buf(const struct gguf_context * ctx, std::vector<int8_t> & bu
gguf_write_out(ctx, gw, only_meta);
}
bool gguf_write_to_file_ptr(const struct gguf_context * ctx, FILE * file, bool only_meta) {
GGML_ASSERT(file);
try {
gguf_writer_file gw(file);
gguf_write_out(ctx, gw, only_meta);
} catch (const std::runtime_error& ex) {
GGML_LOG_ERROR("%s: failed to write GGUF data: %s\n", __func__, ex.what());
return false;
}
return true;
}
bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
FILE * file = ggml_fopen(fname, "wb");
@ -1516,17 +1533,13 @@ bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, boo
return false;
}
try {
gguf_writer_file gw(file);
gguf_write_out(ctx, gw, only_meta);
} catch (const std::runtime_error& ex) {
GGML_LOG_ERROR("%s: failed to write GGUF data into '%s': %s\n", __func__, fname, ex.what());
fclose(file);
return false;
const bool success = gguf_write_to_file_ptr(ctx, file, only_meta);
if (!success) {
GGML_LOG_ERROR("%s: failed to write GGUF data into '%s'\n", __func__, fname);
}
fclose(file);
return true;
return success;
}
size_t gguf_get_meta_size(const struct gguf_context * ctx) {

93
gguf-py/gguf/constants.py
View File

@ -326,6 +326,11 @@ class Keys:
class Projector:
SCALE_FACTOR = "clip.vision.projector.scale_factor"
class SAM:
BLOCK_COUNT = "clip.vision.sam.block_count"
EMBEDDING_LENGTH = "clip.vision.sam.embedding_length"
HEAD_COUNT = "clip.vision.sam.head_count"
class ClipAudio:
PROJECTOR_TYPE = "clip.audio.projector_type" # for mixed modality models
NUM_MEL_BINS = "clip.audio.num_mel_bins"
@ -434,6 +439,7 @@ class MODEL_ARCH(IntEnum):
ARCTIC = auto()
DEEPSEEK = auto()
DEEPSEEK2 = auto()
DEEPSEEK2OCR = auto()
CHATGLM = auto()
GLM4 = auto()
GLM4_MOE = auto()
@ -755,6 +761,22 @@ class MODEL_TENSOR(IntEnum):
V_MM_GATE = auto() # cogvlm
V_TOK_BOI = auto() # cogvlm
V_TOK_EOI = auto() # cogvlm
V_SAM_POS_EMBD = auto() # Deepseek-OCR
V_SAM_PATCH_EMBD = auto() # Deepseek-OCR
V_SAM_PRE_NORM = auto() # Deepseek-OCR
V_SAM_POST_NORM = auto() # Deepseek-OCR
V_SAM_ATTN_POS_H = auto() # Deepseek-OCR
V_SAM_ATTN_POS_W = auto() # Deepseek-OCR
V_SAM_ATTN_QKV = auto() # Deepseek-OCR
V_SAM_ATTN_OUT = auto() # Deepseek-OCR
V_SAM_MLP_LIN_1 = auto() # Deepseek-OCR
V_SAM_MLP_LIN_2 = auto() # Deepseek-OCR
V_SAM_NECK = auto() # Deepseek-OCR
V_SAM_NET_2 = auto() # Deepseek-OCR
V_SAM_NET_3 = auto() # Deepseek-OCR
V_ENC_EMBD_IMGNL = auto() # Deepseek-OCR
V_ENC_EMBD_VSEP = auto() # Deepseek-OCR
# audio (mtmd)
A_ENC_EMBD_POS = auto()
A_ENC_EMBD_NORM = auto()
@ -880,6 +902,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.ARCTIC: "arctic",
MODEL_ARCH.DEEPSEEK: "deepseek",
MODEL_ARCH.DEEPSEEK2: "deepseek2",
MODEL_ARCH.DEEPSEEK2OCR: "deepseek2-ocr",
MODEL_ARCH.CHATGLM: "chatglm",
MODEL_ARCH.GLM4: "glm4",
MODEL_ARCH.GLM4_MOE: "glm4moe",
@ -1199,6 +1222,22 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.V_MM_GATE: "mm.gate",
MODEL_TENSOR.V_TOK_BOI: "v.boi",
MODEL_TENSOR.V_TOK_EOI: "v.eoi",
# DeepSeek-OCR SAM
MODEL_TENSOR.V_SAM_POS_EMBD: "v.sam.pos_embd",
MODEL_TENSOR.V_SAM_PATCH_EMBD: "v.sam.patch_embd",
MODEL_TENSOR.V_SAM_PRE_NORM: "v.sam.blk.{bid}.pre_ln",
MODEL_TENSOR.V_SAM_POST_NORM: "v.sam.blk.{bid}.post_ln",
MODEL_TENSOR.V_SAM_ATTN_POS_H: "v.sam.blk.{bid}.attn.pos_h",
MODEL_TENSOR.V_SAM_ATTN_POS_W: "v.sam.blk.{bid}.attn.pos_w",
MODEL_TENSOR.V_SAM_ATTN_QKV: "v.sam.blk.{bid}.attn.qkv",
MODEL_TENSOR.V_SAM_ATTN_OUT: "v.sam.blk.{bid}.attn.out",
MODEL_TENSOR.V_SAM_MLP_LIN_1: "v.sam.blk.{bid}.mlp.lin1",
MODEL_TENSOR.V_SAM_MLP_LIN_2: "v.sam.blk.{bid}.mlp.lin2",
MODEL_TENSOR.V_SAM_NECK: "v.sam.neck.{bid}",
MODEL_TENSOR.V_SAM_NET_2: "v.sam.net_2",
MODEL_TENSOR.V_SAM_NET_3: "v.sam.net_3",
MODEL_TENSOR.V_ENC_EMBD_IMGNL: "v.image_newline", # Deepseek-OCR
MODEL_TENSOR.V_ENC_EMBD_VSEP: "v.view_seperator", # Deepseek-OCR
# audio (mtmd)
# note: all audio tensor names must use prefix "a." or "mm.a."
MODEL_TENSOR.A_ENC_EMBD_POS: "a.position_embd",
@ -1265,6 +1304,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.V_ENC_EMBD_PATCH,
MODEL_TENSOR.V_ENC_EMBD_NORM,
MODEL_TENSOR.V_ENC_EMBD_POS,
MODEL_TENSOR.V_ENC_EMBD_IMGNL,
MODEL_TENSOR.V_ENC_EMBD_VSEP,
MODEL_TENSOR.V_ENC_INPUT_NORM,
MODEL_TENSOR.V_ENC_ATTN_QKV,
MODEL_TENSOR.V_ENC_ATTN_Q,
@ -1317,6 +1358,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.V_MM_GATE,
MODEL_TENSOR.V_TOK_BOI,
MODEL_TENSOR.V_TOK_EOI,
MODEL_TENSOR.V_SAM_POS_EMBD,
MODEL_TENSOR.V_SAM_PATCH_EMBD,
MODEL_TENSOR.V_SAM_PRE_NORM,
MODEL_TENSOR.V_SAM_POST_NORM,
MODEL_TENSOR.V_SAM_ATTN_POS_H,
MODEL_TENSOR.V_SAM_ATTN_POS_W,
MODEL_TENSOR.V_SAM_ATTN_QKV,
MODEL_TENSOR.V_SAM_ATTN_OUT,
MODEL_TENSOR.V_SAM_MLP_LIN_1,
MODEL_TENSOR.V_SAM_MLP_LIN_2,
MODEL_TENSOR.V_SAM_NECK,
MODEL_TENSOR.V_SAM_NET_2,
MODEL_TENSOR.V_SAM_NET_3,
# audio
MODEL_TENSOR.A_ENC_EMBD_POS,
MODEL_TENSOR.A_ENC_EMBD_NORM,
@ -2612,7 +2666,41 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ATTN_Q_B,
MODEL_TENSOR.ATTN_KV_A_MQA,
MODEL_TENSOR.ATTN_KV_B,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_K_B,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_V_B,
MODEL_TENSOR.ATTN_Q_A_NORM,
MODEL_TENSOR.ATTN_KV_A_NORM,
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,
MODEL_TENSOR.FFN_GATE_SHEXP,
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
MODEL_TENSOR.FFN_EXP_PROBS_B,
],
MODEL_ARCH.DEEPSEEK2OCR: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_A,
MODEL_TENSOR.ATTN_Q_B,
MODEL_TENSOR.ATTN_KV_A_MQA,
MODEL_TENSOR.ATTN_KV_B,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_K_B,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_V_B,
MODEL_TENSOR.ATTN_Q_A_NORM,
MODEL_TENSOR.ATTN_KV_A_NORM,
@ -3741,6 +3829,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.DEEPSEEK2OCR: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.CHATGLM: [
MODEL_TENSOR.ROPE_FREQS,
],
@ -3938,6 +4030,7 @@ class VisionProjectorType:
LIGHTONOCR = "lightonocr"
COGVLM = "cogvlm"
JANUS_PRO = "janus_pro"
DEEPSEEKOCR = "deepseekocr"
LFM2A = "lfm2a" # audio
MUSIC_FLAMINGO = "musicflamingo" # audio
GLM4V = "glm4v"

9
gguf-py/gguf/gguf_writer.py
View File

@ -1218,6 +1218,15 @@ class GGUFWriter:
def add_vision_window_size(self, value: int) -> None:
self.add_uint32(Keys.ClipVision.WINDOW_SIZE, value)
def add_vision_sam_layers_count(self, value: int) -> None:
self.add_uint32(Keys.ClipVision.SAM.BLOCK_COUNT, value)
def add_vision_sam_embedding_length(self, value: int) -> None:
self.add_uint32(Keys.ClipVision.SAM.EMBEDDING_LENGTH, value)
def add_vision_sam_head_count(self, value: int) -> None:
self.add_uint32(Keys.ClipVision.SAM.HEAD_COUNT, value)
# audio models
def add_clip_audio_projector_type(self, value: str) -> None:

76
gguf-py/gguf/tensor_mapping.py
View File

@ -63,6 +63,7 @@ class TensorNameMap:
"transformer.wpe", # gpt2
"embeddings.position_embeddings", # bert
"wpe", # gpt2
"model.embed_positions", # rugpt3xl
),
# Output
@ -1344,6 +1345,7 @@ class TensorNameMap:
MODEL_TENSOR.V_MMPROJ_FC: (
"model.connector.modality_projection.proj", # SmolVLM
"model.vision.linear_proj.linear_proj", # cogvlm
"model.projector.layers", # Deepseek-OCR
"visual.merger.proj", # glm4v
),
@ -1364,6 +1366,7 @@ class TensorNameMap:
"vision_model.class_embedding", # llama 4
"model.vision.patch_embedding.cls_embedding", # cogvlm
"vision_model.radio_model.model.patch_generator.cls_token.token", # Nemotron Nano v2 VL
"model.vision_model.embeddings.class_embedding", # Deepseek-OCR
),
MODEL_TENSOR.V_ENC_EMBD_PATCH: (
@ -1377,6 +1380,7 @@ class TensorNameMap:
"visual.patch_embed.proj", # qwen2vl
"vision_tower.patch_embed.proj", # kimi-vl
"model.vision.patch_embedding.proj", # cogvlm
"model.vision_model.embeddings.patch_embedding", # Deepseek-OCR CLIP
"siglip2.vision_model.embeddings.patch_embedding",
"vision_model.radio_model.model.patch_generator.embedder", # Nemotron Nano v2 VL
),
@ -1398,10 +1402,19 @@ class TensorNameMap:
"vision_model.radio_model.model.patch_generator.pos_embed", # Nemotron Nano v2 VL
),
MODEL_TENSOR.V_ENC_EMBD_IMGNL: (
"model.image_newline", # Deepseek-OCR
),
MODEL_TENSOR.V_ENC_EMBD_VSEP: (
"model.view_seperator", # Deepseek-OCR
),
MODEL_TENSOR.V_ENC_ATTN_QKV: (
"visual.blocks.{bid}.attn.qkv", # qwen3vl
"model.vision.transformer.layers.{bid}.attention.query_key_value", # cogvlm
"vision_tower.encoder.blocks.{bid}.wqkv", # Kimi-K2.5
"model.vision_model.transformer.layers.{bid}.self_attn.qkv_proj", # Deepseek-OCR CLIP
"vision_tower.encoder.blocks.{bid}.wqkv" # Kimi-K2.5
"vision_model.radio_model.model.blocks.{bid}.attn.qkv", # Nemotron Nano v2 VL
),
@ -1416,6 +1429,7 @@ class TensorNameMap:
"visual.blocks.{bid}.attn.q", # qwen2vl, generated
"vision_tower.encoder.blocks.{bid}.wq", # kimi-vl, generated
"siglip2.vision_model.encoder.layers.{bid}.self_attn.q_proj", # youtuvl
"model.vision_model.transformer.layers.{bid}.self_attn.q_proj", # Deepseek-OCR CLIP, generated
),
MODEL_TENSOR.V_ENC_ATTN_Q_NORM: (
@ -1434,6 +1448,7 @@ class TensorNameMap:
"vision_encoder.transformer.layers.{bid}.attention.wk", # pixtral
"visual.blocks.{bid}.attn.k", # qwen2vl, generated
"vision_tower.encoder.blocks.{bid}.wk", # kimi-vl, generated
"model.vision_model.transformer.layers.{bid}.self_attn.k_proj", # Deepseek-OCR CLIP, generated
"siglip2.vision_model.encoder.layers.{bid}.self_attn.k_proj",
),
@ -1454,6 +1469,7 @@ class TensorNameMap:
"visual.blocks.{bid}.attn.v", # qwen2vl, generated
"vision_tower.encoder.blocks.{bid}.wv", # kimi-vl, generated
"siglip2.vision_model.encoder.layers.{bid}.self_attn.v_proj",
"model.vision_model.transformer.layers.{bid}.self_attn.v_proj", # Deepseek-OCR CLIP, generated
),
MODEL_TENSOR.V_ENC_INPUT_NORM: (
@ -1468,6 +1484,7 @@ class TensorNameMap:
"visual.blocks.{bid}.norm1", # qwen2vl
"vision_tower.encoder.blocks.{bid}.norm0", # kimi-vl (norm0/norm1)
"model.vision.transformer.layers.{bid}.input_layernorm", # cogvlm
"model.vision_model.transformer.layers.{bid}.layer_norm1", # Deepseek-OCR CLIP
"siglip2.vision_model.encoder.layers.{bid}.layer_norm1",
"vision_model.radio_model.model.blocks.{bid}.norm1", # Nemotron Nano v2 VL
),
@ -1485,6 +1502,7 @@ class TensorNameMap:
"visual.blocks.{bid}.attn.proj", # qwen2vl
"vision_tower.encoder.blocks.{bid}.wo", # kimi-vl
"model.vision.transformer.layers.{bid}.attention.dense", # cogvlm
"model.vision_model.transformer.layers.{bid}.self_attn.out_proj", # Deepseek-OCR CLIP
"siglip2.vision_model.encoder.layers.{bid}.self_attn.out_proj", # youtuvl
"vision_model.radio_model.model.blocks.{bid}.attn.proj", # Nemotron Nano v2 VL
),
@ -1501,6 +1519,7 @@ class TensorNameMap:
"visual.blocks.{bid}.norm2", # qwen2vl
"vision_tower.encoder.blocks.{bid}.norm1", # kimi-vl (norm0/norm1)
"model.vision.transformer.layers.{bid}.post_attention_layernorm", # cogvlm
"model.vision_model.transformer.layers.{bid}.layer_norm2", # Deepseek-OCR CLIP
"siglip2.vision_model.encoder.layers.{bid}.layer_norm2",
"vision_model.radio_model.model.blocks.{bid}.norm2", # Nemotron Nano v2 VL
),
@ -1517,6 +1536,7 @@ class TensorNameMap:
"visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl
"visual.blocks.{bid}.mlp.linear_fc1", # qwen3vl
"vision_tower.encoder.blocks.{bid}.mlp.fc0", # kimi-vl (fc0/fc1)
"model.vision_model.transformer.layers.{bid}.mlp.fc1", # Deepseek-OCR CLIP
"model.vision.transformer.layers.{bid}.mlp.fc1", # cogvlm
"siglip2.vision_model.encoder.layers.{bid}.mlp.fc1",
"vision_model.radio_model.model.blocks.{bid}.mlp.fc1", # Nemotron Nano v2 VL
@ -1541,6 +1561,7 @@ class TensorNameMap:
"visual.blocks.{bid}.mlp.linear_fc2", # qwen3vl
"vision_tower.encoder.blocks.{bid}.mlp.fc1", # kimi-vl (fc0/fc1)
"model.vision.transformer.layers.{bid}.mlp.fc2", # cogvlm
"model.vision_model.transformer.layers.{bid}.mlp.fc2", # Deepseek-OCR CLIP
"siglip2.vision_model.encoder.layers.{bid}.mlp.fc2",
"vision_model.radio_model.model.blocks.{bid}.mlp.fc2", # Nemotron Nano v2 VL
),
@ -1560,6 +1581,7 @@ class TensorNameMap:
"vision_tower.ln_pre", # pixtral-hf
"vision_encoder.ln_pre", # pixtral
"vision_model.layernorm_pre", # llama4
"model.vision_model.pre_layrnorm", # Deepseek-OCR CLIP
),
MODEL_TENSOR.V_POST_NORM: (
@ -1662,6 +1684,58 @@ class TensorNameMap:
"model.visual.deepstack_merger_list.{bid}.linear_fc2", # deepstack in qwen3vl
),
MODEL_TENSOR.V_SAM_POS_EMBD: (
"model.sam_model.pos_embed",
),
MODEL_TENSOR.V_SAM_PATCH_EMBD: (
"model.sam_model.patch_embed.proj",
),
MODEL_TENSOR.V_SAM_PRE_NORM: (
"model.sam_model.blocks.{bid}.norm1", # deepstack in qwen3vl
),
MODEL_TENSOR.V_SAM_POST_NORM: (
"model.sam_model.blocks.{bid}.norm2", # deepstack in qwen3vl
),
MODEL_TENSOR.V_SAM_ATTN_POS_H: (
"model.sam_model.blocks.{bid}.attn.rel_pos_h",
),
MODEL_TENSOR.V_SAM_ATTN_POS_W: (
"model.sam_model.blocks.{bid}.attn.rel_pos_w",
),
MODEL_TENSOR.V_SAM_ATTN_QKV: (
"model.sam_model.blocks.{bid}.attn.qkv",
),
MODEL_TENSOR.V_SAM_ATTN_OUT: (
"model.sam_model.blocks.{bid}.attn.proj",
),
MODEL_TENSOR.V_SAM_MLP_LIN_1: (
"model.sam_model.blocks.{bid}.mlp.lin1",
),
MODEL_TENSOR.V_SAM_MLP_LIN_2: (
"model.sam_model.blocks.{bid}.mlp.lin2",
),
MODEL_TENSOR.V_SAM_NECK: (
"model.sam_model.neck.{bid}",
),
MODEL_TENSOR.V_SAM_NET_2: (
"model.sam_model.net_2",
),
MODEL_TENSOR.V_SAM_NET_3: (
"model.sam_model.net_3",
),
MODEL_TENSOR.V_MM_POST_FC_NORM: (
"model.vision.linear_proj.norm1", # cogvlm
),

5
include/llama.h
View File

@ -465,6 +465,11 @@ extern "C" {
const char * path_model,
struct llama_model_params params);
// Load a model from an open FILE pointer
LLAMA_API struct llama_model * llama_model_load_from_file_ptr(
FILE * file,
struct llama_model_params params);
// Load a model from multiple splits (support custom naming scheme)
// The paths must be in the correct order
LLAMA_API struct llama_model * llama_model_load_from_splits(

67
scripts/hip/gcn-cdna-vgpr-check.py
View File

@ -2,37 +2,51 @@
import sys
from collections import defaultdict
import re
def parse_log_file(filepath):
"""Parse log file and extract function VGPR usage."""
import re
functions = defaultdict(lambda: {'vgprs': 0, 'spill': 0, 'location': ''})
func_stack = []
try:
with open(filepath, 'r') as f:
content = f.read()
# Find all function entries with VGPR usage including location
pattern = r'([^:]+:\d+):.*?Function Name: (\S+).*?VGPRs: (\d+).*?VGPRs Spill: (\d+)'
matches = re.findall(pattern, content, re.DOTALL)
for line in f:
# Match function name lines
func_match = re.search(r'remark: ([^:]+):(\d+):\d+: Function Name: (\S+)', line)
if func_match:
location = func_match.group(1) + ':' + func_match.group(2)
func_name = func_match.group(3)
# Extract just the filename and line number
parts = location.split('/')
short_location = parts[-1] if len(parts) > 0 else location
functions[func_name]['location'] = short_location
# Push function onto stack with its location
func_stack.append({'name': func_name, 'location': location})
continue
for location, func_name, vgprs, spill in matches:
functions[func_name]['vgprs'] = int(vgprs)
functions[func_name]['spill'] = int(spill)
# Extract just the filename and line number
parts = location.split('/')
if len(parts) > 0:
short_location = parts[-1] # Get last part (filename)
# Check if there's a line number after filename
if ':' in short_location:
functions[func_name]['location'] = short_location
else:
functions[func_name]['location'] = location
else:
functions[func_name]['location'] = location
# Match VGPR usage lines (only if we have functions in stack)
vgpr_match = re.search(r'remark: ([^:]+):(\d+):\d+:\s+VGPRs: (\d+)', line)
if vgpr_match:
location = vgpr_match.group(1) + ':' + vgpr_match.group(2)
# Find the most recent function with matching location
for i in range(len(func_stack) - 1, -1, -1):
if func_stack[i]['location'] == location:
functions[func_stack[i]['name']]['vgprs'] = int(vgpr_match.group(3))
break
continue
spill_match = re.search(r'remark: ([^:]+):(\d+):\d+:\s+VGPRs Spill: (\d+)', line)
if spill_match:
location = spill_match.group(1) + ':' + spill_match.group(2)
# Find the most recent function with matching location
for i in range(len(func_stack) - 1, -1, -1):
if func_stack[i]['location'] == location:
functions[func_stack[i]['name']]['spill'] = int(spill_match.group(3))
break
continue
except FileNotFoundError:
print(f"Error: File {filepath} not found", file=sys.stderr) # noqa: NP100
print(f"Error: File {filepath} not found", file=sys.stderr) # noqa: NP100
sys.exit(1)
return functions
@ -40,7 +54,7 @@ def parse_log_file(filepath):
def main():
if len(sys.argv) < 2:
print("Usage: ./vgpr_check.py <log_file>", file=sys.stderr) # noqa: NP100
print("Usage: ./vgpr_check.py <log_file>", file=sys.stderr) # noqa: NP100
sys.exit(1)
log_file = sys.argv[1]
@ -123,6 +137,9 @@ def main():
'_ZL18flash_attn_ext_f16ILi128ELi128ELi32ELi2ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
'_ZL18flash_attn_ext_f16ILi128ELi128ELi4ELi8ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
'_ZL18flash_attn_ext_f16ILi96ELi96ELi4ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
'_ZL18flash_attn_ext_vecILi128ELi2EL9ggml_type2ELS0_2ELb0EEvPKcS2_S2_S2_S2_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS6_IjLj3EEiiiiiiiiiiiliiliiiiil',
'_ZL9mul_mat_qIL9ggml_type10ELi16ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
'_ZL9mul_mat_qIL9ggml_type12ELi128ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii'
}
functions = parse_log_file(log_file)
@ -134,7 +151,7 @@ def main():
total_vgprs = int(data['vgprs']) + int(data['spill'])
if total_vgprs > 256 and func_name in ignored and func_name not in printed_ignored:
location = data.get('location', log_file)
print(f"{location}: {func_name} - Total VGPRs: {total_vgprs} ({data['vgprs']} + {data['spill']}) [IGNORED]") # noqa: NP100
print(f"{location}: {func_name} - Total VGPRs: {total_vgprs} ({data['vgprs']} + {data['spill']}) [IGNORED]") # noqa: NP100
printed_ignored.add(func_name)
# Then print new functions with issues in red
@ -146,7 +163,7 @@ def main():
# Print in red if not ignored
color_code = "\033[91m" if func_name not in ignored else ""
reset_code = "\033[0m" if func_name not in ignored else ""
print(f"{color_code}{location}: {func_name} - Total VGPRs: {total_vgprs} ({data['vgprs']} + {data['spill']}) {status}{reset_code}") # noqa: NP100
print(f"{color_code}{location}: {func_name} - Total VGPRs: {total_vgprs} ({data['vgprs']} + {data['spill']}) {status}{reset_code}") # noqa: NP100
if func_name not in ignored:
found_issues = True

12
scripts/snapdragon/windows/run-bench.ps1
View File

@ -20,6 +20,14 @@ if ($null -ne $env:V) {
$env:GGML_HEXAGON_VERBOSE=$env:V
}
if ($null -ne $env:E) {
$env:GGML_HEXAGON_EXPERIMENTAL=$env:E
}
if ($null -ne $env:PROF) {
$env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1
}
if ($null -ne $env:OPMASK) {
$env:GGML_HEXAGON_OPMASK=$env:OPMASK
}
@ -32,6 +40,10 @@ if ($null -ne $env:NDEV) {
$env:GGML_HEXAGON_NDEV=$env:NDEV
}
if ($null -ne $env:HB) {
$env:GGML_HEXAGON_HOSTBUF=$env:HB
}
$env:ADSP_LIBRARY_PATH="$basedir\lib"
& "$basedir\bin\llama-bench.exe" `

6
scripts/snapdragon/windows/run-cli.ps1
View File

@ -44,10 +44,14 @@ if ($null -ne $env:NDEV) {
$env:GGML_HEXAGON_NDEV=$env:NDEV
}
if ($null -ne $env:HB) {
$env:GGML_HEXAGON_HOSTBUF=$env:HB
}
$env:ADSP_LIBRARY_PATH="$basedir\lib"
& "$basedir\bin\llama-cli.exe" `
--no-mmap -m $basedir\..\..\gguf\$model `
--poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
--ctx-size 8192 --ubatch-size 128 -fa on `
--ctx-size 8192 --ubatch-size 256 -fa on `
-ngl 99 --device $device $cli_opts

6
scripts/snapdragon/windows/run-completion.ps1
View File

@ -44,10 +44,14 @@ if ($null -ne $env:NDEV) {
$env:GGML_HEXAGON_NDEV=$env:NDEV
}
if ($null -ne $env:HB) {
$env:GGML_HEXAGON_HOSTBUF=$env:HB
}
$env:ADSP_LIBRARY_PATH="$basedir\lib"
& "$basedir\bin\llama-completion.exe" `
--no-mmap -m $basedir\..\..\gguf\$model `
--poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
--ctx-size 8192 --batch-size 128 -fa on `
--ctx-size 8192 --batch-size 256 -fa on `
-ngl 99 -no-cnv --device $device $cli_opts

74
scripts/snapdragon/windows/run-mtmd.ps1 Normal file
View File

@ -0,0 +1,74 @@
#!/usr/bin/env pwsh
# Basedir on device
$basedir=".\pkg-snapdragon"
$cli_opts=$args
$model="gemma-3-4b-it-Q4_0.gguf"
if ($null -ne $env:M) {
$model=$env:M
}
$mmproj="mmproj-F16.gguf"
if ($null -ne $env:MMPROJ) {
$mmproj=$env:MMPROJ
}
$image=""
if ($null -ne $env:IMG) {
$image=$env:IMG
}
$device="HTP0"
if ($null -ne $env:D) {
$device=$env:D
}
if ($null -ne $env:V) {
$env:GGML_HEXAGON_VERBOSE=$env:V
}
# Default experimental to 1
$env:GGML_HEXAGON_EXPERIMENTAL=1
if ($null -ne $env:E) {
$env:GGML_HEXAGON_EXPERIMENTAL=$env:E
}
if ($null -ne $env:SCHED) {
$env:GGML_SCHED_DEBUG=$env:SCHED; $cli_opts="$cli_opts -v"
}
if ($null -ne $env:PROF) {
$env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1
}
if ($null -ne $env:OPMASK) {
$env:GGML_HEXAGON_OPMASK=$env:OPMASK
}
if ($null -ne $env:NHVX) {
$env:GGML_HEXAGON_NHVX=$env:NHVX
}
if ($null -ne $env:NDEV) {
$env:GGML_HEXAGON_NDEV=$env:NDEV
}
if ($null -ne $env:HB) {
$env:GGML_HEXAGON_HOSTBUF=$env:HB
}
if ($null -ne $env:MTMD_DEVICE) {
$env:MTMD_BACKEND_DEVICE=$env:MTMD_DEVICE
}
$env:ADSP_LIBRARY_PATH="$basedir\lib"
& "$basedir\bin\llama-mtmd-cli.exe" `
--no-mmap -m $basedir\..\..\gguf\$model `
--mmproj $basedir\..\..\gguf\$mmproj `
--image $basedir\..\..\gguf\$image `
--poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
--ctx-size 8192 --ubatch-size 256 -fa on `
-ngl 99 --device $device -v $cli_opts

4
scripts/snapdragon/windows/run-tool.ps1
View File

@ -50,6 +50,10 @@ if ($null -ne $env:NDEV) {
$env:GGML_HEXAGON_NDEV=$env:NDEV
}
if ($null -ne $env:HB) {
$env:GGML_HEXAGON_HOSTBUF=$env:HB
}
$env:ADSP_LIBRARY_PATH="$basedir\lib"
& "$basedir\bin\$tool" `

65
src/llama-arch.cpp
View File

@ -73,6 +73,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_ARCTIC, "arctic" },
{ LLM_ARCH_DEEPSEEK, "deepseek" },
{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
{ LLM_ARCH_DEEPSEEK2OCR, "deepseek2-ocr" },
{ LLM_ARCH_CHATGLM, "chatglm" },
{ LLM_ARCH_GLM4, "glm4" },
{ LLM_ARCH_GLM4_MOE, "glm4moe" },
@ -544,6 +545,10 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
case LLM_ARCH_CLIP:
return {};
case LLM_ARCH_LLAMA:
case LLM_ARCH_REFACT:
case LLM_ARCH_MINICPM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_DECI:
case LLM_ARCH_MISTRAL3:
case LLM_ARCH_LLAMA_EMBED:
@ -744,11 +749,9 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
};
case LLM_ARCH_REFACT:
case LLM_ARCH_QWEN2:
case LLM_ARCH_QWEN2VL:
case LLM_ARCH_INTERNLM2:
case LLM_ARCH_GRANITE:
case LLM_ARCH_ERNIE4_5:
case LLM_ARCH_PADDLEOCR:
case LLM_ARCH_SMOLLM3:
@ -759,6 +762,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
@ -1232,29 +1236,6 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_UP,
};
case LLM_ARCH_MINICPM:
return {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ROPE_FACTORS_LONG,
LLM_TENSOR_ROPE_FACTORS_SHORT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
LLM_TENSOR_ATTN_V,
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_ROT_EMBD,
LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_GATE,
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_UP,
LLM_TENSOR_FFN_GATE_EXP,
LLM_TENSOR_FFN_DOWN_EXP,
LLM_TENSOR_FFN_UP_EXP,
};
case LLM_ARCH_MINICPM3:
return {
LLM_TENSOR_TOKEN_EMBD,
@ -1442,6 +1423,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
@ -1590,6 +1572,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_FFN_UP_SHEXP,
};
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_DEEPSEEK2OCR:
case LLM_ARCH_MISTRAL4:
return {
LLM_TENSOR_TOKEN_EMBD,
@ -1598,6 +1581,8 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_Q_A_NORM,
LLM_TENSOR_ATTN_KV_A_NORM,
LLM_TENSOR_ATTN_K, // deepseek-ocr
LLM_TENSOR_ATTN_V, // deepseek-ocr
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_Q_A,
LLM_TENSOR_ATTN_Q_B,
@ -1657,7 +1642,9 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_QKV,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
LLM_TENSOR_ATTN_V,
@ -2061,30 +2048,12 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_UP,
};
case LLM_ARCH_GRANITE_MOE:
return {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
LLM_TENSOR_ATTN_V,
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_GATE_EXPS,
LLM_TENSOR_FFN_DOWN_EXPS,
LLM_TENSOR_FFN_UP_EXPS,
LLM_TENSOR_FFN_GATE_SHEXP,
LLM_TENSOR_FFN_DOWN_SHEXP,
LLM_TENSOR_FFN_UP_SHEXP,
};
case LLM_ARCH_GRANITE_HYBRID:
return {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_SSM_IN,
LLM_TENSOR_SSM_CONV1D,
@ -2412,6 +2381,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_QKV,
LLM_TENSOR_ATTN_OUT,
@ -2789,7 +2759,12 @@ std::string LLM_TN_IMPL::str() const {
}
if (model_tensors.find(tensor) == model_tensors.end()) {
return LLM_TENSOR_NAMES.at(tensor);
const char * name = LLM_TENSOR_NAMES.at(tensor);
if (suffix != nullptr || bid != -1 || xid != -1) {
LLAMA_LOG_WARN("%s: cannot properly format tensor name %s with suffix=%s bid=%d xid=%d\n",
__func__, name, suffix, bid, xid);
}
return name;
}
std::string name = ::format(LLM_TENSOR_NAMES.at(tensor), bid, xid);

1
src/llama-arch.h
View File

@ -77,6 +77,7 @@ enum llm_arch {
LLM_ARCH_ARCTIC,
LLM_ARCH_DEEPSEEK,
LLM_ARCH_DEEPSEEK2,
LLM_ARCH_DEEPSEEK2OCR,
LLM_ARCH_CHATGLM,
LLM_ARCH_GLM4,
LLM_ARCH_GLM4_MOE,

6
src/llama-chat.cpp
View File

@ -49,6 +49,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
{ "deepseek", LLM_CHAT_TEMPLATE_DEEPSEEK },
{ "deepseek2", LLM_CHAT_TEMPLATE_DEEPSEEK_2 },
{ "deepseek3", LLM_CHAT_TEMPLATE_DEEPSEEK_3 },
{ "deepseek-ocr", LLM_CHAT_TEMPLATE_DEEPSEEK_OCR },
{ "command-r", LLM_CHAT_TEMPLATE_COMMAND_R },
{ "llama3", LLM_CHAT_TEMPLATE_LLAMA_3 },
{ "chatglm3", LLM_CHAT_TEMPLATE_CHATGLM_3 },
@ -548,6 +549,11 @@ int32_t llm_chat_apply_template(
if (add_ass) {
ss << LU8("<｜Assistant｜>");
}
} else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_OCR) {
for (auto message : chat) {
// no template
ss << message->content;
}
} else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) {
// ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
// EXAONE-3.0-7.8B-Instruct

1
src/llama-chat.h
View File

@ -28,6 +28,7 @@ enum llm_chat_template {
LLM_CHAT_TEMPLATE_DEEPSEEK,
LLM_CHAT_TEMPLATE_DEEPSEEK_2,
LLM_CHAT_TEMPLATE_DEEPSEEK_3,
LLM_CHAT_TEMPLATE_DEEPSEEK_OCR,
LLM_CHAT_TEMPLATE_COMMAND_R,
LLM_CHAT_TEMPLATE_LLAMA_3,
LLM_CHAT_TEMPLATE_CHATGLM_3,

2
src/llama-graph.cpp
View File

@ -1516,7 +1516,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
if (!weight_before_ffn) {
experts = ggml_mul(ctx0, experts, weights);
cb(cur, "ffn_moe_weighted", il);
cb(experts, "ffn_moe_weighted", il);
}
ggml_tensor * cur_experts[LLAMA_MAX_EXPERTS] = { nullptr };

1
src/llama-kv-cache.cpp
View File

@ -1561,7 +1561,6 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
// ref: https://github.com/ggml-org/llama.cpp/pull/13870
? LLAMA_ROPE_TYPE_NEOX
: hparams.rope_type;
ggml_tensor * tmp;
if (ggml_is_quantized(cur->type)) {

23
src/llama-mmap.cpp
View File

@ -86,6 +86,14 @@ struct llama_file::impl {
seek(0, SEEK_SET);
}
impl(FILE * file) : owns_fp(false) {
fp = file;
fp_win32 = (HANDLE) _get_osfhandle(_fileno(fp));
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
size_t tell() const {
LARGE_INTEGER li;
li.QuadPart = 0;
@ -159,7 +167,7 @@ struct llama_file::impl {
}
~impl() {
if (fp) {
if (fp && owns_fp) {
std::fclose(fp);
}
}
@ -209,6 +217,13 @@ struct llama_file::impl {
seek(0, SEEK_SET);
}
impl(FILE * file) : fname("(file*)"), owns_fp(false) {
fp = file;
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
size_t tell() const {
if (fd == -1) {
long ret = std::ftell(fp);
@ -353,7 +368,7 @@ struct llama_file::impl {
~impl() {
if (fd != -1) {
close(fd);
} else {
} else if (owns_fp) {
std::fclose(fp);
}
}
@ -369,10 +384,14 @@ struct llama_file::impl {
FILE * fp{};
size_t size{};
bool owns_fp = true;
};
llama_file::llama_file(const char * fname, const char * mode, const bool use_direct_io) :
pimpl(std::make_unique<impl>(fname, mode, use_direct_io)) {}
llama_file::llama_file(FILE * file) : pimpl(std::make_unique<impl>(file)) {}
llama_file::~llama_file() = default;
size_t llama_file::tell() const { return pimpl->tell(); }

1
src/llama-mmap.h
View File

@ -15,6 +15,7 @@ using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
struct llama_file {
llama_file(const char * fname, const char * mode, bool use_direct_io = false);
llama_file(FILE * file);
~llama_file();
size_t tell() const;

33
src/llama-model-loader.cpp
View File

@ -511,6 +511,7 @@ llama_model_loader::llama_model_loader(
void * set_tensor_data_ud,
const std::string & fname,
std::vector<std::string> & splits,
FILE * file,
bool use_mmap,
bool use_direct_io,
bool check_tensors,
@ -658,6 +659,36 @@ llama_model_loader::llama_model_loader(
LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1);
}
} else if (file != nullptr) {
struct ggml_context * ctx = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx,
};
metadata_ptr.reset(gguf_init_from_file_ptr(file, params));
metadata = metadata_ptr.get();
if (metadata == nullptr) {
throw std::runtime_error(format("%s: failed to load model from file pointer", __func__));
}
get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
llm_kv = LLM_KV(llm_arch_from_string(arch_name));
files.emplace_back(new llama_file(file));
contexts.emplace_back(ctx);
// Save tensors data offset info of the main file.
for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
std::string tensor_name = std::string(cur->name);
// make sure there is no duplicated tensor names
if (weights_map.find(tensor_name) != weights_map.end()) {
throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
}
n_elements += ggml_nelements(cur);
n_bytes += ggml_nbytes(cur);
weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, metadata, cur));
}
} else {
get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
llm_kv = LLM_KV(llm_arch_from_string(arch_name));
@ -669,7 +700,7 @@ llama_model_loader::llama_model_loader(
fver = (enum llama_fver) gguf_get_version(metadata);
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
__func__, n_kv, n_tensors, fname.empty() ? "(file*)" : fname.c_str(), llama_file_version_name(fver));
// determine file type based on the number of tensors for each quantization and print meta data
// TODO: make optional

1
src/llama-model-loader.h
View File

@ -125,6 +125,7 @@ struct llama_model_loader {
void * set_tensor_data_ud,
const std::string & fname,
std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme
FILE * file,
bool use_mmap,
bool use_direct_io,
bool check_tensors,

127
src/llama-model-saver.cpp
View File

@ -1,7 +1,9 @@
#include "llama-model-saver.h"
#include "ggml.h"
#include "gguf.h"
#include "llama-arch.h"
#include "llama.h"
#include "llama-hparams.h"
#include "llama-model.h"
@ -10,8 +12,33 @@
#include <cstdint>
#include <string>
bool llama_model_saver_supports_arch(llm_arch arch) {
switch (arch) {
case LLM_ARCH_QWEN3NEXT:
case LLM_ARCH_QWEN35:
case LLM_ARCH_QWEN35MOE:
case LLM_ARCH_PLAMO3:
case LLM_ARCH_GEMMA3:
case LLM_ARCH_GEMMA3N:
case LLM_ARCH_COHERE2:
case LLM_ARCH_OLMO2:
case LLM_ARCH_BITNET:
case LLM_ARCH_T5:
case LLM_ARCH_EXAONE_MOE:
case LLM_ARCH_AFMOE:
case LLM_ARCH_APERTUS:
case LLM_ARCH_MIMO2:
case LLM_ARCH_STEP35:
return false;
default:
return true;
}
}
llama_model_saver::llama_model_saver(const struct llama_model * model) :
gguf_ctx(gguf_init_empty()), gguf_ctx_owned(true), model(model), llm_kv(model->arch) {}
gguf_ctx(gguf_init_empty()), gguf_ctx_owned(true), model(model), llm_kv(model->arch) {
GGML_ASSERT(llama_model_saver_supports_arch(model->arch));
}
llama_model_saver::llama_model_saver(enum llm_arch arch, struct gguf_context * gguf_ctx) :
gguf_ctx(gguf_ctx == nullptr ? gguf_init_empty() : gguf_ctx), gguf_ctx_owned(gguf_ctx == nullptr), model(nullptr), llm_kv(arch) {}
@ -105,7 +132,10 @@ void llama_model_saver::add_tensor(const struct ggml_tensor * tensor) {
return;
}
if (gguf_find_tensor(gguf_ctx, tensor->name) >= 0) {
GGML_ASSERT(std::string(tensor->name) == "rope_freqs.weight"); // FIXME
const std::string tensor_name = tensor->name;
GGML_ASSERT(
tensor_name == "rope_freqs.weight" || tensor_name == "rope_factors_long.weight" ||
tensor_name == "rope_factors_short.weight"); // FIXME
return;
}
gguf_add_tensor(gguf_ctx, tensor);
@ -127,6 +157,7 @@ void llama_model_saver::add_kv_from_model() {
tokens[id] = token_data.text;
scores[id] = token_data.score;
// FIXME should this be treated as flags?
switch(token_data.attr) {
case LLAMA_TOKEN_ATTR_UNKNOWN: token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN; break;
case LLAMA_TOKEN_ATTR_UNUSED: token_types[id] = LLAMA_TOKEN_TYPE_UNUSED; break;
@ -134,6 +165,9 @@ void llama_model_saver::add_kv_from_model() {
case LLAMA_TOKEN_ATTR_CONTROL: token_types[id] = LLAMA_TOKEN_TYPE_CONTROL; break;
case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break;
case LLAMA_TOKEN_ATTR_BYTE: token_types[id] = LLAMA_TOKEN_TYPE_BYTE; break;
// case LLAMA_TOKEN_ATTR_NORMALIZED: ???
// case LLAMA_TOKEN_ATTR_LSTRIP: ???
// case LLAMA_TOKEN_ATTR_RSTRIP: ???
case LLAMA_TOKEN_ATTR_UNDEFINED:
default: token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED; break;
}
@ -144,6 +178,19 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_GENERAL_ARCHITECTURE, model->arch_name());
// add_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION, ???);
// add_kv(LLM_KV_GENERAL_ALIGNMENT, ???);
// add_kv(LLM_KV_GENERAL_FILE_TYPE, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_SEQUENCE, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_TOP_K, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_TOP_P, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_MIN_P, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_XTC_PROBABILITY, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_XTC_THRESHOLD, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_TEMP, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_PENALTY_LAST_N, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_PENALTY_REPEAT, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_MIROSTAT, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_MIROSTAT_TAU, ???);
// add_kv(LLM_KV_GENERAL_SAMPLING_MIROSTAT_ETA, ???);
add_kv(LLM_KV_GENERAL_NAME, model->name);
// add_kv(LLM_KV_GENERAL_AUTHOR, ???);
// add_kv(LLM_KV_GENERAL_VERSION, ???);
@ -163,17 +210,31 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
add_kv(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, true);
add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);
add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_chexp);
add_kv(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp);
add_kv(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp);
add_kv(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
// add_kv(LLM_KV_TENSOR_DATA_LAYOUT, ???);
add_kv(LLM_KV_EXPERT_COUNT, hparams.n_expert);
add_kv(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
add_kv(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
add_kv(LLM_KV_EXPERT_GROUP_COUNT, hparams.n_expert_groups);
add_kv(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used);
add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
add_kv(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm);
add_kv(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
add_kv(LLM_KV_EXPERT_GROUP_SCALE, hparams.expert_group_scale);
add_kv(LLM_KV_EXPERTS_PER_GROUP, hparams.n_group_experts);
add_kv(LLM_KV_MOE_EVERY_N_LAYERS, hparams.moe_every_n_layers);
add_kv(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers);
add_kv(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers);
add_kv(LLM_KV_POOLING_TYPE, uint32_t(hparams.pooling_type));
add_kv(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
add_kv(LLM_KV_DECODER_START_TOKEN_ID, hparams.dec_start_token_id);
add_kv(LLM_KV_DECODER_BLOCK_COUNT, hparams.dec_n_layer);
add_kv(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping);
add_kv(LLM_KV_ROUTER_LOGIT_SOFTCAPPING, hparams.f_router_logit_softcapping);
add_kv(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping);
add_kv(LLM_KV_SWIN_NORM, hparams.swin_norm);
add_kv(LLM_KV_RESCALE_EVERY_N_LAYERS, hparams.rescale_every_n_layers);
@ -181,6 +242,9 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_TIME_DECAY_EXTRA_DIM, hparams.time_decay_extra_dim);
add_kv(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
add_kv(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
add_kv(LLM_KV_TOKEN_SHIFT_COUNT, hparams.token_shift_count);
add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step);
// add_kv(LLM_KV_FULL_ATTENTION_INTERVAL, ???);
add_kv(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, true);
add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, true);
@ -188,22 +252,39 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv);
add_kv(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k_full);
add_kv(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v_full);
add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa);
add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa);
add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
add_kv(LLM_KV_ATTENTION_GROUPNORM_EPS, hparams.f_norm_group_eps);
add_kv(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
add_kv(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
add_kv(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
add_kv(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
add_kv(LLM_KV_ATTENTION_DECAY_LORA_RANK, hparams.n_lora_decay);
add_kv(LLM_KV_ATTENTION_ICLR_LORA_RANK, hparams.n_lora_iclr);
add_kv(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);
add_kv(LLM_KV_ATTENTION_GATE_LORA_RANK, hparams.n_lora_gate);
add_kv(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
// add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, ???);
add_kv(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
add_kv(LLM_KV_ATTENTION_OUTPUT_SCALE, hparams.f_attn_out_scale);
add_kv(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.attn_temp_length);
add_kv(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale);
add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl);
add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl);
add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa);
add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa);
add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;
add_kv(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot_full);
add_kv(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa);
add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections);
add_kv(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train);
add_kv(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa);
// add_kv(LLM_KV_ROPE_SCALE_LINEAR, rope_scaling_factor); // old name
add_kv(LLM_KV_ROPE_SCALING_TYPE, llama_rope_scaling_type_name(hparams.rope_scaling_type_train));
add_kv(LLM_KV_ROPE_SCALING_FACTOR, rope_scaling_factor);
@ -211,6 +292,10 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn);
add_kv(LLM_KV_ROPE_SCALING_FINETUNED, hparams.rope_finetuned);
add_kv(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
add_kv(LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR, hparams.yarn_ext_factor);
add_kv(LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, hparams.yarn_attn_factor);
add_kv(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast);
add_kv(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow);
// TODO: implement split file support
// add_kv(LLM_KV_SPLIT_NO, ???);
@ -221,8 +306,11 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
add_kv(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
add_kv(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
add_kv(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
add_kv(LLM_KV_SSM_DT_B_C_RMS, hparams.ssm_dt_b_c_rms);
add_kv(LLM_KV_KDA_HEAD_DIM, hparams.n_embd_head_kda);
add_kv(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
add_kv(LLM_KV_TOKENIZER_MODEL, vocab.get_tokenizer_model());
@ -260,15 +348,39 @@ void llama_model_saver::add_kv_from_model() {
// TODO: implement LoRA support
// add_kv(LLM_KV_ADAPTER_TYPE, ???);
// add_kv(LLM_KV_ADAPTER_LORA_ALPHA, ???);
// add_kv(LLM_KV_ADAPTER_LORA_TASK_NAME, ???);
// add_kv(LLM_KV_ADAPTER_LORA_PROMPT_PREFIX, ???);
// add_kv(LLM_KV_ADAPTER_ALORA_INVOCATION_TOKENS, ???);
add_kv(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
add_kv(LLM_KV_POSNET_BLOCK_COUNT, hparams.posnet.n_layer);
add_kv(LLM_KV_CONVNEXT_EMBEDDING_LENGTH, hparams.convnext.n_embd);
add_kv(LLM_KV_CONVNEXT_BLOCK_COUNT, hparams.convnext.n_layer);
add_kv(LLM_KV_CLASSIFIER_OUTPUT_LABELS, model->classifier_labels);
add_kv(LLM_KV_SHORTCONV_L_CACHE, hparams.n_shortconv_l_cache);
add_kv(LLM_KV_XIELU_ALPHA_N, hparams.xielu_alpha_n);
add_kv(LLM_KV_XIELU_ALPHA_P, hparams.xielu_alpha_p);
add_kv(LLM_KV_XIELU_BETA, hparams.xielu_beta);
add_kv(LLM_KV_XIELU_EPS, hparams.xielu_eps);
// deprecated
// add_kv(LLM_KV_TOKENIZER_PREFIX_ID, ???);
// add_kv(LLM_KV_TOKENIZER_SUFFIX_ID, ???);
// add_kv(LLM_KV_TOKENIZER_MIDDLE_ID, ???);
add_kv(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in);
add_kv(LLM_KV_DENSE_2_FEAT_OUT, hparams.dense_2_feat_out);
add_kv(LLM_KV_DENSE_3_FEAT_IN, hparams.dense_3_feat_in);
add_kv(LLM_KV_DENSE_3_FEAT_OUT, hparams.dense_3_feat_out);
}
void llama_model_saver::add_tensors_from_model() {
if (std::string(model->output->name) != std::string(model->tok_embd->name)) {
if (model->output != nullptr &&
std::string(model->output->name) != std::string(model->tok_embd->name)) {
add_tensor(model->tok_embd); // some models use the same tensor for tok_embd and output
}
add_tensor(model->type_embd);
@ -297,3 +409,6 @@ void llama_model_saver::save(const std::string & path_model) {
gguf_write_to_file(gguf_ctx, path_model.c_str(), false);
}
void llama_model_saver::save(FILE * file) {
gguf_write_to_file_ptr(gguf_ctx, file, false);
}

4
src/llama-model-saver.h
View File

@ -6,6 +6,9 @@
#include <vector>
// FIXME temporary function for better error messages
bool llama_model_saver_supports_arch(llm_arch arch);
struct llama_model_saver {
struct gguf_context * gguf_ctx = nullptr;
const bool gguf_ctx_owned;
@ -37,4 +40,5 @@ struct llama_model_saver {
void add_tensors_from_model();
void save(const std::string & path_model);
void save(FILE * file);
};

166
src/llama-model.cpp
View File

@ -370,6 +370,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
ml.get_key(LLM_KV_EMBEDDING_LENGTH_OUT, hparams.n_embd_out_impl, false);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
@ -748,8 +750,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case LLM_ARCH_BERT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
switch (hparams.n_layer) {
case 3:
@ -781,8 +781,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
switch (hparams.n_layer) {
case 12:
@ -797,8 +795,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case LLM_ARCH_JINA_BERT_V2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
hparams.f_max_alibi_bias = 8.0f;
switch (hparams.n_layer) {
@ -810,8 +806,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case LLM_ARCH_JINA_BERT_V3:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
switch (hparams.n_layer) {
case 24:
@ -823,8 +817,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case LLM_ARCH_NOMIC_BERT_MOE:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS, hparams.moe_every_n_layers, 0);
if (hparams.n_layer == 12 && hparams.n_embd == 768) {
@ -838,8 +830,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case LLM_ARCH_NEO_BERT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
if (hparams.n_layer == 28) {
type = LLM_TYPE_250M;
@ -848,8 +838,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case LLM_ARCH_EUROBERT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
if (hparams.n_layer == 12) {
type = LLM_TYPE_SMALL; // 0.2B
@ -913,7 +901,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
// fall through
case LLM_ARCH_QWEN2:
{
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_5B : LLM_TYPE_1B; break;
@ -995,7 +982,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
} break;
case LLM_ARCH_QWEN3:
{
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 28: type = hparams.n_embd == 1024 ? LLM_TYPE_0_6B : LLM_TYPE_1_7B; break;
@ -1287,7 +1273,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
//applied only if model converted with --sentence-transformers-dense-modules
ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false);
@ -1624,7 +1609,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
// (optional) temperature tuning - used by mistral-large
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false);
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); // FIXME why not use temperature_length?
hparams.f_attn_temp_offset = 0.0f;
@ -1636,6 +1621,26 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_DEEPSEEK2OCR:
{
// similar to deepseek2, but without MLA
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
}
switch (hparams.n_layer) {
case 12: type = LLM_TYPE_3B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_PLM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -2084,7 +2089,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS, hparams.f_norm_group_eps);
ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
} break;
case LLM_ARCH_BAILINGMOE:
{
@ -4967,6 +4971,60 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
// Shared expert branch
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
}
}
} break;
case LLM_ARCH_DEEPSEEK2OCR:
{
// similar to deepseek2, but without MLA
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_expert_shared = hparams.n_expert_shared;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
// try to load output.weight, if not found, use token_embd (tied embeddings)
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
if (!output) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
// norm
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
if (i < (int) hparams.n_layer_dense_lead) {
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
} else {
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
if (n_expert == 0) {
throw std::runtime_error("n_expert must be > 0");
}
if (n_expert_used == 0) {
throw std::runtime_error("n_expert_used must be > 0");
}
// MoE branch
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
// Shared expert branch
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
@ -7520,6 +7578,65 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
if (!layer.ssm_beta_s && layer.ssm_beta) {
layer.ssm_beta_s = create_tensor(tn(LLM_TENSOR_SSM_BETA, "scale", i), {1}, TENSOR_NOT_REQUIRED);
}
// input scales
if (!layer.wq_in_s && layer.wq) {
layer.wq_in_s = create_tensor(tn(LLM_TENSOR_ATTN_Q, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.wk_in_s && layer.wk) {
layer.wk_in_s = create_tensor(tn(LLM_TENSOR_ATTN_K, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.wv_in_s && layer.wv) {
layer.wv_in_s = create_tensor(tn(LLM_TENSOR_ATTN_V, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.wo_in_s && layer.wo) {
layer.wo_in_s = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.wqkv_in_s && layer.wqkv) {
layer.wqkv_in_s = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.wqkv_gate_in_s && layer.wqkv_gate) {
layer.wqkv_gate_in_s = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ffn_gate_in_s && layer.ffn_gate) {
layer.ffn_gate_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ffn_down_in_s && layer.ffn_down) {
layer.ffn_down_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ffn_up_in_s && layer.ffn_up) {
layer.ffn_up_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ffn_gate_exps_in_s && layer.ffn_gate_exps) {
layer.ffn_gate_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
}
if (!layer.ffn_down_exps_in_s && layer.ffn_down_exps) {
layer.ffn_down_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
}
if (!layer.ffn_up_exps_in_s && layer.ffn_up_exps) {
layer.ffn_up_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
}
if (!layer.ffn_gate_shexp_in_s && layer.ffn_gate_shexp) {
layer.ffn_gate_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ffn_down_shexp_in_s && layer.ffn_down_shexp) {
layer.ffn_down_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ffn_up_shexp_in_s && layer.ffn_up_shexp) {
layer.ffn_up_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ssm_in_in_s && layer.ssm_in) {
layer.ssm_in_in_s = create_tensor(tn(LLM_TENSOR_SSM_IN, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ssm_out_in_s && layer.ssm_out) {
layer.ssm_out_in_s = create_tensor(tn(LLM_TENSOR_SSM_OUT, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ssm_alpha_in_s && layer.ssm_alpha) {
layer.ssm_alpha_in_s = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
if (!layer.ssm_beta_in_s && layer.ssm_beta) {
layer.ssm_beta_in_s = create_tensor(tn(LLM_TENSOR_SSM_BETA, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
}
}
}
@ -7607,14 +7724,15 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
buf_map.emplace(idx, buf);
}
}
pimpl->ctxs_bufs.emplace_back(std::move(ctx_ptr), std::move(bufs));
for (auto & buf : buf_map) {
for (auto & buf : bufs) {
// indicate that this buffer contains weights
// this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight
ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
ggml_backend_buffer_set_usage(buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
}
pimpl->ctxs_bufs.emplace_back(std::move(ctx_ptr), std::move(bufs));
ctx_buf_maps.emplace_back(ctx, buf_map);
}
@ -7857,7 +7975,7 @@ void llama_model::print_info() const {
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
}
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
@ -8434,6 +8552,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
llm = std::make_unique<llm_build_deepseek>(*this, params);
} break;
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_DEEPSEEK2OCR:
case LLM_ARCH_GLM_DSA:
case LLM_ARCH_MISTRAL4:
{
@ -8834,6 +8953,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_ARCTIC:
case LLM_ARCH_DEEPSEEK:
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_DEEPSEEK2OCR:
case LLM_ARCH_PLM:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GRANITE:

21
src/llama-model.h
View File

@ -414,6 +414,27 @@ struct llama_layer {
struct ggml_tensor * ssm_alpha_s = nullptr;
struct ggml_tensor * ssm_beta_s = nullptr;
// input scales
struct ggml_tensor * wq_in_s = nullptr;
struct ggml_tensor * wk_in_s = nullptr;
struct ggml_tensor * wv_in_s = nullptr;
struct ggml_tensor * wo_in_s = nullptr;
struct ggml_tensor * wqkv_in_s = nullptr;
struct ggml_tensor * wqkv_gate_in_s = nullptr;
struct ggml_tensor * ffn_gate_in_s = nullptr;
struct ggml_tensor * ffn_up_in_s = nullptr;
struct ggml_tensor * ffn_down_in_s = nullptr;
struct ggml_tensor * ffn_gate_exps_in_s = nullptr;
struct ggml_tensor * ffn_down_exps_in_s = nullptr;
struct ggml_tensor * ffn_up_exps_in_s = nullptr;
struct ggml_tensor * ffn_gate_shexp_in_s= nullptr;
struct ggml_tensor * ffn_up_shexp_in_s = nullptr;
struct ggml_tensor * ffn_down_shexp_in_s= nullptr;
struct ggml_tensor * ssm_in_in_s = nullptr;
struct ggml_tensor * ssm_out_in_s = nullptr;
struct ggml_tensor * ssm_alpha_in_s = nullptr;
struct ggml_tensor * ssm_beta_in_s = nullptr;
// altup & laurel
struct ggml_tensor * per_layer_inp_gate = nullptr;
struct ggml_tensor * per_layer_proj = nullptr;

10
src/llama-quant.cpp
View File

@ -344,7 +344,13 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param
quantize &= name.find("attn_rel_b.weight") == std::string::npos;
// do not quantize specific multimodal tensors
quantize &= name.find(".position_embd.") == std::string::npos;
quantize &= name.find(".position_embd") == std::string::npos;
quantize &= name.find("sam.pos_embd") == std::string::npos;
quantize &= name.find("sam.neck.") == std::string::npos;
quantize &= name.find("sam.net_") == std::string::npos;
quantize &= name.find(".rel_pos") == std::string::npos;
quantize &= name.find(".patch_embd") == std::string::npos;
quantize &= name.find(".patch_merger") == std::string::npos;
return quantize;
}
@ -859,7 +865,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
std::vector<std::string> splits = {};
llama_model_loader ml(/*metadata*/ nullptr, /*set_tensor_data*/ nullptr, /*set_tensor_data_ud*/ nullptr,
fname_inp, splits, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
fname_inp, splits, /*file*/ nullptr, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
ml.init_mappings(false); // no prefetching
llama_model model(llama_model_default_params());

4
src/llama-vocab.cpp
View File

@ -1952,7 +1952,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
} else if (
tokenizer_pre == "qwen2" ||
tokenizer_pre == "deepseek-r1-qwen" ||
tokenizer_pre == "kormo") {
tokenizer_pre == "kormo" ||
tokenizer_pre == "f2llmv2") {
pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
clean_spaces = false;
} else if (
@ -2489,6 +2490,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|| t.first == "[EOS]" // Kimi-K2
|| t.first == "<|end_of_text|>"
|| t.first == "<end_of_utterance>" // smoldocling
|| t.first == "<｜end▁of▁sentence｜>" // deepseek-ocr
) {
special_eog_ids.insert(t.second);
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {

40
src/llama.cpp
View File

@ -828,7 +828,7 @@ int64_t llama_time_us(void) {
// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
static int llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud,
const std::string & fname, std::vector<std::string> & splits, llama_model & model, llama_model_params & params) {
const std::string & fname, std::vector<std::string> & splits, FILE * file, llama_model & model, llama_model_params & params) {
// loading time will be recalculated after the first eval, so
// we take page faults deferred by mmap() into consideration
model.t_load_us = 0;
@ -837,7 +837,7 @@ static int llama_model_load(struct gguf_context * metadata, llama_model_set_tens
model.t_start_us = tm.t_start_us;
try {
llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, params.use_mmap, params.use_direct_io,
llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.use_mmap, params.use_direct_io,
params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);
ml.print_info();
@ -889,8 +889,24 @@ static struct llama_model * llama_model_load_from_file_impl(
void * set_tensor_data_ud,
const std::string & path_model,
std::vector<std::string> & splits,
FILE * file,
struct llama_model_params params) {
GGML_ASSERT((metadata == nullptr) != path_model.empty() && "exactly one out of metadata and path_model needs to be defined");
{
int n_sources_defined = 0;
if (metadata != nullptr) {
n_sources_defined++;
}
if (!path_model.empty()) {
n_sources_defined++;
}
if (file != nullptr) {
n_sources_defined++;
}
if (n_sources_defined != 1) {
LLAMA_LOG_ERROR("%s: exactly one out metadata, path_model, and file must be defined\n", __func__);
return nullptr;
}
}
ggml_time_init();
if (!params.vocab_only && ggml_backend_reg_count() == 0) {
@ -1011,7 +1027,7 @@ static struct llama_model * llama_model_load_from_file_impl(
props.memory_free/1024/1024);
}
const int status = llama_model_load(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, *model, params);
const int status = llama_model_load(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, file, *model, params);
GGML_ASSERT(status <= 0);
if (status < 0) {
if (status == -1) {
@ -1037,7 +1053,7 @@ struct llama_model * llama_model_init_from_user(
std::vector<std::string> splits = {};
params.use_mmap = false;
params.use_extra_bufts = false;
return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, params);
return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, /*file*/ nullptr, params);
}
// deprecated
struct llama_model * llama_load_model_from_file(
@ -1050,7 +1066,7 @@ struct llama_model * llama_model_load_from_file(
const char * path_model,
struct llama_model_params params) {
std::vector<std::string> splits = {};
return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, path_model, splits, params);
return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, path_model, splits, /*file*/ nullptr, params);
}
struct llama_model * llama_model_load_from_splits(
@ -1066,7 +1082,17 @@ struct llama_model * llama_model_load_from_splits(
for (size_t i = 0; i < n_paths; ++i) {
splits.push_back(paths[i]);
}
return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, splits.front(), splits, params);
return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, splits.front(), splits, /*file*/ nullptr, params);
}
struct llama_model * llama_model_load_from_file_ptr(FILE * file, struct llama_model_params params) {
if (!file) {
LLAMA_LOG_ERROR("%s: file is NULL\n", __func__);
return nullptr;
}
std::string path_model;
std::vector<std::string> splits = {};
return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, path_model, splits, file, params);
}
void llama_model_save_to_file(const struct llama_model * model, const char * path_model) {

36
src/models/deepseek2.cpp
View File

@ -2,6 +2,9 @@
llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
// lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
bool is_ocr = model.arch == LLM_ARCH_DEEPSEEK2OCR;
const bool is_mla = hparams.is_mla();
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
@ -54,7 +57,38 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
cb(cur, "attn_norm", il);
// self_attention
{
if (is_ocr) {
const int n_embed_head = hparams.n_embd / hparams.n_head();
const int ocr_rope_type = GGML_ROPE_TYPE_NEOX;
GGML_ASSERT(n_embed_head == n_embd_head_k && n_embed_head == n_embd_head_v);
ggml_tensor * Qcur = NULL;
ggml_tensor * Kcur = NULL;
ggml_tensor * Vcur = NULL;
Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(Qcur, "q", il);
cb(Kcur, "k", il);
cb(Vcur, "v", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embed_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embed_head, n_head, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embed_head, n_head, n_tokens);
GGML_ASSERT(fabs(freq_base - 10000.0) < 1e-4);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);
cb(Qcur, "q_pe", il);
cb(Kcur, "k_pe", il);
cur = build_attn(inp_attn_kv,
model.layers[il].wo, NULL,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_out", il);
}
else {
ggml_tensor * q = NULL;
const bool is_lite = model.layers[il].wq;

37
tests/test-backend-ops.cpp
View File

@ -4823,28 +4823,33 @@ struct test_conv_transpose_1d : public test_case {
// GGML_OP_CONV_TRANSPOSE_2D
struct test_conv_transpose_2d : public test_case {
// Dimensions
const std::array<int64_t, 4> ne_input;
const std::array<int64_t, 4> ne_kernel;
const int stride;
// Types
const ggml_type kernel_type;
std::string vars() override {
return VARS_TO_STR3(ne_input, ne_kernel, stride);
return VARS_TO_STR4(kernel_type, ne_input, ne_kernel, stride);
}
double max_nmse_err() override {
return 5e-4; // The default 1e-7 is too small for Vulkan.
}
test_conv_transpose_2d(std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
int stride = 1)
: ne_input(ne_input), ne_kernel(ne_kernel), stride(stride){}
test_conv_transpose_2d(
std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
int stride = 1,
ggml_type kernel_type = GGML_TYPE_F16
) : ne_input(ne_input), ne_kernel(ne_kernel), stride(stride), kernel_type(kernel_type) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data());
ggml_set_name(input, "input");
ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne_kernel.data());
ggml_tensor * kernel = ggml_new_tensor(ctx, kernel_type, 4, ne_kernel.data());
ggml_set_name(kernel, "kernel");
ggml_tensor * out = ggml_conv_transpose_2d_p0(ctx, kernel, input, stride);
@ -7279,7 +7284,7 @@ static const ggml_type all_types[] = {
GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
GGML_TYPE_Q8_0,
GGML_TYPE_MXFP4,
GGML_TYPE_MXFP4, GGML_TYPE_NVFP4,
GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
GGML_TYPE_Q6_K,
@ -7295,7 +7300,7 @@ static const ggml_type base_types[] = {
GGML_TYPE_Q4_0,
GGML_TYPE_Q4_1, // for I8MM tests
GGML_TYPE_Q4_K,
GGML_TYPE_MXFP4, // TODO: or "other"
GGML_TYPE_MXFP4, GGML_TYPE_NVFP4, // TODO: or "other"
GGML_TYPE_IQ2_XXS
};
@ -7704,9 +7709,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1));
test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));
test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1));
test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2));
test_cases.emplace_back(new test_conv_transpose_2d({129, 63, 35, 1}, {3, 3, 48, 35}, 1));
for (ggml_type kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1, kernel_type));
test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2, kernel_type));
test_cases.emplace_back(new test_conv_transpose_2d({129, 63, 35, 1}, {3, 3, 48, 35}, 1, kernel_type));
}
test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 500, 1, 1}));
test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 5000, 1, 1}));
@ -8892,9 +8899,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, false));
test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, true));
test_cases.emplace_back(new test_conv_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1));
test_cases.emplace_back(new test_conv_transpose_2d({16, 16, 16, 1}, {3, 3, 8, 16}, 1));
test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2));
for (ggml_type kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
test_cases.emplace_back(new test_conv_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1, kernel_type));
test_cases.emplace_back(new test_conv_transpose_2d({16, 16, 16, 1}, {3, 3, 8, 16}, 1, kernel_type));
test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2, kernel_type));
}
test_cases.emplace_back(new test_mean(GGML_TYPE_F32, {256, 256, 3, 1}));

12
tests/test-gguf.cpp
View File

@ -742,7 +742,7 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
/*ctx =*/ hft >= offset_has_data ? &ctx : nullptr,
};
struct gguf_context * gguf_ctx = gguf_init_from_file_impl(file, gguf_params);
struct gguf_context * gguf_ctx = gguf_init_from_file_ptr(file, gguf_params);
if (expect_context_not_null(hft)) {
printf("%s: - context_not_null: ", __func__);
@ -1125,19 +1125,15 @@ static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned
GGML_ASSERT(file);
#endif // _WIN32
{
std::vector<int8_t> buf;
gguf_write_to_buf(gguf_ctx_0, buf, only_meta);
GGML_ASSERT(fwrite(buf.data(), 1, buf.size(), file) == buf.size());
rewind(file);
}
gguf_write_to_file_ptr(gguf_ctx_0, file, only_meta);
rewind(file);
struct ggml_context * ctx_1 = nullptr;
struct gguf_init_params gguf_params = {
/*no_alloc =*/ false,
/*ctx =*/ only_meta ? nullptr : &ctx_1,
};
struct gguf_context * gguf_ctx_1 = gguf_init_from_file_impl(file, gguf_params);
struct gguf_context * gguf_ctx_1 = gguf_init_from_file_ptr(file, gguf_params);
printf("%s: same_version: ", __func__);
if (gguf_get_version(gguf_ctx_0) == gguf_get_version(gguf_ctx_1)) {

18
tests/test-jinja.cpp
View File

@ -884,6 +884,24 @@ static void test_macros(testing & t) {
json::object(),
"Hi Guest"
);
test_template(t, "macro kwargs input",
"{% macro my_func(a, b=False) %}{% if b %}{{ a }}{% else %}nope{% endif %}{% endmacro %}{{ my_func(1, b=True) }}",
json::object(),
"1"
);
test_template(t, "macro with multiple args",
"{% macro add(a, b, c=0) %}{{ a + b + c }}{% endmacro %}{{ add(1, 2) }},{{ add(1, 2, 3) }},{{ add(1, b=10) }},{{ add(1, 2, c=5) }}",
json::object(),
"3,6,11,8"
);
test_template(t, "macro with kwarg out-of-order input",
"{% macro greet(first, last, greeting='Hello') %}{{ greeting }}, {{ first }} {{ last }}{% endmacro %}{{ greet(last='Smith', first='John') }},{{ greet(last='Doe', greeting='Hi', first='Jane') }}",
json::object(),
"Hello, John Smith,Hi, Jane Doe"
);
}
static void test_namespace(testing & t) {

86
tests/test-llama-archs.cpp
View File

@ -90,6 +90,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
n_embd = 64;
n_head = 1;
n_ff = 96;
n_layer = 22; // hparams.n_layer_kv_from_start = 20 is hardcoded
} else if (arch == LLM_ARCH_DEEPSEEK2
|| arch == LLM_ARCH_GLM_DSA
|| arch == LLM_ARCH_KIMI_LINEAR
@ -101,8 +102,6 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
n_layer = 3;
} else if (arch == LLM_ARCH_CHAMELEON) {
n_vocab = 10240;
} else if (arch == LLM_ARCH_GEMMA3N) {
n_layer = 22; // hparams.n_layer_kv_from_start = 20 is hardcoded
}
const uint32_t n_embd_head = n_embd / n_head;
@ -231,9 +230,15 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
return ret;
}
static bool silent_model_load_progress(float /*progress*/, void * /*user_data*/) {
return true;
}
static std::pair<llama_model_ptr, llama_context_ptr> get_model_and_ctx(
struct gguf_context * gguf_ctx, const size_t seed, const std::vector<ggml_backend_dev_t> & devs) {
struct gguf_context * gguf_ctx, FILE * file, const size_t seed, const std::vector<ggml_backend_dev_t> & devs) {
GGML_ASSERT((gguf_ctx == nullptr) != (file == nullptr));
llama_model_params model_params = llama_model_default_params();
model_params.progress_callback = silent_model_load_progress;
std::vector<ggml_backend_dev_t> devs_copy = devs;
devs_copy.push_back(nullptr);
model_params.devices = devs_copy.data();
@ -244,7 +249,9 @@ static std::pair<llama_model_ptr, llama_context_ptr> get_model_and_ctx(
ctx_params.n_threads_batch = 4;
size_t tmp = seed;
llama_model_ptr model(llama_model_init_from_user(gguf_ctx, set_tensor_data, &tmp, model_params));
llama_model_ptr model(gguf_ctx != nullptr ?
llama_model_init_from_user(gguf_ctx, set_tensor_data, &tmp, model_params) :
llama_model_load_from_file_ptr(file, model_params));
if (!model) {
throw std::runtime_error("failed to create llama model");
}
@ -351,7 +358,6 @@ static bool moe_implemented(const llm_arch arch) {
}
static int save_models(const llm_arch target_arch, const size_t seed, const ggml_log_level log_level, const std::string & dir) {
GGML_ABORT("llama_model_save_to_file is broken");
struct user_data_t {
struct {
ggml_log_callback callback;
@ -376,6 +382,19 @@ static int save_models(const llm_arch target_arch, const size_t seed, const ggml
if (arch == LLM_ARCH_CLIP || arch == LLM_ARCH_GPTJ || arch == LLM_ARCH_UNKNOWN) {
continue; // These models don't have usable implementations.
}
if (arch == LLM_ARCH_CHAMELEON) {
continue; // Only half-implemented and to be removed in the future.
}
if (arch == LLM_ARCH_RWKV6 || arch == LLM_ARCH_RWKV6QWEN2 || arch == LLM_ARCH_RWKV7 || arch == LLM_ARCH_ARWKV7) {
continue; // FIXME
}
if (arch == LLM_ARCH_BERT || arch == LLM_ARCH_MODERN_BERT || arch == LLM_ARCH_NOMIC_BERT || arch == LLM_ARCH_NOMIC_BERT_MOE ||
arch == LLM_ARCH_NEO_BERT || arch == LLM_ARCH_JINA_BERT_V2 || arch == LLM_ARCH_JINA_BERT_V3 || arch == LLM_ARCH_EUROBERT) {
continue; // TODO vocab
}
if (arch == LLM_ARCH_PLM) {
continue; // TODO tensor shapes
}
for (bool moe : {false, true}) {
if (moe && !moe_implemented(arch)) {
continue;
@ -383,8 +402,12 @@ static int save_models(const llm_arch target_arch, const size_t seed, const ggml
if (!moe && moe_mandatory(arch)) {
continue;
}
if (!llama_model_saver_supports_arch(arch)) {
LOG_INF("%s: %s model (%s) is unsupported, skipping\n", __func__, llm_arch_name(arch), moe ? "MoE" : "dense");
continue;
}
gguf_context_ptr gguf_ctx = get_gguf_ctx(arch, moe);
auto model_and_ctx = get_model_and_ctx(gguf_ctx.get(), seed, {});
auto model_and_ctx = get_model_and_ctx(gguf_ctx.get(), nullptr, seed, {});
const std::string path = dir + "/" + llm_arch_name(arch) + (moe ? "-moe.gguf" : "-dense.gguf");
LOG_INF("%s: Saving %s model (%s) to %s...\n", __func__, llm_arch_name(arch), moe ? "MoE" : "dense", path.c_str());
llama_model_save_to_file(model_and_ctx.first.get(), path.c_str());
@ -416,8 +439,8 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
bool all_ok = true;
common_log_flush(common_log_main());
printf("|%15s|%30s|%6s|%8s|%6s|\n", "Model arch.", "Device", "Config", "NMSE", "Status");
printf("|---------------|------------------------------|------|--------|------|\n");
printf("|%15s|%30s|%6s|%15s|%9s|\n", "Model arch.", "Device", "Config", "NMSE vs. CPU", "Roundtrip");
printf("|---------------|------------------------------|------|---------------|---------|\n");
for (const llm_arch & arch : llm_arch_all()) {
if (target_arch != LLM_ARCH_UNKNOWN && arch != target_arch) {
continue;
@ -425,6 +448,9 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
if (arch == LLM_ARCH_CLIP || arch == LLM_ARCH_GPTJ || arch == LLM_ARCH_UNKNOWN) {
continue; // These models don't have usable implementations.
}
if (arch == LLM_ARCH_CHAMELEON) {
continue; // Only half-implemented and to be removed in the future.
}
if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
continue; // FIXME CUDA backend crashes.
}
@ -441,6 +467,9 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
if (arch == LLM_ARCH_PLM) {
continue; // TODO tensor shapes
}
if (arch == LLM_ARCH_DEEPSEEK2OCR) {
continue; // TODO tensor shapes
}
// FIXME some models are segfaulting with WebGPU:
#ifdef GGML_USE_WEBGPU
@ -458,22 +487,50 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
continue;
}
gguf_context_ptr gguf_ctx = get_gguf_ctx(arch, moe);
auto model_and_ctx_cpu = get_model_and_ctx(gguf_ctx.get(), seed, {});
auto model_and_ctx_cpu = get_model_and_ctx(gguf_ctx.get(), nullptr, seed, {});
const std::vector<float> logits_cpu = get_logits(model_and_ctx_cpu.first.get(), model_and_ctx_cpu.second.get(), tokens, encode);
for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
continue;
}
auto model_and_ctx_dev = get_model_and_ctx(gguf_ctx.get(), seed, {dev});
auto model_and_ctx_dev = get_model_and_ctx(gguf_ctx.get(), nullptr, seed, {dev});
std::string config_name = moe ? "MoE" : "Dense";
const std::vector<float> logits_dev = get_logits(model_and_ctx_dev.first.get(), model_and_ctx_dev.second.get(), tokens, encode);
const double nmse_val = nmse(logits_cpu, logits_dev);
const bool ok = nmse_val <= 1e-4;
all_ok = all_ok && ok;
char nmse_str[10];
snprintf(nmse_str, sizeof(nmse_str), "%.2e", nmse_val);
printf("|%15s|%30s|%6s|%8s|%17s|\n", llm_arch_name(arch), ggml_backend_dev_description(dev),
moe ? "MoE" : "Dense", nmse_str, ok ? "\033[1;32mOK\033[0m" : "\033[1;31mFAIL\033[0m");
std::string status_nmse = "\033[1;32mOK\033[0m";
if (nmse_val > 1e-4) {
all_ok = false;
status_nmse = "\033[1;31mFAIL\033[0m";
}
std::string status_roundtrip = "\033[1;33mSKIP\033[0m";
FILE * file = tmpfile(); // Can be null on Windows without administrator privileges.
if (file != nullptr && llama_model_saver_supports_arch(arch)) {
llama_model_saver ms = llama_model_saver(model_and_ctx_dev.first.get());
ms.add_kv_from_model();
ms.add_tensors_from_model();
ms.save(file);
rewind(file);
auto model_and_ctx_roundtrip = get_model_and_ctx(nullptr, file, seed, {dev});
const std::vector<float> logits_roundtrip = get_logits(
model_and_ctx_roundtrip.first.get(), model_and_ctx_roundtrip.second.get(), tokens, encode);
status_roundtrip = "\033[1;32mOK\033[0m";
GGML_ASSERT(logits_roundtrip.size() == logits_dev.size());
for (size_t i = 0; i < logits_roundtrip.size(); i++) {
if (logits_roundtrip[i] != logits_dev[i]) {
all_ok = false;
status_roundtrip = "\033[1;31mFAIL\033[0m";
break;
}
}
}
printf("|%15s|%30s|%6s|%15s (%8s)|%20s|\n", llm_arch_name(arch), ggml_backend_dev_description(dev),
config_name.c_str(), status_nmse.c_str(), nmse_str, status_roundtrip.c_str());
}
}
}
@ -526,6 +583,7 @@ int main(int argc, char ** argv) {
}
}
}
printf("%s: using seed %zu\n", __func__, seed);
try {
if (!out.empty()) {

10
tools/completion/completion.cpp
View File

@ -146,13 +146,19 @@ int main(int argc, char ** argv) {
ctx = llama_init->context();
model = llama_init->model();
smpl = llama_init->sampler(0);
if (ctx == NULL) {
LOG_ERR("%s: error: unable to create context\n", __func__);
return 1;
}
if (model == NULL) {
LOG_ERR("%s: error: unable to load model\n", __func__);
return 1;
}
smpl = llama_init->sampler(0);
llama_memory_t mem = llama_get_memory(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
@ -695,7 +701,7 @@ int main(int argc, char ** argv) {
if (!common_prompt_batch_decode(ctx, embd, n_past, params.n_batch, path_session, save_now)) {
return 1;
}
session_tokens.insert(session_tokens.end(), embd.begin(), embd.begin());
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
n_session_consumed = session_tokens.size();
session_do_save = false;

2
tools/gguf-split/README.md
View File

@ -7,4 +7,4 @@ CLI to split / merge GGUF files.
- `--split`: split GGUF to multiple GGUF, default operation.
- `--split-max-size`: max size per split in `M` or `G`, f.ex. `500M` or `2G`.
- `--split-max-tensors`: maximum tensors in each split: default(128)
- `--merge`: merge multiple GGUF to a single GGUF.
- `--merge`: merge multiple GGUF to a single GGUF. You only need to specify the name of the first GGUF to merge, the name of the merged GGUF, and the CLI will find the other GGUFs it needs within the same folder.

13
tools/imatrix/imatrix.cpp
View File

@ -143,11 +143,20 @@ static void compute_statistics(std::vector<tensor_statistics> & tstats, const st
activations.reserve(e.values.size());
for (int i = 0; i < n_mat; ++i) {
if (e.counts[i] == 0) {
LOG_DBG("%s: skipping tensor %s due to zero count at index %d\n", __func__, name.c_str(), i);
continue;
}
for (int j = 0; j < row_size; ++j) {
activations.push_back(e.values[i*row_size + j] / e.counts[i]);
}
}
if (activations.empty()) {
LOG_ERR("%s: all counts are zero for tensor %s, skipping statistics computation\n", __func__, name.c_str());
return;
}
const float act_total = std::accumulate(activations.begin(), activations.end(), 0.0f);
const float act_max = *std::max_element(activations.begin(), activations.end());
const float act_min = *std::min_element(activations.begin(), activations.end());
@ -1142,10 +1151,12 @@ static bool show_statistics(const common_params & params) {
blk = -1; // not a block layer
}
const float entropy_norm = (tstat.elements > 0) ? 100.0f * (tstat.entropy / std::log2(tstat.elements)) : 0.0f;
LOG_INF("%5s\t%-20s\t%10.2f\t%8.4f\t%11.4f\t%6.2f\t%6.2f\t%8.2f%%\t%6d\t%10.4f\t%6.2f%%\t%10.2f%%\t%8.4f\n",
layer.c_str(), name.c_str(), tstat.total_sqract, tstat.min_sqract, tstat.max_sqract, tstat.mean_sqract,
tstat.stddev, tstat.active * 100.0f, tstat.elements, tstat.entropy,
100.0f * (tstat.entropy / std::log2(tstat.elements)), 100.0f * tstat.zd, tstat.cossim);
entropy_norm, 100.0f * tstat.zd, tstat.cossim);
const float weighted_bias = tstat.elements * tstat.total_sqract;
const float weighted_zd = tstat.elements * tstat.zd;

2
tools/llama-bench/llama-bench.cpp
View File

@ -1807,7 +1807,7 @@ struct markdown_printer : public printer {
if (!is_cpu_backend) {
fields.emplace_back("n_gpu_layers");
}
if (params.n_cpu_moe.size() > 1) {
if (params.n_cpu_moe.size() > 1 || params.n_cpu_moe != cmd_params_defaults.n_cpu_moe) {
fields.emplace_back("n_cpu_moe");
}
if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {

2
tools/mtmd/CMakeLists.txt
View File

@ -5,6 +5,7 @@ find_package(Threads REQUIRED)
add_library(mtmd
mtmd.cpp
mtmd-audio.cpp
mtmd-image.cpp
mtmd.h
mtmd-helper.cpp
mtmd-helper.h
@ -30,6 +31,7 @@ add_library(mtmd
models/qwen3vl.cpp
models/siglip.cpp
models/whisper-enc.cpp
models/deepseekocr.cpp
models/mobilenetv5.cpp
models/youtuvl.cpp
)

25
tools/mtmd/clip-impl.h
View File

@ -51,13 +51,14 @@
#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
#define KEY_WIN_ATTN_PATTERN "clip.vision.n_wa_pattern"
#define KEY_WIN_ATTN_LAYER_INDEXES "clip.vision.wa_layer_indexes"
#define KEY_ATTN_WINDOW_SIZE "clip.vision.window_size"
#define KEY_MINICPMV_VERSION "clip.minicpmv_version"
#define KEY_MINICPMV_QUERY_NUM "clip.minicpmv_query_num"
#define KEY_SAM_N_HEAD "clip.vision.sam.head_count"
#define KEY_SAM_N_BLOCK "clip.vision.sam.block_count"
#define KEY_SAM_N_EMBD "clip.vision.sam.embedding_length"
// audio-specific
#define KEY_AUDIO_PROJ_TYPE "clip.audio.projector_type" // for models with mixed modalities
#define KEY_A_NUM_MEL_BINS "clip.audio.num_mel_bins"
@ -99,12 +100,13 @@
#define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s"
#define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s"
#define TN_IMAGE_NEWLINE "model.image_newline"
#define TN_IMAGE_NEWLINE "v.image_newline"
#define TN_IMAGE_SEPERATOR "v.view_seperator"
#define TN_MM_INP_NORM "mm.input_norm.weight"
#define TN_MM_INP_NORM_B "mm.input_norm.bias"
#define TN_MM_INP_PROJ "mm.input_projection.weight" // gemma3
#define TN_MM_SOFT_EMB_N "mm.soft_emb_norm.weight" // gemma3
#define TN_MM_PROJECTOR "mm.model.fc.weight" // idefics3
#define TN_MM_PROJECTOR "mm.model.fc.%s" // idefics3, deepseekocr
#define TN_MM_PATCH_MERGER "mm.patch_merger.%s" // mistral small 3.1, glm4v
#define TN_TOK_IMG_BREAK "v.token_embd.img_break" // pixtral
#define TN_TOK_GLM_BOI "adapter.boi" // glm-edge (these embeddings are not in text model)
@ -143,6 +145,19 @@
#define TN_TOK_BOI "v.boi"
#define TN_TOK_EOI "v.eoi"
// deepseek-ocr
#define TN_SAM_POS_EMBD "v.sam.pos_embd.%s"
#define TN_SAM_PATCH_EMBD "v.sam.patch_embd.%s"
#define TN_SAM_PRE_NORM "v.sam.blk.%d.pre_ln.%s"
#define TN_SAM_POST_NORM "v.sam.blk.%d.post_ln.%s"
#define TN_SAM_ATTN_POS_H "v.sam.blk.%d.attn.pos_h.%s"
#define TN_SAM_ATTN_POS_W "v.sam.blk.%d.attn.pos_w.%s"
#define TN_SAM_ATTN_QKV "v.sam.blk.%d.attn.qkv.%s"
#define TN_SAM_ATTN_OUT "v.sam.blk.%d.attn.out.%s"
#define TN_SAM_FFN_UP "v.sam.blk.%d.mlp.lin1.%s"
#define TN_SAM_FFN_DOWN "v.sam.blk.%d.mlp.lin2.%s"
#define TN_SAM_NECK "v.sam.neck.%d.%s"
#define TN_SAM_NET "v.sam.net_%d.%s"
// (conformer) lfm2
#define TN_PRE_ENCODE_OUT "a.pre_encode.out.%s"
#define TN_FFN_NORM "%s.blk.%d.ffn_norm.%s"
@ -236,6 +251,7 @@ enum projector_type {
PROJECTOR_TYPE_LIGHTONOCR,
PROJECTOR_TYPE_COGVLM,
PROJECTOR_TYPE_JANUS_PRO,
PROJECTOR_TYPE_DEEPSEEKOCR,
PROJECTOR_TYPE_LFM2A,
PROJECTOR_TYPE_GLM4V,
PROJECTOR_TYPE_YOUTUVL,
@ -273,6 +289,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
{ PROJECTOR_TYPE_LIGHTONOCR,"lightonocr"},
{ PROJECTOR_TYPE_COGVLM, "cogvlm"},
{ PROJECTOR_TYPE_JANUS_PRO, "janus_pro"},
{ PROJECTOR_TYPE_DEEPSEEKOCR,"deepseekocr"},
{ PROJECTOR_TYPE_LFM2A, "lfm2a"},
{ PROJECTOR_TYPE_GLM4V, "glm4v"},
{ PROJECTOR_TYPE_YOUTUVL, "youtuvl"},

67
tools/mtmd/clip-model.h
View File

@ -28,6 +28,13 @@ enum patch_merge_type {
PATCH_MERGE_SPATIAL_UNPAD,
};
enum resize_algo {
RESIZE_ALGO_BILINEAR, // stretch to target resolution
RESIZE_ALGO_BICUBIC, // center-crop when aspect ratio doesn't match
RESIZE_ALGO_BICUBIC_PILLOW,
// RESIZE_ALGO_LANCZOS, // TODO
};
struct clip_hparams {
int32_t image_size = 0;
int32_t patch_size = 0;
@ -37,13 +44,26 @@ struct clip_hparams {
int32_t n_head = 0;
int32_t n_layer = 0;
// idefics3
int32_t n_merge = 0; // number of patch merges **per-side**
// for preprocessor
int32_t image_longest_edge = 0;
int32_t image_min_pixels = -1;
int32_t image_max_pixels = -1;
int32_t n_merge = 0; // number of patch merges **per-side**
resize_algo image_resize_algo = RESIZE_ALGO_BICUBIC;
bool image_resize_pad = true; // if false, center-crop will be applied when resizing
std::array<uint8_t, 3> image_pad_color = {0, 0, 0};
// (preprocessor) for llava-uhd style models
std::vector<clip_image_size> image_res_candidates;
int32_t preproc_min_tiles = 0;
int32_t preproc_max_tiles = 0;
resize_algo image_resize_algo_rf = RESIZE_ALGO_BICUBIC;
resize_algo image_resize_algo_ov = RESIZE_ALGO_BILINEAR;
bool image_pad_rf = true; // if true, refined image will be padded (e.g. llava-1.6)
bool image_pad_ov = false; // if true, overview image will be padded (e.g. llava-1.6)
std::array<uint8_t, 3> image_pad_color_rf = {0, 0, 0}; // padding color for refined image
std::array<uint8_t, 3> image_pad_color_ov = {0, 0, 0}; // padding color for overview image
float image_mean[3];
float image_std[3];
@ -60,13 +80,16 @@ struct clip_hparams {
float eps = 1e-6;
float rope_theta = 0.0;
std::vector<clip_image_size> image_res_candidates; // for llava-uhd style models
int32_t image_crop_resolution;
std::unordered_set<int32_t> vision_feature_layer;
int32_t attn_window_size = 0;
int32_t n_wa_pattern = 0;
std::unordered_set<int32_t> wa_layer_indexes; // explicit layer indexes that use full attention (for irregular patterns like YoutuVL)
// deepseek-ocr (sam)
int32_t sam_n_layer = 0;
int32_t sam_n_head = 0;
int32_t sam_n_embd = 0;
// audio
int32_t n_mel_bins = 0; // whisper preprocessor
int32_t proj_stack_factor = 0; // ultravox
@ -102,6 +125,21 @@ struct clip_hparams {
warmup_image_size = n_tok_per_side * patch_size * cur_merge;
// TODO: support warmup size for custom token numbers
}
// sam vit deepseek-ocr
std::vector<int32_t> global_attn_indices() const {
return { 2, 5, 8, 11 };
}
bool is_global_attn(int32_t layer) const {
const auto indices = global_attn_indices();
for (const auto & idx : indices) {
if (layer == idx) {
return true;
}
}
return false;
}
};
struct clip_layer {
@ -148,6 +186,9 @@ struct clip_layer {
ggml_tensor * deepstack_fc2_w = nullptr;
ggml_tensor * deepstack_fc2_b = nullptr;
// sam rel_pos
ggml_tensor * rel_pos_w = nullptr;
ggml_tensor * rel_pos_h = nullptr;
// lfm2
ggml_tensor * ff_norm_w = nullptr;
ggml_tensor * ff_norm_b = nullptr;
@ -240,7 +281,6 @@ struct clip_model {
ggml_tensor * post_ln_w;
ggml_tensor * post_ln_b;
ggml_tensor * projection; // TODO: rename it to fc (fully connected layer)
ggml_tensor * mm_fc_w;
ggml_tensor * mm_fc_b;
ggml_tensor * mm_ffn_up_w = nullptr;
@ -261,6 +301,8 @@ struct clip_model {
ggml_tensor * mm_2_b = nullptr;
ggml_tensor * image_newline = nullptr;
ggml_tensor * view_seperator = nullptr;
// Yi type models with mlp+normalization projection
ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4
@ -372,6 +414,23 @@ struct clip_model {
ggml_tensor * mm_boi = nullptr;
ggml_tensor * mm_eoi = nullptr;
// deepseek ocr sam
ggml_tensor * patch_embed_proj_w = nullptr;
ggml_tensor * patch_embed_proj_b = nullptr;
ggml_tensor * pos_embed = nullptr;
ggml_tensor * neck_0_w;
ggml_tensor * neck_1_w;
ggml_tensor * neck_1_b;
ggml_tensor * neck_2_w;
ggml_tensor * neck_3_w;
ggml_tensor * neck_3_b;
ggml_tensor * net_2;
ggml_tensor * net_3;
int32_t n_sam_layers = 12; // used by deepseek-ocr sam encoder
std::vector<clip_layer> sam_layers;
// lfm2 audio
std::array<ggml_tensor *, 7> pre_encode_conv_X_w = {nullptr};
std::array<ggml_tensor *, 7> pre_encode_conv_X_b = {nullptr};

1220
tools/mtmd/clip.cpp
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File diff suppressed because it is too large Load Diff

3
tools/mtmd/clip.h
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@ -97,9 +97,6 @@ struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch
*/
void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img);
/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);

324
tools/mtmd/models/deepseekocr.cpp Normal file
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@ -0,0 +1,324 @@
#include "models.h"
// Implementation based on approach suggested by Acly
// See: https://github.com/ggml-org/llama.cpp/pull/17383#issuecomment-3554227091
static ggml_tensor * window_partition(ggml_context * ctx0, ggml_tensor * x, const int window) {
auto [c, w, h, b] = x->ne;
// same as
// x = ggml_win_part(m, x, window);
// x = ggml_reshape_3d(m, x, c, window * window, x->ne[3]);
const int64_t px = (window - w % window) % window;
const int64_t py = (window - h % window) % window;
const int64_t npw = (w + px) / window;
const int64_t nph = (h + py) / window;
ggml_tensor * cur = x;
if (px > 0 || py > 0) {
cur = ggml_pad(ctx0, cur, 0, static_cast<int>(px), static_cast<int>(py), 0);
}
cur = ggml_reshape_4d(ctx0, cur, c * window, npw, window, nph * b);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
cur = ggml_reshape_4d(ctx0, cur, c, window, window, npw * nph * b);
return cur;
}
// Implementation based on approach suggested by Acly
// See: https://github.com/ggml-org/llama.cpp/pull/17383#issuecomment-3554227091
static ggml_tensor * window_unpartition(ggml_context * ctx0,
ggml_tensor * x,
const int w,
const int h,
const int window) {
const int64_t c = x->ne[0];
// same as
// x = ggml_reshape_4d(m, x, c, window, window, x->ne[2]);
// x = ggml_win_unpart(m, x, w, h, window);
const int64_t px = (window - w % window) % window;
const int64_t py = (window - h % window) % window;
const int64_t npw = (w + px) / window;
const int64_t nph = (h + py) / window;
const int64_t b = x->ne[3] / (npw * nph);
ggml_tensor * cur = x;
cur = ggml_reshape_4d(ctx0, cur, c * window, window, npw, nph * b);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
cur = ggml_reshape_4d(ctx0, cur, c, w + px, h + py, b);
cur = ggml_view_4d(ctx0, cur, cur->ne[0], w, h, cur->ne[3], cur->nb[1], cur->nb[2], cur->nb[3], 0);
cur = ggml_cont(ctx0, cur);
return cur;
}
static ggml_tensor * get_rel_pos(ggml_context * ctx0,
ggml_tensor * rel_pos, // [L, C]
ggml_tensor * indices, // [q_size, k_size]
const int q_size,
const int k_size) {
const int64_t C = rel_pos->ne[0]; // channels
const int64_t L = rel_pos->ne[1]; // length
GGML_ASSERT(indices != nullptr);
GGML_ASSERT(indices->type == GGML_TYPE_I32);
GGML_ASSERT(indices->ne[0] == k_size);
GGML_ASSERT(indices->ne[1] == q_size);
const auto max_rel_dist = 2 * std::max(q_size, k_size) - 1;
ggml_tensor * cur = rel_pos;
if (max_rel_dist != L) {
// Linear interpolation
const int64_t ne0 = cur->ne[0];
const int64_t ne1 = cur->ne[1];
const int64_t ne2 = cur->ne[2];
const int64_t ne3 = cur->ne[3];
cur = ggml_reshape_3d(ctx0, ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 0, 2, 3)), ne1, 1, ne0 * ne2 * ne3);
cur = ggml_reshape_4d(
ctx0, ggml_interpolate(ctx0, cur, max_rel_dist, 1, ne0 * ne2 * ne3, 1, GGML_SCALE_MODE_BILINEAR),
max_rel_dist, ne0, ne2, ne3);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 0, 2, 3));
}
// Flatten indices to 1D for ggml_get_rows
const int qk = q_size * k_size;
cur = ggml_reshape_3d(ctx0, ggml_get_rows(ctx0, cur, ggml_reshape_1d(ctx0, indices, qk)), C, k_size, q_size);
return cur; // [C, k_size, q_size]
}
ggml_cgraph * clip_graph_deepseekocr::build() {
// patch embedding
ggml_tensor * inp_raw = build_inp_raw();
ggml_tensor * sam_out;
// Building SAM
{
const int n_embd = hparams.sam_n_embd;
const int n_layer = hparams.sam_n_layer;
const int n_heads = hparams.sam_n_head;
const int d_heads = n_embd / n_heads;
const int window = hparams.attn_window_size;
ggml_tensor * inpL;
inpL = ggml_conv_2d_sk_p0(ctx0, model.patch_embed_proj_w, inp_raw);
inpL = ggml_add(ctx0, inpL, ggml_reshape_3d(ctx0, model.patch_embed_proj_b, 1, 1, n_embd));
inpL = ggml_cont(ctx0, ggml_permute(ctx0, inpL, 1, 2, 0, 3));
ggml_tensor * rel_pos_indices_local;
ggml_tensor * rel_pos_indices_global;
rel_pos_indices_local = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, window, window);
rel_pos_indices_global = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, inpL->ne[1], inpL->ne[2]);
ggml_set_name(rel_pos_indices_local, "rel_pos_indices_local");
ggml_set_name(rel_pos_indices_global, "rel_pos_indices_global");
ggml_set_input(rel_pos_indices_local);
ggml_set_input(rel_pos_indices_global);
ggml_tensor * cur;
const auto tgt_size = inpL->ne[1];
const auto str_size = model.pos_embed->ne[1];
if (str_size != tgt_size) {
ggml_tensor * old_pos_embed = nullptr;
old_pos_embed = ggml_cont(ctx0, ggml_permute(ctx0, model.pos_embed, 2, 0, 1, 3));
ggml_tensor * new_pos_embed =
ggml_interpolate(ctx0, old_pos_embed, tgt_size, tgt_size, n_embd, 1, GGML_SCALE_MODE_BICUBIC);
new_pos_embed = ggml_cont(ctx0, ggml_permute(ctx0, new_pos_embed, 1, 2, 0, 3));
cur = ggml_add(ctx0, inpL, new_pos_embed);
} else {
cur = ggml_add(ctx0, inpL, model.pos_embed);
}
// loop over layers
for (int il = 0; il < n_layer; il++) {
auto & layer = model.sam_layers[il];
ggml_tensor * shortcut = cur;
// layernorm1
cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
const int64_t w0 = cur->ne[1];
const int64_t h0 = cur->ne[2];
ggml_tensor * indices;
if (hparams.is_global_attn(il)) {
indices = rel_pos_indices_global;
} else {
// local attention layer - apply window partition
cur = window_partition(ctx0, cur, window);
indices = rel_pos_indices_local;
}
const int64_t W = cur->ne[1];
const int64_t H = cur->ne[2];
// self-attention
{
const int B = cur->ne[3];
cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
cur = ggml_add(ctx0, cur, layer.qkv_b);
cur = ggml_cont(ctx0, cur); // Ensure tensor is contiguous before reshape
cur = ggml_reshape_4d(ctx0, cur, n_embd, 3, W * H, B);
ggml_tensor * Q;
ggml_tensor * K;
ggml_tensor * V;
Q = ggml_view_3d(ctx0, cur, n_embd, W * H, B, cur->nb[2], cur->nb[3], 0 * cur->nb[1]);
Q = ggml_reshape_4d(ctx0, ggml_cont(ctx0, Q), d_heads, n_heads, W * H, B);
K = ggml_view_3d(ctx0, cur, n_embd, W * H, B, cur->nb[2], cur->nb[3], 1 * cur->nb[1]);
K = ggml_reshape_4d(ctx0, ggml_cont(ctx0, K), d_heads, n_heads, W * H, B);
V = ggml_view_3d(ctx0, cur, n_embd, W * H, B, cur->nb[2], cur->nb[3], 2 * cur->nb[1]);
V = ggml_reshape_4d(ctx0, ggml_cont(ctx0, V), d_heads, n_heads, W * H, B);
ggml_tensor * mask;
ggml_tensor * rw;
ggml_tensor * rh;
ggml_tensor * qr;
rw = get_rel_pos(ctx0, layer.rel_pos_w, indices, W, W); // [W, W, C]
rh = get_rel_pos(ctx0, layer.rel_pos_h, indices, H, H); // [H, H, C]
qr = ggml_permute(ctx0, Q, 0, 2, 1, 3);
qr = ggml_reshape_4d(ctx0, ggml_cont(ctx0, qr), d_heads, W, H, B * n_heads);
rw = ggml_mul_mat(ctx0, rw,
ggml_cont(ctx0, ggml_permute(ctx0, qr, 0, 2, 1, 3))); // [B*n_heads, W, H, W]
rw = ggml_cont(ctx0, ggml_permute(ctx0, rw, 0, 2, 1, 3)); // [B*n_heads, H, W, W]
rw = ggml_reshape_4d(ctx0, rw, W, 1, W * H, n_heads * B);
rw = ggml_repeat_4d(ctx0, rw, W, H, W * H, n_heads * B);
rh = ggml_mul_mat(ctx0, rh, qr); // [B*n_heads, H, W, H]
rh = ggml_reshape_4d(ctx0, rh, 1, H, W * H, n_heads * B);
mask = ggml_add(ctx0, rw, rh); // [B*n_heads, H*W, H, W]
mask = ggml_reshape_4d(ctx0, mask, W * H, W * H, n_heads, B);
mask = ggml_cast(ctx0, mask, GGML_TYPE_F16);
const float scale = 1.0f / sqrtf(static_cast<float>(d_heads));
cur = build_attn(layer.o_w, layer.o_b, Q, K, V, mask, scale,
il); // [B, H*W, n_embd]
cur = ggml_reshape_4d(ctx0, ggml_cont(ctx0, cur), n_embd, W, H, B);
}
if (hparams.is_global_attn(il) == false) {
// local attention layer - reverse window partition
cur = window_unpartition(ctx0, cur, w0, h0, window);
}
// re-add the layer input, e.g., residual
cur = ggml_add(ctx0, cur, shortcut);
ggml_tensor * inpFF = cur;
// layernorm2
cur = build_norm(inpFF, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
// ffn
cur = build_ffn(cur, layer.ff_up_w, layer.ff_up_b, nullptr, nullptr, layer.ff_down_w, layer.ff_down_b,
hparams.ffn_op, il);
// residual 2
cur = ggml_add(ctx0, cur, inpFF);
cb(cur, "sam_layer_out", il);
}
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 2, 0, 1, 3));
cur = ggml_conv_2d(ctx0, model.neck_0_w, cur, 1, 1, 0, 0, 1, 1);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3));
cur = build_norm(cur, model.neck_1_w, model.neck_1_b, NORM_TYPE_NORMAL, hparams.eps, -1);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 2, 0, 1, 3));
cur = ggml_conv_2d(ctx0, model.neck_2_w, cur, 1, 1, 1, 1, 1, 1);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3));
cur = build_norm(cur, model.neck_3_w, model.neck_3_b, NORM_TYPE_NORMAL, hparams.eps, -1);
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 2, 0, 1, 3));
cur = ggml_conv_2d(ctx0, model.net_2, cur, 2, 2, 1, 1, 1, 1);
cur = ggml_conv_2d(ctx0, model.net_3, cur, 2, 2, 1, 1, 1, 1);
cb(cur, "sam_output", -1);
ggml_build_forward_expand(gf, cur);
sam_out = cur;
}
ggml_tensor * clip_out;
// Building DS-OCR CLIP
{
ggml_tensor * inp;
inp = ggml_cpy(ctx0, sam_out, ggml_dup_tensor(ctx0, sam_out));
inp = ggml_reshape_2d(ctx0, inp, inp->ne[0] * inp->ne[1], inp->ne[2]);
inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
ggml_tensor * new_pos_embd =
ggml_cpy(ctx0, model.position_embeddings, ggml_dup_tensor(ctx0, model.position_embeddings));
int n_pos = new_pos_embd->ne[1]; // +1 for [CLS]
const auto tgt_size = static_cast<int>(std::sqrt(inp->ne[1]));
const auto src_size = static_cast<int>(std::sqrt(n_pos - 1));
if (tgt_size != src_size) {
ggml_tensor * old_pos_embd;
ggml_tensor * cls_tok;
old_pos_embd = ggml_view_2d(ctx0, new_pos_embd, new_pos_embd->ne[0], src_size * src_size,
ggml_row_size(new_pos_embd->type, new_pos_embd->ne[0]), 0);
cls_tok = ggml_view_2d(ctx0, new_pos_embd, new_pos_embd->ne[0], 1,
ggml_row_size(new_pos_embd->type, new_pos_embd->ne[0]), src_size * src_size);
new_pos_embd = ggml_interpolate(ctx0, old_pos_embd, tgt_size, tgt_size, new_pos_embd->ne[0], 1,
GGML_SCALE_MODE_BICUBIC);
new_pos_embd = ggml_reshape_3d(ctx0, new_pos_embd, n_embd, tgt_size * tgt_size, 1);
new_pos_embd = ggml_concat(ctx0, new_pos_embd, cls_tok, 1);
n_pos = tgt_size * tgt_size + 1;
}
// add CLS token
inp = ggml_concat(ctx0, model.class_embedding, inp, 1);
// for selecting learned pos embd, used by ViT
ggml_tensor * positions = ggml_cast(ctx0, ggml_arange(ctx0, 0, n_pos, 1), GGML_TYPE_I32);
ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, new_pos_embd, positions);
ggml_tensor * cur = build_vit(inp, n_pos, NORM_TYPE_NORMAL, FFN_GELU_QUICK, learned_pos_embd, nullptr);
ggml_build_forward_expand(gf, cur);
clip_out = cur;
}
const int clip_n_patches = sam_out->ne[0] * sam_out->ne[1];
sam_out = ggml_cont(ctx0, ggml_permute(ctx0, sam_out, 1, 2, 0, 3));
sam_out = ggml_reshape_2d(ctx0, sam_out, sam_out->ne[0], clip_n_patches);
clip_out = ggml_view_2d(ctx0, clip_out, n_embd, clip_n_patches, clip_out->nb[1], clip_out->nb[1]);
ggml_tensor * cur;
cur = ggml_concat(ctx0, clip_out, sam_out, 0);
cur = ggml_reshape_2d(ctx0, cur, 2 * n_embd, clip_n_patches);
cur = ggml_cont(ctx0, cur);
cur = ggml_mul_mat(ctx0, model.mm_fc_w, cur);
cur = ggml_add(ctx0, cur, model.mm_fc_b);
const auto h = static_cast<int>(std::sqrt(static_cast<float>(cur->ne[1])));
const auto w = h;
const auto n_dim = cur->ne[0];
ggml_tensor * imgnl;
ggml_tensor * vs;
imgnl = ggml_repeat_4d(ctx0, model.image_newline, n_dim, 1, h, 1);
vs = ggml_reshape_2d(ctx0, model.view_seperator, n_dim, 1); // (n_dim, 1)
cur = ggml_reshape_3d(ctx0, cur, n_dim, w, h);
cur = ggml_reshape_2d(ctx0, ggml_concat(ctx0, cur, imgnl, 1), n_dim, (w + 1) * h);
cur = ggml_concat(ctx0, cur, vs, 1); // (n_dim, h*(w+1) + 1)
cb(cur, "dsocr_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}

2
tools/mtmd/models/glm4v.cpp
View File

@ -97,7 +97,7 @@ ggml_cgraph * clip_graph_glm4v::build() {
// FC projector
{
cur = build_mm(model.projection, cur);
cur = build_mm(model.mm_fc_w, cur);
// default LayerNorm (post_projection_norm)
cur = build_norm(cur, model.mm_post_norm_w, model.mm_post_norm_b, NORM_TYPE_NORMAL, 1e-5, -1);
cur = ggml_gelu_erf(ctx0, cur);

5
tools/mtmd/models/models.h
View File

@ -77,6 +77,11 @@ struct clip_graph_whisper_enc : clip_graph {
ggml_cgraph * build() override;
};
struct clip_graph_deepseekocr : clip_graph {
clip_graph_deepseekocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_conformer : clip_graph {
clip_graph_conformer(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;

2
tools/mtmd/models/siglip.cpp
View File

@ -43,7 +43,7 @@ ggml_cgraph * clip_graph_siglip::build() {
// https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578
const int scale_factor = model.hparams.n_merge;
cur = build_patch_merge_permute(cur, scale_factor);
cur = build_mm(model.projection, cur);
cur = build_mm(model.mm_fc_w, cur);
} else if (proj_type == PROJECTOR_TYPE_LFM2) {
// pixel unshuffle block

18
tools/mtmd/mtmd-audio.cpp
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@ -13,23 +13,20 @@
constexpr bool DEBUG = false;
void mtmd_audio_cache::fill_sin_cos_table(int n) {
void mtmd_audio_cache::fill_sin_cos_table(uint32_t n) {
sin_vals.resize(n);
cos_vals.resize(n);
for (int i = 0; i < n; i++) {
for (uint32_t i = 0; i < n; i++) {
double theta = (2 * M_PI * i) / n;
sin_vals[i] = sinf(theta);
cos_vals[i] = cosf(theta);
}
}
void mtmd_audio_cache::fill_hann_window(int length, bool periodic) {
void mtmd_audio_cache::fill_hann_window(uint32_t length, bool periodic) {
hann_window.resize(length);
int offset = -1;
if (periodic) {
offset = 0;
}
for (int i = 0; i < length; i++) {
int offset = periodic ? 0 : -1;
for (uint32_t i = 0; i < length; i++) {
hann_window[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
}
}
@ -165,6 +162,7 @@ static void dft_impl(const mtmd_audio_cache & cache, const float * in, int N, fl
// false = input is complex-valued (interleaved real/imag, stride 2)
template <bool Inverse, bool RealInput>
static void fft_impl(const mtmd_audio_cache & cache, float * in, int N, float * out) {
GGML_ASSERT(N > 0);
const int n_sin_cos_vals = cache.sin_vals.size();
if (N == 1) {
@ -407,6 +405,8 @@ static bool log_mel_spectrogram(
}
GGML_ASSERT(params.n_fft_bins > 0);
GGML_ASSERT(params.hop_length > 0);
out.n_mel = params.n_mel;
out.n_len = (n_samples - frame_size) / frame_step + 1;
// TODO: handle these checks better
@ -438,6 +438,7 @@ static bool log_mel_spectrogram(
const int effective_n_len = n_samples_in / frame_step;
if (params.norm_per_feature) {
GGML_ASSERT(effective_n_len > 1);
for (int i = 0; i < out.n_mel; i++) {
double mean = 0;
for (int j = 0; j < effective_n_len; ++j) {
@ -639,6 +640,7 @@ mtmd_audio_streaming_istft::mtmd_audio_streaming_istft(int n_fft, int hop_length
padding_to_remove((n_fft - hop_length) / 2),
ifft_in(n_fft * 2 * 4, 0.0f), // extra space for recursive IFFT
ifft_out(n_fft * 2 * 4, 0.0f) {
GGML_ASSERT(n_fft > 0 && hop_length > 0 && hop_length <= n_fft);
cache.fill_sin_cos_table(n_fft);
cache.fill_hann_window(n_fft, true);
}

4
tools/mtmd/mtmd-audio.h
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@ -33,9 +33,9 @@ struct mtmd_audio_cache {
mtmd_audio_mel_filters filters;
void fill_sin_cos_table(int n);
void fill_sin_cos_table(uint32_t n);
void fill_hann_window(int length, bool periodic);
void fill_hann_window(uint32_t length, bool periodic);
// Build mel filterbank matrix [n_mel × n_fft_bins] at runtime.
// n_fft_bins must be (N_fft / 2 + 1). Example: if N_fft=512 -> n_fft_bins=257.

10
tools/mtmd/mtmd-helper.cpp
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@ -127,6 +127,7 @@ struct decode_embd_batch {
std::vector<int8_t> logits;
llama_batch batch;
decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) {
GGML_ASSERT(n_tokens > 0 && n_pos_per_embd > 0 && n_mmproj_embd > 0);
pos .resize(n_tokens * n_pos_per_embd);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
@ -157,6 +158,7 @@ struct decode_embd_batch {
// M-RoPE for image
void set_position_mrope_2d(llama_pos pos_0, int nx, int ny, llama_seq_id seq_id) {
GGML_ASSERT(n_pos_per_embd == 4);
GGML_ASSERT(nx > 0 && ny > 0 && nx * ny == batch.n_tokens);
seq_id_0[0] = seq_id;
for (int y = 0; y < ny; y++) {
for (int x = 0; x < nx; x++) {
@ -192,6 +194,7 @@ struct decode_embd_batch {
}
llama_batch get_view(int offset, int n_tokens) {
GGML_ASSERT(offset >= 0 && n_tokens > 0 && offset + n_tokens <= batch.n_tokens);
llama_pos * pos_ptr;
pos_view.clear();
pos_view.reserve(n_tokens * n_pos_per_embd);
@ -235,6 +238,7 @@ int32_t mtmd_helper_decode_image_chunk(
llama_seq_id seq_id,
int32_t n_batch,
llama_pos * new_n_past) {
GGML_ASSERT(n_batch > 0);
auto chunk_type = mtmd_input_chunk_get_type(chunk);
const char * name = chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE ? "image" : "audio";
if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
@ -312,6 +316,7 @@ int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx,
int32_t n_batch,
bool logits_last,
llama_pos * new_n_past) {
GGML_ASSERT(n_batch > 0);
int32_t ret;
llama_batch text_batch = llama_batch_init(n_batch, 0, 1);
auto chunk_type = mtmd_input_chunk_get_type(chunk);
@ -508,6 +513,11 @@ mtmd_bitmap * mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char *
fseek(f, 0, SEEK_END);
long file_size = ftell(f);
fseek(f, 0, SEEK_SET);
if (file_size < 0) {
LOG_ERR("Failed to get file size of %s\n", fname);
fclose(f);
return nullptr;
}
buf.resize(file_size);
size_t n_read = fread(buf.data(), 1, file_size, f);

1170
tools/mtmd/mtmd-image.cpp Normal file
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150
tools/mtmd/mtmd-image.h Normal file
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@ -0,0 +1,150 @@
#pragma once
#include "ggml.h"
#include "clip-model.h"
#include <vector>
#include <string>
#define MTMD_INTERNAL_HEADER
// base class, models must inherit from this class
struct mtmd_image_preprocessor {
const clip_hparams & hparams;
mtmd_image_preprocessor(const clip_ctx * ctx): hparams(*clip_get_hparams(ctx)) {}
virtual ~mtmd_image_preprocessor() = default;
virtual bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) = 0;
void img_u8_to_f32(const clip_image_u8 & src, clip_image_f32 & dst, const float mean[3], const float std[3]);
void img_u8_to_f32(const clip_image_u8 & src, clip_image_f32 & dst);
};
/**
* implementation of LLaVA-UHD:
* - https://arxiv.org/pdf/2403.11703
* - https://github.com/thunlp/LLaVA-UHD
* - https://github.com/thunlp/LLaVA-UHD/blob/302301bc2175f7e717fb8548516188e89f649753/llava_uhd/train/llava-uhd/slice_logic.py#L118
*
* overview:
* - an image always have a single overview (downscaled image)
* - an image can have 0 or multiple slices, depending on the image size
* - each slice can then be considered as a separate image
*
* note: the term "slice" and "tile" are used interchangeably
*
* for example:
*
* [overview] --> [slice 1] --> [slice 2]
* | |
* +--> [slice 3] --> [slice 4]
*/
struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor {
mtmd_image_preprocessor_llava_uhd(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
struct slice_coordinates {
int x;
int y;
clip_image_size size;
};
struct slice_instructions {
clip_image_size overview_size; // size of downscaled image
clip_image_size refined_size; // size of image right before slicing (must be multiple of slice size)
clip_image_size grid_size; // grid_size.width * grid_size.height = number of slices
std::vector<slice_coordinates> slices;
};
// LFM2 override this function to implement its custom slicing logic
virtual slice_instructions get_slice_instructions(const clip_image_size & original_size);
std::vector<clip_image_u8_ptr> slice_image(const clip_image_u8 & img, const slice_instructions & inst, bool overview_first = true);
private:
clip_image_size get_best_resize(const clip_image_size & original_size, int scale_resolution, int patch_size, bool allow_upscale = false);
clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max);
/**
* Selects the best resolution from a list of possible resolutions based on the original size.
*
* For example, when given a list of resolutions:
* - 100x100
* - 200x100
* - 100x200
* - 200x200
*
* And an input image of size 111x200, then 100x200 is the best fit (least wasted resolution).
*
* @param original_size The original size of the image
* @param possible_resolutions A list of possible resolutions
* @return The best fit resolution
*/
clip_image_size select_best_resolution(const clip_image_size & original_size, const std::vector<clip_image_size> & possible_resolutions);
int ensure_divide(int length, int patch_size);
clip_image_size get_refine_size(const clip_image_size & original_size, const clip_image_size & grid, int scale_resolution, int patch_size, bool allow_upscale = false);
clip_image_size get_best_grid(const int max_slice_nums, const int multiple, const float log_ratio);
};
// downscale or upscale the input image to fixed size
struct mtmd_image_preprocessor_fixed_size : mtmd_image_preprocessor {
mtmd_image_preprocessor_fixed_size(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
};
// resize image to multiple of patch_size*n_merge, while preserving aspect ratio
// if image_resize_pad is true, the resized image will be padded, otherwise it will be either stretched or center-cropped depending on image_resize_pad
// this is used by models with native support for dynamic image size, for example: Qwen-VL, Pixtral, Kimi-VL, etc
struct mtmd_image_preprocessor_dyn_size : mtmd_image_preprocessor {
mtmd_image_preprocessor_dyn_size(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
};
// similar to mtmd_image_preprocessor_dyn_size, but resize the image to have longest edge equal to hparams.image_longest_edge, while preserving aspect ratio
struct mtmd_image_preprocessor_longest_edge : mtmd_image_preprocessor {
mtmd_image_preprocessor_longest_edge(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
};
// custom llava-uhd slicing logic for LFM2
// ref: https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/lfm2_vl/image_processing_lfm2_vl_fast.py
struct mtmd_image_preprocessor_lfm2 : mtmd_image_preprocessor_llava_uhd {
// ref: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B/blob/main/processor_config.json
static constexpr int min_tiles = 2;
static constexpr int max_tiles = 10;
static constexpr float max_pixels_tolerance = 2.0f;
static constexpr int tile_size = 512;
using mtmd_image_preprocessor_llava_uhd::mtmd_image_preprocessor_llava_uhd;
slice_instructions get_slice_instructions(const clip_image_size & original_size) override;
private:
clip_image_size find_closest_aspect_ratio(
float aspect_ratio,
const std::vector<clip_image_size> & target_ratios,
int width, int height);
std::vector<clip_image_size> get_target_ratios();
clip_image_size get_grid_layout(int height, int width);
};
struct mtmd_image_preprocessor_idefics3 : mtmd_image_preprocessor_llava_uhd {
mtmd_image_preprocessor_idefics3(const clip_ctx * ctx) : mtmd_image_preprocessor_llava_uhd(ctx) {}
bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
};
struct mtmd_image_preprocessor_internvl : mtmd_image_preprocessor_llava_uhd {
mtmd_image_preprocessor_internvl(const clip_ctx * ctx) : mtmd_image_preprocessor_llava_uhd(ctx) {}
bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
};
struct mtmd_image_preprocessor_deepseekocr : mtmd_image_preprocessor {
mtmd_image_preprocessor_deepseekocr(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
};
struct mtmd_image_preprocessor_youtuvl : mtmd_image_preprocessor {
mtmd_image_preprocessor_youtuvl(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
};

355
tools/mtmd/mtmd.cpp
View File

@ -2,6 +2,7 @@
#include "clip-impl.h"
#include "mtmd.h"
#include "mtmd-audio.h"
#include "mtmd-image.h"
#include "debug/mtmd-debug.h"
#include "llama.h"
@ -138,7 +139,7 @@ struct mtmd_context {
// for llava-uhd style models, we need special tokens in-between slices
// minicpmv calls them "slices", llama 4 calls them "tiles"
mtmd_slice_tmpl slice_tmpl = MTMD_SLICE_TMPL_NONE;
mtmd_slice_tmpl slice_tmpl = MTMD_SLICE_TMPL_NONE;
std::vector<llama_token> tok_ov_img_start; // overview image
std::vector<llama_token> tok_ov_img_end; // overview image
std::vector<llama_token> tok_slices_start; // start of all slices
@ -147,13 +148,14 @@ struct mtmd_context {
std::vector<llama_token> tok_sli_img_end; // single slice end
std::vector<llama_token> tok_sli_img_mid; // between 2 slices
std::vector<llama_token> tok_row_end; // end of row
bool tok_row_end_trail = false;
bool ov_img_first = false;
bool tok_row_end_trail = false;
bool ov_img_first = false;
// string template for slice image delimiters with row/col (idefics3)
std::string sli_img_start_tmpl;
std::unique_ptr<mtmd_audio_preprocessor> audio_preproc;
std::unique_ptr<mtmd_image_preprocessor> image_preproc;
// TODO @ngxson : add timings
@ -221,123 +223,193 @@ struct mtmd_context {
void init_vision() {
GGML_ASSERT(ctx_v != nullptr);
image_preproc.reset();
projector_type proj = clip_get_projector_type(ctx_v);
int minicpmv_version = clip_is_minicpmv(ctx_v);
if (minicpmv_version == 2) {
// minicpmv 2.5 format:
// <image> (overview) </image><slice><image> (slice) </image><image> (slice) </image>\n ... </slice>
slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_5;
tok_ov_img_start = {lookup_token("<image>")};
tok_ov_img_end = {lookup_token("</image>")};
tok_slices_start = {lookup_token("<slice>")};
tok_slices_end = {lookup_token("</slice>")};
tok_sli_img_start = tok_ov_img_start;
tok_sli_img_end = tok_ov_img_end;
tok_row_end = {lookup_token("\n")};
tok_row_end_trail = false; // no trailing end-of-row token
ov_img_first = true;
} else if (minicpmv_version == 3 || minicpmv_version == 4 || minicpmv_version == 5 || minicpmv_version == 6 || minicpmv_version == 100045) {
// minicpmv 2.6 format:
// <image> (overview) </image><slice> (slice) </slice><slice> (slice) </slice>\n ...
slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_6;
tok_ov_img_start = {lookup_token("<image>")};
tok_ov_img_end = {lookup_token("</image>")};
tok_sli_img_start = {lookup_token("<slice>")};
tok_sli_img_end = {lookup_token("</slice>")};
tok_row_end = {lookup_token("\n")};
tok_row_end_trail = false; // no trailing end-of-row token
ov_img_first = true;
switch (proj) {
case PROJECTOR_TYPE_MLP:
case PROJECTOR_TYPE_MLP_NORM:
case PROJECTOR_TYPE_LDP:
case PROJECTOR_TYPE_LDPV2:
case PROJECTOR_TYPE_COGVLM:
case PROJECTOR_TYPE_JANUS_PRO:
case PROJECTOR_TYPE_GLM_EDGE:
{
bool has_pinpoints = !clip_get_hparams(ctx_v)->image_res_candidates.empty();
if (has_pinpoints) {
image_preproc = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
} else {
image_preproc = std::make_unique<mtmd_image_preprocessor_fixed_size>(ctx_v);
}
} break;
case PROJECTOR_TYPE_MINICPMV:
{
int minicpmv_version = clip_is_minicpmv(ctx_v);
if (minicpmv_version == 2) {
// minicpmv 2.5 format:
// <image> (overview) </image><slice><image> (slice) </image><image> (slice) </image>\n ... </slice>
slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_5;
tok_ov_img_start = {lookup_token("<image>")};
tok_ov_img_end = {lookup_token("</image>")};
tok_slices_start = {lookup_token("<slice>")};
tok_slices_end = {lookup_token("</slice>")};
tok_sli_img_start = tok_ov_img_start;
tok_sli_img_end = tok_ov_img_end;
tok_row_end = {lookup_token("\n")};
tok_row_end_trail = false; // no trailing end-of-row token
ov_img_first = true;
} else if (minicpmv_version != 0) {
GGML_ASSERT(false && "unsupported minicpmv version");
} else if (proj == PROJECTOR_TYPE_LLAMA4) {
// llama 4 format:
// <|image_start|>
// (slice) <|tile_x_separator|> (slice) <|tile_x_separator|> ... <|tile_y_separator|>
// (slice) <|tile_x_separator|> (slice) <|tile_x_separator|> ... <|tile_y_separator|>
// ... <|tile_y_separator|> <-- trailing end-of-row token
// <|image|> (overview) <-- overview image is last
// <|image_end|>
slice_tmpl = MTMD_SLICE_TMPL_LLAMA4;
tok_ov_img_start = {lookup_token("<|image|>")};
tok_sli_img_mid = {lookup_token("<|tile_x_separator|>")};
tok_row_end = {lookup_token("<|tile_y_separator|>")};
tok_row_end_trail = true; // add trailing end-of-row token
ov_img_first = false; // overview image is last
} else if (minicpmv_version == 3 || minicpmv_version == 4 || minicpmv_version == 5 || minicpmv_version == 6 || minicpmv_version == 100045) {
// minicpmv 2.6 format:
// <image> (overview) </image><slice> (slice) </slice><slice> (slice) </slice>\n ...
slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_6;
tok_ov_img_start = {lookup_token("<image>")};
tok_ov_img_end = {lookup_token("</image>")};
tok_sli_img_start = {lookup_token("<slice>")};
tok_sli_img_end = {lookup_token("</slice>")};
tok_row_end = {lookup_token("\n")};
tok_row_end_trail = false; // no trailing end-of-row token
ov_img_first = true;
} else if (minicpmv_version != 0) {
throw std::runtime_error(string_format("unsupported minicpmv version: %d\n", minicpmv_version));
}
image_preproc = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
} break;
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
{
// <|vision_start|> ... (image embeddings) ... <|vision_end|>
img_beg = "<|vision_start|>";
img_end = "<|vision_end|>";
image_preproc = std::make_unique<mtmd_image_preprocessor_dyn_size>(ctx_v);
} break;
case PROJECTOR_TYPE_YOUTUVL:
{
// <|vision_start|> ... (image embeddings) ... <|vision_end|>
img_beg = "<|vision_start|>";
img_end = "<|vision_end|>";
image_preproc = std::make_unique<mtmd_image_preprocessor_youtuvl>(ctx_v);
} break;
case PROJECTOR_TYPE_GEMMA3:
case PROJECTOR_TYPE_GEMMA3NV:
{
// <start_of_image> ... (image embeddings) ... <end_of_image>
img_beg = "<start_of_image>";
img_end = "<end_of_image>";
image_preproc = std::make_unique<mtmd_image_preprocessor_fixed_size>(ctx_v);
} break;
case PROJECTOR_TYPE_IDEFICS3:
{
// https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215
slice_tmpl = MTMD_SLICE_TMPL_IDEFICS3;
tok_ov_img_start = {lookup_token("\n\n"), lookup_token("<fake_token_around_image>"), lookup_token("<global-img>")};
tok_ov_img_end = {lookup_token("<fake_token_around_image>")};
tok_row_end = {lookup_token("\n")};
sli_img_start_tmpl = "<fake_token_around_image><row_%d_col_%d>";
image_preproc = std::make_unique<mtmd_image_preprocessor_idefics3>(ctx_v);
} break;
case PROJECTOR_TYPE_PIXTRAL:
{
// https://github.com/huggingface/transformers/blob/1cd110c6cb6a6237614130c470e9a902dbc1a4bd/docs/source/en/model_doc/pixtral.md
img_end = "[IMG_END]";
image_preproc = std::make_unique<mtmd_image_preprocessor_dyn_size>(ctx_v);
} break;
case PROJECTOR_TYPE_PHI4:
{
// Phi-4 uses media marker insertion only. Keep image boundary text empty.
image_preproc = std::make_unique<mtmd_image_preprocessor_dyn_size>(ctx_v);
} break;
case PROJECTOR_TYPE_LLAMA4:
{
// (more details in mtmd_context constructor)
img_beg = "<|image_start|>";
img_end = "<|image_end|>";
LOG_WRN("%s: llama 4 vision is known to have degraded quality:\n"
" https://github.com/ggml-org/llama.cpp/pull/13282\n", __func__);
image_preproc = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
} break;
case PROJECTOR_TYPE_INTERNVL:
{
// <img> ... (image embeddings) ... </img>
img_beg = "<img>";
img_end = "</img>";
image_preproc = std::make_unique<mtmd_image_preprocessor_internvl>(ctx_v);
} break;
case PROJECTOR_TYPE_KIMIVL:
{
// <|media_start|> ... (image embeddings) ... <|media_end|>
img_beg = "<|media_start|>";
img_end = "<|media_end|>";
image_preproc = std::make_unique<mtmd_image_preprocessor_dyn_size>(ctx_v);
} break;
case PROJECTOR_TYPE_KIMIK25:
{
// <|media_begin|> ... (image embeddings) ... <|media_end|>
img_beg = "<|media_begin|>";
img_end = "<|media_end|>";
image_preproc = std::make_unique<mtmd_image_preprocessor_dyn_size>(ctx_v);
} break;
case PROJECTOR_TYPE_LIGHTONOCR:
{
// <|im_start|> ... (image embeddings) ... <|im_end|>
img_beg = "<|im_start|>";
img_end = "<|im_end|>";
image_preproc = std::make_unique<mtmd_image_preprocessor_longest_edge>(ctx_v);
} break;
case PROJECTOR_TYPE_NEMOTRON_V2_VL:
{
image_preproc = std::make_unique<mtmd_image_preprocessor_fixed_size>(ctx_v);
} break;
case PROJECTOR_TYPE_LFM2:
{
// multi-tile:
// <|image_start|>
// <|img_row_1_col_1|> (tile) <|img_row_1_col_2|> (tile) ...
// <|img_thumbnail|> (thumbnail)
// <|image_end|>
// single-tile:
// <|image_start|> (image) <|image_end|>
img_beg = "<|image_start|>";
img_end = "<|image_end|>";
slice_tmpl = MTMD_SLICE_TMPL_LFM2;
sli_img_start_tmpl = "<|img_row_%d_col_%d|>";
tok_ov_img_start = {lookup_token("<|img_thumbnail|>")};
ov_img_first = false;
image_preproc = std::make_unique<mtmd_image_preprocessor_lfm2>(ctx_v);
} break;
case PROJECTOR_TYPE_GLM4V:
{
// <|begin_of_image|> ... (image embeddings) ... <|end_of_image|>
img_beg = "<|begin_of_image|>";
img_end = "<|end_of_image|>";
image_preproc = std::make_unique<mtmd_image_preprocessor_dyn_size>(ctx_v);
} break;
case PROJECTOR_TYPE_PADDLEOCR:
{
// <|IMAGE_START|> ... (image embeddings) ... <|IMAGE_END|>
img_beg = "<|IMAGE_START|>";
img_end = "<|IMAGE_END|>";
image_preproc = std::make_unique<mtmd_image_preprocessor_dyn_size>(ctx_v);
} break;
case PROJECTOR_TYPE_DEEPSEEKOCR:
{
img_end = "\n"; // prevent empty batch on llama-server
image_preproc = std::make_unique<mtmd_image_preprocessor_deepseekocr>(ctx_v);
} break;
default:
throw std::runtime_error(string_format("%s: unexpected vision projector type %d\n", __func__, proj));
}
// set boi/eoi
if (proj == PROJECTOR_TYPE_GEMMA3 || proj == PROJECTOR_TYPE_GEMMA3NV) {
// <start_of_image> ... (image embeddings) ... <end_of_image>
img_beg = "<start_of_image>";
img_end = "<end_of_image>";
} else if (proj == PROJECTOR_TYPE_IDEFICS3) {
// https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215
slice_tmpl = MTMD_SLICE_TMPL_IDEFICS3;
tok_ov_img_start = {lookup_token("\n\n"), lookup_token("<fake_token_around_image>"), lookup_token("<global-img>")};
tok_ov_img_end = {lookup_token("<fake_token_around_image>")};
tok_row_end = {lookup_token("\n")};
sli_img_start_tmpl = "<fake_token_around_image><row_%d_col_%d>";
} else if (proj == PROJECTOR_TYPE_PIXTRAL) {
// https://github.com/huggingface/transformers/blob/1cd110c6cb6a6237614130c470e9a902dbc1a4bd/docs/source/en/model_doc/pixtral.md
img_end = "[IMG_END]";
} else if (proj == PROJECTOR_TYPE_QWEN2VL || proj == PROJECTOR_TYPE_QWEN25VL || proj == PROJECTOR_TYPE_QWEN3VL || proj == PROJECTOR_TYPE_YOUTUVL) {
// <|vision_start|> ... (image embeddings) ... <|vision_end|>
img_beg = "<|vision_start|>";
img_end = "<|vision_end|>";
} else if (proj == PROJECTOR_TYPE_PHI4) {
// Phi-4 uses media marker insertion only. Keep image boundary text empty.
} else if (proj == PROJECTOR_TYPE_LLAMA4) {
// (more details in mtmd_context constructor)
img_beg = "<|image_start|>";
img_end = "<|image_end|>";
LOG_WRN("%s: llama 4 vision is known to have degraded quality:\n"
" https://github.com/ggml-org/llama.cpp/pull/13282\n", __func__);
} else if (proj == PROJECTOR_TYPE_INTERNVL) {
// <img> ... (image embeddings) ... </img>
img_beg = "<img>";
img_end = "</img>";
} else if (proj == PROJECTOR_TYPE_LIGHTONOCR) {
// <|im_start|> ... (image embeddings) ... <|im_end|>
img_beg = "<|im_start|>";
img_end = "<|im_end|>";
} else if (proj == PROJECTOR_TYPE_LFM2) {
// multi-tile:
// <|image_start|>
// <|img_row_1_col_1|> (tile) <|img_row_1_col_2|> (tile) ...
// <|img_thumbnail|> (thumbnail)
// <|image_end|>
// single-tile:
// <|image_start|> (image) <|image_end|>
img_beg = "<|image_start|>";
img_end = "<|image_end|>";
slice_tmpl = MTMD_SLICE_TMPL_LFM2;
sli_img_start_tmpl = "<|img_row_%d_col_%d|>";
tok_ov_img_start = {lookup_token("<|img_thumbnail|>")};
ov_img_first = false;
} else if (proj == PROJECTOR_TYPE_GLM4V) {
img_beg = "<|begin_of_image|>";
img_end = "<|end_of_image|>";
} else if (proj == PROJECTOR_TYPE_PADDLEOCR) {
// <|IMAGE_START|> ... (image embeddings) ... <|IMAGE_END|>
img_beg = "<|IMAGE_START|>";
img_end = "<|IMAGE_END|>";
}
GGML_ASSERT(image_preproc != nullptr);
}
void init_audio() {
GGML_ASSERT(ctx_a != nullptr);
audio_preproc.reset();
projector_type proj = clip_get_projector_type(ctx_a);
LOG_WRN("%s: audio input is in experimental stage and may have reduced quality:\n"
@ -347,36 +419,40 @@ struct mtmd_context {
switch (proj) {
case PROJECTOR_TYPE_QWEN2A:
case PROJECTOR_TYPE_QWEN25O:
case PROJECTOR_TYPE_ULTRAVOX:
{
// <|audio_bos|> ... (embeddings) ... <|audio_eos|>
aud_beg = "<|audio_bos|>";
aud_end = "<|audio_eos|>";
audio_preproc = std::make_unique<mtmd_audio_preprocessor_whisper>(ctx_a);
} break;
case PROJECTOR_TYPE_VOXTRAL:
case PROJECTOR_TYPE_GLMA:
{
// [BEGIN_AUDIO] ... (embeddings) ...
aud_beg = "[BEGIN_AUDIO]";
audio_preproc = std::make_unique<mtmd_audio_preprocessor_whisper>(ctx_a);
} break;
case PROJECTOR_TYPE_MUSIC_FLAMINGO:
audio_preproc = std::make_unique<mtmd_audio_preprocessor_whisper>(ctx_a);
break;
{
// <sound> ... (embeddings) ...
aud_beg = "<sound>";
audio_preproc = std::make_unique<mtmd_audio_preprocessor_whisper>(ctx_a);
} break;
case PROJECTOR_TYPE_ULTRAVOX:
case PROJECTOR_TYPE_GLMA:
{
audio_preproc = std::make_unique<mtmd_audio_preprocessor_whisper>(ctx_a);
} break;
case PROJECTOR_TYPE_LFM2A:
audio_preproc = std::make_unique<mtmd_audio_preprocessor_conformer>(ctx_a);
break;
{
audio_preproc = std::make_unique<mtmd_audio_preprocessor_conformer>(ctx_a);
} break;
default:
GGML_ABORT("unsupported audio projector type");
throw std::runtime_error(string_format("%s: unexpected audio projector type %d\n", __func__, proj));
}
// initialize audio preprocessor
GGML_ASSERT(audio_preproc != nullptr);
audio_preproc->initialize();
// set special tokens
if (proj == PROJECTOR_TYPE_QWEN2A) {
// <|audio_bos|> ... (embeddings) ... <|audio_eos|>
aud_beg = "<|audio_bos|>";
aud_end = "<|audio_eos|>";
} else if (proj == PROJECTOR_TYPE_ULTRAVOX) {
// [BEGIN_AUDIO] ... (embeddings) ...
aud_beg = "[BEGIN_AUDIO]";
} else if (proj == PROJECTOR_TYPE_MUSIC_FLAMINGO) {
// <sound> ... (embeddings) ...
aud_beg = "<sound>";
}
}
// get clip ctx based on chunk type
@ -565,6 +641,11 @@ struct mtmd_tokenizer {
add_text(ctx->img_beg, true); // add image begin token
}
// sanity check
GGML_ASSERT(bitmap->nx > 0 && bitmap->ny > 0);
GGML_ASSERT(bitmap->data.size() == (size_t)bitmap->nx * bitmap->ny * 3);
GGML_ASSERT(ctx->image_preproc != nullptr);
// convert mtmd_bitmap to clip_image_u8
clip_image_u8_ptr img_u8(clip_image_u8_init());
img_u8->nx = bitmap->nx;
@ -574,7 +655,7 @@ struct mtmd_tokenizer {
// preprocess image
clip_image_f32_batch batch_f32;
bool ok = clip_image_preprocess(ctx->ctx_v, img_u8.get(), &batch_f32);
bool ok = ctx->image_preproc->preprocess(*img_u8, batch_f32);
if (!ok) {
LOG_ERR("Unable to preprocess image\n");
return 2;
@ -696,6 +777,11 @@ struct mtmd_tokenizer {
add_text(ctx->aud_beg, true); // add audio begin token
}
// sanity check
GGML_ASSERT(ctx->audio_preproc != nullptr);
GGML_ASSERT(bitmap->data.size() > sizeof(float));
GGML_ASSERT(bitmap->data.size() % sizeof(float) == 0);
// preprocess audio
std::vector<mtmd_audio_mel> mel_spec_chunks;
const float * samples = (const float *)bitmap->data.data();
@ -1225,7 +1311,8 @@ void mtmd_debug_preprocess_image(mtmd_context * ctx, const std::vector<uint8_t>
img_u8.ny = ny;
img_u8.buf = rgb_values;
clip_image_f32_batch batch_f32;
bool ok = clip_image_preprocess(ctx->ctx_v, &img_u8, &batch_f32);
GGML_ASSERT(ctx->image_preproc != nullptr);
bool ok = ctx->image_preproc->preprocess(img_u8, batch_f32);
if (!ok) {
LOG_ERR("%s: failed to preprocess image\n", __func__);
return;

2
tools/mtmd/tests.sh
View File

@ -88,6 +88,7 @@ add_test_vision "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"
add_test_vision "ggml-org/LFM2-VL-450M-GGUF:Q8_0"
add_test_vision "ggml-org/granite-docling-258M-GGUF:Q8_0"
add_test_vision "ggml-org/LightOnOCR-1B-1025-GGUF:Q8_0"
add_test_vision "ggml-org/DeepSeek-OCR-GGUF:Q8_0" -p "Free OCR." --chat-template deepseek-ocr
add_test_audio "ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF:Q8_0"
add_test_audio "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"
@ -108,6 +109,7 @@ if [ "$RUN_BIG_TESTS" = true ]; then
add_test_vision "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
# add_test_vision "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra
# add_test_vision "ggml-org/Kimi-VL-A3B-Thinking-2506-GGUF:Q4_K_M" # not always working
add_test_vision "ggml-org/GLM-4.6V-Flash-GGUF:Q4_K_M" -p "extract all texts from this image"
add_test_audio "ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF:Q4_K_M"
add_test_audio "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"

85
tools/mtmd/tests/test-1-extracted.md Normal file
View File

@ -0,0 +1,85 @@
<|ref|>title<|/ref|><|det|>[[61, 255, 907, 533]]<|/det|>
# MEN WALK ON MOON
ASTRONAUTS LAND ON PLAIN;
COLLECT ROCKS, PLANT FLAG
<|ref|>text<|/ref|><|det|>[[56, 559, 268, 629]]<|/det|>
Voice From Moon:
Eagle Has Landed'
<|ref|>text<|/ref|><|det|>[[74, 645, 262, 675]]<|/det|>
EAGLE (the lunar surface, Houston, Truesquily)
Base here, The Eagle has landed.
<|ref|>text<|/ref|><|det|>[[74, 675, 262, 720]]<|/det|>
BOOTHROOM: Lounge, Truesquily, we enjoy you on the ground. You've got a bunch of guys about to toss bikes. We're breaking again. Thanks a lot.
<|ref|>text<|/ref|><|det|>[[74, 720, 262, 750]]<|/det|>
TRAVELLING MADE: Time you. BOOTHROOM: You're looking good here.
<|ref|>text<|/ref|><|det|>[[74, 750, 262, 780]]<|/det|>
TRAVELLING MADE: A very smooth touchdown. BEDROOM: Eagle, you are very far. I'll. (The first sign in the lunar appearance) (Over.)
<|ref|>text<|/ref|><|det|>[[74, 780, 262, 810]]<|/det|>
TRAVELLING MADE: Eagle, stay for I'll. BOOTHROOM: Bumper and we are you waiting the cue.
<|ref|>text<|/ref|><|det|>[[74, 810, 262, 830]]<|/det|>
TRAVELLING MADE: Eagle, and service mobility.
<|ref|>text<|/ref|><|det|>[[74, 830, 262, 850]]<|/det|>
How do you read me?
<|ref|>text<|/ref|><|det|>[[74, 850, 262, 880]]<|/det|>
TRAVELLING COLUMBIA, he has landed Truesquily. Base, Eagle is at Truesquily. I read you first by. Over.
<|ref|>text<|/ref|><|det|>[[74, 880, 262, 900]]<|/det|>
COLUMBIA: Yes, I heard the whole thing.
<|ref|>text<|/ref|><|det|>[[74, 900, 262, 920]]<|/det|>
BOOTHROOM: Well, it's a good show.
<|ref|>text<|/ref|><|det|>[[74, 920, 262, 940]]<|/det|>
COLUMBIA: Fantastic.
<|ref|>text<|/ref|><|det|>[[74, 940, 262, 960]]<|/det|>
TRAVELLING MADE: I'll read that.
<|ref|>text<|/ref|><|det|>[[74, 960, 262, 980]]<|/det|>
APOLLO CONTROL: The most major sky to sky will be for the 23 event, that is at 21 minutes 26 sec-
<|ref|>text<|/ref|><|det|>[[74, 980, 262, 990]]<|/det|>
tion of lunar descent.
<|ref|>image<|/ref|><|det|>[[270, 545, 697, 990]]<|/det|>
<|ref|>text<|/ref|><|det|>[[715, 559, 911, 629]]<|/det|>
A Powdery Surface
Is Closely Explored
<|ref|>text<|/ref|><|det|>[[733, 645, 851, 665]]<|/det|>
BY JOHN NOBLE WILFORD
<|ref|>text<|/ref|><|det|>[[715, 669, 911, 700]]<|/det|>
HOUSTON, Monday, July 21—New hires landed and walked on the moon.
<|ref|>text<|/ref|><|det|>[[715, 700, 911, 750]]<|/det|>
Two Americans, astronauts of Apollo 11, steered their Eagle-shaped lunar module safely and smoothly to the lunar landing yesterday at 4:17:40 P.M., Eastern day-light time.
<|ref|>text<|/ref|><|det|>[[715, 750, 911, 780]]<|/det|>
Neil A. Armstrong, the 38-year-old civilian commander, radioed to earth and the landing team here.
<|ref|>text<|/ref|><|det|>[[715, 780, 911, 830]]<|/det|>
"Boom, Truesquily! Base here. The Eagle has landed," the first man to reach the moon—Neil Armstrong and his engineer, Capt. Charles E. Alder, of the Jet Propulsion Laboratory, the space agency's rocket and space program manager.
<|ref|>text<|/ref|><|det|>[[715, 830, 911, 880]]<|/det|>
About six and a half hours later, Mr. Armstrong opened the landing craft's hatch, stepped slowly down the ladder and descended as he pointed his first landing footguard on the lunar crater.
<|ref|>text<|/ref|><|det|>[[715, 880, 911, 920]]<|/det|>
"That's one small step for man, one giant leap for mankind."
<|ref|>text<|/ref|><|det|>[[715, 920, 911, 960]]<|/det|>
His first step on the moon came on 10:56:29 P.M., as a television camera recorded the craft's transmitted his every word to an aerial and excited audiences of hundreds of millions of people on earth.
<|ref|>text<|/ref|><|det|>[[749, 960, 861, 974]]<|/det|>
Testable Slope Test Soil

42
tools/mtmd/tests/test-1-extracted.txt Normal file
View File

@ -0,0 +1,42 @@
MEN WALK ON MOON
ASTRONAUTS LAND ON PLAIN;
COLLECT ROCKS, PLANT FLAG
Voice From Moon:
'Eagle Has Landed'
A Powder Surface
Is Closely Explored
By JOHN NOBLE WILFORD
NOVEMBER, Monday, July 21—New York Herald and
wished on the moon.
Two American astronauts of Apollo 11, steered their
frigate Eagle toward the moon's surface and smoothly to
the lunar landing yesterday at 4:17:40 P.M., Eastern day-
light time.
Neil A. Armstrong, the 38-year-old civilian commander,
landed on the soft sand of the moon's surface here.
"Beautiful, Triumph!" he said. "The Eagle has landed."
The first man to reach the moon—Neil Armstrong and
his co-pilot, Charles E. "Pete" Conrad, 26, of the Pentagon,
brought their ship to rest on a level, rock-strewn plain near
the moon's surface. The two men and two of the three
astronauts on board, Armstrong, Conrad and Edwin E.
Aldrin, 38, of Houston, stepped slowly down the ladder
and descended as he pointed his first full-flaming footpad
at the lunar crater.
"That's one small step for man, one giant leap for
mankind."
His first step on the moon came at 10:56:20 P.M., as
a television camera rolled the earth's thousandth line every
second to an aerial and studied audiences of hundreds of
millions of people on earth.
Textile Slope Test Soil

186
tools/mtmd/tests/test-deepseek-ocr.py Normal file
View File

@ -0,0 +1,186 @@
#!/usr/bin/env python3
"""
Test script to compare llama.cpp mtmd-cli output with HuggingFace reference implementation
for DeepSeek-OCR model using embedding similarity.
"""
import argparse
import subprocess
import sys
from pathlib import Path
from sentence_transformers import SentenceTransformer
from sentence_transformers import util
def run_mtmd_deepseek_ocr(
model_path: str,
mmproj_path: str,
image_path: str,
bin_path: str,
prompt: str = "Free OCR."
) -> str:
"""
Run inference using llama.cpp mtmd-cli.
"""
cmd = [
bin_path,
"-m", model_path,
"--mmproj", mmproj_path,
"--image", image_path,
# "-p", "<|grounding|>Convert the document to markdown.",
"-p", prompt,
"--chat-template", "deepseek-ocr",
"--temp", "0",
"-n", "1024",
# "--verbose"
]
print(f"Running llama.cpp command: {' '.join(cmd)}")
result = subprocess.run(
cmd,
capture_output=True,
text=False,
timeout=300
)
if result.returncode != 0:
stderr = result.stderr.decode('utf-8', errors='replace')
print(f"llama.cpp stderr: {stderr}")
raise RuntimeError(f"llama-mtmd-cli failed with code {result.returncode}")
output = result.stdout.decode('utf-8', errors='replace').strip()
print(f"llama.cpp output length: {len(output)} chars")
return output
def compute_embedding_similarity(text1: str, text2: str, model_name: str) -> float:
"""
Compute cosine similarity between two texts using embedding model.
"""
print(f"Loading embedding model: {model_name}")
# Use sentence-transformers for easier embedding extraction
embed_model = SentenceTransformer(model_name)
print("Computing embeddings...")
embeddings = embed_model.encode([text1, text2], convert_to_numpy=True)
similarity = util.similarity.cos_sim([embeddings[0]], [embeddings[1]])[0][0]
return float(similarity)
def read_expected_output(file_path: str) -> str:
"""
Read expected OCR output from file.
"""
cur_path = Path(__file__).parent
expected_path = str(cur_path / file_path)
with open(expected_path, "r", encoding="utf-8") as f:
return f.read().strip()
def main():
ap = argparse.ArgumentParser(description="Compare llama.cpp and HuggingFace DeepSeek-OCR outputs")
ap.add_argument("--llama-model", default="gguf_models/deepseek-ai/deepseek-ocr-f16.gguf",
help="Path to llama.cpp GGUF model")
ap.add_argument("--mmproj", default="gguf_models/deepseek-ai/mmproj-deepseek-ocr-f16.gguf",
help="Path to mmproj GGUF file")
ap.add_argument("--image", default="test-1.jpeg",
help="Path to test image")
ap.add_argument("--llama-bin", default="build/bin/llama-mtmd-cli",
help="Path to llama-mtmd-cli binary")
ap.add_argument("--embedding-model", default="Qwen/Qwen3-Embedding-0.6B",
help="Embedding model for similarity computation")
ap.add_argument("--threshold", type=float, default=0.7,
help="Minimum similarity threshold for pass")
args = ap.parse_args()
# Validate paths
# script directory + image
mtmd_dir = Path(__file__).parent.parent
args.image = str(mtmd_dir / args.image)
# project directory + llama model
args.llama_model = str(mtmd_dir.parent.parent / args.llama_model)
# project directory + mmproj
args.mmproj = str(mtmd_dir.parent.parent / args.mmproj)
args.llama_bin = str(mtmd_dir.parent.parent / args.llama_bin)
if not Path(args.image).exists():
print(f"Error: Image not found: {args.image}")
sys.exit(1)
if not Path(args.llama_model).exists():
print(f"Error: Model not found: {args.llama_model}")
sys.exit(1)
if not Path(args.mmproj).exists():
print(f"Error: mmproj not found: {args.mmproj}")
sys.exit(1)
print("=" * 60)
print("DeepSeek-OCR: llama.cpp vs HuggingFace Comparison")
print("=" * 60)
# Default paths based on your command
# Run llama.cpp inference
print("\n[2/3] Running llama.cpp implementation...")
llama_free_ocr = run_mtmd_deepseek_ocr(
args.llama_model,
args.mmproj,
args.image,
args.llama_bin
)
llama_md_ocr = run_mtmd_deepseek_ocr(
args.llama_model,
args.mmproj,
args.image,
args.llama_bin,
prompt="<|grounding|>Convert the document to markdown."
)
expected_free_ocr = read_expected_output("test-1-extracted.txt")
expected_md_ocr = read_expected_output("test-1-extracted.md")
# Compute similarity
print("\n[3/3] Computing embedding similarity...")
free_ocr_similarity = compute_embedding_similarity(
expected_free_ocr,
llama_free_ocr,
args.embedding_model
)
md_ocr_similarity = compute_embedding_similarity(
expected_md_ocr,
llama_md_ocr,
args.embedding_model
)
# Results
print("\n" + "=" * 60)
print("RESULTS")
print("=" * 60)
print(f"\nReference Model output:\n{'-' * 40}")
print(expected_free_ocr)
print(f"\nDeepSeek-OCR output:\n{'-' * 40}")
print(llama_free_ocr)
print(f"\n{'=' * 60}")
print(f"Cosine Similarity: {free_ocr_similarity:.4f}")
print(f"Threshold: {args.threshold}")
print(f"Result: {'PASS' if free_ocr_similarity >= args.threshold else 'FAIL'}")
print("=" * 60)
# Markdown OCR results
print(f"\nReference Model Markdown output:\n{'-' * 40}")
print(expected_md_ocr)
print(f"\nDeepSeek-OCR Markdown output:\n{'-' * 40}")
print(llama_md_ocr)
print(f"\n{'=' * 60}")
print(f"Cosine Similarity (Markdown): {md_ocr_similarity:.4f}")
print(f"Threshold: {args.threshold}")
print(f"Result: {'PASS' if md_ocr_similarity >= args.threshold else 'FAIL'}")
print("=" * 60)
if __name__ == "__main__":
main()

5
tools/mtmd/tests/tests-requirements.txt Normal file
View File

@ -0,0 +1,5 @@
sentence-transformers
transformers
tokenizers
torch
torchvision

Some files were not shown because too many files have changed in this diff Show More