vbatts/llama.cpp
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Merge branch 'ggerganov:master' into master

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Giovanni Petrantoni 2025-02-10 11:16:22 +08:00 • committed by GitHub
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384 changed files with 54631 additions and 25978 deletions
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13
.devops/cpu.Dockerfile
View file

@ -2,6 +2,10 @@ ARG UBUNTU_VERSION=22.04
FROM ubuntu:$UBUNTU_VERSION AS build
ARG TARGETARCH
ARG GGML_CPU_ARM_ARCH=armv8-a
RUN apt-get update && \
apt-get install -y build-essential git cmake libcurl4-openssl-dev
@ -9,7 +13,14 @@ WORKDIR /app
COPY . .
RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
RUN if [ "$TARGETARCH" = "amd64" ]; then \
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \
elif [ "$TARGETARCH" = "arm64" ]; then \
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \
else \
echo "Unsupported architecture"; \
exit 1; \
fi && \
cmake --build build -j $(nproc)
RUN mkdir -p /app/lib && \

10
.devops/tools.sh
View file

@ -13,9 +13,13 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
exec ./llama-quantize "$@"
elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
exec ./llama-cli "$@"
elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then
exec ./llama-bench "$@"
elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then
exec ./llama-perplexity "$@"
elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
echo "Converting PTH to GGML..."
for i in `ls $1/$2/ggml-model-f16.bin*`; do
for i in $(ls $1/$2/ggml-model-f16.bin*); do
if [ -f "${i/f16/q4_0}" ]; then
echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
else
@ -30,6 +34,10 @@ else
echo "Available commands: "
echo " --run (-r): Run a model previously converted into ggml"
echo " ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512"
echo " --bench (-b): Benchmark the performance of the inference for various parameters."
echo " ex: -m model.gguf"
echo " --perplexity (-p): Measure the perplexity of a model over a given text."
echo " ex: -m model.gguf -f file.txt"
echo " --convert (-c): Convert a llama model into ggml"
echo " ex: --outtype f16 \"/models/7B/\" "
echo " --quantize (-q): Optimize with quantization process ggml"

11
.devops/vulkan.Dockerfile
View file

@ -1,4 +1,4 @@
ARG UBUNTU_VERSION=jammy
ARG UBUNTU_VERSION=24.04
FROM ubuntu:$UBUNTU_VERSION AS build
@ -7,7 +7,7 @@ RUN apt update && apt install -y git build-essential cmake wget
# Install Vulkan SDK and cURL
RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \
apt update -y && \
apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
@ -34,7 +34,7 @@ RUN mkdir -p /app/full \
FROM ubuntu:$UBUNTU_VERSION AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt-get install -y libgomp1 curl libvulkan-dev \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
@ -55,8 +55,9 @@ RUN apt-get update \
git \
python3 \
python3-pip \
&& pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt \
python3-wheel \
&& pip install --break-system-packages --upgrade setuptools \
&& pip install --break-system-packages -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \

8
.editorconfig
View file

@ -40,3 +40,11 @@ indent_style = tab
[examples/cvector-generator/*.txt]
trim_trailing_whitespace = unset
insert_final_newline = unset
[models/templates/*.jinja]
indent_style = unset
indent_size = unset
end_of_line = unset
charset = unset
trim_trailing_whitespace = unset
insert_final_newline = unset

12
.github/ISSUE_TEMPLATE/010-bug-compilation.yml vendored
View file

@ -65,12 +65,22 @@ body:
If possible, please do a git bisect and identify the exact commit that introduced the bug.
validations:
required: false
- type: textarea
id: command
attributes:
label: Compile command
description: >
Please provide the exact command you used to compile llama.cpp. For example: `cmake -B ...`.
This will be automatically formatted into code, so no need for backticks.
render: shell
validations:
required: true
- type: textarea
id: logs
attributes:
label: Relevant log output
description: >
Please copy and paste any relevant log output, including the command that you entered and any generated text.
Please copy and paste any relevant log output, including any generated text.
This will be automatically formatted into code, so no need for backticks.
render: shell
validations:

12
.github/ISSUE_TEMPLATE/019-bug-misc.yml vendored
View file

@ -52,6 +52,16 @@ body:
- Other (Please specify in the next section)
validations:
required: false
- type: textarea
id: command
attributes:
label: Command line
description: >
Please provide the exact commands you entered, if applicable. For example: `llama-server -m ... -c ...`, `llama-cli -m ...`, etc.
This will be automatically formatted into code, so no need for backticks.
render: shell
validations:
required: false
- type: textarea
id: info
attributes:
@ -74,7 +84,7 @@ body:
attributes:
label: Relevant log output
description: >
If applicable, please copy and paste any relevant log output, including the command that you entered and any generated text.
If applicable, please copy and paste any relevant log output, including any generated text.
This will be automatically formatted into code, so no need for backticks.
render: shell
validations:

440
.github/workflows/build.yml vendored
View file

@ -10,10 +10,10 @@ on:
push:
branches:
- master
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp']
pull_request:
types: [opened, synchronize, reopened]
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp']
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@ -43,6 +43,12 @@ jobs:
with:
fetch-depth: 0
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: macOS-latest-cmake-arm64
evict-old-files: 1d
- name: Dependencies
id: depends
continue-on-error: true
@ -53,16 +59,14 @@ jobs:
id: cmake_build
run: |
sysctl -a
mkdir build
cd build
cmake .. \
cmake -B build \
-DCMAKE_BUILD_RPATH="@loader_path" \
-DLLAMA_FATAL_WARNINGS=ON \
-DLLAMA_CURL=ON \
-DGGML_METAL_USE_BF16=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DGGML_RPC=ON \
-DBUILD_SHARED_LIBS=OFF
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
-DGGML_RPC=ON
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
id: cmake_test
@ -88,6 +92,7 @@ jobs:
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
run: |
cp LICENSE ./build/bin/
cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp
zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/*
- name: Upload artifacts
@ -107,6 +112,12 @@ jobs:
with:
fetch-depth: 0
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: macOS-latest-cmake-x64
evict-old-files: 1d
- name: Dependencies
id: depends
continue-on-error: true
@ -120,11 +131,11 @@ jobs:
# Metal is disabled due to intermittent failures with Github runners not having a GPU:
# https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313
cmake -B build \
-DCMAKE_BUILD_RPATH="@loader_path" \
-DLLAMA_FATAL_WARNINGS=ON \
-DLLAMA_CURL=ON \
-DGGML_METAL=OFF \
-DGGML_RPC=ON \
-DBUILD_SHARED_LIBS=OFF
-DGGML_RPC=ON
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
@ -151,6 +162,7 @@ jobs:
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
run: |
cp LICENSE ./build/bin/
cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp
zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/*
- name: Upload artifacts
@ -160,8 +172,8 @@ jobs:
path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
name: llama-bin-macos-x64.zip
ubuntu-latest-cmake:
runs-on: ubuntu-latest
ubuntu-cpu-cmake:
runs-on: ubuntu-22.04
steps:
- name: Clone
@ -170,6 +182,12 @@ jobs:
with:
fetch-depth: 0
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ubuntu-cpu-cmake
evict-old-files: 1d
- name: Dependencies
id: depends
run: |
@ -179,10 +197,11 @@ jobs:
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF
cmake --build . --config Release -j $(nproc)
cmake -B build \
-DLLAMA_FATAL_WARNINGS=ON \
-DLLAMA_CURL=ON \
-DGGML_RPC=ON
cmake --build build --config Release -j $(nproc)
- name: Test
id: cmake_test
@ -219,6 +238,7 @@ jobs:
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
run: |
cp LICENSE ./build/bin/
cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp
zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip ./build/bin/*
- name: Upload artifacts
@ -236,7 +256,54 @@ jobs:
strategy:
matrix:
sanitizer: [ADDRESS, THREAD, UNDEFINED]
build_type: [Debug, Release]
build_type: [Debug]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }}
evict-old-files: 1d
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential
- name: Build
id: cmake_build
if: ${{ matrix.sanitizer != 'THREAD' }}
run: |
cmake -B build \
-DLLAMA_FATAL_WARNINGS=ON \
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
- name: Build (no OpenMP)
id: cmake_build_no_openmp
if: ${{ matrix.sanitizer == 'THREAD' }}
run: |
cmake -B build \
-DLLAMA_FATAL_WARNINGS=ON \
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-DGGML_OPENMP=OFF
cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
- name: Test
id: cmake_test
run: |
cd build
ctest -L main --verbose --timeout 900
ubuntu-latest-llguidance:
runs-on: ubuntu-latest
steps:
- name: Clone
@ -251,21 +318,13 @@ jobs:
- name: Build
id: cmake_build
if: ${{ matrix.sanitizer != 'THREAD' }}
run: |
mkdir build
cd build
cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
- name: Build (no OpenMP)
id: cmake_build_no_openmp
if: ${{ matrix.sanitizer == 'THREAD' }}
run: |
mkdir build
cd build
cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -DGGML_OPENMP=OFF
cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
cmake .. \
-DLLAMA_FATAL_WARNINGS=ON \
-DLLAMA_LLGUIDANCE=ON
cmake --build . --config Release -j $(nproc)
- name: Test
id: cmake_test
@ -283,6 +342,12 @@ jobs:
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ubuntu-latest-cmake-rpc
evict-old-files: 1d
- name: Dependencies
id: depends
run: |
@ -292,10 +357,9 @@ jobs:
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake -DGGML_RPC=ON ..
cmake --build . --config Release -j $(nproc)
cmake -B build \
-DGGML_RPC=ON
cmake --build build --config Release -j $(nproc)
- name: Test
id: cmake_test
@ -311,6 +375,12 @@ jobs:
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ubuntu-22-cmake-vulkan
evict-old-files: 1d
- name: Dependencies
id: depends
run: |
@ -322,16 +392,16 @@ jobs:
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake -DGGML_VULKAN=ON ..
cmake --build . --config Release -j $(nproc)
cmake -B build \
-DGGML_VULKAN=ON
cmake --build build --config Release -j $(nproc)
- name: Test
id: cmake_test
run: |
cd build
ctest -L main --verbose --timeout 900
# This is using llvmpipe and runs slower than other backends
ctest -L main --verbose --timeout 1800
ubuntu-22-cmake-hip:
runs-on: ubuntu-22.04
@ -348,16 +418,27 @@ jobs:
sudo apt-get update
sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ubuntu-22-cmake-hip
evict-old-files: 1d
- name: Build with native CMake HIP support
id: cmake_build
run: |
cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIP=ON
cmake -B build -S . \
-DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
-DGGML_HIP=ON
cmake --build build --config Release -j $(nproc)
- name: Build with legacy HIP support
id: cmake_build_legacy_hip
run: |
cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIP=ON
cmake -B build2 -S . \
-DCMAKE_C_COMPILER=hipcc \
-DCMAKE_CXX_COMPILER=hipcc \
-DGGML_HIP=ON
cmake --build build2 --config Release -j $(nproc)
ubuntu-22-cmake-musa:
@ -375,10 +456,17 @@ jobs:
apt-get update
apt-get install -y build-essential git cmake libcurl4-openssl-dev
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ubuntu-22-cmake-musa
evict-old-files: 1d
- name: Build with native CMake MUSA support
id: cmake_build
run: |
cmake -B build -S . -DGGML_MUSA=ON
cmake -B build -S . \
-DGGML_MUSA=ON
cmake --build build --config Release -j $(nproc)
ubuntu-22-cmake-sycl:
@ -413,14 +501,21 @@ jobs:
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ubuntu-22-cmake-sycl
evict-old-files: 1d
- name: Build
id: cmake_build
run: |
source /opt/intel/oneapi/setvars.sh
mkdir build
cd build
cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
cmake --build . --config Release -j $(nproc)
cmake -B build \
-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx
cmake --build build --config Release -j $(nproc)
ubuntu-22-cmake-sycl-fp16:
runs-on: ubuntu-22.04
@ -454,47 +549,22 @@ jobs:
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ubuntu-22-cmake-sycl-fp16
evict-old-files: 1d
- name: Build
id: cmake_build
run: |
source /opt/intel/oneapi/setvars.sh
mkdir build
cd build
cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON ..
cmake --build . --config Release -j $(nproc)
# TODO: build with GGML_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# how to debug it.
# ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584
# would be great if we fix these
macOS-latest-cmake:
runs-on: macos-latest
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Dependencies
id: depends
continue-on-error: true
run: |
brew update
- name: Build
id: cmake_build
run: |
sysctl -a
mkdir build
cd build
cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF ..
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
id: cmake_test
run: |
cd build
ctest -L main --verbose --timeout 900
cmake -B build \
-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx \
-DGGML_SYCL_F16=ON
cmake --build build --config Release -j $(nproc)
macOS-latest-cmake-ios:
runs-on: macos-latest
@ -504,6 +574,12 @@ jobs:
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: macOS-latest-cmake-ios
evict-old-files: 1d
- name: Dependencies
id: depends
continue-on-error: true
@ -514,9 +590,7 @@ jobs:
id: cmake_build
run: |
sysctl -a
mkdir build
cd build
cmake -G Xcode .. \
cmake -B build -G Xcode \
-DGGML_METAL_USE_BF16=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
@ -525,7 +599,7 @@ jobs:
-DCMAKE_SYSTEM_NAME=iOS \
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
macOS-latest-cmake-tvos:
runs-on: macos-latest
@ -535,6 +609,12 @@ jobs:
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: macOS-latest-cmake-tvos
evict-old-files: 1d
- name: Dependencies
id: depends
continue-on-error: true
@ -545,9 +625,7 @@ jobs:
id: cmake_build
run: |
sysctl -a
mkdir build
cd build
cmake -G Xcode .. \
cmake -B build -G Xcode \
-DGGML_METAL_USE_BF16=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
@ -556,7 +634,7 @@ jobs:
-DCMAKE_SYSTEM_NAME=tvOS \
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
macOS-latest-swift:
runs-on: macos-latest
@ -570,6 +648,12 @@ jobs:
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: macOS-latest-swift
evict-old-files: 1d
- name: Dependencies
id: depends
continue-on-error: true
@ -580,17 +664,15 @@ jobs:
id: cmake_build
run: |
sysctl -a
mkdir build
cd build
cmake -G Xcode .. \
cmake -B build -G Xcode \
-DGGML_METAL_USE_BF16=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
-DLLAMA_BUILD_TESTS=OFF \
-DLLAMA_BUILD_SERVER=OFF \
-DCMAKE_OSX_ARCHITECTURES="arm64;x86_64"
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
sudo cmake --install . --config Release
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
sudo cmake --install build --config Release
- name: xcodebuild for swift package
id: xcodebuild
@ -611,6 +693,13 @@ jobs:
- name: Clone
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: windows-msys2
variant: sccache
evict-old-files: 1d
- name: Setup ${{ matrix.sys }}
uses: msys2/setup-msys2@v2
with:
@ -618,6 +707,7 @@ jobs:
msystem: ${{matrix.sys}}
install: >-
base-devel
git
mingw-w64-${{matrix.env}}-toolchain
mingw-w64-${{matrix.env}}-cmake
mingw-w64-${{matrix.env}}-openblas
@ -651,23 +741,23 @@ jobs:
matrix:
include:
- build: 'noavx-x64'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF'
- build: 'avx2-x64'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON'
- build: 'avx-x64'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF'
- build: 'avx512-x64'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON'
- build: 'openblas-x64'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DBUILD_SHARED_LIBS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
- build: 'kompute-x64'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON'
- build: 'vulkan-x64'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON'
- build: 'llvm-arm64'
defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'
- build: 'msvc-arm64'
defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'
- build: 'llvm-arm64-opencl-adreno'
defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'
@ -678,6 +768,13 @@ jobs:
with:
fetch-depth: 0
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: windows-latest-cmake-${{ matrix.build }}
variant: sccache
evict-old-files: 1d
- name: Clone Kompute submodule
id: clone_kompute
if: ${{ matrix.build == 'kompute-x64' }}
@ -717,21 +814,19 @@ jobs:
run: |
git clone https://github.com/KhronosGroup/OpenCL-Headers
cd OpenCL-Headers
mkdir build && cd build
cmake .. `
cmake -B build `
-DBUILD_TESTING=OFF `
-DOPENCL_HEADERS_BUILD_TESTING=OFF `
-DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
-DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
cmake --build . --target install
cmake --build build --target install
git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
cd OpenCL-ICD-Loader
mkdir build-arm64-release && cd build-arm64-release
cmake .. `
cmake -B build-arm64-release `
-A arm64 `
-DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" `
-DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
cmake --build . --target install --config release
cmake --build build-arm64-release --target install --config release
- name: Build
id: cmake_build
@ -798,6 +893,7 @@ jobs:
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
run: |
Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt
Copy-Item .\examples\run\linenoise.cpp\LICENSE .\build\bin\Release\linenoise.cpp.txt
7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip .\build\bin\Release\*
- name: Upload artifacts
@ -815,6 +911,8 @@ jobs:
- name: Clone
id: checkout
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Install dependencies
env:
@ -823,9 +921,21 @@ jobs:
apt update
apt install -y cmake build-essential ninja-build libgomp1 git
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ubuntu-latest-cmake-cuda
evict-old-files: 1d
- name: Build with CMake
run: |
cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=89-real -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined -DLLAMA_FATAL_WARNINGS=ON
cmake -S . -B build -G Ninja \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_CUDA_ARCHITECTURES=89-real \
-DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined \
-DLLAMA_FATAL_WARNINGS=ON \
-DGGML_NATIVE=OFF \
-DGGML_CUDA=ON
cmake --build build
windows-2019-cmake-cuda:
@ -843,6 +953,13 @@ jobs:
with:
fetch-depth: 0
- name: Install ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }}
variant: sccache
evict-old-files: 1d
- name: Install Cuda Toolkit 11.7
if: ${{ matrix.cuda == '11.7' }}
run: |
@ -899,11 +1016,6 @@ jobs:
echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
echo "CUDA_PATH_V12_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
- name: Install ccache
uses: hendrikmuhs/ccache-action@v1.2
with:
key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }}
- name: Install Ninja
id: install_ninja
run: |
@ -914,7 +1026,11 @@ jobs:
shell: cmd
run: |
call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat"
cmake -S . -B build -G "Ninja Multi-Config" -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON -DGGML_RPC=ON
cmake -S . -B build -G "Ninja Multi-Config" ^
-DLLAMA_BUILD_SERVER=ON ^
-DGGML_NATIVE=OFF ^
-DGGML_CUDA=ON ^
-DGGML_RPC=ON
set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
cmake --build build --config Release -j %NINJA_JOBS% -t ggml
cmake --build build --config Release
@ -979,6 +1095,13 @@ jobs:
with:
fetch-depth: 0
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: windows-latest-cmake-sycl
variant: sccache
evict-old-files: 1d
- name: Install
run: |
scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
@ -1058,16 +1181,22 @@ jobs:
& 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
- name: Install ccache
uses: hendrikmuhs/ccache-action@v1.2
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: ${{ github.job }}
evict-old-files: 1d
- name: Build
id: cmake_build
run: |
$env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
$env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DGGML_RPC=ON
cmake -G "Unix Makefiles" -B build -S . `
-DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
-DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
-DCMAKE_BUILD_TYPE=Release `
-DGGML_HIP=ON `
-DGGML_RPC=ON
cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
windows-latest-cmake-hip-release:
@ -1085,6 +1214,12 @@ jobs:
with:
fetch-depth: 0
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: windows-latest-cmake-hip-release
evict-old-files: 1d
- name: Install
id: depends
run: |
@ -1105,7 +1240,13 @@ jobs:
run: |
$env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
$env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=${{ matrix.gpu_target }} -DGGML_RPC=ON
cmake -G "Unix Makefiles" -B build -S . `
-DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
-DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
-DCMAKE_BUILD_TYPE=Release `
-DAMDGPU_TARGETS=${{ matrix.gpu_target }} `
-DGGML_HIP=ON `
-DGGML_RPC=ON
cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
md "build\bin\rocblas\library\"
cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\"
@ -1147,9 +1288,7 @@ jobs:
id: cmake_build
run: |
sysctl -a
mkdir build
cd build
cmake -G Xcode .. \
cmake -B build -G Xcode \
-DGGML_METAL_USE_BF16=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
@ -1158,8 +1297,8 @@ jobs:
-DCMAKE_SYSTEM_NAME=iOS \
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
sudo cmake --install . --config Release
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
sudo cmake --install build --config Release
- name: xcodebuild for swift package
id: xcodebuild
@ -1176,6 +1315,12 @@ jobs:
- name: Clone
uses: actions/checkout@v4
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: android-build
evict-old-files: 1d
- name: Set up JDK
uses: actions/setup-java@v3
with:
@ -1199,8 +1344,7 @@ jobs:
runs-on: ubuntu-latest
needs:
- ubuntu-latest-cmake
- macOS-latest-cmake
- ubuntu-cpu-cmake
- windows-latest-cmake
- windows-2019-cmake-cuda
- windows-latest-cmake-hip-release
@ -1214,6 +1358,12 @@ jobs:
with:
fetch-depth: 0
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: release
evict-old-files: 1d
- name: Determine tag name
id: tag
shell: bash
@ -1239,7 +1389,7 @@ jobs:
- name: Create release
id: create_release
uses: anzz1/action-create-release@v1
uses: ggml-org/action-create-release@v1
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
@ -1459,3 +1609,37 @@ jobs:
# popd
# emcmake cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }}
# make
openEuler-latest-cmake-cann:
if: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'Ascend NPU') }}
defaults:
run:
shell: bash -el {0}
runs-on: ubuntu-24.04-arm
strategy:
matrix:
cann:
- '8.0.rc3.beta1-910b-openeuler22.03-py3.10'
device:
- 'ascend910b3'
build:
- 'Release'
container: ascendai/cann:${{ matrix.cann }}
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Dependencies
run: |
yum update -y
yum install -y git gcc gcc-c++ make cmake
- name: Build
run: |
export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
cmake -S . -B build \
-DCMAKE_BUILD_TYPE=${{ matrix.build }} \
-DGGML_CANN=on \
-DSOC_TYPE=${{ matrix.device }}
cmake --build build -j $(nproc)

2
.github/workflows/close-issue.yml vendored
View file

@ -17,7 +17,7 @@ jobs:
steps:
- uses: actions/stale@v5
with:
exempt-issue-labels: "refactor,help wanted,good first issue,research,bug"
exempt-issue-labels: "refactor,help wanted,good first issue,research,bug,roadmap"
days-before-issue-stale: 30
days-before-issue-close: 14
stale-issue-label: "stale"

6
.github/workflows/docker.yml vendored
View file

@ -28,10 +28,11 @@ jobs:
push_to_registry:
name: Push Docker image to Docker Hub
runs-on: ubuntu-latest
runs-on: ubuntu-22.04
env:
COMMIT_SHA: ${{ github.sha }}
strategy:
fail-fast: false
matrix:
config:
# Multi-stage build
@ -97,10 +98,9 @@ jobs:
GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
# https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example
- name: Free Disk Space (Ubuntu)
if: ${{ matrix.config.free_disk_space == true }}
uses: jlumbroso/free-disk-space@main
uses: ggml-org/free-disk-space@v1.3.1
with:
# this might remove tools that are actually needed,
# if set to "true" but frees about 6 GB

4
.github/workflows/editorconfig.yml vendored
View file

@ -23,5 +23,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: editorconfig-checker/action-editorconfig-checker@main
- uses: editorconfig-checker/action-editorconfig-checker@v2
with:
version: v3.0.3
- run: editorconfig-checker

52
.github/workflows/server.yml vendored
View file

@ -81,13 +81,36 @@ jobs:
with:
node-version: '22.11.0'
- name: WebUI - Install dependencies
id: webui_lint
run: |
cd examples/server/webui
npm ci
- name: WebUI - Check code format
id: webui_format
run: |
git config --global --add safe.directory $(realpath .)
cd examples/server/webui
git status
npm run format
git status
modified_files="$(git status -s)"
echo "Modified files: ${modified_files}"
if [ -n "${modified_files}" ]; then
echo "Files do not follow coding style. To fix: npm run format"
echo "${modified_files}"
exit 1
fi
- name: Verify bundled index.html
id: verify_server_index_html
run: |
git config --global --add safe.directory $(realpath .)
cd examples/server/webui
git status
npm ci
npm run build
git status
modified_files="$(git status -s)"
@ -112,9 +135,9 @@ jobs:
-DGGML_OPENMP=OFF ;
cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
- name: Build
id: cmake_build
if: ${{ matrix.sanitizer != 'THREAD' }}
- name: Build (sanitizers)
id: cmake_build_sanitizers
if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
run: |
cmake -B build \
-DGGML_NATIVE=OFF \
@ -124,12 +147,31 @@ jobs:
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
- name: Build (sanitizers)
id: cmake_build
if: ${{ matrix.sanitizer == '' }}
run: |
cmake -B build \
-DGGML_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
- name: Tests
id: server_integration_tests
if: ${{ matrix.sanitizer == '' }}
run: |
cd examples/server/tests
./tests.sh
- name: Tests (sanitizers)
id: server_integration_tests_sanitizers
if: ${{ matrix.sanitizer != '' }}
run: |
cd examples/server/tests
LLAMA_SANITIZE=1 ./tests.sh
- name: Slow tests
id: server_integration_tests_slow
if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
@ -186,7 +228,7 @@ jobs:
run: |
cd examples/server/tests
$env:PYTHONIOENCODING = ":replace"
pytest -v -x
pytest -v -x -m "not slow"
- name: Slow tests
id: server_integration_tests_slow

1
.gitignore vendored
View file

@ -18,6 +18,7 @@
*.metallib
*.o
*.so
*.swp
*.tmp
# IDE / OS

83
AUTHORS
View file

@ -1,4 +1,4 @@
# date: Thu Nov 28 20:46:15 EET 2024
# date: Tue Feb 4 13:04:05 EET 2025
# this file is auto-generated by scripts/gen-authors.sh
0cc4m <picard12@live.de>
@ -20,6 +20,8 @@ Adithya Balaji <adithya.b94@gmail.com>
AdithyanI <adithyan.i4internet@gmail.com>
Adrian <smith.adriane@gmail.com>
Adrian Hesketh <a-h@users.noreply.github.com>
Adrien Gallouët <adrien@gallouet.fr>
Adrien Gallouët <angt@huggingface.co>
Ahmad Tameem <113388789+Tameem-10xE@users.noreply.github.com>
Ahmet Zeer <ahmed.zeer@std.yildiz.edu.tr>
AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
@ -55,6 +57,7 @@ Ananta Bastola <anantarajbastola@gmail.com>
Anas Ahouzi <112881240+aahouzi@users.noreply.github.com>
András Salamon <ott2@users.noreply.github.com>
Andreas (Andi) Kunar <andreask@msn.com>
Andreas Kieslinger <47689530+aendk@users.noreply.github.com>
Andrei <abetlen@gmail.com>
Andrew Canis <andrew.canis@gmail.com>
Andrew Downing <andrew2085@gmail.com>
@ -91,13 +94,17 @@ Ben Siraphob <bensiraphob@gmail.com>
Ben Williams <ben@719ben.com>
Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com>
Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com>
Benson Wong <mostlygeek@gmail.com>
Bernat Vadell <hounter.caza@gmail.com>
Bernhard M. Wiedemann <githubbmwprimary@lsmod.de>
Bert Wagner <github@bertwagner.com>
Billel Mokeddem <billel.mokeddem.ml@gmail.com>
Bingan <70050083+binganao@users.noreply.github.com>
Bjarke Viksøe <164612031+bviksoe@users.noreply.github.com>
Bodo Graumann <mail@bodograumann.de>
Bono Lv <lvscar@users.noreply.github.com>
Borislav Stanimirov <b.stanimirov@abv.bg>
Borislav Stanimirov <b@ibob.bg>
Branden Butler <bwtbutler@hotmail.com>
Brandon Squizzato <35474886+bsquizz@users.noreply.github.com>
Brian <mofosyne@gmail.com>
@ -117,6 +124,7 @@ Casey Primozic <casey@cprimozic.net>
Casey Primozic <me@ameo.link>
CausalLM <148736309+CausalLM@users.noreply.github.com>
Cebtenzzre <cebtenzzre@gmail.com>
CentricStorm <CentricStorm@users.noreply.github.com>
Chad Brewbaker <crb002@gmail.com>
Changyeon Kim <cyzero.kim@samsung.com>
Chao Jiang <jc19chaoj@zoho.com>
@ -131,12 +139,15 @@ Chris Kuehl <ckuehl@ckuehl.me>
Christian Demsar <christian@github.email.demsar.us>
Christian Demsar <crasm@git.vczf.us>
Christian Falch <875252+chrfalch@users.noreply.github.com>
Christian Kastner <ckk@kvr.at>
Christian Kögler <ck3d@gmx.de>
Christian Köhnenkamp <cvk5@me.com>
Christian Zhou-Zheng <59622928+christianazinn@users.noreply.github.com>
Christopher Nielsen <62156882+mascguy@users.noreply.github.com>
Clark Saben <76020733+csaben@users.noreply.github.com>
Clint Herron <hanclinto@gmail.com>
Conrad Kramer <conrad@conradkramer.com>
Corentin REGAL <corentin.regal@gmail.com>
CrispStrobe <154636388+CrispStrobe@users.noreply.github.com>
Csaba Kecskemeti <csaba.kecskemeti@gmail.com>
Cuong Trinh Manh <nguoithichkhampha@gmail.com>
@ -176,6 +187,7 @@ Dibakar Gope <dibakar.gope@arm.com>
Didzis Gosko <didzis@users.noreply.github.com>
Diego Devesa <slarengh@gmail.com>
Diogo Teles Sant'Anna <diogoteles@google.com>
Djip007 <3705339+Djip007@users.noreply.github.com>
Djip007 <djip.perois@free.fr>
Don Mahurin <dmahurin@users.noreply.github.com>
DooWoong Lee (David) <manics99@naver.com>
@ -193,6 +205,7 @@ Edward Taylor <edeetee@gmail.com>
Elaine <elaine.zosa@gmail.com>
Elbios <141279586+Elbios@users.noreply.github.com>
Elton Kola <eltonkola@gmail.com>
Emreerdog <34742675+Emreerdog@users.noreply.github.com>
Engininja2 <139037756+Engininja2@users.noreply.github.com>
Equim <sayaka@ekyu.moe>
Eric Curtin <ecurtin@redhat.com>
@ -233,6 +246,7 @@ Fred Douglas <43351173+fredlas@users.noreply.github.com>
Frederik Vogel <Schaltfehler@users.noreply.github.com>
Gabe Goodhart <gabe.l.hart@gmail.com>
Gabe Goodhart <ghart@us.ibm.com>
Gaetan Bisson <gaetan@fenua.org>
GainLee <perfecter.gen@gmail.com>
Galunid <karolek1231456@gmail.com>
Gary Linscott <glinscott@gmail.com>
@ -249,6 +263,7 @@ Guillaume "Vermeille" Sanchez <Guillaume.V.Sanchez@gmail.com>
Guillaume Wenzek <gwenzek@users.noreply.github.com>
Guoliang Hua <32868157+nbcsm@users.noreply.github.com>
Guoteng <32697156+SolenoidWGT@users.noreply.github.com>
Guspan Tanadi <36249910+guspan-tanadi@users.noreply.github.com>
Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com>
Haggai Nuchi <h.nuchi@gmail.com>
Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com>
@ -259,11 +274,13 @@ Haoxiang Fei <tonyfettes@tonyfettes.com>
Harald Fernengel <harald.fernengel@here.com>
Hatsune Miku <129688334+at8u@users.noreply.github.com>
HatsuneMikuUwU33 <173229399+HatsuneMikuUwU33@users.noreply.github.com>
Haus1 <haus.xda@gmail.com>
Henk Poley <HenkPoley@gmail.com>
Henri Vasserman <henv@hot.ee>
Henrik Forstén <henrik.forsten@gmail.com>
Herman Semenov <GermanAizek@yandex.ru>
Hesen Peng <hesen.peng@gmail.com>
HimariO <dsfhe49854@gmail.com>
Hoang Nguyen <hugo53@users.noreply.github.com>
Hong Bo PENG <penghb@cn.ibm.com>
Hongyu Ouyang <96765450+casavaca@users.noreply.github.com>
@ -280,6 +297,7 @@ Icecream95 <the.real.icecream95@gmail.com>
Ido S <ido.pluto@gmail.com>
IgnacioFDM <ignaciofdm@gmail.com>
Igor Okulist <okigan@gmail.com>
Ihar Hrachyshka <ihrachys@redhat.com>
Ikko Eltociear Ashimine <eltociear@gmail.com>
Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com>
Ionoclast Laboratories <brigham@ionoclast.com>
@ -289,12 +307,14 @@ Ivan <nekotekina@gmail.com>
Ivan Filipov <159561759+vanaka11@users.noreply.github.com>
Ivan Komarov <Ivan.Komarov@dfyz.info>
Ivan Stepanov <ivanstepanovftw@gmail.com>
JFLFY2255 <JFLFY2255@163.com>
JH23X <165871467+JH23X@users.noreply.github.com>
Jack Mousseau <jack@software.inc>
Jack Mousseau <jmousseau@users.noreply.github.com>
JackJollimore <130917767+JackJollimore@users.noreply.github.com>
Jaeden Amero <jaeden@patater.com>
Jaemin Son <woalsdnd@gmail.com>
Jafar Uruç <jafar.uruc@gmail.com>
Jag Chadha <jagtesh@gmail.com>
Jakub N <jakubniemczyk97@gmail.com>
James A Capozzoli <157492257+jac-jim@users.noreply.github.com>
@ -315,6 +335,7 @@ Jeffrey Morgan <jmorganca@gmail.com>
Jeffrey Quesnelle <emozilla@nousresearch.com>
Jeroen Mostert <jeroen.mostert@cm.com>
Jesse Jojo Johnson <williamsaintgeorge@gmail.com>
Jett Janiak <jettjaniak@gmail.com>
Jeximo <jeximo@gmail.com>
Jhen-Jie Hong <iainst0409@gmail.com>
Jiahao Li <liplus17@163.com>
@ -343,6 +364,7 @@ Josh Ramer <josh.ramer@icloud.com>
Joyce <joycebrum@google.com>
Juan Calderon-Perez <835733+gaby@users.noreply.github.com>
Judd <foldl@users.noreply.github.com>
Juk Armstrong <69222624+jukofyork@users.noreply.github.com>
Julius Arkenberg <arki05@users.noreply.github.com>
Jun Hee Yoo <contact.jhyoo@gmail.com>
Jun Jie <71215065+junnjiee16@users.noreply.github.com>
@ -357,6 +379,7 @@ Justine Tunney <jtunney@mozilla.com>
Juuso Alasuutari <juuso.alasuutari@gmail.com>
KASR <karim.asrih@gmail.com>
Kamil Tomšík <info@tomsik.cz>
Karol Kontny <82021046+kkontny@users.noreply.github.com>
Karsten Weiss <knweiss@gmail.com>
Karthick <j.karthic2004@gmail.com>
Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com>
@ -376,6 +399,7 @@ Kolen Cheung <ickc@users.noreply.github.com>
Konstantin Herud <konstantin.herud@denkbares.com>
Konstantin Zhuravlyov <konstantin.zhuravlyov@amd.com>
Kunshang Ji <kunshang.ji@intel.com>
Kyle Bruene <KyleBruene@users.noreply.github.com>
Kyle Liang <liangmanlai@gmail.com>
Kyle Mistele <kyle@mistele.com>
Kylin <56434533+KyL0N@users.noreply.github.com>
@ -394,6 +418,7 @@ Liu Jia <jia3.liu@intel.com>
LoganDark <github@logandark.mozmail.com>
Loïc Carrère <loic.carrere@gmail.com>
LostRuins <39025047+LostRuins@users.noreply.github.com>
LostRuins Concedo <39025047+LostRuins@users.noreply.github.com>
Luciano <lucianostrika44@gmail.com>
Luo Tian <lt@basecity.com>
Lyle Dean <dean@lyle.dev>
@ -423,6 +448,7 @@ MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com>
Mateusz Charytoniuk <mateusz.charytoniuk@protonmail.com>
Matheus C. França <matheus-catarino@hotmail.com>
Matheus Gabriel Alves Silva <matheusgasource@gmail.com>
Mathieu Baudier <mbaudier@argeo.org>
Mathieu Geli <mathieu.geli@gmail.com>
Mathieu Nayrolles <MathieuNls@users.noreply.github.com>
Mathijs Henquet <mathijs.henquet@gmail.com>
@ -444,6 +470,7 @@ Meng, Hengyu <hengyu.meng@intel.com>
Mengqing Cao <cmq0113@163.com>
Merrick Christensen <merrick.christensen@gmail.com>
Michael Coppola <m18coppola@gmail.com>
Michael Engel <mengel@redhat.com>
Michael Francis <edude03@gmail.com>
Michael Hueschen <m@mhueschen.dev>
Michael Kesper <mkesper@schokokeks.org>
@ -452,7 +479,9 @@ Michael Podvitskiy <podvitskiymichael@gmail.com>
Michael Potter <NanoTekGuy@Gmail.com>
Michael de Gans <michael.john.degans@gmail.com>
Michaël de Vries <vriesdemichael@gmail.com>
Michał Moskal <michal@moskal.me>
Michał Tuszyński <srgtuszy@gmail.com>
Michelle Tan <41475767+MichelleTanPY@users.noreply.github.com>
Mihai <mihai.chirculescu@yahoo.com>
Mike <ytianhui2004@gmail.com>
Mikko Juola <mikjuo@gmail.com>
@ -477,6 +506,7 @@ Neo Zhang <14088817+arthw@users.noreply.github.com>
Neo Zhang <zhang.jianyu@outlook.com>
Neo Zhang Jianyu <jianyu.zhang@intel.com>
Neuman Vong <neuman.vong@gmail.com>
NeverLucky <92274250+nvrxq@users.noreply.github.com>
Nexes the Old <124105151+Nexesenex@users.noreply.github.com>
Nexesenex <124105151+Nexesenex@users.noreply.github.com>
Niall Coates <1349685+Niall-@users.noreply.github.com>
@ -484,11 +514,15 @@ Nicholai Tukanov <nicholaitukanov@gmail.com>
Nico Bosshard <nico@bosshome.ch>
Nicolai Weitkemper <kontakt@nicolaiweitkemper.de>
Nicolás Pérez <nicolas_perez@brown.edu>
Nicolò Scipione <nicolo.scipione@codeplay.com>
Nigel Bosch <pnigelb@gmail.com>
Nikita Sarychev <42014488+sARY77@users.noreply.github.com>
Niklas Korz <niklas@niklaskorz.de>
NikolaiLyssogor <59844691+NikolaiLyssogor@users.noreply.github.com>
Nikolaos Pothitos <pothitos@di.uoa.gr>
Nikolas <127742645+nneubacher@users.noreply.github.com>
Nindaleth <Nindaleth@users.noreply.github.com>
Nuno <rare-magma@posteo.eu>
OSecret <135510162+OLSecret@users.noreply.github.com>
Oleksandr Nikitin <oleksandr@tvori.info>
Oleksii Maryshchenko <oleksii.maryshchenko@gmail.com>
@ -504,6 +538,7 @@ Pavel Zloi <github.com@drteam.rocks>
Pavol Rusnak <pavol@rusnak.io>
Paweł Wodnicki <151604+32bitmicro@users.noreply.github.com>
Pedro Cuenca <pedro@huggingface.co>
Peter <peter277@users.noreply.github.com>
Peter Sugihara <peter@campsh.com>
Phil H <5756783+phiharri@users.noreply.github.com>
Philip Taron <philip.taron@gmail.com>
@ -529,9 +564,12 @@ Rand Xie <randxiexyy29@gmail.com>
Randall Fitzgerald <randall@dasaku.net>
Random Fly <renfei8@live.cn>
Reinforce-II <fate@eastal.com>
Rémy Oudompheng <oudomphe@phare.normalesup.org>
Ren Xuancheng <jklj077@users.noreply.github.com>
Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com>
Reza Kakhki <rezakakhki.de@gmail.com>
RhinoDevel <RhinoDevel@users.noreply.github.com>
Riccardo Orlando <Riccorl@users.noreply.github.com>
Riceball LEE <snowyu.lee@gmail.com>
Rich Dougherty <rich@rd.nz>
Richard Kiss <him@richardkiss.com>
@ -544,6 +582,8 @@ Riley Stewart <ristew@users.noreply.github.com>
Rinne <AsakusaRinne@gmail.com>
Rinne <liu_yaohui1998@126.com>
Robert Brisita <986796+rbrisita@users.noreply.github.com>
Robert Collins <roberto.tomas.cuentas@gmail.com>
Robert Ormandi <52251610+ormandi@users.noreply.github.com>
Robert Sung-wook Shin <edp1096@users.noreply.github.com>
Robey Holderith <robey@flaminglunchbox.net>
Robyn <robyngraf@users.noreply.github.com>
@ -559,7 +599,9 @@ Roni <sulpher@gmx.net>
Ronny Brendel <ronnybrendel@gmail.com>
Ronsor <ronsor@ronsor.pw>
Rowan Hart <rowanbhart@gmail.com>
Ruan <47767371+ruanych@users.noreply.github.com>
Ruchira Hasaranga <ruchira66@gmail.com>
Rudi Servo <rudiservo@gmail.com>
Ruixin Huang <18860020911@163.com>
Rune <43761327+Rune-AI@users.noreply.github.com>
RunningLeon <maningsheng@sensetime.com>
@ -623,12 +665,14 @@ Steven Roussey <sroussey@gmail.com>
Steward Garcia <57494570+FSSRepo@users.noreply.github.com>
StrangeBytesDev <141275258+StrangeBytesDev@users.noreply.github.com>
Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com>
Sukriti Sharma <Ssukriti@users.noreply.github.com>
SuperUserNameMan <yoann@terminajones.com>
Sutou Kouhei <kou@cozmixng.org>
Tai Duc Nguyen <taiducnguyen.drexel@gmail.com>
Taikono-Himazin <kazu@po.harenet.ne.jp>
Tameem <113388789+AhmadTameem@users.noreply.github.com>
Tamotsu Takahashi <ttakah+github@gmail.com>
Tei Home <taiteitonghome@proton.me>
Thái Hoàng Tâm <75922889+RoyalHeart@users.noreply.github.com>
Thatcher Chamberlin <j.thatcher.c@gmail.com>
Theia Vogel <theia@vgel.me>
@ -640,6 +684,7 @@ Tim Miller <drasticactions@users.noreply.github.com>
Tim Wang <overocean@gmail.com>
Timmy Knight <r2d2fish@gmail.com>
Timothy Cronin <40186632+4imothy@users.noreply.github.com>
Ting Lou <louting@189.cn>
Ting Lou <ting.lou@gmail.com>
Ting Sun <suntcrick@gmail.com>
Tobias Lütke <tobi@shopify.com>
@ -661,6 +706,7 @@ Uzo Nweke <uzoechi@gmail.com>
Vaibhav Srivastav <vaibhavs10@gmail.com>
Val Kharitonov <mail@kharvd.com>
Valentin Konovalov <valle.ketsujin@gmail.com>
Valentin Mamedov <45292985+Inf1delis@users.noreply.github.com>
Valentyn Bezshapkin <61702053+valentynbez@users.noreply.github.com>
Vali Malinoiu <0x4139@gmail.com>
Victor Nogueira <felladrin@gmail.com>
@ -673,13 +719,17 @@ Vladimir Malyutin <first-leon@yandex.ru>
Vladimir Zorin <vladimir@deviant.guru>
VoidIsVoid <343750470@qq.com>
Volodymyr Vitvitskyi <72226+signalpillar@users.noreply.github.com>
Wang Qin <37098874+wangqin0@users.noreply.github.com>
Wang Ran (汪然) <wangr@smail.nju.edu.cn>
WangHaoranRobin <56047610+WangHaoranRobin@users.noreply.github.com>
Weird Constructor <weirdconstructor@gmail.com>
Welby Seely <welbyseely@gmail.com>
Wentai Zhang <rchardx@gmail.com>
WillCorticesAI <150854901+WillCorticesAI@users.noreply.github.com>
William Tambellini <william.tambellini@gmail.com>
William Tambellini <wtambellini@sdl.com>
Willy Tarreau <w@1wt.eu>
Woof Dog <197125663+woof-dog@users.noreply.github.com>
Wouter <9594229+DifferentialityDevelopment@users.noreply.github.com>
Wu Jian Ping <wujjpp@hotmail.com>
Wu Jian Ping <wujp@greatld.com>
@ -692,6 +742,7 @@ Xie Yanbo <xieyanbo@gmail.com>
Xingchen Song(宋星辰) <xingchensong1996@163.com>
Xinpeng Dou <81913537+Dou-Git@users.noreply.github.com>
Xuan Son Nguyen <thichthat@gmail.com>
Xuan-Son Nguyen <thichthat@gmail.com>
Yaiko <elyaiko@hotmail.com>
Yann Follet <131855179+YannFollet@users.noreply.github.com>
Yaroslav <yaroslav.yashin@me.com>
@ -702,7 +753,9 @@ Yoshi Suhara <y.suhara@gmail.com>
Yoshi Suhara <ysuhara@nvidia.com>
Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com>
Yüg <eugeniosegalaweb@gmail.com>
Yui <dev@sleepyyui.com>
Yun Dou <dixyes@gmail.com>
Yuri Khrustalev <ykhrustalev@users.noreply.github.com>
Yusuf Kağan Hanoğlu <hanoglu@yahoo.com>
Yuval Peled <31162840+Yuval-Peled@users.noreply.github.com>
@ -714,18 +767,23 @@ Zhang Peiyuan <a1286225768@gmail.com>
Zheng.Deng <32841220+dengzheng-cloud@users.noreply.github.com>
Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com>
Zhiyuan Li <lizhiyuan@uniartisan.com>
Zhiyuan Li <uniartisan2017@gmail.com>
ZhouYuChen <zhouyuchen@naver.com>
Ziad Ben Hadj-Alouane <zied.benhadjalouane@gmail.com>
Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com>
Zsapi <martin1.zsapka@gmail.com>
a-n-n-a-l-e-e <150648636+a-n-n-a-l-e-e@users.noreply.github.com>
a3sh <38979186+A3shTnT@users.noreply.github.com>
adel boussaken <netdur@gmail.com>
afrideva <95653597+afrideva@users.noreply.github.com>
ag2s20150909 <19373730+ag2s20150909@users.noreply.github.com>
agray3 <agray3@users.noreply.github.com>
akawrykow <142945436+akawrykow@users.noreply.github.com>
alek3y <44779186+alek3y@users.noreply.github.com>
alexpinel <93524949+alexpinel@users.noreply.github.com>
alonfaraj <alonfaraj@gmail.com>
alwqx <kenan3015@gmail.com>
amd-dwang <dong.wang@amd.com>
amd-lalithnc <lalithnc@amd.com>
amritahs-ibm <amritahs@linux.vnet.ibm.com>
andrijdavid <david@geek.mg>
@ -737,6 +795,7 @@ arch-btw <57669023+arch-btw@users.noreply.github.com>
arcrank <arcrank@gmail.com>
ardfork <134447697+ardfork@users.noreply.github.com>
arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com>
aryantandon01 <80969509+aryantandon01@users.noreply.github.com>
at8u <129688334+at8u@users.noreply.github.com>
automaticcat <daogiatuank54@gmail.com>
awatuna <23447591+awatuna@users.noreply.github.com>
@ -751,12 +810,14 @@ bryanSwk <93190252+bryanSwk@users.noreply.github.com>
bsilvereagle <bsilvereagle@users.noreply.github.com>
bssrdf <merlintiger@hotmail.com>
byte-6174 <88070277+byte-6174@users.noreply.github.com>
cduk <19917266+cduk@users.noreply.github.com>
cebtenzzre <cebtenzzre@gmail.com>
chaihahaha <chai836275709@gmail.com>
chiranko <96988916+chiranko@users.noreply.github.com>
clibdev <52199778+clibdev@users.noreply.github.com>
clyang <clyang@clyang.net>
cocktailpeanut <121128867+cocktailpeanut@users.noreply.github.com>
codezjx <code.zjx@gmail.com>
coezbek <c.oezbek@gmail.com>
comex <comexk@gmail.com>
compilade <113953597+compilade@users.noreply.github.com>
@ -780,14 +841,17 @@ drbh <david.richard.holtz@gmail.com>
ds5t5 <145942675+ds5t5@users.noreply.github.com>
dylan <canardleteer@users.noreply.github.com>
eastriver <lee@eastriver.dev>
ebraminio <ebrahim@gnu.org>
ebraminio <ebraminio@gmail.com>
eiery <19350831+eiery@users.noreply.github.com>
eric8607242 <e0928021388@gmail.com>
fairydreaming <166155368+fairydreaming@users.noreply.github.com>
fengerhu1 <2748250768@qq.com>
fj-y-saito <85871716+fj-y-saito@users.noreply.github.com>
fraxy-v <65565042+fraxy-v@users.noreply.github.com>
github-actions[bot] <github-actions[bot]@users.noreply.github.com>
gliptic <gliptic@users.noreply.github.com>
gn64 <yukikaze.jp@gmail.com>
goerch <jhr.walter@t-online.de>
grahameth <96447521+grahameth@users.noreply.github.com>
gtygo <gtydoit@gmail.com>
@ -812,10 +876,12 @@ icppWorld <124377669+icppWorld@users.noreply.github.com>
igarnier <igarnier@protonmail.com>
intelmatt <61025942+intelmatt@users.noreply.github.com>
iohub <rickyang.pro@gmail.com>
issixx <46835150+issixx@users.noreply.github.com>
jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com>
jaime-m-p <167997752+jaime-m-p@users.noreply.github.com>
jameswu2014 <545426914@qq.com>
jdomke <28772296+jdomke@users.noreply.github.com>
jiahao su <damow890@gmail.com>
jiez <373447296@qq.com>
jneem <joeneeman@gmail.com>
joecryptotoo <80373433+joecryptotoo@users.noreply.github.com>
@ -828,6 +894,7 @@ junchao-loongson <68935141+junchao-loongson@users.noreply.github.com>
jwj7140 <32943891+jwj7140@users.noreply.github.com>
k.h.lai <adrian.k.h.lai@outlook.com>
kaizau <kaizau@users.noreply.github.com>
kallewoof <kalle.alm@gmail.com>
kalomaze <66376113+kalomaze@users.noreply.github.com>
kang <tpdns9032100@gmail.com>
katsu560 <118887472+katsu560@users.noreply.github.com>
@ -835,6 +902,7 @@ kchro3 <62481661+kchro3@users.noreply.github.com>
khimaros <me@khimaros.com>
kiltyj <kiltyj@gmail.com>
klosax <131523366+klosax@users.noreply.github.com>
krystiancha <krystian@krystianch.com>
kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
kunnis <kunnis@users.noreply.github.com>
kuronekosaiko <EvanChanJ@163.com>
@ -847,6 +915,8 @@ ldwang <ftgreat@163.com>
le.chang <cljs118@126.com>
leejet <leejet714@gmail.com>
leo-pony <nengjunma@outlook.com>
lexasub <lexakopp2212@gmail.com>
lhez <quic_lih@quicinc.com>
limitedAtonement <limitedAtonement@users.noreply.github.com>
liuwei-git <14815172+liuwei-git@users.noreply.github.com>
lon <114724657+longregen@users.noreply.github.com>
@ -855,10 +925,13 @@ ltoniazzi <61414566+ltoniazzi@users.noreply.github.com>
luoyu-intel <yu.luo@intel.com>
m3ndax <adrian.goessl@outlook.com>
maddes8cht <55592906+maddes8cht@users.noreply.github.com>
mahorozte <41834471+mahorozte@users.noreply.github.com>
makomk <makosoft@googlemail.com>
manikbhandari <mbbhandarimanik2@gmail.com>
maor-ps <154728172+maor-ps@users.noreply.github.com>
mashdragon <122402293+mashdragon@users.noreply.github.com>
matiaslin <45382001+matiaslin@users.noreply.github.com>
matt23654 <matthew.webber@protonmail.com>
matteo <matteogeniaccio@yahoo.it>
mdrokz <mohammadmunshi@gmail.com>
mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com>
@ -868,6 +941,7 @@ mmyjona <jonathan.gonse@gmail.com>
momonga <115213907+mmnga@users.noreply.github.com>
momonga <146910567+mmngays@users.noreply.github.com>
moritzbrantner <31051084+moritzbrantner@users.noreply.github.com>
musoles <135031143+musoles@users.noreply.github.com>
mzcu <milos.cubrilo@gmail.com>
nanahi <130121847+na-na-hi@users.noreply.github.com>
ngc92 <7938269+ngc92@users.noreply.github.com>
@ -885,6 +959,7 @@ oobabooga <112222186+oobabooga@users.noreply.github.com>
opparco <parco.opaai@gmail.com>
ostix360 <55257054+ostix360@users.noreply.github.com>
pculliton <phillipculliton@gmail.com>
peidaqi <peidaqi@gmail.com>
pengxin99 <pengxin.yuan@intel.com>
perserk <perserk@gmail.com>
piDack <104877312+piDack@users.noreply.github.com>
@ -892,10 +967,12 @@ pmysl <piotr.myslinski@outlook.com>
postmasters <namnguyen@google.com>
pudepiedj <pudepiedj@gmail.com>
qingfengfenga <41416092+qingfengfenga@users.noreply.github.com>
qingy1337 <qxli2@students.everettcc.edu>
qouoq <qouoq@fastmail.com>
qunash <anzoria@gmail.com>
rabidcopy <rabidcopy@yahoo.com>
rankaiyx <rankaiyx@rankaiyx.com>
redbeard <bharrington@alticon.net>
rhjdvsgsgks <26178113+rhjdvsgsgks@users.noreply.github.com>
rhuddleston <ryan.huddleston@percona.com>
rimoliga <53384203+rimoliga@users.noreply.github.com>
@ -912,6 +989,7 @@ sjxx <63994076+ylsdamxssjxxdd@users.noreply.github.com>
slaren <2141330+slaren@users.noreply.github.com>
slaren <slarengh@gmail.com>
snadampal <87143774+snadampal@users.noreply.github.com>
someone13574 <81528246+someone13574@users.noreply.github.com>
standby24x7 <standby24x7@gmail.com>
staviq <staviq@gmail.com>
stduhpf <stephduh@live.fr>
@ -931,6 +1009,7 @@ uint256_t <konndennsa@gmail.com>
uint256_t <maekawatoshiki1017@gmail.com>
unbounded <haakon@likedan.net>
uvos <devnull@uvos.xyz>
uvos <philipp@uvos.xyz>
valiray <133289098+valiray@users.noreply.github.com>
vb <vaibhavs10@gmail.com>
vik <vikhyatk@gmail.com>
@ -951,6 +1030,7 @@ xaedes <xaedes@googlemail.com>
xctan <axunlei@gmail.com>
xloem <0xloem@gmail.com>
yangli2 <yangli2@gmail.com>
ymcki <84055651+ymcki@users.noreply.github.com>
yuiseki <yuiseki@gmail.com>
yuri@FreeBSD <yurivict@users.noreply.github.com>
zakkor <edward.partenie@gmail.com>
@ -963,4 +1043,5 @@ zrm <trustiosity.zrm@gmail.com>
杨朱 · Kiki <baofa.fan@daocloud.io>
源文雨 <41315874+fumiama@users.noreply.github.com>
蕭澧邦 <45505768+shou692199@users.noreply.github.com>
谢乃闻 <sienaiwun@users.noreply.github.com>
Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>

102
CMakeLists.txt
View file

@ -16,6 +16,7 @@ endif()
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
set(LLAMA_STANDALONE ON)
@ -49,6 +50,8 @@ endif()
if (MSVC)
add_compile_options("$<$<COMPILE_LANGUAGE:C>:/utf-8>")
add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
add_compile_options("$<$<COMPILE_LANGUAGE:C>:/bigobj>")
add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/bigobj>")
endif()
#
@ -77,17 +80,15 @@ option(LLAMA_BUILD_SERVER "llama: build server example" ${LLAMA_STANDALONE})
# 3rd party libs
option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
# Required for relocatable CMake package
include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake)
# override ggml options
set(GGML_SANITIZE_THREAD ${LLAMA_SANITIZE_THREAD})
set(GGML_SANITIZE_ADDRESS ${LLAMA_SANITIZE_ADDRESS})
set(GGML_SANITIZE_UNDEFINED ${LLAMA_SANITIZE_UNDEFINED})
set(GGML_ALL_WARNINGS ${LLAMA_ALL_WARNINGS})
set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS})
set(GGML_ALL_WARNINGS ${LLAMA_ALL_WARNINGS})
set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS})
# change the default for these ggml options
if (NOT DEFINED GGML_LLAMAFILE)
@ -117,16 +118,62 @@ llama_option_depr(WARNING LLAMA_SYCL GGML_SYCL)
llama_option_depr(WARNING LLAMA_SYCL_F16 GGML_SYCL_F16)
llama_option_depr(WARNING LLAMA_CANN GGML_CANN)
if (NOT MSVC)
if (LLAMA_SANITIZE_THREAD)
message(STATUS "Using -fsanitize=thread")
add_compile_options(-fsanitize=thread)
link_libraries (-fsanitize=thread)
endif()
if (LLAMA_SANITIZE_ADDRESS)
message(STATUS "Using -fsanitize=address")
add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
link_libraries (-fsanitize=address)
endif()
if (LLAMA_SANITIZE_UNDEFINED)
message(STATUS "Using -fsanitize=undefined")
add_compile_options(-fsanitize=undefined)
link_libraries (-fsanitize=undefined)
endif()
endif()
#
# build the library
# 3rd-party
#
if (NOT TARGET ggml)
add_subdirectory(ggml)
# ... otherwise assume ggml is added by a parent CMakeLists.txt
endif()
#
# build the library
#
add_subdirectory(src)
#
# utils, programs, examples and tests
#
if (LLAMA_BUILD_COMMON)
add_subdirectory(common)
endif()
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
include(CTest)
add_subdirectory(tests)
endif()
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
add_subdirectory(examples)
add_subdirectory(pocs)
endif()
#
# install
#
@ -142,27 +189,14 @@ set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location o
set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files")
set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files")
# At the moment some compile definitions are placed within the ggml/src
# directory but not exported on the `ggml` target. This could be improved by
# determining _precisely_ which defines are necessary for the llama-config
# package.
#
set(GGML_TRANSIENT_DEFINES)
get_target_property(GGML_DIRECTORY ggml SOURCE_DIR)
get_directory_property(GGML_DIR_DEFINES DIRECTORY ${GGML_DIRECTORY} COMPILE_DEFINITIONS)
if (GGML_DIR_DEFINES)
list(APPEND GGML_TRANSIENT_DEFINES ${GGML_DIR_DEFINES})
endif()
get_target_property(GGML_TARGET_DEFINES ggml COMPILE_DEFINITIONS)
if (GGML_TARGET_DEFINES)
list(APPEND GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES})
endif()
get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES)
# all public headers
set(LLAMA_PUBLIC_HEADERS
${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h
${CMAKE_CURRENT_SOURCE_DIR}/include/llama-cpp.h)
set_target_properties(llama PROPERTIES PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}")
set_target_properties(llama
PROPERTIES
PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}")
install(TARGETS llama LIBRARY PUBLIC_HEADER)
configure_package_config_file(
@ -199,22 +233,4 @@ configure_file(cmake/llama.pc.in
@ONLY)
install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
DESTINATION lib/pkgconfig)
#
# utils, programs, examples and tests
#
if (LLAMA_BUILD_COMMON)
add_subdirectory(common)
endif()
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
include(CTest)
add_subdirectory(tests)
endif()
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
add_subdirectory(examples)
add_subdirectory(pocs)
endif()
DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)

8
CODEOWNERS
View file

@ -1,5 +1,11 @@
# collaborators can optionally add themselves here to indicate their availability for reviewing related PRs
/ci/ @ggerganov
/.devops/ @ngxson
/.devops/*.Dockerfile @ngxson
/examples/server/ @ngxson
/ggml/src/ggml-cuda/fattn* @JohannesGaessler
/ggml/src/ggml-cuda/mmq.* @JohannesGaessler
/ggml/src/ggml-cuda/mmv.* @JohannesGaessler
/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
/ggml/src/ggml-opt.cpp @JohannesGaessler
/ggml/src/gguf.cpp @JohannesGaessler

102
CONTRIBUTING.md
View file

@ -1,10 +1,10 @@
# Pull requests (for contributors)
- Test your changes:
- Execute [the full CI locally on your machine](ci/README.md) before publishing
- Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
- If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
- If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
- Execute [the full CI locally on your machine](ci/README.md) before publishing
- Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
- If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
- If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
- Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
- If your PR becomes stale, don't hesitate to ping the maintainers in the comments
@ -20,14 +20,104 @@
- Avoid adding third-party dependencies, extra files, extra headers, etc.
- Always consider cross-compatibility with other operating systems and architectures
- Avoid fancy-looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
- Vertical alignment makes things more readable and easier to batch edit
- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
- Naming usually optimizes for common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963)
- Use sized integer types such as `int32_t` in the public API, e.g. `size_t` may also be appropriate for allocation sizes or byte offsets
- Declare structs with `struct foo {}` instead of `typedef struct foo {} foo`
- In C++ code omit optional `struct` and `enum` keyword whenever they are not necessary
```cpp
// OK
llama_context * ctx;
const llama_rope_type rope_type;
// not OK
struct llama_context * ctx;
const enum llama_rope_type rope_type;
```
_(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline.)_
- Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use `clang-format` to format the added code
- For anything not covered in the current guidelines, refer to the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines)
- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
![matmul](media/matmul.png)
# Naming guidelines
- Use `snake_case` for function, variable and type names
- Naming usually optimizes for longest common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963)
```cpp
// not OK
int small_number;
int big_number;
// OK
int number_small;
int number_big;
```
- Enum values are always in upper case and prefixed with the enum name
```cpp
enum llama_vocab_type {
LLAMA_VOCAB_TYPE_NONE = 0,
LLAMA_VOCAB_TYPE_SPM = 1,
LLAMA_VOCAB_TYPE_BPE = 2,
LLAMA_VOCAB_TYPE_WPM = 3,
LLAMA_VOCAB_TYPE_UGM = 4,
LLAMA_VOCAB_TYPE_RWKV = 5,
};
```
- The general naming pattern is `<class>_<method>`, with `<method>` being `<action>_<noun>`
```cpp
llama_model_init(); // class: "llama_model", method: "init"
llama_sampler_chain_remove(); // class: "llama_sampler_chain", method: "remove"
llama_sampler_get_seed(); // class: "llama_sampler", method: "get_seed"
llama_set_embeddings(); // class: "llama_context", method: "set_embeddings"
llama_n_threads(); // class: "llama_context", method: "n_threads"
llama_adapter_lora_free(); // class: "llama_adapter_lora", method: "free"
```
- The `get` `<action>` can be omitted
- The `<noun>` can be omitted if not necessary
- The `_context` suffix of the `<class>` is optional. Use it to disambiguate symbols when needed
- Use `init`/`free` for constructor/destructor `<action>`
- Use the `_t` suffix when a type is supposed to be opaque to the user - it's not relevant to them if it is a struct or anything else
```cpp
typedef struct llama_context * llama_context_t;
enum llama_pooling_type llama_pooling_type(const llama_context_t ctx);
```
_(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline)_
- C/C++ filenames are all lowercase with dashes. Headers use the `.h` extension. Source files use the `.c` or `.cpp` extension
- Python filenames are all lowercase with underscores
- _(TODO: abbreviations usage)_
# Preprocessor directives
- _(TODO: add guidelines with examples and apply them to the codebase)_
```cpp
#ifdef FOO
#endif // FOO
```
# Documentation
- Documentation is a community effort
- When you need to look into the source code to figure out how to use an API consider adding a short summary to the header file for future reference
- When you notice incorrect or outdated documentation, please update it
# Resources
The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects:

13
Makefile
View file

@ -52,6 +52,7 @@ TEST_TARGETS = \
tests/test-arg-parser \
tests/test-autorelease \
tests/test-backend-ops \
tests/test-chat \
tests/test-chat-template \
tests/test-double-float \
tests/test-grammar-integration \
@ -595,7 +596,7 @@ ifdef GGML_RPC
OBJ_GGML_EXT += ggml/src/ggml-rpc.o
endif # GGML_RPC
OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu))
OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-mma*.cu))
OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu))
ifdef GGML_CUDA_FA_ALL_QUANTS
@ -983,6 +984,7 @@ OBJ_COMMON = \
$(DIR_COMMON)/ngram-cache.o \
$(DIR_COMMON)/sampling.o \
$(DIR_COMMON)/speculative.o \
$(DIR_COMMON)/chat.o \
$(DIR_COMMON)/build-info.o \
$(DIR_COMMON)/json-schema-to-grammar.o
@ -1361,7 +1363,11 @@ llama-server: \
examples/server/httplib.h \
examples/server/index.html.hpp \
examples/server/loading.html.hpp \
common/chat.cpp \
common/chat.hpp \
common/chat-template.hpp \
common/json.hpp \
common/minja.hpp \
$(OBJ_ALL)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
@ -1469,6 +1475,11 @@ tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \
$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-chat: tests/test-chat.cpp \
$(OBJ_ALL)
$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-opt: tests/test-opt.cpp \
$(OBJ_GGML)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)

55
README.md
View file

@ -16,7 +16,11 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
## Hot topics
- **Introducing GGUF-my-LoRA** https://github.com/ggerganov/llama.cpp/discussions/10123
- **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggerganov/llama.cpp/pull/11427
- **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode
- Universal tool call support in `llama-server`: https://github.com/ggerganov/llama.cpp/pull/9639
- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
- Introducing GGUF-my-LoRA https://github.com/ggerganov/llama.cpp/discussions/10123
- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669
- Hugging Face GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
@ -69,6 +73,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
- [x] [PhiMoE](https://github.com/ggerganov/llama.cpp/pull/11003)
- [x] [GPT-2](https://huggingface.co/gpt2)
- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
@ -91,13 +96,14 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
- [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1)
- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
#### Multimodal
@ -111,6 +117,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
</details>
@ -129,6 +136,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client)
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html)
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
@ -181,6 +189,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- [ramalama](https://github.com/containers/ramalama) (MIT)
- [semperai/amica](https://github.com/semperai/amica) (MIT)
- [withcatai/catai](https://github.com/withcatai/catai) (MIT)
- [Autopen](https://github.com/blackhole89/autopen) (GPL)
</details>
@ -201,6 +210,8 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
- [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
- [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server
- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale
</details>
@ -242,6 +253,8 @@ The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](htt
- [Trending](https://huggingface.co/models?library=gguf&sort=trending)
- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)
You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from Hugging Face by using this CLI argument: `-hf <user>/<model>[:quant]`
After downloading a model, use the CLI tools to run it locally - see below.
`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo.
@ -260,21 +273,12 @@ To learn more about model quantization, [read this documentation](examples/quant
#### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
- <details open>
<summary>Run simple text completion</summary>
```bash
llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128
# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
```
</details>
- <details>
<summary>Run in conversation mode</summary>
Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME`
```bash
llama-cli -m model.gguf -p "You are a helpful assistant" -cnv
llama-cli -m model.gguf
# > hi, who are you?
# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
@ -286,17 +290,28 @@ To learn more about model quantization, [read this documentation](examples/quant
</details>
- <details>
<summary>Run with custom chat template</summary>
<summary>Run in conversation mode with custom chat template</summary>
```bash
# use the "chatml" template
llama-cli -m model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml
# use the "chatml" template (use -h to see the list of supported templates)
llama-cli -m model.gguf -cnv --chat-template chatml
# use a custom template
llama-cli -m model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
```
[Supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
</details>
- <details>
<summary>Run simple text completion</summary>
To disable conversation mode explicitly, use `-no-cnv`
```bash
llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv
# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
```
</details>
@ -411,7 +426,7 @@ To learn more about model quantization, [read this documentation](examples/quant
</details>
[^1]: [examples/perplexity/README.md](examples/perplexity/README.md)
[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md)
[^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
## [`llama-bench`](examples/llama-bench)

66
ci/run.sh
View file

@ -326,17 +326,17 @@ function gg_run_open_llama_7b_v2 {
./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
(time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-cli -no-cnv --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@ -460,17 +460,17 @@ function gg_run_pythia_1_4b {
./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
(time ./bin/llama-cli --model ${model_f16} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-cli --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-cli --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-cli --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-cli --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-cli --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-cli --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-cli --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-cli --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-cli --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-cli --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-cli -no-cnv --model ${model_f16} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@ -591,17 +591,17 @@ function gg_run_pythia_2_8b {
./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
(time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-cli -no-cnv --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log

2
cmake/build-info.cmake
View file

@ -44,7 +44,7 @@ if(MSVC)
set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
else()
execute_process(
COMMAND sh -c "$@ --version | head -1" _ ${CMAKE_C_COMPILER}
COMMAND sh -c "\"$@\" --version | head -1" _ ${CMAKE_C_COMPILER}
OUTPUT_VARIABLE OUT
OUTPUT_STRIP_TRAILING_WHITESPACE
)

156
cmake/llama-config.cmake.in
View file

@ -3,159 +3,13 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
set(GGML_STATIC @GGML_STATIC@)
set(GGML_NATIVE @GGML_NATIVE@)
set(GGML_LTO @GGML_LTO@)
set(GGML_CCACHE @GGML_CCACHE@)
set(GGML_AVX @GGML_AVX@)
set(GGML_AVX2 @GGML_AVX2@)
set(GGML_AVX512 @GGML_AVX512@)
set(GGML_AVX512_VBMI @GGML_AVX512_VBMI@)
set(GGML_AVX512_VNNI @GGML_AVX512_VNNI@)
set(GGML_AVX512_BF16 @GGML_AVX512_BF16@)
set(GGML_AMX_TILE @GGML_AMX_TILE@)
set(GGML_AMX_INT8 @GGML_AMX_INT8@)
set(GGML_AMX_BF16 @GGML_AMX_BF16@)
set(GGML_FMA @GGML_FMA@)
set(GGML_LASX @GGML_LASX@)
set(GGML_LSX @GGML_LSX@)
set(GGML_RVV @GGML_RVV@)
set(GGML_SVE @GGML_SVE@)
set(GGML_ACCELERATE @GGML_ACCELERATE@)
set(GGML_OPENMP @GGML_OPENMP@)
set(GGML_CPU_HBM @GGML_CPU_HBM@)
set(GGML_BLAS_VENDOR @GGML_BLAS_VENDOR@)
set(GGML_CUDA_FORCE_MMQ @GGML_CUDA_FORCE_MMQ@)
set(GGML_CUDA_FORCE_CUBLAS @GGML_CUDA_FORCE_CUBLAS@)
set(GGML_CUDA_F16 @GGML_CUDA_F16@)
set(GGML_CUDA_PEER_MAX_BATCH_SIZE @GGML_CUDA_PEER_MAX_BATCH_SIZE@)
set(GGML_CUDA_NO_PEER_COPY @GGML_CUDA_NO_PEER_COPY@)
set(GGML_CUDA_NO_VMM @GGML_CUDA_NO_VMM@)
set(GGML_CUDA_FA_ALL_QUANTS @GGML_CUDA_FA_ALL_QUANTS@)
set(GGML_CUDA_GRAPHS @GGML_CUDA_GRAPHS@)
set(GGML_HIP_UMA @GGML_HIP_UMA@)
set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@)
set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@)
set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@)
set(GGML_VULKAN_SHADER_DEBUG_INFO @GGML_VULKAN_SHADER_DEBUG_INFO@)
set(GGML_VULKAN_PERF @GGML_VULKAN_PERF@)
set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@)
set(GGML_VULKAN_RUN_TESTS @GGML_VULKAN_RUN_TESTS@)
set(GGML_METAL_USE_BF16 @GGML_METAL_USE_BF16@)
set(GGML_METAL_NDEBUG @GGML_METAL_NDEBUG@)
set(GGML_METAL_SHADER_DEBUG @GGML_METAL_SHADER_DEBUG@)
set(GGML_METAL_EMBED_LIBRARY @GGML_METAL_EMBED_LIBRARY@)
set(GGML_METAL_MACOSX_VERSION_MIN @GGML_METAL_MACOSX_VERSION_MIN@)
set(GGML_METAL_STD @GGML_METAL_STD@)
set(GGML_SYCL_F16 @GGML_SYCL_F16@)
set(GGML_SYCL_TARGET @GGML_SYCL_TARGET@)
set(GGML_SYCL_DEVICE_ARCH @GGML_SYCL_DEVICE_ARCH@)
@PACKAGE_INIT@
set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
find_package(Threads REQUIRED)
set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@")
set(_llama_link_deps "")
set(_llama_link_opts "")
foreach(_ggml_lib ggml ggml-base)
string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY")
find_library(${_ggml_lib_var} ${_ggml_lib}
REQUIRED
HINTS ${LLAMA_LIB_DIR}
NO_CMAKE_FIND_ROOT_PATH
)
list(APPEND _llama_link_deps "${${_ggml_lib_var}}")
message(STATUS "Found ${${_ggml_lib_var}}")
endforeach()
foreach(backend amx blas cann cpu cuda hip kompute metal musa rpc sycl vulkan)
string(TOUPPER "GGML_${backend}" backend_id)
set(_ggml_lib "ggml-${backend}")
string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY")
find_library(${_ggml_lib_var} ${_ggml_lib}
HINTS ${LLAMA_LIB_DIR}
NO_CMAKE_FIND_ROOT_PATH
)
if(${_ggml_lib_var})
list(APPEND _llama_link_deps "${${_ggml_lib_var}}")
set(${backend_id} ON)
message(STATUS "Found backend ${${_ggml_lib_var}}")
else()
set(${backend_id} OFF)
endif()
endforeach()
if (NOT LLAMA_SHARED_LIB)
if (APPLE AND GGML_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
list(APPEND _llama_link_deps ${ACCELERATE_FRAMEWORK})
endif()
if (GGML_OPENMP)
find_package(OpenMP REQUIRED)
list(APPEND _llama_link_deps OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
endif()
if (GGML_CPU_HBM)
find_library(memkind memkind REQUIRED)
list(APPEND _llama_link_deps memkind)
endif()
if (GGML_BLAS)
find_package(BLAS REQUIRED)
list(APPEND _llama_link_deps ${BLAS_LIBRARIES})
list(APPEND _llama_link_opts ${BLAS_LINKER_FLAGS})
endif()
if (GGML_CUDA)
find_package(CUDAToolkit REQUIRED)
endif()
if (GGML_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
list(APPEND _llama_link_deps ${FOUNDATION_LIBRARY}
${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK})
endif()
if (GGML_VULKAN)
find_package(Vulkan REQUIRED)
list(APPEND _llama_link_deps Vulkan::Vulkan)
endif()
if (GGML_HIP)
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
find_package(rocblas REQUIRED)
list(APPEND _llama_link_deps hip::host roc::rocblas roc::hipblas)
endif()
if (GGML_SYCL)
find_package(DNNL)
if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
list(APPEND _llama_link_deps DNNL::dnnl)
endif()
if (WIN32)
find_package(IntelSYCL REQUIRED)
find_package(MKL REQUIRED)
list(APPEND _llama_link_deps IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
endif()
endif()
endif()
find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake)
find_library(llama_LIBRARY llama
REQUIRED
@ -167,12 +21,10 @@ add_library(llama UNKNOWN IMPORTED)
set_target_properties(llama
PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
INTERFACE_LINK_LIBRARIES "${_llama_link_deps}"
INTERFACE_LINK_OPTIONS "${_llama_link_opts}"
INTERFACE_COMPILE_DEFINITIONS "${_llama_transient_defines}"
INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;"
IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
IMPORTED_LOCATION "${llama_LIBRARY}"
INTERFACE_COMPILE_FEATURES cxx_std_11
POSITION_INDEPENDENT_CODE ON )
INTERFACE_COMPILE_FEATURES c_std_90
POSITION_INDEPENDENT_CODE ON)
check_required_components(Llama)

10
cmake/llama.pc.in
View file

@ -1,10 +1,10 @@
prefix=@CMAKE_INSTALL_PREFIX@
exec_prefix=${prefix}
libdir=${exec_prefix}/lib
includedir=${prefix}/include
exec_prefix=@CMAKE_INSTALL_PREFIX@
libdir=@CMAKE_INSTALL_FULL_LIBDIR@
includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@
Name: llama
Description: Port of Facebook's LLaMA model in C/C++
Version: @PROJECT_VERSION@
Libs: -L${libdir} -lggml -lggml-base -lllama
Version: @LLAMA_INSTALL_VERSION@
Libs: -L${libdir} -lggml -lggml-base -lllama
Cflags: -I${includedir}

32
common/CMakeLists.txt
View file

@ -56,14 +56,19 @@ add_library(${TARGET} STATIC
arg.cpp
arg.h
base64.hpp
chat.cpp
chat.hpp
chat-template.hpp
common.cpp
common.h
console.cpp
console.h
json-schema-to-grammar.cpp
json.hpp
llguidance.cpp
log.cpp
log.h
minja.hpp
ngram-cache.cpp
ngram-cache.h
sampling.cpp
@ -87,6 +92,33 @@ if (LLAMA_CURL)
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
endif ()
if (LLAMA_LLGUIDANCE)
include(ExternalProject)
set(LLGUIDANCE_SRC ${CMAKE_BINARY_DIR}/llguidance/source)
set(LLGUIDANCE_PATH ${LLGUIDANCE_SRC}/target/release)
ExternalProject_Add(llguidance_ext
GIT_REPOSITORY https://github.com/guidance-ai/llguidance
# v0.6.12:
GIT_TAG ced1c9023d47ec194fa977932d35ce65c2ebfc09
PREFIX ${CMAKE_BINARY_DIR}/llguidance
SOURCE_DIR ${LLGUIDANCE_SRC}
BUILD_IN_SOURCE TRUE
CONFIGURE_COMMAND ""
BUILD_COMMAND cargo build --release
INSTALL_COMMAND ""
BUILD_BYPRODUCTS ${LLGUIDANCE_PATH}/libllguidance.a ${LLGUIDANCE_PATH}/llguidance.h
UPDATE_COMMAND ""
)
target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_LLGUIDANCE)
add_library(llguidance STATIC IMPORTED)
set_target_properties(llguidance PROPERTIES IMPORTED_LOCATION ${LLGUIDANCE_PATH}/libllguidance.a)
add_dependencies(llguidance llguidance_ext)
target_include_directories(${TARGET} PRIVATE ${LLGUIDANCE_PATH})
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance)
endif ()
target_include_directories(${TARGET} PUBLIC .)
target_compile_features (${TARGET} PUBLIC cxx_std_17)
target_link_libraries (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)

226
common/arg.cpp
View file

@ -22,6 +22,11 @@ common_arg & common_arg::set_examples(std::initializer_list<enum llama_example>
return *this;
}
common_arg & common_arg::set_excludes(std::initializer_list<enum llama_example> excludes) {
this->excludes = std::move(excludes);
return *this;
}
common_arg & common_arg::set_env(const char * env) {
help = help + "\n(env: " + env + ")";
this->env = env;
@ -37,6 +42,10 @@ bool common_arg::in_example(enum llama_example ex) {
return examples.find(ex) != examples.end();
}
bool common_arg::is_exclude(enum llama_example ex) {
return excludes.find(ex) != excludes.end();
}
bool common_arg::get_value_from_env(std::string & output) {
if (env == nullptr) return false;
char * value = std::getenv(env);
@ -121,17 +130,27 @@ std::string common_arg::to_string() {
static void common_params_handle_model_default(
std::string & model,
std::string & model_url,
const std::string & model_url,
std::string & hf_repo,
std::string & hf_file) {
std::string & hf_file,
const std::string & hf_token,
const std::string & model_default) {
if (!hf_repo.empty()) {
// short-hand to avoid specifying --hf-file -> default it to --model
if (hf_file.empty()) {
if (model.empty()) {
throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
auto auto_detected = common_get_hf_file(hf_repo, hf_token);
if (auto_detected.first.empty() || auto_detected.second.empty()) {
exit(1); // built without CURL, error message already printed
}
hf_repo = auto_detected.first;
hf_file = auto_detected.second;
} else {
hf_file = model;
}
hf_file = model;
} else if (model.empty()) {
}
// make sure model path is present (for caching purposes)
if (model.empty()) {
// this is to avoid different repo having same file name, or same file name in different subdirs
std::string filename = hf_repo + "_" + hf_file;
// to make sure we don't have any slashes in the filename
@ -145,7 +164,7 @@ static void common_params_handle_model_default(
model = fs_get_cache_file(string_split<std::string>(f, '/').back());
}
} else if (model.empty()) {
model = DEFAULT_MODEL_PATH;
model = model_default;
}
}
@ -281,8 +300,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
}
// TODO: refactor model params in a common struct
common_params_handle_model_default(params.model, params.model_url, params.hf_repo, params.hf_file);
common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file);
common_params_handle_model_default(params.model, params.model_url, params.hf_repo, params.hf_file, params.hf_token, DEFAULT_MODEL_PATH);
common_params_handle_model_default(params.speculative.model, params.speculative.model_url, params.speculative.hf_repo, params.speculative.hf_file, params.hf_token, "");
common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file, params.hf_token, "");
if (params.escape) {
string_process_escapes(params.prompt);
@ -305,6 +325,14 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
throw std::invalid_argument("error: either --embedding or --reranking can be specified, but not both");
}
if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
throw std::runtime_error(string_format(
"error: the supplied chat template is not supported: %s%s\n",
params.chat_template.c_str(),
params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates"
));
}
return true;
}
@ -358,6 +386,30 @@ static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & val
return devices;
}
static void add_rpc_devices(std::string servers) {
auto rpc_servers = string_split<std::string>(servers, ',');
if (rpc_servers.empty()) {
throw std::invalid_argument("no RPC servers specified");
}
ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
if (!rpc_reg) {
throw std::invalid_argument("failed to find RPC backend");
}
typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
if (!ggml_backend_rpc_add_device_fn) {
throw std::invalid_argument("failed to find RPC device add function");
}
for (const auto & server : rpc_servers) {
ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
if (dev) {
ggml_backend_device_register(dev);
} else {
throw std::invalid_argument("failed to register RPC device");
}
}
}
bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
auto ctx_arg = common_params_parser_init(params, ex, print_usage);
const common_params params_org = ctx_arg.params; // the example can modify the default params
@ -420,7 +472,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
* - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
*/
auto add_opt = [&](common_arg arg) {
if (arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) {
if ((arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) && !arg.is_exclude(ex)) {
ctx_arg.options.push_back(std::move(arg));
}
};
@ -649,7 +701,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params, const std::string & value) {
params.prompt = value;
}
));
).set_excludes({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--no-perf"},
string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
@ -673,7 +725,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.prompt.pop_back();
}
}
));
).set_excludes({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--in-file"}, "FNAME",
"an input file (repeat to specify multiple files)",
@ -700,7 +752,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.prompt = ss.str();
fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str());
}
));
).set_excludes({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-e", "--escape"},
string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
@ -759,15 +811,19 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-cnv", "--conversation"},
string_format(
"run in conversation mode:\n"
"- does not print special tokens and suffix/prefix\n"
"- interactive mode is also enabled\n"
"(default: %s)",
params.conversation ? "true" : "false"
),
"run in conversation mode:\n"
"- does not print special tokens and suffix/prefix\n"
"- interactive mode is also enabled\n"
"(default: auto enabled if chat template is available)",
[](common_params & params) {
params.conversation = true;
params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(common_arg(
{"-no-cnv", "--no-conversation"},
"force disable conversation mode (default: false)",
[](common_params & params) {
params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
add_opt(common_arg(
@ -821,7 +877,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params) {
params.warmup = false;
}
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING}));
add_opt(common_arg(
{"--spm-infill"},
string_format(
@ -1363,7 +1419,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--rpc"}, "SERVERS",
"comma separated list of RPC servers",
[](common_params & params, const std::string & value) {
params.rpc_servers = value;
add_rpc_devices(value);
GGML_UNUSED(params);
}
).set_env("LLAMA_ARG_RPC"));
}
@ -1408,15 +1465,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--list-devices"},
"print list of available devices and exit",
[](common_params &) {
printf("Available devices:\n");
std::vector<ggml_backend_dev_t> rpc_devices;
std::vector<ggml_backend_dev_t> all_devices;
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
auto * dev = ggml_backend_dev_get(i);
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
size_t free, total;
ggml_backend_dev_memory(dev, &free, &total);
printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
if (ggml_backend_reg_name(reg) == std::string("RPC")) {
rpc_devices.push_back(dev);
} else {
all_devices.push_back(dev);
}
}
}
// insert RPC devices in front
all_devices.insert(all_devices.begin(), rpc_devices.begin(), rpc_devices.end());
printf("Available devices:\n");
for (size_t i = 0; i < all_devices.size(); ++i) {
auto * dev = all_devices[i];
size_t free, total;
ggml_backend_dev_memory(dev, &free, &total);
printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
}
exit(0);
}
));
@ -1512,7 +1582,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--lora"}, "FNAME",
"path to LoRA adapter (can be repeated to use multiple adapters)",
[](common_params & params, const std::string & value) {
params.lora_adapters.push_back({ std::string(value), 1.0 });
params.lora_adapters.push_back({ std::string(value), 1.0, nullptr });
}
// we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
@ -1520,7 +1590,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--lora-scaled"}, "FNAME", "SCALE",
"path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
[](common_params & params, const std::string & fname, const std::string & scale) {
params.lora_adapters.push_back({ fname, std::stof(scale) });
params.lora_adapters.push_back({ fname, std::stof(scale), nullptr });
}
// we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
@ -1574,21 +1644,30 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_env("LLAMA_ARG_MODEL_URL"));
add_opt(common_arg(
{"-hfr", "--hf-repo"}, "REPO",
"Hugging Face model repository (default: unused)",
{"-hf", "-hfr", "--hf-repo"}, "<user>/<model>[:quant]",
"Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"
"example: unsloth/phi-4-GGUF:q4_k_m\n"
"(default: unused)",
[](common_params & params, const std::string & value) {
params.hf_repo = value;
}
).set_env("LLAMA_ARG_HF_REPO"));
add_opt(common_arg(
{"-hfd", "-hfrd", "--hf-repo-draft"}, "<user>/<model>[:quant]",
"Same as --hf-repo, but for the draft model (default: unused)",
[](common_params & params, const std::string & value) {
params.speculative.hf_repo = value;
}
).set_env("LLAMA_ARG_HFD_REPO"));
add_opt(common_arg(
{"-hff", "--hf-file"}, "FILE",
"Hugging Face model file (default: unused)",
"Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)",
[](common_params & params, const std::string & value) {
params.hf_file = value;
}
).set_env("LLAMA_ARG_HF_FILE"));
add_opt(common_arg(
{"-hfrv", "--hf-repo-v"}, "REPO",
{"-hfv", "-hfrv", "--hf-repo-v"}, "<user>/<model>[:quant]",
"Hugging Face model repository for the vocoder model (default: unused)",
[](common_params & params, const std::string & value) {
params.vocoder.hf_repo = value;
@ -1889,24 +1968,44 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--jinja"},
"use jinja template for chat (default: disabled)",
[](common_params & params) {
params.use_jinja = true;
}
).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA"));
add_opt(common_arg(
{"--chat-template"}, "JINJA_TEMPLATE",
string_format(
"set custom jinja chat template (default: template taken from model's metadata)\n"
"if suffix/prefix are specified, template will be disabled\n"
"only commonly used templates are accepted (unless --jinja is set before this flag):\n"
"list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
),
[](common_params & params, const std::string & value) {
if (!common_chat_verify_template(value)) {
throw std::runtime_error(string_format(
"error: the supplied chat template is not supported: %s\n"
"note: llama.cpp does not use jinja parser, we only support commonly used templates\n",
value.c_str()
));
}
params.chat_template = value;
}
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
add_opt(common_arg(
{"--chat-template-file"}, "JINJA_TEMPLATE_FILE",
string_format(
"set custom jinja chat template file (default: template taken from model's metadata)\n"
"if suffix/prefix are specified, template will be disabled\n"
"only commonly used templates are accepted (unless --jinja is set before this flag):\n"
"list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
),
[](common_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
}
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(params.chat_template));
}
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
add_opt(common_arg(
{"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
@ -2205,6 +2304,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.vocoder.model = value;
}
).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--tts-use-guide-tokens"},
"Use guide tokens to improve TTS word recall",
[](common_params & params) {
params.vocoder.use_guide_tokens = true;
}
).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
// model-specific
add_opt(common_arg(
@ -2218,5 +2324,47 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_examples({LLAMA_EXAMPLE_TTS}));
add_opt(common_arg(
{"--embd-bge-small-en-default"},
string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"),
[](common_params & params) {
params.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF";
params.hf_file = "bge-small-en-v1.5-q8_0.gguf";
params.pooling_type = LLAMA_POOLING_TYPE_NONE;
params.embd_normalize = 2;
params.n_ctx = 512;
params.verbose_prompt = true;
params.embedding = true;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--embd-e5-small-en-default"},
string_format("use default e5-small-v2 model (note: can download weights from the internet)"),
[](common_params & params) {
params.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF";
params.hf_file = "e5-small-v2-q8_0.gguf";
params.pooling_type = LLAMA_POOLING_TYPE_NONE;
params.embd_normalize = 2;
params.n_ctx = 512;
params.verbose_prompt = true;
params.embedding = true;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--embd-gte-small-default"},
string_format("use default gte-small model (note: can download weights from the internet)"),
[](common_params & params) {
params.hf_repo = "ggml-org/gte-small-Q8_0-GGUF";
params.hf_file = "gte-small-q8_0.gguf";
params.pooling_type = LLAMA_POOLING_TYPE_NONE;
params.embd_normalize = 2;
params.n_ctx = 512;
params.verbose_prompt = true;
params.embedding = true;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
return ctx_arg;
}

3
common/arg.h
View file

@ -12,6 +12,7 @@
struct common_arg {
std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
std::set<enum llama_example> excludes = {};
std::vector<const char *> args;
const char * value_hint = nullptr; // help text or example for arg value
const char * value_hint_2 = nullptr; // for second arg value
@ -53,9 +54,11 @@ struct common_arg {
) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
common_arg & set_examples(std::initializer_list<enum llama_example> examples);
common_arg & set_excludes(std::initializer_list<enum llama_example> excludes);
common_arg & set_env(const char * env);
common_arg & set_sparam();
bool in_example(enum llama_example ex);
bool is_exclude(enum llama_example ex);
bool get_value_from_env(std::string & output);
bool has_value_from_env();
std::string to_string();

515
common/chat-template.hpp Normal file
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@ -0,0 +1,515 @@
/*
Copyright 2024 Google LLC
Use of this source code is governed by an MIT-style
license that can be found in the LICENSE file or at
https://opensource.org/licenses/MIT.
*/
// SPDX-License-Identifier: MIT
#pragma once
#include "minja.hpp"
#include <json.hpp>
#include <string>
#include <vector>
using json = nlohmann::ordered_json;
namespace minja {
struct chat_template_caps {
bool supports_tools = false;
bool supports_tool_calls = false;
bool supports_tool_responses = false;
bool supports_system_role = false;
bool supports_parallel_tool_calls = false;
bool supports_tool_call_id = false;
// meta-llama/Llama-3.1-8B-Instruct expects arguments to be an object.
// Most other templates (and OpenAI's API) expect the arguments object to be stringified.
bool requires_object_arguments = false;
// CohereForAI/c4ai-command-r-plus simple variant
bool requires_non_null_content = false;
// MiniMaxAI/MiniMax-Text-01 special
bool requires_typed_content = false;
};
struct chat_template_inputs {
nlohmann::ordered_json messages;
nlohmann::ordered_json tools;
bool add_generation_prompt = true;
nlohmann::ordered_json extra_context;
std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
};
struct chat_template_options {
bool apply_polyfills = true;
bool use_bos_token = true;
bool use_eos_token = true;
bool define_strftime_now = true;
bool polyfill_tools = true;
bool polyfill_tool_call_examples = true;
bool polyfill_tool_calls = true;
bool polyfill_tool_responses = true;
bool polyfill_system_role = true;
bool polyfill_object_arguments = true;
bool polyfill_typed_content = true;
};
class chat_template {
private:
chat_template_caps caps_;
std::string source_;
std::string bos_token_;
std::string eos_token_;
std::shared_ptr<minja::TemplateNode> template_root_;
std::string tool_call_example_;
std::string try_raw_render(
const nlohmann::ordered_json & messages,
const nlohmann::ordered_json & tools,
bool add_generation_prompt,
const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const
{
try {
chat_template_inputs inputs;
inputs.messages = messages;
inputs.tools = tools;
inputs.add_generation_prompt = add_generation_prompt;
inputs.extra_context = extra_context;
// Use fixed date for tests
inputs.now = std::chrono::system_clock::from_time_t(0);
chat_template_options opts;
opts.apply_polyfills = false;
auto prompt = apply(inputs, opts);
// fprintf(stderr, "try_raw_render: %s\n", prompt.c_str());
return prompt;
} catch (const std::exception & e) {
// fprintf(stderr, "try_raw_render error: %s\n", e.what());
return "";
}
}
public:
chat_template(const std::string & source, const std::string & bos_token, const std::string & eos_token)
: source_(source), bos_token_(bos_token), eos_token_(eos_token)
{
template_root_ = minja::Parser::parse(source_, {
/* .trim_blocks = */ true,
/* .lstrip_blocks = */ true,
/* .keep_trailing_newline = */ false,
});
auto contains = [](const std::string & haystack, const std::string & needle) {
return haystack.find(needle) != std::string::npos;
};
const std::string user_needle = "<User Needle>";
const std::string sys_needle = "<System Needle>";
const json dummy_str_user_msg = {{"role", "user"}, {"content", user_needle}};
const json dummy_typed_user_msg = {{"role", "user"}, {"content", json::array({{{"type", "text"}, {"text", user_needle}}})}};
caps_.requires_typed_content =
!contains(try_raw_render(json::array({dummy_str_user_msg}), {}, false), user_needle)
&& contains(try_raw_render(json::array({dummy_typed_user_msg}), {}, false), user_needle);
const auto dummy_user_msg = caps_.requires_typed_content
? dummy_typed_user_msg
: dummy_str_user_msg;
const json needle_system_msg = {
{"role", "system"},
{"content", caps_.requires_typed_content ? json::array({{{"type", "text"}, {"text", sys_needle}}}) : json(sys_needle)},
};
caps_.supports_system_role = contains(try_raw_render({needle_system_msg, dummy_user_msg,}, {}, false), sys_needle);
auto out = try_raw_render(json::array({
dummy_user_msg
}), json::array({
{
{"name", "some_tool"},
{"type", "function"},
{"function", {
{"name", "some_tool"},
{"description", "Some tool."},
{"parameters", {
{"type", "object"},
{"properties", {
{"arg", {
{"type", "string"},
{"description", "Some argument."},
}},
}},
{"required", json::array({ "arg" })},
}},
}},
},
}), false);
caps_.supports_tools = contains(out, "some_tool");
auto make_tool_calls_msg = [&](const json & tool_calls) {
return json {
{"role", "assistant"},
{"content", nullptr},
{"tool_calls", tool_calls},
};
};
auto make_tool_call = [](const std::string & tool_name, const json & arguments) {
return json {
{"id", "call_1___"},
{"type", "function"},
{"function", {
{"arguments", arguments},
{"name", tool_name},
}},
};
};
const json dummy_args_obj {{"argument_needle", "print('Hello, World!')"}};
// Note: the arguments are rendered in both cases, but may be double-escaped, which we don't want.
out = try_raw_render(json::array({
dummy_user_msg,
make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj.dump())})),
}), {}, false);
auto tool_call_renders_str_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
out = try_raw_render(json::array({
dummy_user_msg,
make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj)})),
}), {}, false);
auto tool_call_renders_obj_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
caps_.supports_tool_calls = tool_call_renders_str_arguments || tool_call_renders_obj_arguments;
caps_.requires_object_arguments = !tool_call_renders_str_arguments && tool_call_renders_obj_arguments;
auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false);
auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false);
caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle);
if (caps_.supports_tool_calls) {
auto dummy_args = caps_.requires_object_arguments ? dummy_args_obj : json(dummy_args_obj.dump());
auto tc1 = make_tool_call("test_tool1", dummy_args);
auto tc2 = make_tool_call("test_tool2", dummy_args);
auto out = try_raw_render(json::array({
dummy_user_msg,
make_tool_calls_msg(json::array({tc1, tc2})),
}), {}, false);
caps_.supports_parallel_tool_calls = contains(out, "test_tool1") && contains(out, "test_tool2");
out = try_raw_render(json::array({
dummy_user_msg,
make_tool_calls_msg(json::array({tc1})),
{
{"role", "tool"},
{"name", "test_tool1"},
{"content", "Some response!"},
{"tool_call_id", "call_911_"},
}
}), {}, false);
caps_.supports_tool_responses = contains(out, "Some response!");
caps_.supports_tool_call_id = contains(out, "call_911_");
}
try {
if (!caps_.supports_tools) {
const json user_msg {
{"role", "user"},
{"content", "Hey"},
};
const json args {
{"arg1", "some_value"},
};
const json tool_call_msg {
{"role", "assistant"},
{"content", nullptr},
{"tool_calls", json::array({
{
// TODO: detect if requires numerical id or fixed length == 6 like Nemo
{"id", "call_1___"},
{"type", "function"},
{"function", {
{"name", "tool_name"},
{"arguments", (caps_.requires_object_arguments ? args : json(minja::Value(args).dump(-1, /* to_json= */ true)))},
}},
},
})},
};
std::string prefix, full;
{
chat_template_inputs inputs;
inputs.messages = json::array({user_msg});
inputs.add_generation_prompt = true;
prefix = apply(inputs);
}
{
chat_template_inputs inputs;
inputs.messages = json::array({user_msg, tool_call_msg});
inputs.add_generation_prompt = false;
full = apply(inputs);
}
if (full.find(prefix) != 0) {
if (prefix.rfind(eos_token_) == prefix.size() - eos_token_.size()) {
prefix = prefix.substr(0, prefix.size() - eos_token_.size());
}
}
if (full.find(prefix) != 0) {
fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n");
}
tool_call_example_ = full.substr(prefix.size());
}
} catch (const std::exception & e) {
fprintf(stderr, "Failed to generate tool call example: %s\n", e.what());
}
}
const std::string & source() const { return source_; }
const std::string & bos_token() const { return bos_token_; }
const std::string & eos_token() const { return eos_token_; }
const chat_template_caps & original_caps() const { return caps_; }
// Deprecated, please use the form with chat_template_inputs and chat_template_options
std::string apply(
const nlohmann::ordered_json & messages,
const nlohmann::ordered_json & tools,
bool add_generation_prompt,
const nlohmann::ordered_json & extra_context = nlohmann::ordered_json(),
bool apply_polyfills = true)
{
fprintf(stderr, "[%s] Deprecated!\n", __func__);
chat_template_inputs inputs;
inputs.messages = messages;
inputs.tools = tools;
inputs.add_generation_prompt = add_generation_prompt;
inputs.extra_context = extra_context;
inputs.now = std::chrono::system_clock::now();
chat_template_options opts;
opts.apply_polyfills = apply_polyfills;
return apply(inputs, opts);
}
std::string apply(
const chat_template_inputs & inputs,
const chat_template_options & opts = chat_template_options()) const
{
json actual_messages;
auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
auto has_tool_calls = false;
auto has_tool_responses = false;
auto has_string_content = false;
for (const auto & message : inputs.messages) {
if (message.contains("tool_calls") && !message["tool_calls"].is_null()) {
has_tool_calls = true;
}
if (message.contains("role") && message["role"] == "tool") {
has_tool_responses = true;
}
if (message.contains("content") && message["content"].is_string()) {
has_string_content = true;
}
}
auto polyfill_system_role = opts.polyfill_system_role && !caps_.supports_system_role;
auto polyfill_tools = opts.polyfill_tools && has_tools && !caps_.supports_tools;
auto polyfill_tool_call_example = polyfill_tools && opts.polyfill_tool_call_examples;
auto polyfill_tool_calls = opts.polyfill_tool_calls && has_tool_calls && !caps_.supports_tool_calls;
auto polyfill_tool_responses = opts.polyfill_tool_responses && has_tool_responses && !caps_.supports_tool_responses;
auto polyfill_object_arguments = opts.polyfill_object_arguments && has_tool_calls && caps_.requires_object_arguments;
auto polyfill_typed_content = opts.polyfill_typed_content && has_string_content && caps_.requires_typed_content;
auto needs_polyfills = opts.apply_polyfills && (false
|| polyfill_system_role
|| polyfill_tools
|| polyfill_tool_calls
|| polyfill_tool_responses
|| polyfill_object_arguments
|| polyfill_typed_content
);
if (needs_polyfills) {
actual_messages = json::array();
auto add_message = [&](const json & msg) {
if (polyfill_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
actual_messages.push_back({
{"role", msg.at("role")},
{"content", {{
{"type", "text"},
{"text", msg.at("content")},
}}},
});
} else {
actual_messages.push_back(msg);
}
};
std::string pending_system;
auto flush_sys = [&]() {
if (!pending_system.empty()) {
add_message({
{"role", "user"},
{"content", pending_system},
});
pending_system.clear();
}
};
json adjusted_messages;
if (polyfill_tools) {
adjusted_messages = add_system(inputs.messages,
"You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) +
(!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_));
} else {
adjusted_messages = inputs.messages;
}
for (const auto & message_ : adjusted_messages) {
auto message = message_;
if (!message.contains("role") || !message.contains("content")) {
throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump());
}
std::string role = message.at("role");
if (message.contains("tool_calls")) {
if (polyfill_object_arguments || polyfill_tool_calls) {
for (auto & tool_call : message.at("tool_calls")) {
if (tool_call["type"] == "function") {
auto & function = tool_call.at("function");
auto & arguments = function.at("arguments");
if (arguments.is_string()) {
try {
arguments = json::parse(arguments.get<std::string>());
} catch (const std::exception & ecvt) {
fprintf(stderr, "Failed to parse arguments: %s\n", ecvt.what());
}
}
}
}
}
if (polyfill_tool_calls) {
auto content = message.at("content");
auto tool_calls = json::array();
for (const auto & tool_call : message.at("tool_calls")) {
if (tool_call.at("type") != "function") {
continue;
}
const auto & function = tool_call.at("function");
auto tc = json {
{"name", function.at("name")},
{"arguments", function.at("arguments")},
};
if (tool_call.contains("id")) {
tc["id"] = tool_call["id"];
}
tool_calls.push_back(tc);
}
auto obj = json {
{"tool_calls", tool_calls},
};
if (!content.is_null() && content != "") {
obj["content"] = content;
}
message["content"] = obj.dump(2);
message.erase("tool_calls");
}
}
if (polyfill_tool_responses && role == "tool") {
message["role"] = "user";
auto obj = json {
{"tool_response", {
{"content", message.at("content")},
}},
};
if (message.contains("name")) {
obj["tool_response"]["name"] = message.at("name");
}
if (message.contains("tool_call_id")) {
obj["tool_response"]["tool_call_id"] = message.at("tool_call_id");
}
message["content"] = obj.dump(2);
message.erase("name");
}
if (!message["content"].is_null() && polyfill_system_role) {
std::string content = message.at("content");
if (role == "system") {
if (!pending_system.empty()) pending_system += "\n";
pending_system += content;
continue;
} else {
if (role == "user") {
if (!pending_system.empty()) {
message["content"] = pending_system + (content.empty() ? "" : "\n" + content);
pending_system.clear();
}
} else {
flush_sys();
}
}
}
add_message(message);
}
flush_sys();
} else {
actual_messages = inputs.messages;
}
auto context = minja::Context::make(json({
{"messages", actual_messages},
{"add_generation_prompt", inputs.add_generation_prompt},
}));
context->set("bos_token", opts.use_bos_token ? bos_token_ : "");
context->set("eos_token", opts.use_eos_token ? eos_token_ : "");
if (opts.define_strftime_now) {
auto now = inputs.now;
context->set("strftime_now", Value::callable([now](const std::shared_ptr<minja::Context> &, minja::ArgumentsValue & args) {
args.expectArgs("strftime_now", {1, 1}, {0, 0});
auto format = args.args[0].get<std::string>();
auto time = std::chrono::system_clock::to_time_t(now);
auto local_time = *std::localtime(&time);
std::ostringstream ss;
ss << std::put_time(&local_time, format.c_str());
return ss.str();
}));
}
if (!inputs.tools.is_null()) {
context->set("tools", minja::Value(inputs.tools));
}
if (!inputs.extra_context.is_null()) {
for (auto & kv : inputs.extra_context.items()) {
context->set(kv.key(), minja::Value(kv.value()));
}
}
auto ret = template_root_->render(context);
// fprintf(stderr, "actual_messages: %s\n", actual_messages.dump(2).c_str());
// fprintf(stderr, "apply: %s\n\n", ret.c_str());
return ret;
}
static nlohmann::ordered_json add_system(const nlohmann::ordered_json & messages, const std::string & system_prompt) {
json messages_with_system = messages;
if (messages_with_system.size() > 0 && messages_with_system[0].at("role") == "system") {
std::string existing_system = messages_with_system.at(0).at("content");
messages_with_system[0] = json {
{"role", "system"},
{"content", existing_system + "\n\n" + system_prompt},
};
} else {
messages_with_system.insert(messages_with_system.begin(), json {
{"role", "system"},
{"content", system_prompt},
});
}
return messages_with_system;
}
};
} // namespace minja

966
common/chat.cpp Normal file
View file

@ -0,0 +1,966 @@
#include "chat.hpp"
#include "chat-template.hpp"
#include "json-schema-to-grammar.h"
#include "log.h"
#include "minja.hpp"
std::string common_chat_format_name(common_chat_format format) {
switch (format) {
case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only";
case COMMON_CHAT_FORMAT_GENERIC: return "Generic";
case COMMON_CHAT_FORMAT_MISTRAL_NEMO: return "Mistral Nemo";
case COMMON_CHAT_FORMAT_LLAMA_3_X: return "Llama 3.x";
case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: return "Llama 3.x with builtin tools";
case COMMON_CHAT_FORMAT_DEEPSEEK_R1: return "DeepSeek R1";
case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2";
case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2";
case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
default:
throw std::runtime_error("Unknown chat format");
}
}
const common_grammar_options grammar_options {
/* .dotall = */ false,
/* .compact_spaces = */ false,
// /* .compact_spaces = */ true,
};
static bool parse_json(std::string::const_iterator & it, const std::string::const_iterator & end, json & out) {
// // https://json.nlohmann.me/features/parsing/sax_interface/
struct json_error_locator : public nlohmann::json_sax<json> {
std::size_t position;
bool found_error;
json_error_locator() : position(0), found_error(false) {}
bool parse_error(std::size_t position, const std::string &, const json::exception &) override {
this->position = position - 1;
this->found_error = true;
return false;
}
bool null() override { return true; }
bool boolean(bool) override { return true; }
bool number_integer(number_integer_t) override { return true; }
bool number_unsigned(number_unsigned_t) override { return true; }
bool number_float(number_float_t, const string_t &) override { return true; }
bool string(string_t &) override { return true; }
bool binary(binary_t &) override { return true; }
bool start_object(std::size_t) override { return true; }
bool key(string_t &) override { return true; }
bool end_object() override { return true; }
bool start_array(std::size_t) override { return true; }
bool end_array() override { return true; }
};
json_error_locator err_loc;
json::sax_parse(it, end, &err_loc);
std::string::const_iterator temptative_end;
if (err_loc.found_error) {
temptative_end = it + err_loc.position;
} else {
temptative_end = end;
}
std::string json_sub {it, temptative_end};
try {
out = json::parse(json_sub);
it = temptative_end;
return true;
} catch (const std::exception &) {
return false;
}
}
/**
* Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between.
* Aggregates the prefix, suffix and in-between text into the content.
*/
static common_chat_msg parse_json_tool_calls(
const std::string& input,
const std::optional<std::regex> & trigger_opt,
const std::regex & function_regex,
const std::regex & close_regex) {
std::smatch match;
common_chat_msg result;
result.role = "assistant";
auto end = input.end();
auto it = input.begin();
if (trigger_opt) {
if (!std::regex_search(it, end, match, *trigger_opt)) {
result.content = input;
return result;
}
result.content = match.prefix().str();
it = match.suffix().first;
}
while (it != end) {
std::sregex_iterator rend;
std::sregex_iterator rit(it, end, function_regex);
if (rit == rend) {
fprintf(stderr, "No more tool calls found\n");
result.content += std::string(it, end);
break;
}
auto name = rit->str(1);
result.content += std::string(it, rit->prefix().second);
it = rit->suffix().first;
json arguments;
if (!parse_json(it, end, arguments)) {
throw std::runtime_error("Failed to parse json tool call arguments");
}
if (!std::regex_search(it, end, match, close_regex)) {
throw std::runtime_error("Malformed input, missing closing pattern");
}
it = match.suffix().first;
result.tool_calls.push_back({name, arguments.is_string() ? arguments.get<std::string>() : arguments.dump(), /* id= */ ""});
}
return result;
}
static common_chat_msg parse_prefixed_json_tool_call_array(const std::string& input, const std::string & prefix, size_t rstrip_prefix = 0) {
auto content_end = input.find(prefix);
size_t tc_start = std::string::npos;
common_chat_msg result;
result.role = "assistant";
const auto process_tool_calls = [&](const json & tool_calls) {
for (const auto & tool_call : tool_calls) {
const auto & arguments = tool_call["arguments"];
result.tool_calls.push_back({
tool_call["name"],
arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
tool_call.contains("id") ? tool_call["id"] : "",
});
}
};
if (content_end == std::string::npos) {
result.content = input;
} else {
tc_start = content_end + prefix.size() - rstrip_prefix;
result.content = input.substr(0, content_end);
auto tool_calls = json::parse(input.substr(tc_start));
process_tool_calls(tool_calls);
}
return result;
}
static void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
for (const auto & tool : tools) {
if (!tool.contains("type") || tool["type"] != "function" || !tool.contains("function")) {
LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str());
continue;
}
fn(tool);
}
}
static std::string apply(
const common_chat_template & tmpl,
const nlohmann::ordered_json & messages,
const nlohmann::ordered_json & tools,
bool add_generation_prompt,
const nlohmann::ordered_json & extra_context = nlohmann::ordered_json())
{
minja::chat_template_inputs tmpl_inputs;
tmpl_inputs.messages = messages;
tmpl_inputs.tools = tools;
tmpl_inputs.add_generation_prompt = add_generation_prompt;
tmpl_inputs.extra_context = extra_context;
// TODO: add flag to control date/time, if only for testing purposes.
// tmpl_inputs.now = std::chrono::system_clock::now();
minja::chat_template_options tmpl_opts;
tmpl_opts.use_bos_token = false;
tmpl_opts.use_eos_token = false;
return tmpl.apply(tmpl_inputs, tmpl_opts);
}
static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
common_chat_params data;
auto tool_call_schemas = json::array();
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool["function"];
auto tool_schema = json {
{"type", "object"},
{"properties", {
{"name", {
{"type", "string"},
{"const", function["name"]},
}},
{"arguments", function["parameters"]},
}},
{"required", json::array({"name", "arguments"})},
};
if (function.contains("description")) {
tool_schema["description"] = function["description"];
}
if (inputs.parallel_tool_calls) {
tool_schema["properties"]["id"] = {
{"type", "string"},
{"minLength", 4},
};
tool_schema["required"].push_back("id");
}
tool_call_schemas.emplace_back(tool_schema);
});
const auto tool_call =
inputs.parallel_tool_calls
? json {
{"type", "object"},
{"properties", {
{"tool_calls", {
{"type", "array"},
{"items", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
{"anyOf", tool_call_schemas},
}},
{"minItems", 1},
}},
}},
{"required", json::array({"tool_calls"})},
}
: json {
{"type", "object"},
{"properties", {
{"tool_call", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
{"anyOf", tool_call_schemas},
}},
}},
{"required", json::array({"tool_call"})},
};
const auto schema =
inputs.tool_choice != "required"
? json {
{"anyOf", json::array({
tool_call,
{
{"type", "object"},
{"properties", {
{"response", inputs.json_schema.is_null()
? json {{"type", "string"}}
: inputs.json_schema
},
}},
{"required", json::array({"response"})},
},
})}
}
: tool_call;
data.grammar_lazy = false;
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
builder.add_schema("root", schema);
}, grammar_options);
auto tweaked_messages = common_chat_template::add_system(
inputs.messages,
"Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request");
data.prompt = apply(tmpl, tweaked_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
data.format = COMMON_CHAT_FORMAT_GENERIC;
return data;
}
static common_chat_msg common_chat_parse_generic(const std::string & input) {
json data = json::parse(input);
common_chat_msg result;
result.role = "assistant";
if (data.contains("tool_calls")) {
for (const auto & tool_call : data["tool_calls"]) {
result.tool_calls.push_back({
tool_call["name"],
tool_call["arguments"].dump(),
tool_call.contains("id") ? tool_call["id"] : "",
});
}
} else if (data.contains("tool_call")) {
result.tool_calls.push_back({
data["tool_call"]["name"],
data["tool_call"]["arguments"].dump(),
/* id= */ "",
});
} else if (data.contains("response")) {
const auto & response = data["response"];
result.content = response.is_string() ? response.get<std::string>() : response.dump(2);
}
return result;
}
static common_chat_params common_chat_params_init_mistral_nemo(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
common_chat_params data;
data.grammar_lazy = inputs.tool_choice != "required";
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
auto schemas = json::array();
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool["function"];
schemas.push_back({
{"type", "object"},
{"properties", {
// Important note: the model is probably trained to take a JSON stringified arguments value.
// It's hard to constrain that for now (while reusing the JSON schema conversion), so we're just expecting a plain object.
{"name", {
{"type", "string"},
{"const", function["name"]},
}},
{"arguments", function["parameters"]},
{"id", {
{"type", "string"},
// Nemo's template expects a 9-character alphanumeric ID.
{"pattern", "^[a-zA-Z0-9]{9}$"},
}},
}},
{"required", json::array({"name", "arguments", "id"})},
});
});
auto schema = json {
{"type", "array"},
{"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
{"minItems", 1},
};
if (!inputs.parallel_tool_calls) {
schema["maxItems"] = 1;
}
builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema));
}, grammar_options);
data.grammar_triggers.push_back({"[TOOL_CALLS]", /* .at_start = */ true});
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO;
return data;
}
static common_chat_msg common_chat_parse_mistral_nemo(const std::string & input) {
return parse_prefixed_json_tool_call_array(input, "[TOOL_CALLS]");
}
static common_chat_params common_chat_params_init_command_r7b(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
common_chat_params data;
data.grammar_lazy = inputs.tool_choice != "required";
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
auto schemas = json::array();
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool["function"];
schemas.push_back({
{"type", "object"},
{"properties", {
{"tool_call_id", {
{"type", "string"},
// Command-R's template expects an integer string.
{"pattern", "^[0-9]{1,10}$"},
}},
{"tool_name", {
{"type", "string"},
{"const", function["name"]},
}},
{"parameters", function["parameters"]},
}},
{"required", json::array({"tool_call_id", "tool_name", "parameters"})},
});
});
auto schema = json {
{"type", "array"},
{"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
{"minItems", 1},
};
if (!inputs.parallel_tool_calls) {
schema["maxItems"] = 1;
}
builder.add_rule("root", "\"<|START_ACTION|>\" " + builder.add_schema("tool_calls", schema) + " \"<|END_ACTION|>\"");
}, grammar_options);
data.grammar_triggers.push_back({"<|START_ACTION|>", /* .at_start = */ false});
data.preserved_tokens = {
"<|START_RESPONSE|>",
"<|END_RESPONSE|>",
"<|START_THINKING|>",
"<|END_THINKING|>",
"<|END_ACTION|>",
};
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
data.format = COMMON_CHAT_FORMAT_COMMAND_R7B;
return data;
}
static common_chat_msg common_chat_parse_command_r7b(const std::string & input) {
static std::regex response_regex("<\\|START_RESPONSE\\|>([\\s\\S\\n\\r]*?)<\\|END_RESPONSE\\|>");
static std::regex thought_action_regex("<\\|START_THINKING\\|>([\\s\\S\\n\\r]*?)<\\|END_THINKING\\|><\\|START_ACTION\\|>([\\s\\S\\n\\r]*?)<\\|END_ACTION\\|>");
std::smatch match;
common_chat_msg result;
result.role = "assistant";
if (std::regex_match(input, match, response_regex)) {
result.content = match[1].str();
} else if (std::regex_match(input, match, thought_action_regex)) {
result.tool_plan = match[1].str();
auto actions_str = match[2].str();
auto actions = json::parse(actions_str);
for (const auto & action : actions) {
result.tool_calls.push_back({
/* .name = */ action["tool_name"],
/* .arguments = */ action["parameters"].dump(),
/* .id = */ action["tool_call_id"],
});
}
} else {
LOG_ERR("Failed to parse command_r output");
result.content = input;
}
return result;
}
static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector<std::string> & expected_properties) {
if (!parameters.is_object() || !parameters.contains("type") || parameters["type"] != "object" || !parameters.contains("properties") || !parameters.contains("required")) {
throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties");
}
const auto & parameters_properties = parameters.at("properties");
const auto & parameters_required = parameters.at("required");
for (const auto & prop : expected_properties) {
if (!parameters_properties.contains(prop)) {
throw std::runtime_error("Parameters of tool " + name + " is missing property: " + prop);
}
if (std::find(parameters_required.begin(), parameters_required.end(), json(prop)) == parameters_required.end()) {
throw std::runtime_error("Parameters of tool " + name + " must have property marked as required: " + prop);
}
}
if (parameters_properties.size() != expected_properties.size()) {
throw std::runtime_error("Parameters of tool " + name + " must only have these properties:" + string_join(expected_properties, ", "));
}
}
static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const common_chat_template & tmpl, const struct common_chat_inputs & inputs, bool allow_python_tag_builtin_tools) {
auto builtin_tools = json::array();
common_chat_params data;
data.grammar_lazy = inputs.tool_choice != "required";
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
std::vector<std::string> tool_rules;
auto handle_builtin_tool = [&](const std::string & name, const json & parameters) {
if (name == "wolfram_alpha") {
// https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py
expect_tool_parameters(name, parameters, {"query"});
} else if (name == "web_search" || name == "brave_search") {
// https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py
expect_tool_parameters(name, parameters, {"query"});
} else if (name == "python" || name == "code_interpreter") {
// https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py
expect_tool_parameters(name, parameters, {"code"});
} else {
return false;
}
std::vector<std::string> kvs;
for (const auto & [key, value] : parameters.at("properties").items()) {
kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value));
}
tool_rules.push_back(
builder.add_rule(
name + "-call",
"\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\""));
builtin_tools.push_back(name);
return true;
};
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool["function"];
std::string name = function["name"];
auto parameters = function["parameters"];
builder.resolve_refs(parameters);
// https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime
if (allow_python_tag_builtin_tools) {
handle_builtin_tool(name, parameters);
}
tool_rules.push_back(
builder.add_rule(
name + "-call",
"\"{\" space "
"( \"\\\"type\\\":\" space \"\\\"function\\\",\" space )? "
"\"\\\"name\\\": \\\"" + name + "\\\", \\\"parameters\\\": \" " +
builder.add_schema(name + "-args", parameters) +
" \"}\""));
data.grammar_triggers.push_back({"{\"name\": \"" + name + "\"", /* .at_start = */ true});
});
data.grammar_triggers.push_back({"{\"name\":", /* .at_start = */ true});
data.grammar_triggers.push_back({"{\n \"name\":", /* .at_start = */ true});
data.grammar_triggers.push_back({"{\n \"name\":", /* .at_start = */ true});
data.grammar_triggers.push_back({"{\"type\": \"function\"", /* .at_start = */ true});
data.grammar_triggers.push_back({"{\n \"type\": \"function\"", /* .at_start = */ true});
data.grammar_triggers.push_back({"{\n \"type\": \"function\"", /* .at_start = */ true});
if (!builtin_tools.empty()) {
data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
}
builder.add_rule("root", string_join(tool_rules, " | "));
}, grammar_options);
data.additional_stops.push_back("<|eom_id|>");
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {
{"tools_in_user_message", false},
{"builtin_tools", builtin_tools.empty() ? json() : builtin_tools},
});
data.format = allow_python_tag_builtin_tools && !builtin_tools.empty()
? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS
: COMMON_CHAT_FORMAT_LLAMA_3_X;
return data;
}
static common_chat_msg common_chat_parse_llama_3_1(const std::string & input, bool with_builtin_tools = false) {
// TODO: tighten & simplify the parser, don't accept leading text context.
static std::regex function_regex("\\{[\\s\\n\\r]*(?:\"type\"[\\s\\n\\r]*:[\\s\\n\\r]*\"function\"[\\s\\n\\r]*,[\\s\\n\\r]*|[\\s\\n\\r]*)\"name\"[\\s\\n\\r]*:[\\s\\n\\r]*\"([^\"]+)\"[\\s\\n\\r]*,[\\s\\n\\r]*\"parameters\": ");
static std::regex close_regex("\\}");
static std::regex builtin_call_regex("<\\|python_tag\\|>([^.(]+)\\.call\\((.*)\\)");
if (with_builtin_tools) {
std::smatch match;
if (std::regex_match(input, match, builtin_call_regex)) {
auto name = match[1].str();
auto raw_args = match[2].str();
// TODO: if/when builtin tools start accepting more than 1 argument, use parse_json for real parsing.
auto it_eq = raw_args.find('=');
auto arg_name = raw_args.substr(0, it_eq);
auto arg_value_str = raw_args.substr(it_eq + 1);
auto arg_value = json::parse(arg_value_str);
return {
/* .role = */ "assistant",
/* .content = */ match.prefix().str(),
/* .tool_calls = */ {
{
/* .name = */ match[1],
/* .arguments = */ (json {
{arg_name, arg_value},
}).dump(),
/* .id = */ "",
},
},
};
}
}
return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
}
static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
common_chat_params data;
data.grammar_lazy = inputs.tool_choice != "required";
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
std::vector<std::string> tool_rules;
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool["function"];
std::string name = function["name"];
auto parameters = function["parameters"];
auto args_rule = builder.add_schema(name + "-args", parameters);
tool_rules.push_back(builder.add_rule(name + "-call",
"\"<｜tool▁call▁begin｜>function<｜tool▁sep｜>" + name + "\\n```json\\n\" " + args_rule + " \"```<｜tool▁call▁end｜>\""));
});
data.grammar_triggers.push_back({"<｜tool▁calls▁begin｜>", /* .at_start = */ false});
data.preserved_tokens = {
"<｜tool▁sep｜>",
"<｜tool▁call▁end｜>",
};
builder.add_rule("root", "\"<｜tool▁calls▁begin｜>\" (" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " space");
}, grammar_options);
auto prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
data.prompt = prompt;
data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1;
return data;
}
static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input) {
static std::regex trigger_regex("<｜tool▁calls▁begin｜>");
static std::regex function_regex("<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n");
static std::regex close_regex("```<｜tool▁call▁end｜>");
return parse_json_tool_calls(input, trigger_regex, function_regex, close_regex);
}
static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
fprintf(stderr, "%s\n", __func__);
common_chat_params data;
data.prompt = apply(tmpl, inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, {
{"datetime", "Jan 29 2025 13:00:00 GMT"},
{"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))},
});
if (!inputs.tools.is_null() && !inputs.tools.empty()) {
data.grammar_lazy = inputs.tool_choice != "required";
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
auto schemas = json::array();
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool["function"];
schemas.push_back({
{"type", "object"},
{"properties", {
{"name", {
{"type", "string"},
{"const", function["name"]},
}},
{"arguments", function["parameters"]},
}},
{"required", json::array({"name", "arguments", "id"})},
});
});
auto schema = json {
{"type", "array"},
{"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
{"minItems", 1},
};
if (!inputs.parallel_tool_calls) {
schema["maxItems"] = 1;
}
builder.add_rule("root", "\" functools\"? " + builder.add_schema("tool_calls", schema));
}, grammar_options);
data.grammar_triggers.push_back({" functools[", /* .at_start = */ false});
data.format = COMMON_CHAT_FORMAT_FIREFUNCTION_V2;
} else {
data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
}
return data;
}
static common_chat_msg common_chat_parse_firefunction_v2(const std::string & input) {
return parse_prefixed_json_tool_call_array(input, " functools[", /* rstrip_prefix= */ 1);
}
static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
// >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}...
// Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar
common_chat_params data;
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2;
if (!inputs.tools.is_null() && !inputs.tools.empty()) {
data.grammar_lazy = inputs.tool_choice != "required";
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
std::vector<std::string> first_tool_rules;
std::vector<std::string> subsequent_tool_rules;
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool["function"];
std::string name = function["name"];
auto parameters = function["parameters"];
auto args_rule = builder.add_schema(name + "-args", parameters);
first_tool_rules.push_back(builder.add_rule(name + "-call", "\"" + name + "\\n\" " + args_rule));
subsequent_tool_rules.push_back(builder.add_rule(name + "-call2", "\">>>" + name + "\\n\" " + args_rule));
data.grammar_triggers.push_back({name, /* .at_start = */ true});
data.grammar_triggers.push_back({">>>" + name, /* .at_start = */ false});
});
auto first_rule = first_tool_rules.empty() ? "" : builder.add_rule("first_tool_call", string_join(first_tool_rules, " | ")) + " space";
if (inputs.parallel_tool_calls) {
auto subsequent_rule = builder.add_rule("subsequent_tool_call", string_join(subsequent_tool_rules, " | ")) + " space";
builder.add_rule("root", first_rule + " (" + subsequent_rule + ")*");
} else {
builder.add_rule("root", first_rule);
}
}, grammar_options);
}
return data;
}
static bool consume(std::string::const_iterator & it, const std::string::const_iterator & end, const std::string & expected) {
auto expected_it = expected.begin();
auto tmp_it = it;
while (tmp_it != end && expected_it != expected.end() && *tmp_it == *expected_it) {
++tmp_it;
++expected_it;
}
if (expected_it == expected.end()) {
it = tmp_it;
return true;
}
return false;
}
static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & input) {
static std::regex function_regex(R"((?:>>>)?(\w+)\n)");
static std::regex close_regex(R"($|(?=>>>))");
std::string content;
auto it = input.begin();
const auto end = input.end();
if (consume(it, end, "all\n")) {
std::smatch match;
if (std::regex_search(it, end, match, function_regex)) {
auto fun_it = match.prefix().second;
content = std::string(it, fun_it);
it = fun_it;
} else {
common_chat_msg res;
res.role = "assistant";
res.content = std::string(it, end);
return res;
}
}
// TODO: tighten & simplify.
try {
auto res = parse_json_tool_calls(std::string(it, end), std::nullopt, function_regex, close_regex);
res.content = content + res.content;
return res;
} catch (const std::exception & e) {
LOG_ERR("Failed to parse functionary v3.2 input: %s\n", e.what());
common_chat_msg res;
res.role = "assistant";
res.content = input;
return res;
}
}
static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
// https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt
common_chat_params data;
json tools = inputs.tools.is_null() ? inputs.tools : json::array();
std::string python_code_argument_name;
auto has_raw_python = false;
data.grammar_lazy = inputs.tool_choice != "required";
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
std::vector<std::string> tool_rules;
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool["function"];
const auto & parameters = function["parameters"];
std::string name = function["name"];
if (name == "python" || name == "ipython") {
if (!parameters.contains("type")) {
throw std::runtime_error("Missing type in python tool");
}
has_raw_python = true;
auto type = parameters.at("type");
if (type == "object") {
auto properties = parameters.at("properties");
for (auto it = properties.begin(); it != properties.end(); ++it) {
if (it.value().at("type") == "string") {
if (!python_code_argument_name.empty()) {
throw std::runtime_error("Multiple string arguments found in python tool");
}
python_code_argument_name = it.key();
}
}
if (python_code_argument_name.empty()) {
throw std::runtime_error("No string argument found in python tool");
}
} else if (type != "string") {
throw std::runtime_error("Invalid type in python tool: " + type.dump());
}
}
tool_rules.push_back(builder.add_rule(name + "-call", "\"<function=" + name + ">\" " + builder.add_schema(name + "-args", parameters) + " \"</function>\" space"));
});
if (has_raw_python) {
tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*"));
data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
}
auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space";
builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
data.grammar_triggers.push_back({"<function=", /* .at_start = */ false});
}, grammar_options);
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
// TODO: if (has_raw_python)
data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1;
return data;
}
static common_chat_msg common_chat_parse_functionary_v3_1_llama_3_1(const std::string & input) {
// This version of Functionary still supports the llama 3.1 tool call format for the python tool.
static std::regex python_tag_regex(R"(<\|python_tag\|>([\s\S\n]*)$)");
std::smatch match;
if (std::regex_search(input, match, python_tag_regex)) {
auto code = match[1].str();
return {
/* .role = */ "assistant",
/* .content = */ match.prefix().str(),
/* .tool_calls = */ {
{
/* .name = */ "python",
/* .arguments = */ (json {{"code", code}}).dump(),
/* .id = */ "",
},
}
};
}
static std::regex function_regex(R"(<function=(\w+)>)");
static std::regex close_regex(R"(</function>)");
// TODO: tighten & simplify.
return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
}
static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
common_chat_params data;
// (content)?(<tool_call>{"name": "foo", "arguments": {"a": 1}}</tool_call>)*
data.grammar_lazy = inputs.tool_choice != "required";
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
std::vector<std::string> tool_rules;
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool["function"];
std::string name = function["name"];
auto parameters = function["parameters"];
builder.resolve_refs(parameters);
tool_rules.push_back(builder.add_schema(name + "-call", {
{"type", "object"},
{"properties", json {
{"name", json {{"const", name}}},
{"arguments", parameters},
}},
{"required", json::array({"name", "arguments"})},
}));
});
auto tool_call = "\"<tool_call>\" space " + builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " \"</tool_call>\" space";
builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
data.grammar_triggers.push_back({"<tool_call>", /* .at_start = */ false});
data.preserved_tokens = { "</tool_call>" };
}, grammar_options);
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO;
return data;
}
static common_chat_msg common_chat_parse_hermes_2_pro(const std::string & input) {
try {
std::regex start_pattern(R"([\n\s]*<tool_call>)");
std::regex middle_pattern(R"([\n\s]*</tool_call>[\n\s]*<tool_call>)");
std::regex end_pattern(R"([\n\s]*</tool_call>[\n\s]*$)");
auto end = input.end();
std::sregex_iterator rend;
std::sregex_iterator rit(input.begin(), end, start_pattern);
if (rit == rend) {
return {
/* .role = */ "assistant",
/* .content = */ input,
/* .tool_calls = */ {},
};
}
common_chat_msg result;
result.role = "assistant";
result.content = rit->prefix();
auto it = rit->suffix().first;
while (it != end) {
json call;
if (!parse_json(it, end, call)) {
throw std::runtime_error("Failed to parse json tool call");
}
const auto & arguments = call["arguments"];
result.tool_calls.push_back({
call["name"],
arguments.dump(),
// arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
/* id= */ "",
});
rit = {it, end, middle_pattern};
if (rit != rend) {
it = rit->suffix().first;
} else {
rit = {it, end, end_pattern};
if (rit == rend) {
throw std::runtime_error("Malformed input, missing </tool_call>");
}
break;
}
}
return result;
} catch (const std::exception & e) {
return {
/* .role = */ "assistant",
/* .content = */ input,
/* .tool_calls = */ {},
};
}
}
static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
common_chat_params data;
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
data.grammar_lazy = false;
if (!inputs.json_schema.is_null()) {
if (!inputs.grammar.empty()) {
throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both");
}
data.grammar = json_schema_to_grammar(inputs.json_schema);
} else {
data.grammar = inputs.grammar.empty();
}
return data;
}
common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
auto has_tools = !inputs.tools.is_null() && inputs.tool_choice != "none";
LOG_DBG("[%s] has_tools=%s\n", __func__, has_tools ? "true" : "false");
if (has_tools && !inputs.grammar.empty()) {
throw std::runtime_error("Cannot specify grammar with tools");
}
const auto & src = tmpl.source();
if (src.find(">>>all") != std::string::npos) {
// Functionary prepends "all\n" to plain content outputs, so we use the parser no matter when
return common_chat_params_init_functionary_v3_2(tmpl, inputs);
}
if (src.find(" functools[") != std::string::npos) {
// Firefunction v2 requires datetime and functions in the context, even w/o tools.
return common_chat_params_init_firefunction_v2(tmpl, inputs);
}
if (!has_tools) {
return common_chat_params_init_without_tools(tmpl, inputs);
}
if (src.find("<tool_call>") != std::string::npos) {
return common_chat_params_init_hermes_2_pro(tmpl, inputs);
}
if (src.find("<|start_header_id|>") != std::string::npos
&& src.find("<function=") != std::string::npos) {
return common_chat_params_init_functionary_v3_1_llama_3_1(tmpl, inputs);
}
if (src.find("<|start_header_id|>ipython<|end_header_id|>") != std::string::npos) {
auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos;
return common_chat_params_init_llama_3_1_tool_calls(tmpl, inputs, allow_python_tag_builtin_tools);
}
if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos) {
return common_chat_params_init_deepseek_r1(tmpl, inputs);
}
if (src.find("[TOOL_CALLS]") != std::string::npos) {
return common_chat_params_init_mistral_nemo(tmpl, inputs);
}
if (src.find("<|END_THINKING|><|START_ACTION|>") != std::string::npos) {
return common_chat_params_init_command_r7b(tmpl, inputs);
}
return common_chat_params_init_generic(tmpl, inputs);
}
static common_chat_msg common_chat_parse_content_only(const std::string & input) {
return {
/* .role = */ "assistant",
/* .content = */ input,
/* .tool_calls = */ {},
};
}
common_chat_msg common_chat_parse(const std::string & input, common_chat_format format) {
switch (format) {
case COMMON_CHAT_FORMAT_CONTENT_ONLY:
return common_chat_parse_content_only(input);
case COMMON_CHAT_FORMAT_GENERIC:
return common_chat_parse_generic(input);
case COMMON_CHAT_FORMAT_MISTRAL_NEMO:
return common_chat_parse_mistral_nemo(input);
case COMMON_CHAT_FORMAT_LLAMA_3_X:
return common_chat_parse_llama_3_1(input);
case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS:
return common_chat_parse_llama_3_1(input, /* with_builtin_tools= */ true);
case COMMON_CHAT_FORMAT_DEEPSEEK_R1:
return common_chat_parse_deepseek_r1(input);
case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2:
return common_chat_parse_functionary_v3_2(input);
case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1:
return common_chat_parse_functionary_v3_1_llama_3_1(input);
case COMMON_CHAT_FORMAT_HERMES_2_PRO:
return common_chat_parse_hermes_2_pro(input);
case COMMON_CHAT_FORMAT_FIREFUNCTION_V2:
return common_chat_parse_firefunction_v2(input);
case COMMON_CHAT_FORMAT_COMMAND_R7B:
return common_chat_parse_command_r7b(input);
default:
throw std::runtime_error("Unsupported format: " + common_chat_format_name(format));
}
}

52
common/chat.hpp Normal file
View file

@ -0,0 +1,52 @@
// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers.
#pragma once
#include "common.h"
#include <json.hpp>
#include <optional>
#include <string>
#include <vector>
using json = nlohmann::ordered_json;
struct common_chat_inputs {
json messages;
json tools;
json tool_choice;
json json_schema;
bool parallel_tool_calls;
bool stream;
std::string grammar;
bool add_generation_prompt = true;
};
enum common_chat_format {
COMMON_CHAT_FORMAT_CONTENT_ONLY,
COMMON_CHAT_FORMAT_GENERIC,
COMMON_CHAT_FORMAT_MISTRAL_NEMO,
COMMON_CHAT_FORMAT_LLAMA_3_X,
COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
COMMON_CHAT_FORMAT_DEEPSEEK_R1,
COMMON_CHAT_FORMAT_FIREFUNCTION_V2,
COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
COMMON_CHAT_FORMAT_HERMES_2_PRO,
COMMON_CHAT_FORMAT_COMMAND_R7B,
COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
};
struct common_chat_params {
common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
json prompt;
std::string grammar;
bool grammar_lazy = false;
std::vector<common_grammar_trigger> grammar_triggers;
std::vector<std::string> preserved_tokens;
std::vector<std::string> additional_stops;
};
struct common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & params);
std::string common_chat_format_name(common_chat_format format);
common_chat_msg common_chat_parse( const std::string & input, common_chat_format format);

441
common/common.cpp
View file

@ -2,6 +2,9 @@
#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
#endif
#include "ggml.h"
#include "gguf.h"
#include "common.h"
#include "log.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
@ -9,6 +12,8 @@
#include "json.hpp"
#include "json-schema-to-grammar.h"
#include "llama.h"
#include "chat.hpp"
#include "chat-template.hpp"
#include <algorithm>
#include <cinttypes>
@ -18,6 +23,7 @@
#include <cstdarg>
#include <cstring>
#include <ctime>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <iterator>
@ -69,11 +75,29 @@ typedef unsigned short u_short;
#ifdef __linux__
#include <linux/limits.h>
#elif defined(_WIN32)
#define PATH_MAX MAX_PATH
# if !defined(PATH_MAX)
# define PATH_MAX MAX_PATH
# endif
#else
#include <sys/syslimits.h>
#endif
#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
//
// CURL utils
//
using curl_ptr = std::unique_ptr<CURL, decltype(&curl_easy_cleanup)>;
// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one
struct curl_slist_ptr {
struct curl_slist * ptr = nullptr;
~curl_slist_ptr() {
if (ptr) {
curl_slist_free_all(ptr);
}
}
};
#endif // LLAMA_USE_CURL
using json = nlohmann::ordered_json;
@ -468,6 +492,48 @@ void string_replace_all(std::string & s, const std::string & search, const std::
s = std::move(builder);
}
std::string string_join(const std::vector<std::string> & values, const std::string & separator) {
std::ostringstream result;
for (size_t i = 0; i < values.size(); ++i) {
if (i > 0) {
result << separator;
}
result << values[i];
}
return result.str();
}
std::vector<std::string> string_split(const std::string & str, const std::string & delimiter) {
std::vector<std::string> parts;
size_t start = 0;
size_t end = str.find(delimiter);
while (end != std::string::npos) {
parts.push_back(str.substr(start, end - start));
start = end + delimiter.length();
end = str.find(delimiter, start);
}
parts.push_back(str.substr(start));
return parts;
}
std::string string_repeat(const std::string & str, size_t n) {
if (n == 0) {
return "";
}
std::string result;
result.reserve(str.length() * n);
for (size_t i = 0; i < n; ++i) {
result += str;
}
return result;
}
std::string string_from(bool value) {
return value ? "true" : "false";
}
@ -850,7 +916,7 @@ struct common_init_result common_init_from_params(common_params & params) {
} else if (!params.model_url.empty()) {
model = common_load_model_from_url(params.model_url, params.model, params.hf_token, mparams);
} else {
model = llama_load_model_from_file(params.model.c_str(), mparams);
model = llama_model_load_from_file(params.model.c_str(), mparams);
}
if (model == NULL) {
@ -858,26 +924,28 @@ struct common_init_result common_init_from_params(common_params & params) {
return iparams;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
if (params.reranking) {
bool ok = true;
if (llama_token_bos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have a BOS token, reranking will not work\n", __func__);
if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__);
ok = false;
}
if (llama_token_eos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have an EOS token, reranking will not work\n", __func__);
if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__);
ok = false;
}
if (llama_token_sep(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have a SEP token, reranking will not work\n", __func__);
if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
ok = false;
}
if (!ok) {
llama_free_model(model);
llama_model_free(model);
return iparams;
}
@ -885,40 +953,40 @@ struct common_init_result common_init_from_params(common_params & params) {
auto cparams = common_context_params_to_llama(params);
llama_context * lctx = llama_new_context_with_model(model, cparams);
llama_context * lctx = llama_init_from_model(model, cparams);
if (lctx == NULL) {
LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
llama_model_free(model);
return iparams;
}
if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) {
LOG_ERR("%s: KV cache shifting is not supported for this model (--no-context-shift to disable)'\n", __func__);
llama_free_model(model);
return iparams;
LOG_WRN("%s: KV cache shifting is not supported for this model, disabling KV cache shifting\n", __func__);
params.ctx_shift = false;
}
if (!params.control_vectors.empty()) {
if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_model_n_layer(model);
const auto cvec = common_control_vector_load(params.control_vectors);
if (cvec.n_embd == -1) {
llama_free(lctx);
llama_free_model(model);
llama_model_free(model);
return iparams;
}
int err = llama_control_vector_apply(lctx,
cvec.data.data(),
cvec.data.size(),
cvec.n_embd,
params.control_vector_layer_start,
params.control_vector_layer_end);
int err = llama_apply_adapter_cvec(
lctx,
cvec.data.data(),
cvec.data.size(),
cvec.n_embd,
params.control_vector_layer_start,
params.control_vector_layer_end);
if (err) {
llama_free(lctx);
llama_free_model(model);
llama_model_free(model);
return iparams;
}
@ -926,30 +994,31 @@ struct common_init_result common_init_from_params(common_params & params) {
// load and optionally apply lora adapters
for (auto & la : params.lora_adapters) {
common_lora_adapter_container loaded_la;
loaded_la.path = la.path;
loaded_la.scale = la.scale;
loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
if (loaded_la.adapter == nullptr) {
llama_adapter_lora_ptr lora;
lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
if (lora == nullptr) {
LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
llama_free(lctx);
llama_free_model(model);
llama_model_free(model);
return iparams;
}
iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
}
if (!params.lora_init_without_apply) {
common_lora_adapters_apply(lctx, iparams.lora_adapters);
la.ptr = lora.get();
iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
}
if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
if (!params.lora_init_without_apply) {
common_set_adapter_lora(lctx, params.lora_adapters);
}
if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sampling.ignore_eos = false;
}
if (params.sampling.ignore_eos) {
for (llama_token i = 0; i < llama_n_vocab(model); i++) {
if (llama_token_is_eog(model, i)) {
for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
if (llama_vocab_is_eog(vocab, i)) {
LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
params.sampling.logit_bias.push_back({i, -INFINITY});
}
@ -970,8 +1039,9 @@ struct common_init_result common_init_from_params(common_params & params) {
LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
std::vector<llama_token> tmp;
llama_token bos = llama_token_bos(model);
llama_token eos = llama_token_eos(model);
llama_token bos = llama_vocab_bos(vocab);
llama_token eos = llama_vocab_eos(vocab);
// some models (e.g. T5) don't have a BOS token
if (bos != LLAMA_TOKEN_NULL) {
tmp.push_back(bos);
@ -986,7 +1056,7 @@ struct common_init_result common_init_from_params(common_params & params) {
if (llama_model_has_encoder(model)) {
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size()));
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
decoder_start_token_id = bos;
}
tmp.clear();
@ -1000,17 +1070,17 @@ struct common_init_result common_init_from_params(common_params & params) {
llama_perf_context_reset(lctx);
}
iparams.model = model;
iparams.context = lctx;
iparams.model.reset(model);
iparams.context.reset(lctx);
return iparams;
}
void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters) {
llama_lora_adapter_clear(ctx);
for (auto & la : lora_adapters) {
void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
llama_clear_adapter_lora(ctx);
for (auto & la : lora) {
if (la.scale != 0.0f) {
llama_lora_adapter_set(ctx, la.adapter, la.scale);
llama_set_adapter_lora(ctx, la.ptr, la.scale);
}
}
}
@ -1024,7 +1094,6 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
if (params.n_gpu_layers != -1) {
mparams.n_gpu_layers = params.n_gpu_layers;
}
mparams.rpc_servers = params.rpc_servers.c_str();
mparams.main_gpu = params.main_gpu;
mparams.split_mode = params.split_mode;
mparams.tensor_split = params.tensor_split;
@ -1127,7 +1196,8 @@ static bool curl_perform_with_retry(const std::string & url, CURL * curl, int ma
static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
// Initialize libcurl
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
curl_slist_ptr http_headers;
if (!curl) {
LOG_ERR("%s: error initializing libcurl\n", __func__);
return false;
@ -1141,11 +1211,9 @@ static bool common_download_file(const std::string & url, const std::string & pa
// Check if hf-token or bearer-token was specified
if (!hf_token.empty()) {
std::string auth_header = "Authorization: Bearer ";
auth_header += hf_token.c_str();
struct curl_slist *http_headers = NULL;
http_headers = curl_slist_append(http_headers, auth_header.c_str());
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
std::string auth_header = "Authorization: Bearer " + hf_token;
http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
}
#if defined(_WIN32)
@ -1155,8 +1223,7 @@ static bool common_download_file(const std::string & url, const std::string & pa
#endif
// Check if the file already exists locally
struct stat model_file_info;
auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
auto file_exists = std::filesystem::exists(path);
// If the file exists, check its JSON metadata companion file.
std::string metadata_path = path + ".json";
@ -1416,7 +1483,7 @@ struct llama_model * common_load_model_from_url(
}
}
return llama_load_model_from_file(local_path.c_str(), params);
return llama_model_load_from_file(local_path.c_str(), params);
}
struct llama_model * common_load_model_from_hf(
@ -1442,6 +1509,80 @@ struct llama_model * common_load_model_from_hf(
return common_load_model_from_url(model_url, local_path, hf_token, params);
}
/**
* Allow getting the HF file from the HF repo with tag (like ollama), for example:
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
* - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s
* Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo)
*
* Return pair of <repo, file> (with "repo" already having tag removed)
*
* Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
*/
std::pair<std::string, std::string> common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & hf_token) {
auto parts = string_split<std::string>(hf_repo_with_tag, ':');
std::string tag = parts.size() > 1 ? parts.back() : "latest";
std::string hf_repo = parts[0];
if (string_split<std::string>(hf_repo, '/').size() != 2) {
throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
}
// fetch model info from Hugging Face Hub API
json model_info;
curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
curl_slist_ptr http_headers;
std::string res_str;
std::string url = "https://huggingface.co/v2/" + hf_repo + "/manifests/" + tag;
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
static_cast<std::string *>(data)->append((char * ) ptr, size * nmemb);
return size * nmemb;
};
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_str);
#if defined(_WIN32)
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
#endif
if (!hf_token.empty()) {
std::string auth_header = "Authorization: Bearer " + hf_token;
http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
}
// Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
http_headers.ptr = curl_slist_append(http_headers.ptr, "Accept: application/json");
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
CURLcode res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
throw std::runtime_error("error: cannot make GET request to HF API");
}
long res_code;
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
if (res_code == 200) {
model_info = json::parse(res_str);
} else if (res_code == 401) {
throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
} else {
throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str()));
}
// check response
if (!model_info.contains("ggufFile")) {
throw std::runtime_error("error: model does not have ggufFile");
}
json & gguf_file = model_info.at("ggufFile");
if (!gguf_file.contains("rfilename")) {
throw std::runtime_error("error: ggufFile does not have rfilename");
}
return std::make_pair(hf_repo, gguf_file.at("rfilename"));
}
#else
struct llama_model * common_load_model_from_url(
@ -1463,6 +1604,11 @@ struct llama_model * common_load_model_from_hf(
return nullptr;
}
std::pair<std::string, std::string> common_get_hf_file(const std::string &, const std::string &) {
LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
return std::make_pair("", "");
}
#endif // LLAMA_USE_CURL
//
@ -1561,21 +1707,23 @@ std::vector<llama_token> common_tokenize(
const std::string & text,
bool add_special,
bool parse_special) {
return common_tokenize(llama_get_model(ctx), text, add_special, parse_special);
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
return common_tokenize(vocab, text, add_special, parse_special);
}
std::vector<llama_token> common_tokenize(
const struct llama_model * model,
const struct llama_vocab * vocab,
const std::string & text,
bool add_special,
bool parse_special) {
// upper limit for the number of tokens
int n_tokens = text.length() + 2 * add_special;
std::vector<llama_token> result(n_tokens);
n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
@ -1584,12 +1732,18 @@ std::vector<llama_token> common_tokenize(
}
std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
return common_token_to_piece(vocab, token, special);
}
std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) {
std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
if (n_chars < 0) {
piece.resize(-n_chars);
int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
GGML_ASSERT(check == -n_chars);
}
else {
@ -1599,13 +1753,19 @@ std::string common_token_to_piece(const struct llama_context * ctx, llama_token
return piece;
}
std::string common_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
std::string common_detokenize(const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
return common_detokenize(vocab, tokens, special);
}
std::string common_detokenize(const struct llama_vocab * vocab, const std::vector<llama_token> & tokens, bool special) {
std::string text;
text.resize(std::max(text.capacity(), tokens.size()));
int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
if (n_chars < 0) {
text.resize(-n_chars);
n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization
}
@ -1619,63 +1779,80 @@ std::string common_detokenize(llama_context * ctx, const std::vector<llama_token
// Chat template utils
//
bool common_chat_verify_template(const std::string & tmpl) {
bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) {
if (use_jinja) {
try {
auto chat_template = common_chat_template(tmpl, "<s>", "</s>");
common_chat_inputs inputs;
inputs.messages = json::array({{
{"role", "user"},
{"content", "test"},
}});
common_chat_params_init(chat_template, inputs);
return true;
} catch (const std::exception & e) {
LOG_ERR("%s: failed to apply template: %s\n", __func__, e.what());
return false;
}
}
llama_chat_message chat[] = {{"user", "test"}};
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0);
return res >= 0;
}
std::string common_chat_apply_template(const struct llama_model * model,
const std::string & tmpl,
std::string common_chat_apply_template(
const common_chat_template & tmpl,
const std::vector<common_chat_msg> & msgs,
bool add_ass) {
bool add_ass,
bool use_jinja) {
if (use_jinja) {
auto messages = json::array();
for (const auto & msg : msgs) {
messages.push_back({{"role", msg.role}, {"content", msg.content}});
}
common_chat_inputs inputs;
inputs.messages = messages;
inputs.add_generation_prompt = add_ass;
return common_chat_params_init(tmpl, inputs).prompt;
}
int alloc_size = 0;
bool fallback = false; // indicate if we must fallback to default chatml
std::vector<llama_chat_message> chat;
for (auto & msg : msgs) {
for (const auto & msg : msgs) {
chat.push_back({msg.role.c_str(), msg.content.c_str()});
alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
}
const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
std::vector<char> buf(alloc_size);
// run the first time to get the total output length
int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
int32_t res = llama_chat_apply_template(tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size());
// error: chat template is not supported
if (res < 0) {
if (ptr_tmpl != nullptr) {
// if the custom "tmpl" is not supported, we throw an error
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
throw std::runtime_error("this custom template is not supported");
} else {
// If the built-in template is not supported, we default to chatml
res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
fallback = true;
}
// if the custom "tmpl" is not supported, we throw an error
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
throw std::runtime_error("this custom template is not supported");
}
// if it turns out that our buffer is too small, we resize it
if ((size_t) res > buf.size()) {
buf.resize(res);
res = llama_chat_apply_template(
fallback ? nullptr : model,
fallback ? "chatml" : ptr_tmpl,
chat.data(), chat.size(), add_ass, buf.data(), buf.size());
res = llama_chat_apply_template(tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size());
}
std::string formatted_chat(buf.data(), res);
return formatted_chat;
}
std::string common_chat_format_single(const struct llama_model * model,
const std::string & tmpl,
std::string common_chat_format_single(
const common_chat_template & tmpl,
const std::vector<common_chat_msg> & past_msg,
const common_chat_msg & new_msg,
bool add_ass) {
bool add_ass,
bool use_jinja) {
std::ostringstream ss;
auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(model, tmpl, past_msg, false);
auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(tmpl, past_msg, false, use_jinja);
std::vector<common_chat_msg> chat_new(past_msg);
// if the past_msg ends with a newline, we must preserve it in the formatted version
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
@ -1683,21 +1860,87 @@ std::string common_chat_format_single(const struct llama_model * model,
};
// format chat with new_msg
chat_new.push_back(new_msg);
auto fmt_new_msg = common_chat_apply_template(model, tmpl, chat_new, add_ass);
auto fmt_new_msg = common_chat_apply_template(tmpl, chat_new, add_ass, use_jinja);
// get the diff part
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
return ss.str();
}
std::string common_chat_format_example(const struct llama_model * model,
const std::string & tmpl) {
std::string common_chat_format_example(const common_chat_template & tmpl, bool use_jinja) {
std::vector<common_chat_msg> msgs = {
{"system", "You are a helpful assistant"},
{"user", "Hello"},
{"assistant", "Hi there"},
{"user", "How are you?"},
{"system", "You are a helpful assistant", {}},
{"user", "Hello", {}},
{"assistant", "Hi there", {}},
{"user", "How are you?", {}},
};
return common_chat_apply_template(model, tmpl, msgs, true);
return common_chat_apply_template(tmpl, msgs, true, use_jinja);
}
#define CHATML_TEMPLATE_SRC \
"{%- for message in messages -%}\n" \
" {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' -}}\n" \
"{%- endfor -%}\n" \
"{%- if add_generation_prompt -%}\n" \
" {{- '<|im_start|>assistant\n' -}}\n" \
"{%- endif -%}"
common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override)
{
std::string default_template_src;
std::string template_tool_use_src;
bool has_explicit_template = !chat_template_override.empty();
if (chat_template_override.empty()) {
auto str = llama_model_chat_template(model, /* name */ nullptr);
if (str) {
default_template_src = str;
has_explicit_template = true;
}
str = llama_model_chat_template(model, /* name */ "tool_use");
if (str) {
template_tool_use_src = str;
has_explicit_template = true;
}
} else {
default_template_src = chat_template_override;
}
if (default_template_src.empty() || default_template_src == "chatml") {
if (!template_tool_use_src.empty()) {
default_template_src = template_tool_use_src;
} else {
default_template_src = CHATML_TEMPLATE_SRC;
}
}
auto vocab = llama_model_get_vocab(model);
const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
if (token == LLAMA_TOKEN_NULL) {
if (default_template_src.find(jinja_variable_name) != std::string::npos
|| template_tool_use_src.find(jinja_variable_name) != std::string::npos) {
LOG_WRN("%s: warning: vocab does not have a %s token, jinja template won't work as intended.\n", __func__, name);
}
return std::string();
} else {
return common_token_to_piece(vocab, token, true);
}
};
auto token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
auto token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
try {
return {
has_explicit_template,
std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos),
template_tool_use_src.empty()
? nullptr
: std::make_unique<minja::chat_template>(template_tool_use_src, token_bos, token_eos),
};
} catch (const std::exception & e) {
LOG_ERR("%s: failed to parse chat template: %s\n", __func__, e.what());
return {
has_explicit_template,
std::make_unique<minja::chat_template>(CHATML_TEMPLATE_SRC, token_bos, token_eos),
nullptr,
};
}
}
//

133
common/common.h
View file

@ -2,8 +2,9 @@
#pragma once
#include "llama.h"
#include "llama-cpp.h"
#include <set>
#include <string>
#include <vector>
#include <sstream>
@ -24,13 +25,11 @@
#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
struct common_lora_adapter_info {
struct common_adapter_lora_info {
std::string path;
float scale;
};
struct common_lora_adapter_container : common_lora_adapter_info {
struct llama_lora_adapter * adapter;
struct llama_adapter_lora * ptr;
};
using llama_tokens = std::vector<llama_token>;
@ -105,6 +104,17 @@ enum dimre_method {
DIMRE_METHOD_MEAN,
};
enum common_conversation_mode {
COMMON_CONVERSATION_MODE_DISABLED = 0,
COMMON_CONVERSATION_MODE_ENABLED = 1,
COMMON_CONVERSATION_MODE_AUTO = 2,
};
struct common_grammar_trigger {
std::string word;
bool at_start;
};
// sampling parameters
struct common_params_sampling {
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
@ -150,7 +160,11 @@ struct common_params_sampling {
COMMON_SAMPLER_TYPE_TEMPERATURE,
};
std::string grammar; // optional BNF-like grammar to constrain sampling
std::string grammar; // optional BNF-like grammar to constrain sampling
bool grammar_lazy = false;
std::vector<common_grammar_trigger> grammar_trigger_words; // optional trigger words to trigger lazy grammar
std::vector<llama_token> grammar_trigger_tokens; // optional trigger tokens to trigger lazy grammar and print trigger special tokens.
std::set<llama_token> preserved_tokens;
std::vector<llama_logit_bias> logit_bias; // logit biases to apply
@ -171,7 +185,11 @@ struct common_params_speculative {
struct cpu_params cpuparams;
struct cpu_params cpuparams_batch;
std::string model = ""; // draft model for speculative decoding // NOLINT
std::string hf_repo = ""; // HF repo // NOLINT
std::string hf_file = ""; // HF file // NOLINT
std::string model = ""; // draft model for speculative decoding // NOLINT
std::string model_url = ""; // model url to download // NOLINT
};
struct common_params_vocoder {
@ -180,6 +198,8 @@ struct common_params_vocoder {
std::string model = ""; // model path // NOLINT
std::string model_url = ""; // model url to download // NOLINT
bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy // NOLINT
};
struct common_params {
@ -242,14 +262,13 @@ struct common_params {
std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding // NOLINT
std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding // NOLINT
std::string logits_file = ""; // file for saving *all* logits // NOLINT
std::string rpc_servers = ""; // comma separated list of RPC servers // NOLINT
std::vector<std::string> in_files; // all input files
std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
std::vector<llama_model_kv_override> kv_overrides;
bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_lora_adapter_apply)
std::vector<common_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply)
std::vector<common_adapter_lora_info> lora_adapters; // lora adapter path with user defined scale
std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale
@ -277,7 +296,6 @@ struct common_params {
bool special = false; // enable special token output
bool interactive = false; // interactive mode
bool interactive_first = false; // wait for user input immediately
bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix)
bool prompt_cache_all = false; // save user input and generations to prompt cache
bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it
@ -303,6 +321,8 @@ struct common_params {
ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;
// multimodal models (see examples/llava)
std::string mmproj = ""; // path to multimodal projector // NOLINT
std::vector<std::string> image; // path to image file(s)
@ -324,6 +344,7 @@ struct common_params {
std::string hostname = "127.0.0.1";
std::string public_path = ""; // NOLINT
std::string chat_template = ""; // NOLINT
bool use_jinja = false; // NOLINT
bool enable_chat_template = true;
std::vector<std::string> api_keys;
@ -418,6 +439,10 @@ std::string string_format(const char * fmt, ...);
std::string string_strip(const std::string & str);
std::string string_get_sortable_timestamp();
std::string string_join(const std::vector<std::string> & values, const std::string & separator);
std::vector<std::string> string_split(const std::string & str, const std::string & delimiter);
std::string string_repeat(const std::string & str, size_t n);
void string_replace_all(std::string & s, const std::string & search, const std::string & replace);
template<class T>
@ -456,6 +481,11 @@ static bool string_starts_with(const std::string & str,
return str.rfind(prefix, 0) == 0;
}
static bool string_ends_with(const std::string & str,
const std::string & suffix) { // While we wait for C++20's std::string::ends_with...
return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
}
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
void string_process_escapes(std::string & input);
@ -478,10 +508,12 @@ std::string fs_get_cache_file(const std::string & filename);
// Model utils
//
// note: defines object's lifetime
struct common_init_result {
struct llama_model * model = nullptr;
struct llama_context * context = nullptr;
std::vector<common_lora_adapter_container> lora_adapters;
llama_model_ptr model;
llama_context_ptr context;
std::vector<llama_adapter_lora_ptr> lora;
};
struct common_init_result common_init_from_params(common_params & params);
@ -495,6 +527,7 @@ struct llama_model * common_load_model_from_url(
const std::string & local_path,
const std::string & hf_token,
const struct llama_model_params & params);
struct llama_model * common_load_model_from_hf(
const std::string & repo,
const std::string & remote_path,
@ -502,8 +535,12 @@ struct llama_model * common_load_model_from_hf(
const std::string & hf_token,
const struct llama_model_params & params);
std::pair<std::string, std::string> common_get_hf_file(
const std::string & hf_repo_with_tag,
const std::string & hf_token);
// clear LoRA adapters from context, then apply new list of adapters
void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters);
void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);
//
// Batch utils
@ -541,7 +578,7 @@ std::vector<llama_token> common_tokenize(
bool parse_special = false);
std::vector<llama_token> common_tokenize(
const struct llama_model * model,
const struct llama_vocab * vocab,
const std::string & text,
bool add_special,
bool parse_special = false);
@ -553,11 +590,21 @@ std::string common_token_to_piece(
llama_token token,
bool special = true);
std::string common_token_to_piece(
const struct llama_vocab * vocab,
llama_token token,
bool special = true);
// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
// optionally renders special/control tokens
std::string common_detokenize(
llama_context * ctx,
const struct llama_context * ctx,
const std::vector<llama_token> & tokens,
bool special = true);
std::string common_detokenize(
const struct llama_vocab * vocab,
const std::vector<llama_token> & tokens,
bool special = true);
@ -565,33 +612,57 @@ std::string common_detokenize(
// Chat template utils
//
struct common_tool_call {
std::string name;
std::string arguments;
std::string id;
};
// same with llama_chat_message, but uses std::string
struct common_chat_msg {
std::string role;
std::string content;
std::vector<common_tool_call> tool_calls;
std::string tool_plan = "";
};
// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
bool common_chat_verify_template(const std::string & tmpl);
bool common_chat_verify_template(const std::string & tmpl, bool use_jinja);
namespace minja {
class chat_template;
}
typedef minja::chat_template common_chat_template;
struct common_chat_templates {
bool has_explicit_template; // Model had builtin template or template overridde was specified.
std::unique_ptr<common_chat_template> template_default; // always set (defaults to chatml)
std::unique_ptr<common_chat_template> template_tool_use;
};
// CPP wrapper for llama_chat_apply_template
// If the built-in template is not supported, we default to chatml
// If the custom "tmpl" is not supported, we throw an error
std::string common_chat_apply_template(const struct llama_model * model,
const std::string & tmpl,
std::string common_chat_apply_template(
const common_chat_template & tmpl,
const std::vector<common_chat_msg> & chat,
bool add_ass);
bool add_ass,
bool use_jinja);
// Format single message, while taking into account the position of that message in chat history
std::string common_chat_format_single(const struct llama_model * model,
const std::string & tmpl,
std::string common_chat_format_single(
const common_chat_template & tmpl,
const std::vector<common_chat_msg> & past_msg,
const common_chat_msg & new_msg,
bool add_ass);
bool add_ass,
bool use_jinja);
// Returns an example of formatted chat
std::string common_chat_format_example(const struct llama_model * model,
const std::string & tmpl);
std::string common_chat_format_example(
const common_chat_template & tmpl, bool use_jinja);
common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override);
//
// KV cache utils
@ -637,6 +708,10 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
// Split utils
//
static const char * const LLM_KV_SPLIT_NO = "split.no";
static const char * const LLM_KV_SPLIT_COUNT = "split.count";
static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
namespace {
const char * const LLM_KV_SPLIT_NO = "split.no";
const char * const LLM_KV_SPLIT_COUNT = "split.count";
const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
}

112
common/json-schema-to-grammar.cpp
View file

@ -1,4 +1,6 @@
#include "json-schema-to-grammar.h"
#include "common.h"
#include <algorithm>
#include <fstream>
#include <map>
@ -11,11 +13,6 @@
using json = nlohmann::ordered_json;
template <typename Iterator>
static std::string join(Iterator begin, Iterator end, const std::string & separator);
static std::string repeat(const std::string & str, size_t n);
static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
auto has_max = max_items != std::numeric_limits<int>::max();
@ -128,8 +125,8 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream &
if (sub_len > 0) {
auto from_sub = from.substr(i + 1);
auto to_sub = to.substr(i + 1);
auto sub_zeros = repeat("0", sub_len);
auto sub_nines = repeat("9", sub_len);
auto sub_zeros = string_repeat("0", sub_len);
auto sub_nines = string_repeat("9", sub_len);
auto to_reached = false;
out << "(";
@ -188,8 +185,8 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream &
auto max_digits = max_s.length();
for (auto digits = min_digits; digits < max_digits; digits++) {
uniform_range(min_s, repeat("9", digits));
min_s = "1" + repeat("0", digits);
uniform_range(min_s, string_repeat("9", digits));
min_s = "1" + string_repeat("0", digits);
out << " | ";
}
uniform_range(min_s, max_s);
@ -318,49 +315,6 @@ std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
template <typename Iterator>
std::string join(Iterator begin, Iterator end, const std::string & separator) {
std::ostringstream result;
if (begin != end) {
result << *begin;
for (Iterator it = begin + 1; it != end; ++it) {
result << separator << *it;
}
}
return result.str();
}
static std::vector<std::string> split(const std::string & str, const std::string & delimiter) {
std::vector<std::string> tokens;
size_t start = 0;
size_t end = str.find(delimiter);
while (end != std::string::npos) {
tokens.push_back(str.substr(start, end - start));
start = end + delimiter.length();
end = str.find(delimiter, start);
}
tokens.push_back(str.substr(start));
return tokens;
}
static std::string repeat(const std::string & str, size_t n) {
if (n == 0) {
return "";
}
std::string result;
result.reserve(str.length() * n);
for (size_t i = 0; i < n; ++i) {
result += str;
}
return result;
}
static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function<std::string(const std::smatch &)> & replacement) {
std::smatch match;
std::string result;
@ -389,6 +343,7 @@ static std::string format_literal(const std::string & literal) {
class SchemaConverter {
private:
friend std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options);
std::function<json(const std::string &)> _fetch_json;
bool _dotall;
std::map<std::string, std::string> _rules;
@ -418,7 +373,7 @@ private:
for (size_t i = 0; i < alt_schemas.size(); i++) {
rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i)));
}
return join(rules.begin(), rules.end(), " | ");
return string_join(rules, " | ");
}
std::string _visit_pattern(const std::string & pattern, const std::string & name) {
@ -481,7 +436,7 @@ private:
for (const auto & item : ret) {
results.push_back(to_rule(item));
}
return std::make_pair(join(results.begin(), results.end(), " "), false);
return std::make_pair(string_join(results, " "), false);
};
while (i < length) {
@ -539,7 +494,7 @@ private:
}
curly_brackets += '}';
i++;
auto nums = split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
auto nums = string_split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
int min_times = 0;
int max_times = std::numeric_limits<int>::max();
try {
@ -809,10 +764,11 @@ private:
public:
SchemaConverter(
const std::function<json(const std::string &)> & fetch_json,
bool dotall)
bool dotall,
bool compact_spaces)
: _fetch_json(fetch_json), _dotall(dotall)
{
_rules["space"] = SPACE_RULE;
_rules["space"] = compact_spaces ? "\" \"?" : SPACE_RULE;
}
void resolve_refs(json & schema, const std::string & url) {
@ -854,7 +810,7 @@ public:
return;
}
std::string pointer = ref.substr(ref.find('#') + 1);
std::vector<std::string> tokens = split(pointer, "/");
std::vector<std::string> tokens = string_split(pointer, "/");
for (size_t i = 1; i < tokens.size(); ++i) {
std::string sel = tokens[i];
if (target.is_null() || !target.contains(sel)) {
@ -905,7 +861,7 @@ public:
for (const auto & v : schema["enum"]) {
enum_values.push_back(_generate_constant_rule(v));
}
return _add_rule(rule_name, "(" + join(enum_values.begin(), enum_values.end(), " | ") + ") space");
return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ") space");
} else if ((schema_type.is_null() || schema_type == "object")
&& (schema.contains("properties") ||
(schema.contains("additionalProperties") && schema["additionalProperties"] != true))) {
@ -1019,10 +975,10 @@ public:
void check_errors() {
if (!_errors.empty()) {
throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n"));
throw std::runtime_error("JSON schema conversion failed:\n" + string_join(_errors, "\n"));
}
if (!_warnings.empty()) {
fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", join(_warnings.begin(), _warnings.end(), "; ").c_str());
fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", string_join(_warnings, "; ").c_str());
}
}
@ -1035,11 +991,35 @@ public:
}
};
std::string json_schema_to_grammar(const json & schema) {
SchemaConverter converter([](const std::string &) { return json::object(); }, /* dotall= */ false);
auto copy = schema;
converter.resolve_refs(copy, "input");
converter.visit(copy, "");
std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
#ifdef LLAMA_USE_LLGUIDANCE
if (!force_gbnf) {
return "%llguidance {}\nstart: %json " + schema.dump();
}
#else
(void)force_gbnf;
#endif // LLAMA_USE_LLGUIDANCE
return build_grammar([&](const common_grammar_builder & callbacks) {
auto copy = schema;
callbacks.resolve_refs(copy);
callbacks.add_schema("", copy);
});
}
std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options) {
SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall, options.compact_spaces);
common_grammar_builder builder {
/* .add_rule = */ [&](const std::string & name, const std::string & rule) {
return converter._add_rule(name, rule);
},
/* .add_schema = */ [&](const std::string & name, const nlohmann::ordered_json & schema) {
return converter.visit(schema, name == "root" ? "" : name);
},
/* .resolve_refs = */ [&](nlohmann::ordered_json & schema) {
converter.resolve_refs(schema, "");
}
};
cb(builder);
converter.check_errors();
return converter.format_grammar();
}

16
common/json-schema-to-grammar.h
View file

@ -5,4 +5,18 @@
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
std::string json_schema_to_grammar(const nlohmann::ordered_json& schema);
std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
bool force_gbnf = false);
struct common_grammar_builder {
std::function<std::string(const std::string &, const std::string &)> add_rule;
std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
std::function<void(nlohmann::ordered_json &)> resolve_refs;
};
struct common_grammar_options {
bool dotall = false;
bool compact_spaces = false;
};
std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options = {});

270
common/llguidance.cpp Normal file
View file

@ -0,0 +1,270 @@
#include "sampling.h"
#include "log.h"
#ifdef LLAMA_USE_LLGUIDANCE
# include "llguidance.h"
# include <cmath>
struct llama_sampler_llg {
const llama_vocab * vocab;
std::string grammar_kind;
std::string grammar_data;
LlgTokenizer * tokenizer;
LlgConstraint * grammar;
LlgMaskResult llg_res;
bool has_llg_res;
};
static LlgConstraint * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind,
const char * grammar_data) {
LlgConstraintInit cinit;
llg_constraint_init_set_defaults(&cinit, tokenizer);
const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL");
if (log_level && *log_level) {
cinit.log_stderr_level = atoi(log_level);
}
auto c = llg_new_constraint_any(&cinit, grammar_kind, grammar_data);
if (llg_get_error(c)) {
LOG_ERR("llg error: %s\n", llg_get_error(c));
llg_free_constraint(c);
return nullptr;
}
return c;
}
static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) {
return "llguidance";
}
static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) {
auto * ctx = (llama_sampler_llg *) smpl->ctx;
if (ctx->grammar) {
LlgCommitResult res;
llg_commit_token(ctx->grammar, token, &res);
ctx->has_llg_res = false;
}
}
static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * ctx = (llama_sampler_llg *) smpl->ctx;
if (ctx->grammar) {
if (!ctx->has_llg_res) {
if (llg_compute_mask(ctx->grammar, &ctx->llg_res) == 0) {
ctx->has_llg_res = true;
} else {
LOG_ERR("llg error: %s\n", llg_get_error(ctx->grammar));
llg_free_constraint(ctx->grammar);
ctx->grammar = nullptr;
}
}
if (ctx->has_llg_res) {
if (ctx->llg_res.is_stop) {
for (size_t i = 0; i < cur_p->size; ++i) {
if (!llama_vocab_is_eog(ctx->vocab, cur_p->data[i].id)) {
cur_p->data[i].logit = -INFINITY;
}
}
} else {
const uint32_t * mask = ctx->llg_res.sample_mask;
for (size_t i = 0; i < cur_p->size; ++i) {
auto token = cur_p->data[i].id;
if ((mask[token / 32] & (1 << (token % 32))) == 0) {
cur_p->data[i].logit = -INFINITY;
}
}
}
}
}
}
static void llama_sampler_llg_reset(llama_sampler * smpl) {
auto * ctx = (llama_sampler_llg *) smpl->ctx;
if (!ctx->grammar) {
return;
}
auto * grammar_new = llama_sampler_llg_new(ctx->tokenizer, ctx->grammar_kind.c_str(), ctx->grammar_data.c_str());
llg_free_constraint(ctx->grammar);
ctx->grammar = grammar_new;
ctx->has_llg_res = false;
}
static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) {
const auto * ctx = (const llama_sampler_llg *) smpl->ctx;
auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr);
// copy the state
{
auto * result_ctx = (llama_sampler_llg *) result->ctx;
if (ctx->grammar) {
result_ctx->grammar_kind = ctx->grammar_kind;
result_ctx->grammar_data = ctx->grammar_data;
result_ctx->grammar = llg_clone_constraint(ctx->grammar);
result_ctx->tokenizer = llg_clone_tokenizer(ctx->tokenizer);
}
}
return result;
}
static void llama_sampler_llg_free(llama_sampler * smpl) {
const auto * ctx = (llama_sampler_llg *) smpl->ctx;
if (ctx->grammar) {
llg_free_constraint(ctx->grammar);
llg_free_tokenizer(ctx->tokenizer);
}
delete ctx;
}
static llama_sampler_i llama_sampler_llg_i = {
/* .name = */ llama_sampler_llg_name,
/* .accept = */ llama_sampler_llg_accept_impl,
/* .apply = */ llama_sampler_llg_apply,
/* .reset = */ llama_sampler_llg_reset,
/* .clone = */ llama_sampler_llg_clone,
/* .free = */ llama_sampler_llg_free,
};
static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,
uint32_t * output_tokens, size_t output_tokens_len) {
const llama_vocab * vocab = (const llama_vocab *) user_data;
int r = 0;
try {
r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false,
true);
} catch (const std::exception & e) {
GGML_ABORT("llama_tokenize failed: %s\n", e.what());
}
if (r < 0) {
return -r;
}
return r;
}
static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) {
// TODO store the tokenizer in the vocab somehow
static const llama_vocab * vocab_cache;
static LlgTokenizer * tokenizer_cache;
if (vocab_cache == vocab) {
return llg_clone_tokenizer(tokenizer_cache);
}
auto tok_eos = llama_vocab_eot(vocab);
if (tok_eos == LLAMA_TOKEN_NULL) {
tok_eos = llama_vocab_eos(vocab);
}
size_t vocab_size = llama_vocab_n_tokens(vocab);
auto token_lens = new uint32_t[vocab_size];
// we typically have ~7 bytes per token; let's go on the safe side here
auto token_bytes_size = vocab_size * 16 + 1024 * 1024;
auto token_bytes = new uint8_t[token_bytes_size];
size_t offset = 0;
for (size_t i = 0; i < vocab_size; i++) {
size_t max_token = 1024;
if (token_bytes_size - offset < max_token) {
GGML_ABORT("token_bytes buffer too small\n");
}
llama_token token = i;
auto dp = (char *) token_bytes + offset;
auto size = llama_detokenize(vocab, &token, 1, dp, max_token, false, false);
if (size < 0) {
GGML_ABORT("llama_detokenize failed\n");
}
if (size == 0) {
size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true);
if (size < 0) {
GGML_ABORT("llama_detokenize failed\n");
}
if (size != 0) {
*dp = '\xff'; // special token prefix marker
size += 1;
}
}
token_lens[i] = size;
offset += size;
}
LlgTokenizerInit tinit = {
/* .vocab_size = */ (uint32_t) vocab_size,
/* .tok_eos = */ (uint32_t) tok_eos,
/* .token_lens = */ token_lens,
/* .token_bytes = */ token_bytes,
/* .tokenizer_json = */ nullptr,
/* .tokenize_assumes_string = */ true,
/* .tokenize_fn = */ llama_sampler_llg_tokenize_fn,
/* .use_approximate_greedy_tokenize_fn = */ false,
/* .tokenize_user_data = */ vocab,
};
char error_buffer[1024];
LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer));
delete[] token_bytes;
delete[] token_lens;
if (tokenizer == nullptr) {
LOG_ERR("llg tokenizer error: %s\n", error_buffer);
return tokenizer;
}
if (tokenizer_cache) {
llg_free_tokenizer(tokenizer_cache);
}
vocab_cache = vocab;
tokenizer_cache = tokenizer;
return llg_clone_tokenizer(tokenizer_cache);
}
llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind,
const char * grammar_data) {
auto * ctx = new llama_sampler_llg;
if (grammar_kind != nullptr && grammar_kind[0] != '\0') {
auto tokenizer = llama_sampler_llg_new_tokenizer(vocab);
*ctx = {
/* .vocab = */ vocab,
/* .grammar_kind = */ grammar_kind,
/* .grammar_data = */ grammar_data,
/* .tokenizer = */ tokenizer,
/* .grammar = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data),
/* .llg_res = */ {},
/* .has_llg_res = */ false,
};
} else {
*ctx = {
/* .vocab = */ vocab,
/* .grammar_kind = */ {},
/* .grammar_data = */ {},
/* .tokenizer = */ nullptr,
/* .grammar = */ nullptr,
/* .llg_res = */ {},
/* .has_llg_res = */ false,
};
}
return llama_sampler_init(
/* .iface = */ &llama_sampler_llg_i,
/* .ctx = */ ctx
);
}
#else
llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) {
LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
return nullptr;
}
#endif // LLAMA_USE_LLGUIDANCE

11
common/log.cpp
View file

@ -14,16 +14,6 @@ void common_log_set_verbosity_thold(int verbosity) {
common_log_verbosity_thold = verbosity;
}
#define LOG_COL_DEFAULT "\033[0m"
#define LOG_COL_BOLD "\033[1m"
#define LOG_COL_RED "\033[31m"
#define LOG_COL_GREEN "\033[32m"
#define LOG_COL_YELLOW "\033[33m"
#define LOG_COL_BLUE "\033[34m"
#define LOG_COL_MAGENTA "\033[35m"
#define LOG_COL_CYAN "\033[36m"
#define LOG_COL_WHITE "\033[37m"
static int64_t t_us() {
return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
}
@ -206,6 +196,7 @@ public:
vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args_copy);
}
#endif
va_end(args_copy);
}
entry.level = level;

11
common/log.h
View file

@ -2,6 +2,17 @@
#include "ggml.h" // for ggml_log_level
#define LOG_CLR_TO_EOL "\033[K\r"
#define LOG_COL_DEFAULT "\033[0m"
#define LOG_COL_BOLD "\033[1m"
#define LOG_COL_RED "\033[31m"
#define LOG_COL_GREEN "\033[32m"
#define LOG_COL_YELLOW "\033[33m"
#define LOG_COL_BLUE "\033[34m"
#define LOG_COL_MAGENTA "\033[35m"
#define LOG_COL_CYAN "\033[36m"
#define LOG_COL_WHITE "\033[37m"
#ifndef __GNUC__
# define LOG_ATTRIBUTE_FORMAT(...)
#elif defined(__MINGW32__)

2868
common/minja.hpp Normal file
View file

File diff suppressed because it is too large Load diff

24
common/ngram-cache.cpp
View file

@ -65,13 +65,13 @@ constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) {
common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
if (part_static_it == nc_static.end()) {
return -1;
return LLAMA_TOKEN_NULL;
}
const common_ngram_cache_part part_static = part_static_it->second;
int max_count_static = 0;
int sum_count_static = 0;
llama_token max_token = -1;
llama_token max_token = LLAMA_TOKEN_NULL;
for (std::pair<llama_token, int> token_count_static : part_static) {
const llama_token token = token_count_static.first;
@ -85,10 +85,10 @@ static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram
}
if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
return -1;
return LLAMA_TOKEN_NULL;
}
if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
return -1;
return LLAMA_TOKEN_NULL;
}
return max_token;
}
@ -98,9 +98,9 @@ static llama_token try_draft(
common_ngram_cache & nc_primary, const std::vector<common_ngram> & ngrams_primary, common_ngram_cache_part & part_static,
const int * min_sample_size, const int * min_percent) {
llama_token drafted_token = -1;
llama_token drafted_token = LLAMA_TOKEN_NULL;
for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) {
for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) {
const common_ngram ngram_primary = ngrams_primary[i];
common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
@ -112,7 +112,7 @@ static llama_token try_draft(
int max_count_primary = 0;
int max_count_static = 0;
int sum_count_primary = 0;
llama_token max_token = -1;
llama_token max_token = LLAMA_TOKEN_NULL;
for (std::pair<llama_token, int> token_count_primary : part_primary) {
const llama_token token = token_count_primary.first;
@ -154,7 +154,7 @@ void common_ngram_cache_draft(
}
while ((int) draft.size()-1 < n_draft) {
llama_token drafted_token = -1;
llama_token drafted_token = LLAMA_TOKEN_NULL;
const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
common_ngram ngram_static;
@ -177,17 +177,17 @@ void common_ngram_cache_draft(
}
ngrams_cd.push_back(ngram_cd);
}
if (drafted_token == -1) {
if (drafted_token == LLAMA_TOKEN_NULL) {
drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
}
if (drafted_token == -1) {
if (drafted_token == LLAMA_TOKEN_NULL) {
drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
}
if (drafted_token == -1) {
if (drafted_token == LLAMA_TOKEN_NULL) {
drafted_token = try_draft(nc_static, ngram_static);
}
if (drafted_token == -1) {
if (drafted_token == LLAMA_TOKEN_NULL) {
break;
}

4
common/ngram-cache.h
View file

@ -17,13 +17,13 @@ struct common_ngram {
common_ngram() {
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
tokens[i] = -1;
tokens[i] = LLAMA_TOKEN_NULL;
}
}
common_ngram(const llama_token * input, const int ngram_size) {
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
tokens[i] = i < ngram_size ? input[i] : -1;
tokens[i] = i < ngram_size ? input[i] : LLAMA_TOKEN_NULL;
}
}

38
common/sampling.cpp
View file

@ -113,7 +113,10 @@ struct common_sampler {
void set_logits(struct llama_context * ctx, int idx) {
const auto * logits = llama_get_logits_ith(ctx, idx);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const int n_vocab = llama_vocab_n_tokens(vocab);
cur.resize(n_vocab);
@ -142,13 +145,36 @@ std::string common_params_sampling::print() const {
}
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
const llama_vocab * vocab = llama_model_get_vocab(model);
llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
lparams.no_perf = params.no_perf;
std::vector<const char *> trigger_words;
trigger_words.reserve(params.grammar_trigger_words.size());
for (const auto & str : params.grammar_trigger_words) {
trigger_words.push_back(str.word.c_str());
}
struct llama_sampler * grmr;
if (params.grammar.compare(0, 11, "%llguidance") == 0) {
#ifdef LLAMA_USE_LLGUIDANCE
grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
#else
GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
#endif // LLAMA_USE_LLGUIDANCE
} else {
grmr = params.grammar_lazy
? llama_sampler_init_grammar_lazy(vocab, params.grammar.c_str(), "root",
trigger_words.data(), trigger_words.size(),
params.grammar_trigger_tokens.data(), params.grammar_trigger_tokens.size())
: llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
}
auto * result = new common_sampler {
/* .params = */ params,
/* .grmr = */ llama_sampler_init_grammar(model, params.grammar.c_str(), "root"),
/* .grmr = */ grmr,
/* .chain = */ llama_sampler_chain_init(lparams),
/* .prev = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
/* .cur = */ {},
@ -157,7 +183,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
llama_sampler_chain_add(result->chain,
llama_sampler_init_logit_bias(
llama_n_vocab(model),
llama_vocab_n_tokens(vocab),
params.logit_bias.size(),
params.logit_bias.data()));
@ -172,7 +198,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
c_breakers.push_back(str.c_str());
}
llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
}
break;
case COMMON_SAMPLER_TYPE_TOP_K:
@ -194,7 +220,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
break;
case COMMON_SAMPLER_TYPE_INFILL:
llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model));
llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab));
break;
case COMMON_SAMPLER_TYPE_PENALTIES:
llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
@ -206,7 +232,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
} else if (params.mirostat == 1) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
} else if (params.mirostat == 2) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));

3
common/sampling.h
View file

@ -102,3 +102,6 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr);
std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);
llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
const char * grammar_kind, const char * grammar_data);

33
common/speculative.cpp
View file

@ -79,10 +79,13 @@ bool common_speculative_are_compatible(
const struct llama_model * model_tgt = llama_get_model(ctx_tgt);
const struct llama_model * model_dft = llama_get_model(ctx_dft);
const bool vocab_type_tgt = llama_vocab_type(model_tgt);
const struct llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
const struct llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
const bool vocab_type_tgt = llama_vocab_type(vocab_tgt);
LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt);
const bool vocab_type_dft = llama_vocab_type(model_dft);
const bool vocab_type_dft = llama_vocab_type(vocab_dft);
LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
if (vocab_type_tgt != vocab_type_dft) {
@ -91,34 +94,34 @@ bool common_speculative_are_compatible(
return false;
}
if (llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) ||
llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) ||
llama_token_bos(model_tgt) != llama_token_bos(model_dft) ||
llama_token_eos(model_tgt) != llama_token_eos(model_dft)) {
LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__);
LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_token_bos(model_tgt), llama_add_bos_token(model_tgt), llama_token_eos(model_tgt), llama_add_eos_token(model_tgt));
LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_token_bos(model_dft), llama_add_bos_token(model_dft), llama_token_eos(model_dft), llama_add_eos_token(model_dft));
if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)) {
LOG_ERR("%s: draft vocab special tokens must match target vocab to use speculation\n", __func__);
LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_tgt), llama_vocab_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_tgt));
LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_dft), llama_vocab_get_add_bos(vocab_dft), llama_vocab_eos(vocab_dft), llama_vocab_get_add_eos(vocab_dft));
return false;
}
{
const int n_vocab_tgt = llama_n_vocab(model_tgt);
const int n_vocab_dft = llama_n_vocab(model_dft);
const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt);
const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft);
const int vocab_diff = std::abs(n_vocab_tgt - n_vocab_dft);
if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
LOG_ERR("%s: draft model vocab must closely match target model to use speculation but "
"target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
__func__, n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
__func__, n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
return false;
}
for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
const char * token_text_tgt = llama_token_get_text(model_tgt, i);
const char * token_text_dft = llama_token_get_text(model_dft, i);
const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
LOG_ERR("%s: draft model vocab must match target model to use speculation but "
LOG_ERR("%s: draft vocab vocab must match target vocab to use speculation but "
"token %d content differs - target '%s', draft '%s'\n", __func__, i,
common_token_to_piece(ctx_tgt, i).c_str(),
common_token_to_piece(ctx_dft, i).c_str());

332
convert_hf_to_gguf.py
View file

@ -326,6 +326,7 @@ class Model:
gguf.MODEL_TENSOR.TIME_MIX_W2,
gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED,
gguf.MODEL_TENSOR.POSNET_NORM1,
gguf.MODEL_TENSOR.POSNET_NORM2,
)
@ -477,6 +478,11 @@ class Model:
return modelcls
return func
@classmethod
def print_registered_models(cls):
for name in sorted(cls._model_classes.keys()):
logger.error(f"- {name}")
@classmethod
def from_model_architecture(cls, arch: str) -> type[Model]:
try:
@ -642,7 +648,7 @@ class Model:
if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
res = "jina-v2-code"
if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b" or chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516":
# ref: https://huggingface.co/THUDM/glm-4-9b-chat
res = "chatglm-bpe"
if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
@ -687,6 +693,12 @@ class Model:
if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
# ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
res = "megrez"
if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5":
# ref: https://huggingface.co/deepseek-ai/DeepSeek-V3
res = "deepseek-v3"
if chkhsh == "b3f499bb4255f8ca19fccd664443283318f2fd2414d5e0b040fbdd0cc195d6c5":
# ref: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
res = "deepseek-r1-qwen"
if res is None:
logger.warning("\n")
@ -1764,25 +1776,19 @@ class DeciModel(Model):
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(
self.dir_model, load_merges=True,
special_token_types = ['bos', 'eos', 'eom', 'eot']
)
special_vocab._set_special_token("bos", 128000)
special_vocab._set_special_token("eos", 128001)
special_vocab._set_special_token("eom", 128008)
special_vocab._set_special_token("eot", 128009)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
special_vocab.add_to_gguf(self.gguf_writer)
else:
# DeciLM-7B
self._set_vocab_llama_hf()
# self._set_vocab_gpt2()
def set_gguf_parameters(self):
if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
assert self.block_count == len(self._num_kv_heads)
assert self.block_count == len(self._num_heads)
assert self.block_count == len(self._ffn_dims)
if (rope_theta := self.hparams.get("rope_theta")) is not None:
self.gguf_writer.add_rope_freq_base(rope_theta)
self.gguf_writer.add_head_count_kv(self._num_kv_heads)
self.gguf_writer.add_head_count(self._num_heads)
self.gguf_writer.add_feed_forward_length(self._ffn_dims)
@ -2565,6 +2571,63 @@ class Phi3MiniModel(Model):
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
@Model.register("PhiMoEForCausalLM")
class PhiMoeModel(Phi3MiniModel):
model_arch = gguf.MODEL_ARCH.PHIMOE
_experts: list[dict[str, Tensor]] | None = None
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"])
self.gguf_writer.add_expert_count(self.hparams["num_local_experts"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# process the experts separately
if name.find("block_sparse_moe.experts") != -1:
n_experts = self.hparams["num_local_experts"]
assert bid is not None
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
self._experts[bid][name] = data_torch
if len(self._experts[bid]) >= n_experts * 3:
tensors: list[tuple[str, Tensor]] = []
# merge the experts into a single 3d tensor
for w_name in ["w1", "w2", "w3"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
new_name = self.map_tensor_name(merged_name)
tensors.append((new_name, data_torch))
return tensors
else:
return []
return [(self.map_tensor_name(name), data_torch)]
def prepare_tensors(self):
super().prepare_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
experts = [k for d in self._experts for k in d.keys()]
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts}")
@Model.register("PlamoForCausalLM")
class PlamoModel(Model):
model_arch = gguf.MODEL_ARCH.PLAMO
@ -2822,6 +2885,66 @@ class InternLM2Model(Model):
return [(self.map_tensor_name(name), data_torch)]
@Model.register("InternLM3ForCausalLM")
class InternLM3Model(Model):
model_arch = gguf.MODEL_ARCH.LLAMA
def set_vocab(self):
tokens, scores, toktypes = self._create_vocab_sentencepiece()
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
if "add_prefix_space" in tokenizer_config_json:
self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
if "added_tokens_decoder" in tokenizer_config_json:
for token_id, token_data in tokenizer_config_json["added_tokens_decoder"].items():
if token_data.get("special"):
token_id = int(token_id)
token = token_data["content"]
special_vocab._set_special_token(token, token_id)
# update eos token
if token == '<|im_end|>' and "eos" in special_vocab.special_token_ids:
special_vocab.special_token_ids["eos"] = token_id
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear" or self.hparams["rope_scaling"].get("rope_type") == "linear":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith(("k_proj.weight", "k_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
return [(self.map_tensor_name(name), data_torch)]
@Model.register("BertModel", "BertForMaskedLM", "CamembertModel")
class BertModel(Model):
model_arch = gguf.MODEL_ARCH.BERT
@ -3262,6 +3385,8 @@ class Rwkv6Model(Model):
# required by llama.cpp, unused
self.gguf_writer.add_head_count(0)
lerp_weights: dict[int, dict[str, Tensor]] = {}
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
new_name = self.map_tensor_name(name)
@ -3277,14 +3402,84 @@ class Rwkv6Model(Model):
if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name:
data_torch = data_torch.squeeze()
rescale_every_n_layers = self.hparams["rescale_every"]
if rescale_every_n_layers > 0:
if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):
data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers))
try:
rescale_every_n_layers = self.hparams["rescale_every"]
if rescale_every_n_layers > 0:
if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):
data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers))
except KeyError:
pass
# concat time_mix_lerp weights to reduce some cpu overhead
# also reduces the number of tensors in the model
if bid is not None and "time_mix_lerp" in new_name and "time_mix_lerp_x" not in new_name:
try:
self.lerp_weights[bid][new_name] = data_torch
except KeyError:
self.lerp_weights[bid] = {new_name: data_torch}
if all(f"blk.{bid}.time_mix_lerp_{i}.weight" in self.lerp_weights[bid].keys() for i in ["w", "k", "v", "r", "g"]):
new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
data = torch.stack([self.lerp_weights[bid][f"blk.{bid}.time_mix_lerp_{i}.weight"].unsqueeze(0) for i in ["w", "k", "v", "r", "g"]], dim=0).unsqueeze(1)
yield (new_name, data)
return
yield (new_name, data_torch)
@Model.register("RWKV6Qwen2ForCausalLM")
class RWKV6Qwen2Model(Rwkv6Model):
model_arch = gguf.MODEL_ARCH.RWKV6QWEN2
def set_vocab(self):
try:
self._set_vocab_sentencepiece()
except FileNotFoundError:
self._set_vocab_gpt2()
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
num_attention_heads = self.hparams["num_attention_heads"]
num_key_value_heads = self.hparams["num_key_value_heads"]
hidden_size = self.hparams["hidden_size"]
head_size = hidden_size // num_attention_heads
rms_norm_eps = self.hparams["rms_norm_eps"]
intermediate_size = self.hparams["intermediate_size"]
time_mix_extra_dim = 64 if hidden_size >= 4096 else 32
time_decay_extra_dim = 128 if hidden_size >= 4096 else 64
# RWKV isn't context limited
self.gguf_writer.add_context_length(1048576)
self.gguf_writer.add_embedding_length(hidden_size)
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_wkv_head_size(head_size)
self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
self.gguf_writer.add_feed_forward_length(intermediate_size)
self.gguf_writer.add_file_type(self.ftype)
# special parameters for time_mixing in RWKV6QWEN2
self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
self.gguf_writer.add_token_shift_count(1)
# RWKV6QWEN2 use grouped key/value like GQA
self.gguf_writer.add_head_count_kv(num_key_value_heads)
# required by llama.cpp, unused
self.gguf_writer.add_head_count(0)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
for new_name, data in super().modify_tensors(data_torch, name, bid):
if "time_mix_w1" in new_name or "time_mix_w2" in new_name:
data = data.view(5, -1, data.shape[-1])
# rwkv6qwen2 has a different order of rkvwg instead of the original wkvrg
# permute them here to avoid code changes
data = torch.stack([data[3], data[1], data[2], data[0], data[4]], dim=0).view(-1, data.shape[-1])
if "w2" in new_name:
data = data.view(5, -1, data.shape[-1])
yield (new_name, data)
continue
yield (new_name, data)
@Model.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
class MambaModel(Model):
model_arch = gguf.MODEL_ARCH.MAMBA
@ -3379,6 +3574,24 @@ class CommandR2Model(Model):
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
@Model.register("Cohere2ForCausalLM")
class Cohere2Model(Model):
model_arch = gguf.MODEL_ARCH.COHERE2
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
rotary_pct = self.hparams["rotary_pct"]
hidden_size = self.hparams["hidden_size"]
num_attention_heads = self.hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads)))
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
@Model.register("OlmoForCausalLM")
@Model.register("OLMoForCausalLM")
class OlmoModel(Model):
@ -3837,6 +4050,7 @@ class DeepseekModel(Model):
@Model.register("DeepseekV2ForCausalLM")
@Model.register("DeepseekV3ForCausalLM")
class DeepseekV2Model(Model):
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
@ -3858,6 +4072,15 @@ class DeepseekV2Model(Model):
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
if hparams["scoring_func"] == "sigmoid":
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
elif hparams["scoring_func"] == "softmax":
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
else:
raise ValueError(f"Unsupported scoring_func value: {hparams['scoring_func']}")
self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
@ -3870,6 +4093,16 @@ class DeepseekV2Model(Model):
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# rename e_score_correction_bias tensors
if name.endswith("e_score_correction_bias"):
name = name.replace("e_score_correction_bias", "e_score_correction.bias")
# skip Multi-Token Prediction (MTP) layers
block_count = self.hparams["num_hidden_layers"]
match = re.match(r"model.layers.(\d+)", name)
if match and int(match.group(1)) >= block_count:
return []
# process the experts separately
if name.find("mlp.experts") != -1:
n_experts = self.hparams["n_routed_experts"]
@ -4280,7 +4513,7 @@ class JaisModel(Model):
self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
@Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration")
@Model.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
class ChatGLMModel(Model):
model_arch = gguf.MODEL_ARCH.CHATGLM
@ -4386,47 +4619,15 @@ class ChatGLMModel(Model):
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
vocab_size = hparams["padded_vocab_size"]
vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"])
assert max(tokenizer.get_vocab().values()) < vocab_size
tokpre = self.get_vocab_base_pre(tokenizer)
merges = []
vocab = {}
mergeable_ranks = tokenizer.mergeable_ranks
for token, rank in mergeable_ranks.items():
vocab[ChatGLMModel.token_bytes_to_string(token)] = rank
if len(token) == 1:
continue
merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank)
assert len(merged) >= 2 and len(merged) <= 7
merges.append(' '.join(map(ChatGLMModel.token_bytes_to_string, merged)))
# for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
added_vocab = tokenizer.get_added_vocab()
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.UNUSED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
if tokenizer.added_tokens_decoder[i].special:
toktypes.append(gguf.TokenType.CONTROL)
else:
toktypes.append(gguf.TokenType.USER_DEFINED)
else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
special_vocab.merges = merges
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
# only add special tokens when they were not already loaded from config.json
special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
@ -4437,16 +4638,20 @@ class ChatGLMModel(Model):
def set_gguf_parameters(self):
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
n_head_kv = self.hparams.get("multi_query_group_num", n_head)
n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head))
self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
self.gguf_writer.add_embedding_length(n_embed)
self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", 4 * n_embed))
self.gguf_writer.add_block_count(self.hparams["num_layers"])
self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed)))
self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams["num_hidden_layers"]))
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head_kv)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layernorm_epsilon"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5))
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_rope_dimension_count(64)
if "attention_dim" in self.hparams:
rope_dim = self.hparams["attention_dim"]
else:
rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
self.gguf_writer.add_add_bos_token(False)
rope_freq = 10000
if "rope_ratio" in self.hparams:
@ -4456,7 +4661,7 @@ class ChatGLMModel(Model):
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
if name.endswith(".rotary_pos_emb.inv_freq"):
if name.endswith(".rotary_pos_emb.inv_freq") or name.startswith("model.vision."):
return []
name = name.removeprefix("transformer.")
@ -4764,6 +4969,7 @@ def parse_args() -> argparse.Namespace:
parser.add_argument(
"model", type=Path,
help="directory containing model file",
nargs="?",
)
parser.add_argument(
"--use-temp-file", action="store_true",
@ -4801,8 +5007,15 @@ def parse_args() -> argparse.Namespace:
"--metadata", type=Path,
help="Specify the path for an authorship metadata override file"
)
parser.add_argument(
"--print-supported-models", action="store_true",
help="Print the supported models"
)
return parser.parse_args()
args = parser.parse_args()
if not args.print_supported_models and args.model is None:
parser.error("the following arguments are required: model")
return args
def split_str_to_n_bytes(split_str: str) -> int:
@ -4826,6 +5039,11 @@ def split_str_to_n_bytes(split_str: str) -> int:
def main() -> None:
args = parse_args()
if args.print_supported_models:
logger.error("Supported models:")
Model.print_registered_models()
sys.exit(0)
if args.verbose:
logging.basicConfig(level=logging.DEBUG)
else:

86
convert_hf_to_gguf_update.py
View file

@ -65,48 +65,50 @@ else:
# TODO: add models here, base models preferred
models = [
{"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
{"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
{"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
{"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
{"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
{"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
{"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
{"name": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
{"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
{"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
{"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
{"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
{"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
{"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
{"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
{"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
{"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
{"name": "jina-v1-en", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", },
{"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
{"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
{"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
{"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
{"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
{"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
{"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
{"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
{"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
{"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
{"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
{"name": "codeshell", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", },
{"name": "tekken", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
{"name": "smollm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
{'name': "bloom", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", },
{'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", },
{"name": "exaone", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
{"name": "phi-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
{"name": "chameleon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
{"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
{"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
{"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
{"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
{"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
{"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
{"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
{"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
{"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
{"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
{"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
{"name": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
{"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
{"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
{"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
{"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
{"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
{"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
{"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
{"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
{"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
{"name": "jina-v1-en", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", },
{"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
{"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
{"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
{"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
{"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
{"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
{"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
{"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
{"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
{"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
{"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
{"name": "codeshell", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", },
{"name": "tekken", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
{"name": "smollm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
{'name': "bloom", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", },
{'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", },
{"name": "exaone", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
{"name": "phi-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
{"name": "chameleon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
{"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
{"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
{"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
{"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
{"name": "deepseek-v3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
{"name": "deepseek-r1-qwen", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"},
]

34
convert_lora_to_gguf.py
View file

@ -226,6 +226,9 @@ def get_base_tensor_name(lora_tensor_name: str) -> str:
base_name = lora_tensor_name.replace("base_model.model.", "")
base_name = base_name.replace(".lora_A.weight", ".weight")
base_name = base_name.replace(".lora_B.weight", ".weight")
# models produced by mergekit-extract-lora have token embeddings in the adapter
base_name = base_name.replace(".lora_embedding_A", ".weight")
base_name = base_name.replace(".lora_embedding_B", ".weight")
return base_name
@ -260,6 +263,10 @@ def parse_args() -> argparse.Namespace:
"--base", type=Path,
help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config",
)
parser.add_argument(
"--base-model-id", type=str,
help="the model ID of the base model, if it is not available locally or in the adapter config. If specified, it will ignore --base and load the base model config from the Hugging Face hub (Example: 'meta-llama/Llama-3.2-1B-Instruct')",
)
parser.add_argument(
"lora_path", type=Path,
help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)",
@ -290,6 +297,7 @@ if __name__ == '__main__':
dir_base_model: Path | None = args.base
dir_lora: Path = args.lora_path
base_model_id: str | None = args.base_model_id
lora_config = dir_lora / "adapter_config.json"
input_model = dir_lora / "adapter_model.safetensors"
@ -313,7 +321,10 @@ if __name__ == '__main__':
lparams: dict[str, Any] = json.load(f)
# load base model
if dir_base_model is None:
if base_model_id is not None:
logger.info(f"Loading base model from Hugging Face: {base_model_id}")
hparams = load_hparams_from_hf(base_model_id)
elif dir_base_model is None:
if "base_model_name_or_path" in lparams:
model_id = lparams["base_model_name_or_path"]
logger.info(f"Loading base model from Hugging Face: {model_id}")
@ -371,11 +382,16 @@ if __name__ == '__main__':
if self.lazy:
tensor = LazyTorchTensor.from_eager(tensor)
base_name = get_base_tensor_name(name)
is_lora_a = ".lora_A.weight" in name
is_lora_b = ".lora_B.weight" in name
# note: mergekit-extract-lora also adds token embeddings to the adapter
is_lora_a = ".lora_A.weight" in name or ".lora_embedding_A" in name
is_lora_b = ".lora_B.weight" in name or ".lora_embedding_B" in name
if not is_lora_a and not is_lora_b:
if ".base_layer.weight" in name:
continue
# mergekit-extract-lora add these layernorm to the adapter, we need to keep them
if "_layernorm" in name or ".norm" in name:
yield (base_name, tensor)
continue
logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
if ".embed_tokens.weight" in name or ".lm_head.weight" in name:
logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning")
@ -407,9 +423,21 @@ if __name__ == '__main__':
if name == "lm_head.weight" and len(dest) == 0:
raise ValueError("lm_head is present in adapter, but is ignored in base model")
for dest_name, dest_data in dest:
# mergekit-extract-lora add these layernorm to the adapter
if "_norm" in dest_name:
assert dest_data.dim() == 1
yield (dest_name, dest_data)
continue
# otherwise, we must get the lora_A and lora_B tensors
assert isinstance(dest_data, LoraTorchTensor)
lora_a, lora_b = dest_data.get_lora_A_B()
# note: mergekit-extract-lora flip and transpose A and B
# here we only need to transpose token_embd.lora_a, see llm_build_inp_embd()
if "token_embd.weight" in dest_name:
lora_a = lora_a.T
yield (dest_name + ".lora_a", lora_a)
yield (dest_name + ".lora_b", lora_b)

2
docs/backend/SYCL.md
View file

@ -133,7 +133,7 @@ The docker build option is currently limited to *intel GPU* targets.
### Build image
```sh
# Using FP16
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile .
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
```
*Notes*:

63
docs/build.md
View file

@ -125,19 +125,66 @@ For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
## CUDA
This provides GPU acceleration using an NVIDIA GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from the [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
This provides GPU acceleration using an NVIDIA GPU. Make sure to have the [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit) installed.
- Using `CMake`:
#### Download directly from NVIDIA
You may find the official downloads here: [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
```bash
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
```
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used.
#### Compile and run inside a Fedora Toolbox Container
We also have a [guide](./cuda-fedora.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/).
**Recommended for:**
- ***Particularly*** *convenient* for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/).
- Toolbox is installed by default: [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde).
- *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download)
### Compilation
```bash
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
```
### Override Compute Capability Specifications
If `nvcc` cannot detect your gpu, you may get compile-warnings such as:
```text
nvcc warning : Cannot find valid GPU for '-arch=native', default arch is used
```
To override the `native` GPU detection:
#### 1. Take note of the `Compute Capability` of your NVIDIA devices: ["CUDA: Your GPU Compute > Capability"](https://developer.nvidia.com/cuda-gpus).
```text
GeForce RTX 4090 8.9
GeForce RTX 3080 Ti 8.6
GeForce RTX 3070 8.6
```
#### 2. Manually list each varying `Compute Capability` in the `CMAKE_CUDA_ARCHITECTURES` list.
```bash
cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89"
```
### Runtime CUDA environmental variables
You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) at runtime.
```bash
# Use `CUDA_VISIBLE_DEVICES` to hide the first compute device.
CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf
```
### Unified Memory
The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
### Performance Tuning
The following compilation options are also available to tweak performance:
| Option | Legal values | Default | Description |
@ -284,7 +331,7 @@ You don't need to install Vulkan SDK. It will be installed inside the container.
```sh
# Build the image
docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile .
docker build -t llama-cpp-vulkan --target light -f .devops/vulkan.Dockerfile .
# Then, use it:
docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33

270
docs/cuda-fedora.md Normal file
View file

@ -0,0 +1,270 @@
# Setting Up CUDA on Fedora
In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox container. This guide is applicable for:
- [Fedora Workstation](https://fedoraproject.org/workstation/)
- [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/)
- [Fedora Spins](https://fedoraproject.org/spins)
- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.5`, `Arch Linux`, and `Ubuntu`.
## Table of Contents
- [Prerequisites](#prerequisites)
- [Using the Fedora 41 CUDA Repository](#using-the-fedora-41-cuda-repository)
- [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment)
- [Installing Essential Development Tools](#installing-essential-development-tools)
- [Adding the CUDA Repository](#adding-the-cuda-repository)
- [Installing `nvidia-driver-libs`](#installing-nvidia-driver-libs)
- [Manually Resolving Package Conflicts](#manually-resolving-package-conflicts)
- [Finalizing the Installation of `nvidia-driver-libs`](#finalizing-the-installation-of-nvidia-driver-libs)
- [Installing the CUDA Meta-Package](#installing-the-cuda-meta-package)
- [Configuring the Environment](#configuring-the-environment)
- [Verifying the Installation](#verifying-the-installation)
- [Conclusion](#conclusion)
- [Troubleshooting](#troubleshooting)
- [Additional Notes](#additional-notes)
- [References](#references)
## Prerequisites
- **Toolbox Installed on the Host System** `Fedora Silverblue` and `Fedora Workstation` both have toolbox by default, other distributions may need to install the [toolbox package](https://containertoolbx.org/install/).
- **NVIDIA Drivers and Graphics Card installed on Host System (recommended)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA).
- **Internet connectivity** to download packages.
### Using the Fedora 41 CUDA Repository
The latest release is 41.
- [Fedora 41 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/)
**Note:** We recommend using a toolbox environment to prevent system conflicts.
## Creating a Fedora Toolbox Environment
This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using the Fedora Toolbox allows us to install the necessary packages without affecting the host system.
**Note:** Toolbox is available for other systems, and even without Toolbox, it is possible to use Podman or Docker.
1. **Create a Fedora 41 Toolbox:**
```bash
toolbox create --image registry.fedoraproject.org/fedora-toolbox:41 --container fedora-toolbox-41-cuda
```
2. **Enter the Toolbox:**
```bash
toolbox enter --container fedora-toolbox-41-cuda
```
Inside the toolbox, you have root privileges and can install packages without affecting the host system.
## Installing Essential Development Tools
1. **Synchronize the DNF Package Manager:**
```bash
sudo dnf distro-sync
```
2. **Install the Default Text Editor (Optional):**
```bash
sudo dnf install vim-default-editor --allowerasing
```
The `--allowerasing` flag will allow the removal of the conflicting `nano-default-editor` package.
3. **Install Development Tools and Libraries:**
```bash
sudo dnf install @c-development @development-tools cmake
```
This installs essential packages for compiling software, including `gcc`, `make`, and other development headers.
## Adding the CUDA Repository
Add the NVIDIA CUDA repository to your DNF configuration:
```bash
sudo dnf config-manager addrepo --from-repofile=https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/cuda-fedora41.repo
```
After adding the repository, synchronize the package manager again:
```bash
sudo dnf distro-sync
```
## Installing `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
We need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go).
```bash
ls -la /usr/lib64/libcuda.so.1
```
**Explanation:**
- `nvidia-driver-libs` and `nvidia-driver-cuda-libs` contains necessary NVIDIA driver libraries required by CUDA,
on hosts with NVIDIA drivers installed the Fedora Container will supply the host libraries.
### Install Nvidia Driver Libraries on Guest (if `libcuda.so.1` was NOT found).
```bash
sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
```
### Manually Updating the RPM database for host-supplied NVIDIA drivers (if `libcuda.so.1` was found).
If the installation fails due to conflicts, we'll manually download and install the required packages, excluding conflicting files.
#### 1. Download `nvidia-driver-libs` and `nvidia-driver-cuda-libs` RPM's (with dependencies)
```bash
sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-libs nvidia-driver-cuda-libs
```
#### 2. Update the RPM database to assume the installation of these packages.
```bash
sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/*
```
**Note:**
- The `--justdb` option only updates the RPM database, without touching the filesystem.
#### Finalizing the Installation of `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
After manually installing the dependencies, run:
```bash
sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
```
You should receive a message indicating the package is already installed:
```
Updating and loading repositories:
Repositories loaded.
Package "nvidia-driver-libs-3:570.86.10-1.fc41.x86_64" is already installed.
Package "nvidia-driver-cuda-libs-3:570.86.10-1.fc41.x86_64" is already installed.
Nothing to do.
```
## Installing the CUDA Meta-Package
Now that the driver libraries are installed, proceed to install CUDA:
```bash
sudo dnf install cuda
```
This installs the CUDA toolkit and associated packages.
## Configuring the Environment
To use CUDA, add its binary directory to your system's `PATH`.
1. **Create a Profile Script:**
```bash
sudo sh -c 'echo "export PATH=\$PATH:/usr/local/cuda/bin" >> /etc/profile.d/cuda.sh'
```
**Explanation:**
- We add to `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system.
- The backslash `\` before `$PATH` ensures the variable is correctly written into the script.
2. **Make the Script Executable:**
```bash
sudo chmod +x /etc/profile.d/cuda.sh
```
3. **Source the Script to Update Your Environment:**
```bash
source /etc/profile.d/cuda.sh
```
**Note:** This command updates your current shell session with the new `PATH`. The `/etc/profile.d/cuda.sh` script ensures that the CUDA binaries are available in your `PATH` for all future sessions.
## Verifying the Installation
To confirm that CUDA is correctly installed and configured, check the version of the NVIDIA CUDA Compiler (`nvcc`):
```bash
nvcc --version
```
You should see output similar to:
```
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2025 NVIDIA Corporation
Built on Wed_Jan_15_19:20:09_PST_2025
Cuda compilation tools, release 12.8, V12.8.61
Build cuda_12.8.r12.8/compiler.35404655_0
```
This output confirms that the CUDA compiler is accessible and indicates the installed version.
## Conclusion
You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 41 CUDA repository. By manually updating the RPM db and configuring the environment, you can develop CUDA applications without affecting your host system.
## Troubleshooting
- **Installation Failures:**
- If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies.
- You may use the `--excludepath` option with `rpm` to exclude conflicting files during manual RPM installations.
- **Rebooting the Container:**
- Sometimes there may be a bug in the NVIDIA driver host passthrough (such as missing a shared library). Rebooting the container may solve this issue:
```bash
# on the host system
podman container restart --all
```
- **Environment Variables Not Set:**
- If `nvcc` is not found after installation, ensure that `/usr/local/cuda/bin` is in your `PATH`.
- Run `echo $PATH` to check if the path is included.
- Re-source the profile script or open a new terminal session.
## Additional Notes
- **Updating CUDA in the Future:**
- Keep an eye on the official NVIDIA repositories for updates to your Fedora version.
- When an updated repository becomes available, adjust your `dnf` configuration accordingly.
- **Building `llama.cpp`:**
- With CUDA installed, you can follow these [build instructions for `llama.cpp`](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) to compile it with CUDA support.
- Ensure that any CUDA-specific build flags or paths are correctly set in your build configuration.
- **Using the Toolbox Environment:**
- The toolbox environment is isolated from your host system, which helps prevent conflicts.
- Remember that system files and configurations inside the toolbox are separate from the host. By default the home directory of the user is shared between the host and the toolbox.
---
**Disclaimer:** Manually installing and modifying system packages can lead to instability of the container. The above steps are provided as a guideline and may need adjustments based on your specific system configuration. Always back up important data before making significant system changes, especially as your home folder is writable and shared with he toolbox.
**Acknowledgments:** Special thanks to the Fedora community and NVIDIA documentation for providing resources that assisted in creating this guide.
## References
- [Fedora Toolbox Documentation](https://docs.fedoraproject.org/en-US/fedora-silverblue/toolbox/)
- [NVIDIA CUDA Installation Guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
- [Podman Documentation](https://podman.io/get-started)
---

10
docs/development/HOWTO-add-model.md
View file

@ -28,7 +28,7 @@ The required steps to implement for an HF model are:
```python
@Model.register("MyModelForCausalLM")
class MyModel(Model):
model_arch = gguf.MODEL_ARCH.GROK
model_arch = gguf.MODEL_ARCH.MYMODEL
```
2. Define the layout of the GGUF tensors in [constants.py](/gguf-py/gguf/constants.py)
@ -79,14 +79,14 @@ Depending on the model configuration, tokenizer, code and tensors layout, you wi
- `Model#set_vocab`
- `Model#write_tensors`
NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights.
NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the convention and several tools like `quantize` expect this to proceed the weights.
### 2. Define the model architecture in `llama.cpp`
The model params and tensors layout must be defined in `llama.cpp`:
1. Define a new `llm_arch`
2. Define the tensors layout in `LLM_TENSOR_NAMES`
3. Add any non standard metadata in `llm_load_hparams`
3. Add any non-standard metadata in `llm_load_hparams`
4. Create the tensors for inference in `llm_load_tensors`
5. If the model has a RoPE operation, add the rope type in `llama_rope_type`
@ -96,9 +96,9 @@ NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorc
This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`.
Have a look at existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
Have a look at existing implementations like `build_llama`, `build_dbrx` or `build_bert`.
When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR.
Some `ggml` backends do not support all operations. Backend implementations can be added in a separate PR.
Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/).

12
docs/docker.md
View file

@ -60,9 +60,9 @@ Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia
## Building Docker locally
```bash
docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile .
docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile .
docker build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile .
docker build -t local/llama.cpp:light-cuda --target light -f .devops/cuda.Dockerfile .
docker build -t local/llama.cpp:server-cuda --target server -f .devops/cuda.Dockerfile .
```
You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
@ -95,9 +95,9 @@ Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/
## Building Docker locally
```bash
docker build -t local/llama.cpp:full-musa -f .devops/full-musa.Dockerfile .
docker build -t local/llama.cpp:light-musa -f .devops/llama-cli-musa.Dockerfile .
docker build -t local/llama.cpp:server-musa -f .devops/llama-server-musa.Dockerfile .
docker build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile .
docker build -t local/llama.cpp:light-musa --target light -f .devops/musa.Dockerfile .
docker build -t local/llama.cpp:server-musa --target server -f .devops/musa.Dockerfile .
```
You may want to pass in some different `ARGS`, depending on the MUSA environment supported by your container host, as well as the GPU architecture.

51
docs/llguidance.md Normal file
View file

@ -0,0 +1,51 @@
# LLGuidance Support in llama.cpp
[LLGuidance](https://github.com/guidance-ai/llguidance) is a library for constrained decoding (also called constrained sampling or structured outputs) for Large Language Models (LLMs). Initially developed as the backend for the [Guidance](https://github.com/guidance-ai/guidance) library, it can also be used independently.
LLGuidance supports JSON Schemas and arbitrary context-free grammars (CFGs) written in a [variant](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md) of Lark syntax. It is [very fast](https://github.com/guidance-ai/jsonschemabench/tree/main/maskbench) and has [excellent](https://github.com/guidance-ai/llguidance/blob/main/docs/json_schema.md) JSON Schema coverage but requires the Rust compiler, which complicates the llama.cpp build process.
## Building
To enable LLGuidance support, build llama.cpp with the `LLAMA_LLGUIDANCE` option:
```sh
cmake -B build -DLLAMA_LLGUIDANCE=ON
make -C build -j
```
This requires the Rust compiler and the `cargo` tool to be [installed](https://www.rust-lang.org/tools/install).
## Interface
There are no new command-line arguments or modifications to `common_params`. When enabled, grammars starting with `%llguidance` are passed to LLGuidance instead of the [current](../grammars/README.md) llama.cpp grammars. Additionally, JSON Schema requests (e.g., using the `-j` argument in `llama-cli`) are also passed to LLGuidance.
For your existing GBNF grammars, you can use [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) to convert them to LLGuidance Lark-like format.
## Performance
Computing a "token mask" (i.e., the set of allowed tokens) for a llama3 tokenizer with 128k tokens takes, on average, 50μs of single-core CPU time for the [JSON Schema Bench](https://github.com/guidance-ai/jsonschemabench). The p99 time is 0.5ms, and the p100 time is 20ms. These results are due to the lexer/parser split and several [optimizations](https://github.com/guidance-ai/llguidance/blob/main/docs/optimizations.md).
## JSON Schema
LLGuidance adheres closely to the JSON Schema specification. For example:
- `additionalProperties` defaults to `true`, unlike current grammars, though you can set `"additionalProperties": false` if needed.
- any whitespace is allowed.
- The definition order in the `"properties": {}` object is maintained, regardless of whether properties are required (current grammars always puts required properties first).
Unsupported schemas result in an error message—no keywords are silently ignored.
## Why Not Reuse GBNF Format?
GBNF lacks the concept of a lexer.
Most programming languages, including JSON, use a two-step process: a lexer (built with regular expressions) converts a byte stream into lexemes, which are then processed by a CFG parser. This approach is faster because lexers are cheaper to evaluate, and there is ~10x fewer lexemes than bytes.
LLM tokens often align with lexemes, so the parser is engaged in under 0.5% of tokens, with the lexer handling the rest.
However, the user has to provide the distinction between lexemes and CFG symbols. In [Lark](https://github.com/lark-parser/lark), lexeme names are uppercase, while CFG symbols are lowercase.
The [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) can often take care of this automatically.
See [LLGuidance syntax docs](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#terminals-vs-rules) for more details.
## Error Handling
Errors are currently printed to `stderr`, and generation continues. Improved error handling may be added in the future.

6
examples/batched-bench/batched-bench.cpp
View file

@ -38,7 +38,7 @@ int main(int argc, char ** argv) {
llama_model_params model_params = common_model_params_to_llama(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params);
if (model == NULL) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
@ -50,7 +50,7 @@ int main(int argc, char ** argv) {
// ensure enough sequences are available
ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end());
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
llama_context * ctx = llama_init_from_model(model, ctx_params);
if (ctx == NULL) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
@ -194,7 +194,7 @@ int main(int argc, char ** argv) {
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_model_free(model);
llama_backend_free();

19
examples/batched.swift/Sources/main.swift
View file

@ -23,12 +23,17 @@ defer {
}
let model_params = llama_model_default_params()
guard let model = llama_load_model_from_file(modelPath.cString(using: .utf8), model_params) else {
guard let model = llama_model_load_from_file(modelPath.cString(using: .utf8), model_params) else {
print("Failed to load model")
exit(1)
}
defer {
llama_free_model(model)
llama_model_free(model)
}
guard let vocab = llama_model_get_vocab(model) else {
print("Failed to get vocab")
exit(1)
}
var tokens = tokenize(text: prompt, add_bos: true)
@ -41,7 +46,7 @@ context_params.n_batch = UInt32(max(n_len, n_parallel))
context_params.n_threads = 8
context_params.n_threads_batch = 8
let context = llama_new_context_with_model(model, context_params)
let context = llama_init_from_model(model, context_params)
guard context != nil else {
print("Failed to initialize context")
exit(1)
@ -141,7 +146,7 @@ while n_cur <= n_len {
let new_token_id = llama_sampler_sample(smpl, context, i_batch[i])
// is it an end of stream? -> mark the stream as finished
if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len {
i_batch[i] = -1
// print("")
if n_parallel > 1 {
@ -207,7 +212,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
let utf8Count = text.utf8.count
let n_tokens = utf8Count + (add_bos ? 1 : 0)
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
var swiftTokens: [llama_token] = []
for i in 0 ..< tokenCount {
swiftTokens.append(tokens[Int(i)])
@ -218,12 +223,12 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
var result = [CChar](repeating: 0, count: 8)
let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), 0, false)
let nTokens = llama_token_to_piece(vocab, token, &result, Int32(result.count), 0, false)
if nTokens < 0 {
let actualTokensCount = -Int(nTokens)
result = .init(repeating: 0, count: actualTokensCount)
let check = llama_token_to_piece(
model,
vocab,
token,
&result,
Int32(result.count),

16
examples/batched/batched.cpp
View file

@ -41,17 +41,19 @@ int main(int argc, char ** argv) {
llama_model_params model_params = common_model_params_to_llama(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params);
if (model == NULL) {
LOG_ERR("%s: error: unable to load model\n" , __func__);
return 1;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = common_tokenize(model, params.prompt, true);
tokens_list = common_tokenize(vocab, params.prompt, true);
const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
@ -62,7 +64,7 @@ int main(int argc, char ** argv) {
ctx_params.n_ctx = n_kv_req;
ctx_params.n_batch = std::max(n_predict, n_parallel);
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
llama_context * ctx = llama_init_from_model(model, ctx_params);
auto sparams = llama_sampler_chain_default_params();
sparams.no_perf = false;
@ -120,8 +122,8 @@ int main(int argc, char ** argv) {
}
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
decoder_start_token_id = llama_token_bos(model);
if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
decoder_start_token_id = llama_vocab_bos(vocab);
}
common_batch_clear(batch);
@ -174,7 +176,7 @@ int main(int argc, char ** argv) {
const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
// is it an end of generation? -> mark the stream as finished
if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
i_batch[i] = -1;
LOG("\n");
if (n_parallel > 1) {
@ -236,7 +238,7 @@ int main(int argc, char ** argv) {
llama_sampler_free(smpl);
llama_free(ctx);
llama_free_model(model);
llama_model_free(model);
llama_backend_free();

18
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -1,4 +1,6 @@
#include "ggml.h"
#include "gguf.h"
#include "llama.h"
#include "common.h"
#include "log.h"
@ -434,12 +436,12 @@ static void print_matrix(struct ggml_tensor * probs) {
}
}
struct llama_file {
struct my_llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
size_t size;
llama_file(const char * fname, const char * mode) {
my_llama_file(const char * fname, const char * mode) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
size = 0;
@ -500,7 +502,7 @@ struct llama_file {
return std::string(chars.data(), len);
}
~llama_file() {
~my_llama_file() {
if (fp) {
std::fclose(fp);
}
@ -508,7 +510,7 @@ struct llama_file {
};
static bool is_ggml_file(const char * filename) {
llama_file file(filename, "rb");
my_llama_file file(filename, "rb");
if (file.size < 4) {
return false;
}
@ -576,7 +578,7 @@ static void load_vocab(const char * filename, const Config * config, struct my_l
} else {
// assume llama2.c vocabulary
LOG_INF("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename);
llama_file file(filename, "rb");
my_llama_file file(filename, "rb");
if (!file.fp) {
die_fmt("%s: %s", strerror(errno), filename);
}
@ -689,8 +691,8 @@ static void save_as_llama_model(
gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID);
gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID);
gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID);
gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1);
gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1);
gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, LLAMA_TOKEN_NULL);
gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, LLAMA_TOKEN_NULL);
gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx);
gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
@ -909,7 +911,7 @@ int main(int argc, char ** argv) {
load_vocab(params.fn_vocab_model, &config, &vocab);
struct my_llama_model model;
model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
model.hparams.n_vocab = config.vocab_size; //llama_vocab_n_vocab(lctx);
model.hparams.n_ctx = params.n_ctx;
model.hparams.n_embd = config.dim; //params.n_embd;
model.hparams.n_ff = config.hidden_dim;

19
examples/cvector-generator/cvector-generator.cpp
View file

@ -1,7 +1,9 @@
#include "ggml.h"
#include "gguf.h"
#include "arg.h"
#include "common.h"
#include "llama.h"
#include "ggml.h"
#include "pca.hpp"
#include "mean.hpp"
@ -271,7 +273,9 @@ struct tokenized_prompt {
size_t max_seq_len;
tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const bool add_bos = llama_vocab_get_add_bos(vocab);
tokens_pos = common_tokenize(ctx, pos, add_bos, true);
tokens_neg = common_tokenize(ctx, neg, add_bos, true);
max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
@ -415,12 +419,13 @@ int main(int argc, char ** argv) {
// load the model to get hparams
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
// int n_ctx = llama_n_ctx(ctx);
int n_layers = llama_n_layer(model);
int n_embd = llama_n_embd(model);
int n_layers = llama_model_n_layer(model);
int n_embd = llama_model_n_embd(model);
// get model hint param (a.k.a model arch name)
char model_hint[128];
llama_model_meta_val_str(model, "general.architecture", model_hint, 128);
@ -474,8 +479,6 @@ int main(int argc, char ** argv) {
// done with the model, we can now free it to make gain some memory
printf("Done evaluate prompts, unload model...\n");
llama_free(ctx);
llama_free_model(model);
bool use_pca = params.cvector_dimre_method == DIMRE_METHOD_PCA;

15
examples/embedding/embedding.cpp
View file

@ -97,14 +97,17 @@ int main(int argc, char ** argv) {
// load the model
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
if (model == NULL) {
LOG_ERR("%s: unable to load model\n", __func__);
return 1;
}
const int n_ctx_train = llama_n_ctx_train(model);
const llama_vocab * vocab = llama_model_get_vocab(model);
const int n_ctx_train = llama_model_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
@ -147,7 +150,7 @@ int main(int argc, char ** argv) {
// check if the last token is SEP
// it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true'
for (auto & inp : inputs) {
if (inp.empty() || inp.back() != llama_token_sep(model)) {
if (inp.empty() || inp.back() != llama_vocab_sep(vocab)) {
LOG_WRN("%s: last token in the prompt is not SEP\n", __func__);
LOG_WRN("%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__);
}
@ -180,7 +183,7 @@ int main(int argc, char ** argv) {
}
// allocate output
const int n_embd = llama_n_embd(model);
const int n_embd = llama_model_n_embd(model);
std::vector<float> embeddings(n_embd_count * n_embd, 0);
float * emb = embeddings.data();
@ -316,8 +319,6 @@ int main(int argc, char ** argv) {
// clean up
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;

13
examples/eval-callback/eval-callback.cpp
View file

@ -127,7 +127,10 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
}
static bool run(llama_context * ctx, const common_params & params) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const bool add_bos = llama_vocab_get_add_bos(vocab);
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);
@ -162,8 +165,9 @@ int main(int argc, char ** argv) {
// init
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
if (model == nullptr || ctx == nullptr) {
LOG_ERR("%s : failed to init\n", __func__);
return 1;
@ -184,9 +188,6 @@ int main(int argc, char ** argv) {
LOG("\n");
llama_perf_context_print(ctx);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;

23
examples/export-lora/export-lora.cpp
View file

@ -1,12 +1,13 @@
#include "arg.h"
#include "common.h"
#include "ggml.h"
#include "ggml-alloc.h"
#include "gguf.h"
#include "arg.h"
#include "common.h"
#include <map>
#include <vector>
#include <string>
#include <thread>
#include <fstream>
static bool g_verbose = false;
@ -128,7 +129,7 @@ struct lora_merge_ctx {
lora_merge_ctx(
std::string & base_fname,
std::vector<common_lora_adapter_info> & lora_files,
std::vector<common_adapter_lora_info> & lora_files,
std::string & outfile,
int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) {
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
@ -344,8 +345,18 @@ struct lora_merge_ctx {
gf = ggml_new_graph(ctx0);
struct ggml_tensor * cur = inp_base;
for (size_t i = 0; i < adapters.size(); ++i) {
struct ggml_tensor * a_T = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32)));
struct ggml_tensor * delta = ggml_mul_mat(ctx0, a_T, ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32));
struct ggml_tensor * delta;
bool is_tok_embd = string_starts_with(name_base, "token_embd");
if (is_tok_embd) {
printf("%s : detected token embeddings tensor\n", __func__);
delta = ggml_mul_mat(ctx0,
ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32),
ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32));
} else {
delta = ggml_mul_mat(ctx0,
ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32))),
ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32));
}
// scale
const float alpha = adapters[i]->alpha;
const float rank = (float) inp_b[i]->ne[0];

2
examples/gbnf-validator/gbnf-validator.cpp
View file

@ -76,7 +76,7 @@ int main(int argc, char** argv) {
grammar_str = buffer.str();
}
llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root");
llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0);
if (grammar == nullptr) {
fprintf(stdout, "Failed to initialize llama_grammar\n");
return 1;

1
examples/gguf-hash/gguf-hash.cpp
View file

@ -1,4 +1,5 @@
#include "ggml.h"
#include "gguf.h"
#include <cstdlib> /* abort() */
#include <cstddef>

15
examples/gguf-split/gguf-split.cpp
View file

@ -1,18 +1,19 @@
#include "ggml.h"
#include "gguf.h"
#include "llama.h"
#include "common.h"
#include <algorithm>
#include <cmath>
#include <cinttypes>
#include <climits>
#include <cstdio>
#include <cstdlib>
#include <stdexcept>
#include <cstring>
#include <fstream>
#include <string>
#include <vector>
#include <stdio.h>
#include <string.h>
#include <climits>
#include <stdexcept>
#if defined(_WIN32)
#include <windows.h>
#ifndef PATH_MAX
@ -297,7 +298,7 @@ struct split_strategy {
total_size += ggml_nbytes(t);
}
total_size = total_size / 1000 / 1000; // convert to megabytes
printf("split %05d: n_tensors = %d, total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
printf("split %05d: n_tensors = %" PRIi64 ", total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
i_split++;
}
}

10
examples/gguf-split/tests.sh
View file

@ -41,7 +41,7 @@ echo PASS
echo
# 2b. Test the sharded model is loading properly
$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32
$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32
echo PASS
echo
@ -51,7 +51,7 @@ echo PASS
echo
# 3b. Test the merged model is loading properly
$MAIN --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32
$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32
echo PASS
echo
@ -61,7 +61,7 @@ echo PASS
echo
# 4b. Test the sharded model is loading properly
$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32
$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32
echo PASS
echo
@ -71,7 +71,7 @@ echo
#echo
# 5b. Test the merged model is loading properly
#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32
#$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32
#echo PASS
#echo
@ -81,7 +81,7 @@ echo PASS
echo
# 6b. Test the sharded model is loading properly
$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32
$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32
echo PASS
echo

16
examples/gguf/gguf.cpp
View file

@ -1,10 +1,9 @@
#include "ggml.h"
#include "gguf.h"
#include <cstdio>
#include <cinttypes>
#include <string>
#include <sstream>
#include <fstream>
#include <vector>
#undef MIN
@ -135,9 +134,10 @@ static bool gguf_ex_read_0(const std::string & fname) {
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name (ctx, i);
const size_t size = gguf_get_tensor_size (ctx, i);
const size_t offset = gguf_get_tensor_offset(ctx, i);
printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset);
}
}
@ -182,9 +182,10 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name (ctx, i);
const size_t size = gguf_get_tensor_size (ctx, i);
const size_t offset = gguf_get_tensor_offset(ctx, i);
printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset);
}
}
@ -199,7 +200,8 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, ggml_n_dims(cur), cur->name, cur->data);
printf("%s: tensor[%d]: n_dims = %d, ne = (%d, %d, %d, %d), name = %s, data = %p\n",
__func__, i, ggml_n_dims(cur), int(cur->ne[0]), int(cur->ne[1]), int(cur->ne[2]), int(cur->ne[3]), cur->name, cur->data);
// print first 10 elements
const float * data = (const float *) cur->data;
@ -215,7 +217,7 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
const float * data = (const float *) cur->data;
for (int j = 0; j < ggml_nelements(cur); ++j) {
if (data[j] != 100 + i) {
fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]);
fprintf(stderr, "%s: tensor[%d], data[%d]: found %f, expected %f\n", __func__, i, j, data[j], float(100 + i));
gguf_free(ctx);
return false;
}
@ -245,6 +247,8 @@ int main(int argc, char ** argv) {
check_data = false;
}
srand(123456);
const std::string fname(argv[1]);
const std::string mode (argv[2]);

23
examples/gritlm/gritlm.cpp
View file

@ -11,6 +11,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
std::vector<std::vector<float>> result;
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
@ -19,16 +20,16 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
const std::string input_string = instruction + sentences[i];
std::vector<llama_token> inputs = common_tokenize(model, input_string, true, false);
std::vector<llama_token> inputs = common_tokenize(vocab, input_string, true, false);
const int32_t n_toks = inputs.size();
// GritLM seems to have EOS = ""
// https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18
// inputs.push_back(llama_token_eos(model));
// inputs.push_back(llama_vocab_eos(vocab));
// we want to ignore instruction tokens for mean pooling
const int32_t n_inst = common_tokenize(model, instruction, true, false).size();
const int32_t n_inst = common_tokenize(vocab, instruction, true, false).size();
#ifdef GRIT_DEBUG
// debug tokens - should be matching as referenced in the GritLM sample
@ -52,7 +53,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
llama_decode(ctx, batch);
// get embedding dimensions
uint64_t n_embd = llama_n_embd(model);
uint64_t n_embd = llama_model_n_embd(model);
// allocate embedding output
std::vector<float> emb_unorm(n_embd, 0.0f);
@ -97,7 +98,9 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
std::string result;
const llama_model * model = llama_get_model(ctx);
llama_token eos_token = llama_token_eos(model);
const llama_vocab * vocab = llama_model_get_vocab(model);
llama_token eos_token = llama_vocab_eos(vocab);
llama_kv_cache_clear(ctx);
llama_set_embeddings(ctx, false);
@ -105,7 +108,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
std::vector<llama_token> inputs = common_tokenize(model, prompt, false, true);
std::vector<llama_token> inputs = common_tokenize(vocab, prompt, false, true);
int32_t i_current_token = 0;
while (true) {
@ -165,10 +168,10 @@ int main(int argc, char * argv[]) {
llama_backend_init();
llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams);
llama_model * model = llama_model_load_from_file(params.model.c_str(), mparams);
// create generation context
llama_context * ctx = llama_new_context_with_model(model, cparams);
llama_context * ctx = llama_init_from_model(model, cparams);
auto sparams = llama_sampler_chain_default_params();
@ -197,7 +200,7 @@ int main(int argc, char * argv[]) {
const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
const std::vector<std::vector<float>> q_rep = encode(ctx, queries, gritlm_instruction(instruction));
const int n_embd = llama_n_embd(model);
const int n_embd = llama_model_n_embd(model);
const float cosine_sim_q0_d0 = common_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q0_d1 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
@ -219,7 +222,7 @@ int main(int argc, char * argv[]) {
llama_sampler_free(smpl);
llama_free(ctx);
llama_free_model(model);
llama_model_free(model);
llama_backend_free();
return 0;

25
examples/imatrix/imatrix.cpp
View file

@ -7,7 +7,6 @@
#include <cstdio>
#include <cstring>
#include <ctime>
#include <sstream>
#include <thread>
#include <mutex>
#include <vector>
@ -40,7 +39,7 @@ public:
void set_params(common_params params) { m_params = std::move(params); }
bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
void save_imatrix(int ncall = -1) const;
bool load_imatrix(const char * file_name);
bool load_imatrix(const char * fname);
private:
std::unordered_map<std::string, Stats> m_stats;
common_params m_params;
@ -429,10 +428,14 @@ static void process_logits(
}
static bool compute_imatrix(llama_context * ctx, const common_params & params) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const bool add_bos = llama_vocab_get_add_bos(vocab);
const int n_ctx = llama_n_ctx(ctx);
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
auto tim1 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenizing the input ..\n", __func__);
@ -467,7 +470,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
const int n_chunk_max = tokens.size() / n_ctx;
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_vocab = llama_vocab_n_tokens(vocab);
const int n_batch = params.n_batch;
int count = 0;
@ -507,7 +510,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
// add BOS token for the first batch of each chunk
if (add_bos && j == 0) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
tokens[batch_start] = llama_vocab_bos(vocab);
}
common_batch_clear(batch);
@ -618,14 +621,15 @@ int main(int argc, char ** argv) {
// init
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
if (model == nullptr || ctx == nullptr) {
LOG_ERR("%s : failed to init\n", __func__);
return 1;
}
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx_train = llama_model_n_ctx_train(model);
if (params.n_ctx > n_ctx_train) {
LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, params.n_ctx);
@ -655,9 +659,6 @@ int main(int argc, char ** argv) {
LOG("\n");
llama_perf_context_print(ctx);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;

47
examples/infill/infill.cpp
View file

@ -131,15 +131,17 @@ int main(int argc, char ** argv) {
LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
common_init_result llama_init = common_init_from_params(params);
model = llama_init.model;
ctx = llama_init.context;
model = llama_init.model.get();
ctx = llama_init.context.get();
if (model == NULL) {
LOG_ERR("%s: unable to load model\n", __func__);
return 1;
}
const int n_ctx_train = llama_n_ctx_train(model);
const llama_vocab * vocab = llama_model_get_vocab(model);
const int n_ctx_train = llama_model_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
LOG_DBG("n_ctx: %d\n", n_ctx);
@ -152,28 +154,28 @@ int main(int argc, char ** argv) {
LOG_INF("\n");
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
const bool add_bos = llama_add_bos_token(model);
GGML_ASSERT(!llama_add_eos_token(model));
const bool add_bos = llama_vocab_get_add_bos(vocab);
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
std::vector<llama_token> embd_inp;
std::vector<llama_token> embd_end;
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
GGML_ASSERT(llama_token_fim_pre(model) >= 0);
GGML_ASSERT(llama_token_fim_suf(model) >= 0);
GGML_ASSERT(llama_vocab_fim_pre(vocab) >= 0);
GGML_ASSERT(llama_vocab_fim_suf(vocab) >= 0);
inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model));
inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model));
inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab));
inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab));
embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
embd_end = params.spm_infill ? inp_pfx : inp_sfx;
if (add_bos) {
embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab));
}
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
const llama_token middle_token = llama_token_fim_mid(model);
const llama_token middle_token = llama_vocab_fim_mid(vocab);
if (middle_token >= 0) {
embd_inp.push_back(middle_token);
}
@ -185,7 +187,7 @@ int main(int argc, char ** argv) {
// Should not run without any tokens
if (embd_inp.empty()) {
embd_inp.push_back(llama_token_bos(model));
embd_inp.push_back(llama_vocab_bos(vocab));
LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str());
}
@ -420,10 +422,10 @@ int main(int argc, char ** argv) {
// if not currently processing queued inputs;
if ((int) embd_inp.size() <= n_consumed) {
// deal with eot token in infill mode
if ((common_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){
if ((common_sampler_last(smpl) == llama_vocab_eot(vocab) || is_interacting) && params.interactive){
if (is_interacting && !params.interactive_first) {
// print an eot token
LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str());
LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str());
}
LOG("\n");
console::set_display(console::user_input);
@ -463,13 +465,13 @@ int main(int argc, char ** argv) {
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model));
inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model));
inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab));
inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab));
embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
embd_end = params.spm_infill ? inp_pfx : inp_sfx;
if (add_bos) {
embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab));
}
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
@ -484,7 +486,7 @@ int main(int argc, char ** argv) {
is_interacting = false;
}
// deal with end of generation tokens in interactive mode
else if (llama_token_is_eog(model, common_sampler_last(smpl))) {
else if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) {
LOG_DBG("found EOS token\n");
if (params.interactive) {
@ -500,7 +502,7 @@ int main(int argc, char ** argv) {
if (params.input_prefix_bos) {
LOG_DBG("adding input prefix BOS token\n");
embd_inp.push_back(llama_token_bos(model));
embd_inp.push_back(llama_vocab_bos(vocab));
}
std::string buffer;
@ -563,7 +565,7 @@ int main(int argc, char ** argv) {
}
// end of generation
if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) {
if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !params.interactive) {
break;
}
@ -575,15 +577,12 @@ int main(int argc, char ** argv) {
}
}
if (!params.interactive && n_remain <= 0) {
LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str());
LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str());
}
LOG("\n");
common_perf_print(ctx, smpl);
llama_free(ctx);
llama_free_model(model);
common_sampler_free(smpl);
llama_backend_free();

57
examples/llama-bench/llama-bench.cpp
View file

@ -683,7 +683,7 @@ struct cmd_params_instance {
bool cpu_strict;
int poll;
int n_gpu_layers;
std::string rpc_servers;
std::string rpc_servers_str;
llama_split_mode split_mode;
int main_gpu;
bool no_kv_offload;
@ -696,8 +696,37 @@ struct cmd_params_instance {
llama_model_params mparams = llama_model_default_params();
mparams.n_gpu_layers = n_gpu_layers;
if (!rpc_servers.empty()) {
mparams.rpc_servers = rpc_servers.c_str();
if (!rpc_servers_str.empty()) {
auto rpc_servers = string_split<std::string>(rpc_servers_str, ',');
// add RPC devices
if (!rpc_servers.empty()) {
ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
if (!rpc_reg) {
fprintf(stderr, "%s: failed to find RPC backend\n", __func__);
exit(1);
}
typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
if (!ggml_backend_rpc_add_device_fn) {
fprintf(stderr, "%s: failed to find RPC device add function\n", __func__);
exit(1);
}
static std::vector<ggml_backend_dev_t> devices;
devices.clear();
for (const std::string & server : rpc_servers) {
ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
if (dev) {
devices.push_back(dev);
} else {
fprintf(stderr, "%s: failed to add RPC device for server '%s'\n", __func__, server.c_str());
exit(1);
}
}
devices.push_back(nullptr);
mparams.devices = devices.data();
}
}
mparams.split_mode = split_mode;
mparams.main_gpu = main_gpu;
@ -708,7 +737,7 @@ struct cmd_params_instance {
}
bool equal_mparams(const cmd_params_instance & other) const {
return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers == other.rpc_servers &&
return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers_str == other.rpc_servers_str &&
split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap &&
tensor_split == other.tensor_split;
}
@ -1401,7 +1430,8 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_th
llama_set_n_threads(ctx, n_threads, n_threads);
const llama_model * model = llama_get_model(ctx);
const int32_t n_vocab = llama_n_vocab(model);
const llama_vocab * vocab = llama_model_get_vocab(model);
const int32_t n_vocab = llama_vocab_n_tokens(vocab);
std::vector<llama_token> tokens(n_batch);
@ -1409,7 +1439,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_th
while (n_processed < n_prompt) {
int n_tokens = std::min(n_prompt - n_processed, n_batch);
tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
tokens[0] = n_processed == 0 && llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab;
for (int i = 1; i < n_tokens; i++) {
tokens[i] = std::rand() % n_vocab;
}
@ -1424,9 +1454,10 @@ static void test_gen(llama_context * ctx, int n_gen, int n_threads) {
llama_set_n_threads(ctx, n_threads, n_threads);
const llama_model * model = llama_get_model(ctx);
const int32_t n_vocab = llama_n_vocab(model);
const llama_vocab * vocab = llama_model_get_vocab(model);
const int32_t n_vocab = llama_vocab_n_tokens(vocab);
llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
llama_token token = llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab;
for (int i = 0; i < n_gen; i++) {
llama_decode(ctx, llama_batch_get_one(&token, 1));
@ -1526,10 +1557,10 @@ int main(int argc, char ** argv) {
// keep the same model between tests when possible
if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
if (lmodel) {
llama_free_model(lmodel);
llama_model_free(lmodel);
}
lmodel = llama_load_model_from_file(inst.model.c_str(), inst.to_llama_mparams());
lmodel = llama_model_load_from_file(inst.model.c_str(), inst.to_llama_mparams());
if (lmodel == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, inst.model.c_str());
return 1;
@ -1537,10 +1568,10 @@ int main(int argc, char ** argv) {
prev_inst = &inst;
}
llama_context * ctx = llama_new_context_with_model(lmodel, inst.to_llama_cparams());
llama_context * ctx = llama_init_from_model(lmodel, inst.to_llama_cparams());
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str());
llama_free_model(lmodel);
llama_model_free(lmodel);
return 1;
}
@ -1626,7 +1657,7 @@ int main(int argc, char ** argv) {
ggml_threadpool_free_fn(threadpool);
}
llama_free_model(lmodel);
llama_model_free(lmodel);
if (p) {
p->print_footer();

13
examples/llama.android/llama/src/main/cpp/llama-android.cpp
View file

@ -87,7 +87,7 @@ Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring fi
auto path_to_model = env->GetStringUTFChars(filename, 0);
LOGi("Loading model from %s", path_to_model);
auto model = llama_load_model_from_file(path_to_model, model_params);
auto model = llama_model_load_from_file(path_to_model, model_params);
env->ReleaseStringUTFChars(filename, path_to_model);
if (!model) {
@ -102,7 +102,7 @@ Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring fi
extern "C"
JNIEXPORT void JNICALL
Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) {
llama_free_model(reinterpret_cast<llama_model *>(model));
llama_model_free(reinterpret_cast<llama_model *>(model));
}
extern "C"
@ -347,6 +347,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
jlong context_pointer,
jlong batch_pointer,
jstring jtext,
jboolean format_chat,
jint n_len
) {
@ -356,7 +357,8 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
const auto context = reinterpret_cast<llama_context *>(context_pointer);
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
const auto tokens_list = common_tokenize(context, text, 1);
bool parse_special = (format_chat == JNI_TRUE);
const auto tokens_list = common_tokenize(context, text, true, parse_special);
auto n_ctx = llama_n_ctx(context);
auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
@ -368,7 +370,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
}
for (auto id : tokens_list) {
LOGi("%s", common_token_to_piece(context, id).c_str());
LOGi("token: `%s`-> %d ", common_token_to_piece(context, id).c_str(), id);
}
common_batch_clear(*batch);
@ -405,6 +407,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
const auto sampler = reinterpret_cast<llama_sampler *>(sampler_pointer);
const auto model = llama_get_model(context);
const auto vocab = llama_model_get_vocab(model);
if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur);
if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I");
@ -414,7 +417,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
const auto new_token_id = llama_sampler_sample(sampler, context, -1);
const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) {
return nullptr;
}

5
examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt
View file

@ -65,6 +65,7 @@ class LLamaAndroid {
context: Long,
batch: Long,
text: String,
formatChat: Boolean,
nLen: Int
): Int
@ -115,10 +116,10 @@ class LLamaAndroid {
}
}
fun send(message: String): Flow<String> = flow {
fun send(message: String, formatChat: Boolean = false): Flow<String> = flow {
when (val state = threadLocalState.get()) {
is State.Loaded -> {
val ncur = IntVar(completion_init(state.context, state.batch, message, nlen))
val ncur = IntVar(completion_init(state.context, state.batch, message, formatChat, nlen))
while (ncur.value <= nlen) {
val str = completion_loop(state.context, state.batch, state.sampler, nlen, ncur)
if (str == null) {

16
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -24,6 +24,7 @@ func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama
actor LlamaContext {
private var model: OpaquePointer
private var context: OpaquePointer
private var vocab: OpaquePointer
private var sampling: UnsafeMutablePointer<llama_sampler>
private var batch: llama_batch
private var tokens_list: [llama_token]
@ -47,13 +48,14 @@ actor LlamaContext {
self.sampling = llama_sampler_chain_init(sparams)
llama_sampler_chain_add(self.sampling, llama_sampler_init_temp(0.4))
llama_sampler_chain_add(self.sampling, llama_sampler_init_dist(1234))
vocab = llama_model_get_vocab(model)
}
deinit {
llama_sampler_free(sampling)
llama_batch_free(batch)
llama_model_free(model)
llama_free(context)
llama_free_model(model)
llama_backend_free()
}
@ -65,7 +67,7 @@ actor LlamaContext {
model_params.n_gpu_layers = 0
print("Running on simulator, force use n_gpu_layers = 0")
#endif
let model = llama_load_model_from_file(path, model_params)
let model = llama_model_load_from_file(path, model_params)
guard let model else {
print("Could not load model at \(path)")
throw LlamaError.couldNotInitializeContext
@ -79,7 +81,7 @@ actor LlamaContext {
ctx_params.n_threads = Int32(n_threads)
ctx_params.n_threads_batch = Int32(n_threads)
let context = llama_new_context_with_model(model, ctx_params)
let context = llama_init_from_model(model, ctx_params)
guard let context else {
print("Could not load context!")
throw LlamaError.couldNotInitializeContext
@ -151,7 +153,7 @@ actor LlamaContext {
new_token_id = llama_sampler_sample(sampling, context, batch.n_tokens - 1)
if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len {
print("\n")
is_done = true
let new_token_str = String(cString: temporary_invalid_cchars + [0])
@ -297,7 +299,7 @@ actor LlamaContext {
let utf8Count = text.utf8.count
let n_tokens = utf8Count + (add_bos ? 1 : 0) + 1
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
var swiftTokens: [llama_token] = []
for i in 0..<tokenCount {
@ -316,7 +318,7 @@ actor LlamaContext {
defer {
result.deallocate()
}
let nTokens = llama_token_to_piece(model, token, result, 8, 0, false)
let nTokens = llama_token_to_piece(vocab, token, result, 8, 0, false)
if nTokens < 0 {
let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
@ -324,7 +326,7 @@ actor LlamaContext {
defer {
newResult.deallocate()
}
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, 0, false)
let nNewTokens = llama_token_to_piece(vocab, token, newResult, -nTokens, 0, false)
let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
return Array(bufferPointer)
} else {

7
examples/llava/CMakeLists.txt
View file

@ -50,3 +50,10 @@ set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)
set(TARGET llama-llava-clip-quantize-cli)
add_executable(${TARGET} clip-quantize-cli.cpp)
set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

43
examples/llava/README-glmedge.md Normal file
View file

@ -0,0 +1,43 @@
# GLMV-EDGE
Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b).
## Usage
Build with cmake or run `make llama-llava-cli` to build it.
After building, run: `./llama-llava-cli` to see the usage. For example:
```sh
./llama-llava-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf --image img_path/image.jpg -p "<|system|>\n system prompt <image><|user|>\n prompt <|assistant|>\n"
```
**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
## GGUF conversion
1. Clone a GLMV-EDGE model ([2B](https://huggingface.co/THUDM/glm-edge-v-2b) or [5B](https://huggingface.co/THUDM/glm-edge-v-5b)). For example:
```sh
git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b
```
2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents:
```sh
python ./examples/llava/glmedge-surgery.py -m ../model_path
```
4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF:
```sh
python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
```
5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF:
```sh
python convert_hf_to_gguf.py ../model_path
```
Now both the LLM part and the image encoder are in the `model_path` directory.

46
examples/llava/README-minicpmo2.6.md Normal file
View file

@ -0,0 +1,46 @@
## MiniCPM-o 2.6
Currently, this readme only supports minicpm-omni's image capabilities, and we will update the full-mode support as soon as possible.
### Prepare models and code
Download [MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6) PyTorch model from huggingface to "MiniCPM-o-2_6" folder.
Clone llama.cpp:
```bash
git clone git@github.com:OpenBMB/llama.cpp.git
cd llama.cpp
git checkout minicpm-omni
```
### Usage of MiniCPM-o 2.6
Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us)
```bash
python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-o-2_6
python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4
python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model
# quantize int4 version
./llama-quantize ../MiniCPM-o-2_6/model/ggml-model-f16.gguf ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M
```
Build llama.cpp using `CMake`:
https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md
```bash
cmake -B build
cmake --build build --config Release
```
Inference on Linux or Mac
```
# run f16 version
./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
# run quantized int4 version
./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
# or run in interactive mode
./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i
```

44
examples/llava/README-quantize.md Normal file
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@ -0,0 +1,44 @@
# Quantizing CLIP Visual Projector
This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance.
## Usage
To quantize a CLIP visual projector model, use the following command:
```sh
./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf <type>
```
After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc).
### Arguments
- `/path/to/ggml-model-f32.gguf`: The path to the input model file in FP32 or FP16 format.
- `/path/to/ggml-model-quantized.gguf`: The path where the quantized model will be saved.
- `<type>`: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the `enum ggml_type`.
### Quantization Types
The following quantization types are supported, based on the `enum ggml_type` definition:
- `2` - `q4_0`: 4-bit quantization with a single scale value.
- `3` - `q4_1`: 4-bit quantization with a separate scale value for each block.
- `6` - `q5_0`: 5-bit quantization with a single scale value.
- `7` - `q5_1`: 5-bit quantization with a separate scale value for each block.
- `8` - `q8_0`: 8-bit quantization with a single scale value.
### Example
To quantize a model using the `q4_0` quantization type, you would run:
```sh
./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2
```
This command will generate a quantized model at `/path/to/ggml-model-quantized.gguf` using the `q4_0` quantization method.
## Notes
- Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements.
- The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments.

59
examples/llava/clip-quantize-cli.cpp Normal file
View file

@ -0,0 +1,59 @@
#include "arg.h"
#include "base64.hpp"
#include "log.h"
#include "common.h"
#include "sampling.h"
#include "clip.h"
#include "llava.h"
#include "llama.h"
#include "ggml.h"
static void print_usage(int argc, char ** argv) {
(void) argc;
fprintf(stderr, "usage: %s /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type\n", argv[0]);
fprintf(stderr, " type = 2 - q4_0\n");
fprintf(stderr, " type = 3 - q4_1\n");
fprintf(stderr, " type = 6 - q5_0\n");
fprintf(stderr, " type = 7 - q5_1\n");
fprintf(stderr, " type = 8 - q8_0\n");
}
int main(int argc, char ** argv) {
if (argc != 4) {
print_usage(argc, argv);
return 1;
}
const std::string fname_inp = argv[1];
const std::string fname_out = argv[2];
const int itype = atoi(argv[3]);
const int64_t t_main_start_us = ggml_time_us();
int64_t t_quantize_us = 0;
// load the model
{
const int64_t t_start_us = ggml_time_us();
if (!clip_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}
t_quantize_us = ggml_time_us() - t_start_us;
}
// report timing
{
const int64_t t_main_end_us = ggml_time_us();
printf("\n");
printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us / 1000.0f);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
}
return 0;
}

153
examples/llava/clip.cpp
View file

@ -7,6 +7,7 @@
#include "ggml-cpu.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "gguf.h"
//#ifdef GGML_USE_CUDA
//#include "ggml-cuda.h"
@ -101,6 +102,7 @@ static std::string format(const char * fmt, ...) {
#define KEY_HAS_VIS_ENC "clip.has_vision_encoder"
#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector"
#define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector"
#define KEY_HAS_GLM_PROJ "clip.has_glm_projector"
#define KEY_MINICPMV_VERSION "clip.minicpmv_version"
#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger"
#define KEY_USE_GELU "clip.use_gelu"
@ -159,6 +161,15 @@ static std::string format(const char * fmt, ...) {
#define TN_MINICPMV_ATTN "resampler.attn.%s.%s"
#define TN_MINICPMV_LN "resampler.ln_%s.%s"
#define TN_GLM_ADAPER_CONV "adapter.conv.%s"
#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s"
#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s"
#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s"
#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s"
#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
#define TN_GLM_BOI_W "adapter.boi"
#define TN_GLM_EOI_W "adapter.eoi"
enum projector_type {
PROJECTOR_TYPE_MLP,
@ -166,6 +177,7 @@ enum projector_type {
PROJECTOR_TYPE_LDP,
PROJECTOR_TYPE_LDPV2,
PROJECTOR_TYPE_RESAMPLER,
PROJECTOR_TYPE_GLM_EDGE,
PROJECTOR_TYPE_MERGER,
PROJECTOR_TYPE_UNKNOWN,
};
@ -175,6 +187,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
{ PROJECTOR_TYPE_LDP, "ldp" },
{ PROJECTOR_TYPE_LDPV2, "ldpv2"},
{ PROJECTOR_TYPE_RESAMPLER, "resampler"},
{ PROJECTOR_TYPE_GLM_EDGE, "adapter"},
{ PROJECTOR_TYPE_MERGER, "qwen2vl_merger"},
};
@ -262,7 +275,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
{
const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
int arr_n = gguf_get_arr_n(ctx_gguf, i);
const void * data = gguf_get_arr_data(ctx_gguf, i);
const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i);
std::stringstream ss;
ss << "[";
for (int j = 0; j < arr_n; j++) {
@ -499,6 +512,12 @@ struct clip_vision_model {
struct ggml_tensor * mm_4_w = NULL;
struct ggml_tensor * mm_4_b = NULL;
//GLMV-Edge projection
struct ggml_tensor * mm_model_adapter_conv_w;
struct ggml_tensor * mm_model_adapter_conv_b;
struct ggml_tensor * boi_w;
struct ggml_tensor * eoi_w;
// MobileVLM projection
struct ggml_tensor * mm_model_mlp_1_w;
struct ggml_tensor * mm_model_mlp_1_b;
@ -559,6 +578,7 @@ struct clip_ctx {
bool has_vision_encoder = false;
bool has_llava_projector = false;
bool has_minicpmv_projector = false;
bool has_glm_projector = false;
bool has_qwen2vl_merger = false;
int minicpmv_version = 2;
@ -637,7 +657,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
const int batch_size = imgs->size;
if (ctx->has_llava_projector || ctx->has_minicpmv_projector) {
if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) {
GGML_ASSERT(batch_size == 1);
}
@ -717,6 +737,9 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
else if (ctx->minicpmv_version == 3) {
pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1);
}
else if (ctx->minicpmv_version == 4) {
pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1);
}
ggml_set_name(pos_embed, "pos_embed");
ggml_set_input(pos_embed);
}
@ -730,8 +753,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
}
// loop over layers
if (ctx->has_minicpmv_projector || ctx->has_qwen2vl_merger) {
// TODO: figure out why we doing thing in this way ???
if (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) {
n_layer += 1;
}
for (int il = 0; il < n_layer - 1; il++) {
@ -1052,6 +1074,11 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
n_head = hidden_size/d_head;
num_query = 64;
}
else if (ctx->minicpmv_version == 4) {
hidden_size = 3584;
n_head = hidden_size/d_head;
num_query = 64;
}
struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b);
Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
@ -1086,7 +1113,33 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
GGML_ASSERT(false);
}
}
else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
// glm projector
else if (ctx->has_glm_projector) {
if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
size_t gridsz = (size_t)sqrt(embeddings->ne[1]);
embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3));
embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]);
embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1);
embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size);
embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3));
embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b);
//GLU
{
embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
embeddings = ggml_gelu_inplace(ctx0, embeddings);
struct ggml_tensor * x = embeddings;
embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings);
x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x);
embeddings = ggml_silu_inplace(ctx0, embeddings);
embeddings = ggml_mul(ctx0, embeddings,x);
embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings);
}
} else {
GGML_ABORT("fatel error");
}
} else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
@ -1275,6 +1328,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx);
}
idx = gguf_find_key(ctx, KEY_HAS_GLM_PROJ);
if (idx != -1) {
new_clip->has_glm_projector = gguf_get_val_bool(ctx, idx);
}
idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER);
if (idx != -1) {
new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx);
@ -1299,6 +1357,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
LOG_INF("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
LOG_INF("%s: minicpmv_projector: %d\n", __func__, new_clip->has_minicpmv_projector);
LOG_INF("%s: glm_projector: %d\n", __func__, new_clip->has_glm_projector);
LOG_INF("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
LOG_INF("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
}
@ -1566,6 +1625,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight"));
vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias"));
}
else if (new_clip->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
vision_model.mm_model_adapter_conv_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "weight"));
vision_model.mm_model_adapter_conv_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "bias"));
vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_LINEAR,"weight"));
vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"weight"));
vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"bias"));
vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_H_2_4H,"weight"));
vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_GATE,"weight"));
vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_4H_2_H,"weight"));
vision_model.boi_w = get_tensor(new_clip->ctx_data, TN_GLM_BOI_W);
vision_model.eoi_w = get_tensor(new_clip->ctx_data, TN_GLM_EOI_W);
}
else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) {
vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
@ -2040,6 +2111,7 @@ static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_imag
images[images.size()-1].push_back(patch);
}
}
clip_image_u8_free(refine_image);
}
return images;
}
@ -2078,6 +2150,13 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
clip_image_f32_free(res);
}
}
for (size_t i = 0; i < imgs.size(); ++i) {
for (size_t j = 0; j < imgs[i].size(); ++j) {
if (imgs[i][j] != nullptr) {
clip_image_u8_free(imgs[i][j]);
}
}
}
return true;
}
else if (ctx->has_qwen2vl_merger) {
@ -2098,6 +2177,20 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
return true;
}
if (ctx->has_glm_projector) {
res_imgs->size = 1;
res_imgs->data = new clip_image_f32[res_imgs->size];
clip_image_u8 resized_image;
int32_t sz=ctx->vision_model.hparams.image_size;
bicubic_resize(*img, resized_image,sz,sz);
clip_image_f32 * res = clip_image_f32_init();
//clip_image_save_to_bmp(resized_image, "resized.bmp");
normalize_image_u8_to_f32(&resized_image, res, ctx->image_mean, ctx->image_std);
res_imgs->data[0] = *res;
clip_image_f32_free(res);
return true;
}
bool pad_to_square = true;
if (!ctx->has_vision_encoder) {
LOG_ERR("This gguf file seems to have no vision encoder\n");
@ -2283,7 +2376,8 @@ void clip_free(clip_ctx * ctx) {
}
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
int extra_tokens = ctx->has_glm_projector ? 2 : 0;
return (clip_n_patches(ctx) + extra_tokens) * clip_n_mmproj_embd(ctx) * sizeof(float);
}
size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) {
@ -2325,7 +2419,7 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
n_patches /= 4;
} else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
if (ctx->minicpmv_version == 2) {
@ -2334,6 +2428,9 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i
else if (ctx->minicpmv_version == 3) {
n_patches = 64;
}
else if (ctx->minicpmv_version == 4) {
n_patches = 64;
}
} else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
int patch_size = params.patch_size * 2;
int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
@ -2455,6 +2552,12 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
if (ctx->has_minicpmv_projector) {
GGML_ASSERT(batch_size == 1);
}
if (ctx->has_glm_projector) {
GGML_ASSERT(batch_size == 1);
ggml_tensor * boi = ctx->vision_model.boi_w;
ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi));
vec = (float*)(vec+ggml_nelements(boi)); //offset for boi
}
// build the inference graph
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true);
@ -2513,8 +2616,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
// -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316
struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
int* positions_data = (int*)malloc(ggml_nbytes(positions));
int bucket_coords_h[70];
int bucket_coords_w[70];
int bucket_coords_h[1024];
int bucket_coords_w[1024];
for (int i = 0; i < pos_h; i++){
bucket_coords_h[i] = std::floor(70.0*i/pos_h);
}
@ -2542,6 +2645,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
else if (ctx->minicpmv_version == 3) {
embed_dim = 3584;
}
else if (ctx->minicpmv_version == 4) {
embed_dim = 3584;
}
auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));
float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed));
@ -2604,7 +2710,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
free(positions_data);
{
if (!ctx->has_glm_projector) {
struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
int* patches_data = (int*)malloc(ggml_nbytes(patches));
for (int i = 0; i < num_patches; i++) {
@ -2628,14 +2734,19 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
// copy the embeddings to the location passed by the user
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
if (ctx->has_glm_projector) {
//eoi
ggml_tensor * eoi = ctx->vision_model.eoi_w;
int offset = ggml_nelements(embeddings);
ggml_backend_tensor_get(eoi, vec+offset, 0, ggml_nbytes(eoi));
}
return true;
}
bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {
ggml_type type = GGML_TYPE_Q4_1;
assert(itype < GGML_TYPE_COUNT);
type = static_cast<ggml_type>(itype);
ggml_type type = static_cast<ggml_type>(itype);
auto * ctx_clip = clip_model_load(fname_inp, 2);
@ -2688,8 +2799,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
}
}
// quantize only 2D tensors
quantize &= (ggml_n_dims(cur) == 2);
// quantize only 2D tensors and bigger than block size
quantize &= (ggml_n_dims(cur) == 2) && cur->ne[0] > ggml_blck_size(type);
if (quantize) {
new_type = type;
@ -2734,7 +2845,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
total_size_org += orig_size;
total_size_new += new_size;
gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);
GGML_ASSERT(gguf_get_tensor_size(ctx_out, gguf_find_tensor(ctx_out, name.c_str())) == new_size);
gguf_set_tensor_data(ctx_out, name.c_str(), new_data);
fout.write((const char *)new_data, new_size);
size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size;
for (size_t j = 0; j < pad; ++j) {
@ -2784,6 +2896,12 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
else if (ctx->minicpmv_version == 3) {
return 3584;
}
else if (ctx->minicpmv_version == 4) {
return 3584;
}
}
if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE){
return ctx->vision_model.mm_model_mlp_3_w->ne[1];
}
if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
return ctx->vision_model.mm_1_b->ne[0];
@ -2800,6 +2918,9 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
return 0;
}
bool clip_is_glm(const struct clip_ctx * ctx) {
return ctx->has_glm_projector;
}
bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
return ctx->has_qwen2vl_merger;
}

2
examples/llava/clip.h
View file

@ -93,6 +93,8 @@ CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
CLIP_API bool clip_is_glm(const struct clip_ctx * ctx);
#ifdef __cplusplus
}
#endif

280
examples/llava/glmedge-convert-image-encoder-to-gguf.py Normal file
View file

@ -0,0 +1,280 @@
import argparse
import os
import json
import re
import torch
import numpy as np
from gguf import *
TEXT = "clip.text"
VISION = "clip.vision"
from transformers import SiglipVisionModel, SiglipVisionConfig
def k(raw_key: str, arch: str) -> str:
return raw_key.format(arch=arch)
def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
if name in (
"logit_scale",
"text_model.embeddings.position_ids",
"vision_model.embeddings.position_ids",
):
return True
if name in (
"vision_model.head.probe",
"vision_model.head.attention.in_proj_weight",
"vision_model.head.attention.in_proj_bias",
"vision_model.head.attention.out_proj.weight",
"vision_model.head.attention.out_proj.bias",
"vision_model.head.layernorm.weight",
"vision_model.head.layernorm.bias",
"vision_model.head.mlp.fc1.weight",
"vision_model.head.mlp.fc1.bias",
"vision_model.head.mlp.fc2.weight",
"vision_model.head.mlp.fc2.bias"
):
return True
if name.startswith("v") and not has_vision:
return True
if name.startswith("t") and not has_text:
return True
return False
def get_tensor_name(name: str) -> str:
if "projection" in name:
return name
if "mm_projector" in name:
name = name.replace("model.mm_projector", "mm")
name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
return name
return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = (
list(range(ord("!"), ord("~") + 1))
+ list(range(ord("¡"), ord("¬") + 1))
+ list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
ap.add_argument("--text-only", action="store_true", required=False,
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
help="The clip model is from openclip (for ViT-SO400M type))")
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2","adapter"], default="adapter")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
default_image_mean = [0.5, 0.5, 0.5]
default_image_std = [0.5, 0.5, 0.5]
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
# with proper
args = ap.parse_args()
if args.text_only and args.vision_only:
print("--text-only and --image-only arguments cannot be specified at the same time.")
exit(1)
if args.use_f32:
print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
# output in the same directory as the model if output_dir is None
dir_model = args.model_dir
if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
vocab = None
tokens = None
else:
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
config = json.load(f)
if args.clip_model_is_vision:
v_hparams = config
t_hparams = None
else:
v_hparams = config["vision_config"]
t_hparams = None
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if args.use_f32:
ftype = 0
vision_config = SiglipVisionConfig(**v_hparams)
model = SiglipVisionModel(vision_config)
model.load_state_dict(torch.load(os.path.join(dir_model, "glm.clip")))
fname_middle = None
has_text_encoder = False
has_vision_encoder = True
has_glm_projector = True
if args.text_only:
fname_middle = "text-"
has_vision_encoder = False
elif args.llava_projector is not None:
fname_middle = "mmproj-"
has_text_encoder = False
has_glm_projector = True
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
else:
fname_middle = ""
output_dir = args.output_dir if args.output_dir is not None else dir_model
os.makedirs(output_dir, exist_ok=True)
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
fout = GGUFWriter(path=fname_out, arch="clip")
fout.add_bool("clip.has_text_encoder", has_text_encoder)
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
fout.add_bool("clip.has_glm_projector", has_glm_projector)
fout.add_file_type(ftype)
model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
fout.add_name(model_name)
if has_glm_projector:
fout.add_description("image encoder for glm4v")
fout.add_string("clip.projector_type", "adapter")
else:
fout.add_description("two-tower CLIP model")
if has_text_encoder:
assert t_hparams is not None
assert tokens is not None
# text_model hparams
fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
fout.add_token_list(tokens)
if has_vision_encoder:
# vision_model hparams
fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
fout.add_uint32("clip.vision.projection_dim", 0)
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), v_hparams["num_hidden_layers"])
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
image_std = args.image_std if args.image_std is not None else default_image_std
fout.add_array("clip.vision.image_mean", image_mean)
fout.add_array("clip.vision.image_std", image_std)
fout.add_bool("clip.use_gelu", True)
if has_glm_projector:
# model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue]
projector = torch.load(args.llava_projector)
for name, data in projector.items():
name = get_tensor_name(name)
# pw and dw conv ndim==4
if data.ndim == 2 or data.ndim == 4:
data = data.squeeze().numpy().astype(np.float16)
else:
data = data.squeeze().numpy().astype(np.float32)
if name.startswith("vision."):
name=name.replace("vision.","")
fout.add_tensor(name, data)
print(f"Projector {name} - {data.dtype} - shape = {data.shape}")
# print(f"Projector {name} tensors added\n")
state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue]
for name, data in state_dict.items():
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_glm_projector):
# we don't need this
print(f"skipping parameter: {name}")
continue
name = get_tensor_name(name)
data = data.squeeze().numpy()
n_dims = len(data.shape)
# ftype == 0 -> float32, ftype == 1 -> float16
ftype_cur = 0
if n_dims == 4:
print(f"tensor {name} is always saved in f16")
data = data.astype(np.float16)
ftype_cur = 1
elif ftype == 1:
if name[-7:] == ".weight" and n_dims == 2:
# print(" Converting to float16")
data = data.astype(np.float16)
ftype_cur = 1
else:
# print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
# print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
print(f"siglip {name} - {data.dtype} - shape = {data.shape}")
# print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
fout.add_tensor(name, data)
fout.write_header_to_file()
fout.write_kv_data_to_file()
fout.write_tensors_to_file()
fout.close()
print("Done. Output file: " + fname_out)

33
examples/llava/glmedge-surgery.py Normal file
View file

@ -0,0 +1,33 @@
import argparse
import os
import torch
from transformers import AutoModel
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", help="Path to GLM model")
args = ap.parse_args()
# find the model part that includes the the multimodal projector weights
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
checkpoint = model.state_dict()
# get a list of mm tensor names
mm_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.adapter.")]
# store these tensors in a new dictionary and torch.save them
projector = {name: checkpoint[name].float() for name in mm_tensors}
torch.save(projector, f"{args.model}/glm.projector")
clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.vit.model.vision_model.")]
if len(clip_tensors) > 0:
clip = {name.replace("vision.vit.model.", ""): checkpoint[name].float() for name in clip_tensors}
torch.save(clip, f"{args.model}/glm.clip")
# added tokens should be removed to be able to convert Mistral models
if os.path.exists(f"{args.model}/added_tokens.json"):
with open(f"{args.model}/added_tokens.json", "w") as f:
f.write("{}\n")
print("Done!")
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
print(f"Also, use {args.model}glm.projector to prepare a glm-encoder.gguf file.")

15
examples/llava/llava-cli.cpp
View file

@ -47,8 +47,12 @@ static const char * sample(struct common_sampler * smpl,
int * n_past) {
const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
common_sampler_accept(smpl, id, true);
const llama_model * model = llama_get_model(ctx_llama);
const llama_vocab * vocab = llama_model_get_vocab(model);
static std::string ret;
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
if (llama_vocab_is_eog(vocab, id)) {
ret = "</s>";
} else {
ret = common_token_to_piece(ctx_llama, id);
@ -221,7 +225,7 @@ static struct llama_model * llava_init(common_params * params) {
llama_model_params model_params = common_model_params_to_llama(*params);
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params);
if (model == NULL) {
LOG_ERR("%s: unable to load model\n" , __func__);
return NULL;
@ -239,11 +243,10 @@ static struct llava_context * llava_init_context(common_params * params, llama_m
auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
llama_context_params ctx_params = common_context_params_to_llama(*params);
ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
if (ctx_llama == NULL) {
LOG_ERR("%s: failed to create the llama_context\n" , __func__);
@ -265,7 +268,7 @@ static void llava_free(struct llava_context * ctx_llava) {
}
llama_free(ctx_llava->ctx_llama);
llama_free_model(ctx_llava->model);
llama_model_free(ctx_llava->model);
llama_backend_free();
}
@ -323,7 +326,7 @@ int main(int argc, char ** argv) {
}
}
llama_free_model(model);
llama_model_free(model);
return 0;
}

34
examples/llava/llava.cpp
View file

@ -216,7 +216,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
return true;
}
static clip_image_f32 * only_v2_5_reshape_by_patch(clip_image_f32 * image, int patch_size) {
static clip_image_f32 * reshape_by_patch(clip_image_f32 * image, int patch_size) {
int width = image->nx;
int height = image->ny;
int num_patches = (height / patch_size) * (width / patch_size);
@ -277,13 +277,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
}
else {
int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
if (has_minicpmv_projector == 2) {
encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
}
else if (has_minicpmv_projector == 3) {
encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
}
encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
}
if (!encoded) {
@ -313,6 +307,23 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
load_image_size->height = img->ny;
clip_add_load_image_size(ctx_clip, load_image_size);
LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height);
delete[] img_res_v.data;
img_res_v.size = 0;
img_res_v.data = nullptr;
}
else if (clip_is_glm(ctx_clip)){
struct clip_image_size * load_image_size = clip_image_size_init();
load_image_size->width = img_res_v.data[0].nx;
load_image_size->height = img_res_v.data[0].ny;
clip_add_load_image_size(ctx_clip, load_image_size);
bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd);
int pos = int(load_image_size->width/clip_patch_size(ctx_clip)/2);
*n_img_pos = (pos * pos + 2);
if (!encoded){
LOG_ERR("Unable to encode image \n");
return false;
}
}
else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
// flat / default llava-1.5 type embedding
@ -384,7 +395,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) {
// make sure that the correct mmproj was used, i.e., compare apples to apples
int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
int n_llama_embd = llama_model_n_embd(llama_get_model(ctx_llama));
auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
if (n_image_embd != n_llama_embd) {
LOG_ERR("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
@ -398,6 +409,9 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
if (clip_is_minicpmv(ctx_clip)) {
num_max_patches = 10;
}
if (clip_is_glm(ctx_clip)) {
num_max_patches = 1;
}
float * image_embd;
if (clip_is_qwen2vl(ctx_clip)) {
// qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
@ -456,7 +470,7 @@ struct llava_embd_batch {
};
bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {
int n_embd = llama_n_embd(llama_get_model(ctx_llama));
int n_embd = llama_model_n_embd(llama_get_model(ctx_llama));
for (int i = 0; i < image_embed->n_image_pos; i += n_batch) {
int n_eval = image_embed->n_image_pos - i;

22
examples/llava/minicpmv-cli.cpp
View file

@ -31,7 +31,7 @@ static struct llama_model * llava_init(common_params * params) {
llama_model_params model_params = common_model_params_to_llama(*params);
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params);
if (model == NULL) {
LOG_ERR("%s: unable to load model\n" , __func__);
return NULL;
@ -54,7 +54,7 @@ static struct llava_context * llava_init_context(common_params * params, llama_m
ctx_params.n_ctx = params->n_ctx;
}
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
if (ctx_llama == NULL) {
LOG_ERR("%s: failed to create the llama_context\n" , __func__);
@ -75,7 +75,7 @@ static void llava_free(struct llava_context * ctx_llava) {
}
llama_free(ctx_llava->ctx_llama);
llama_free_model(ctx_llava->model);
llama_model_free(ctx_llava->model);
llama_backend_free();
}
@ -140,6 +140,9 @@ static void process_image(struct llava_context * ctx_llava, struct llava_image_e
else if (has_minicpmv_projector == 3) {
system_prompt = "<|im_start|>user\n";
}
else if (has_minicpmv_projector == 4) {
system_prompt = "<|im_start|>user\n";
}
LOG_INF("%s: image token past: %d\n", __func__, n_past);
eval_string(ctx_llava->ctx_llama, (system_prompt+"<image>").c_str(), params->n_batch, &n_past, false);
process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
@ -167,8 +170,12 @@ static const char * sample(struct common_sampler * smpl,
int * n_past) {
const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
common_sampler_accept(smpl, id, true);
const llama_model * model = llama_get_model(ctx_llama);
const llama_vocab * vocab = llama_model_get_vocab(model);
static std::string ret;
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
if (llama_vocab_is_eog(vocab, id)) {
ret = "</s>";
} else {
ret = common_token_to_piece(ctx_llama, id);
@ -223,6 +230,9 @@ static struct common_sampler * llama_init(struct llava_context * ctx_llava, comm
else if (has_minicpmv_projector == 3) {
user_prompt = "<|im_start|>user\n" + prompt;
}
else if (has_minicpmv_projector == 4) {
user_prompt = "<|im_start|>user\n" + prompt;
}
}
eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
@ -232,6 +242,9 @@ static struct common_sampler * llama_init(struct llava_context * ctx_llava, comm
else if (has_minicpmv_projector == 3) {
eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false);
}
else if (has_minicpmv_projector == 4) {
eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false);
}
// generate the response
@ -304,7 +317,6 @@ int main(int argc, char ** argv) {
const auto * tmp = llama_loop(ctx_llava, smpl, n_past);
response += tmp;
if (strcmp(tmp, "</s>") == 0) break;
if (strstr(tmp, "###")) break; // Yi-VL behavior
printf("%s", tmp);// mistral llava-1.6
if (strstr(response.c_str(), "<user>")) break; // minicpm-v
fflush(stdout);

15
examples/llava/minicpmv-convert-image-encoder-to-gguf.py
View file

@ -501,7 +501,7 @@ default_image_mean = [0.48145466, 0.4578275, 0.40821073]
default_image_std = [0.26862954, 0.26130258, 0.27577711]
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3', default=2)
ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3; MiniCPM-o-2.6 use 4', default=2)
# with proper
args = ap.parse_args()
@ -545,12 +545,19 @@ if args.use_f32:
minicpmv_version = args.minicpmv_version
emb_dim = 4096
block_count = 26
if minicpmv_version == 1:
emb_dim = 2304
block_count = 26
elif minicpmv_version == 2:
emb_dim = 4096
block_count = 27
elif minicpmv_version == 3:
emb_dim = 3584
block_count = 27
elif minicpmv_version == 4:
emb_dim = 3584
block_count = 27
default_vision_config = {
"hidden_size": 1152,
@ -567,6 +574,9 @@ model = Idefics2VisionTransformer(vision_config)
if minicpmv_version == 3:
vision_config = SiglipVisionConfig(**default_vision_config)
model = SiglipVisionTransformer(vision_config)
elif minicpmv_version == 4:
vision_config = SiglipVisionConfig(**default_vision_config)
model = SiglipVisionTransformer(vision_config)
processor = None
# if model.attn_pool is not None:
@ -587,7 +597,7 @@ elif args.minicpmv_projector is not None:
fname_middle = "mmproj-"
has_text_encoder = False
has_minicpmv_projector = True
minicpmv_version = 3
minicpmv_version = 4
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
@ -625,7 +635,6 @@ if has_vision_encoder:
fout.add_uint32("clip.vision.projection_dim", 0)
fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
block_count = 26
fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count)
if processor is not None:

2
examples/llava/minicpmv-surgery.py
View file

@ -8,7 +8,7 @@ ap.add_argument("-m", "--model", help="Path to MiniCPM-V model")
args = ap.parse_args()
# find the model part that includes the the multimodal projector weights
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True, torch_dtype=torch.bfloat16)
checkpoint = model.state_dict()
# get a list of mm tensor names

19
examples/llava/qwen2vl-cli.cpp
View file

@ -27,7 +27,7 @@
static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) {
int n_embd = llama_n_embd(llama_get_model(ctx_llama));
int n_embd = llama_model_n_embd(llama_get_model(ctx_llama));
const int patch_size = 14 * 2;
const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0);
const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0);
@ -132,8 +132,12 @@ static const char * sample(struct common_sampler * smpl,
int * n_past, int * st_pos_id) {
const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
common_sampler_accept(smpl, id, true);
const llama_model * model = llama_get_model(ctx_llama);
const llama_vocab * vocab = llama_model_get_vocab(model);
static std::string ret;
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
if (llama_vocab_is_eog(vocab, id)) {
ret = "</s>";
} else {
ret = common_token_to_piece(ctx_llama, id);
@ -310,7 +314,7 @@ static struct llama_model * llava_init(common_params * params) {
llama_model_params model_params = common_model_params_to_llama(*params);
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params);
if (model == NULL) {
LOG_ERR("%s: unable to load model\n" , __func__);
return NULL;
@ -328,11 +332,10 @@ static struct llava_context * llava_init_context(common_params * params, llama_m
auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
llama_context_params ctx_params = common_context_params_to_llama(*params);
ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
if (ctx_llama == NULL) {
LOG_ERR("%s: failed to create the llama_context\n" , __func__);
@ -354,7 +357,7 @@ static void llava_free(struct llava_context * ctx_llava) {
}
llama_free(ctx_llava->ctx_llama);
llama_free_model(ctx_llava->model);
llama_model_free(ctx_llava->model);
llama_backend_free();
}
@ -481,7 +484,7 @@ static void debug_test_mrope_2d() {
}
static void debug_dump_img_embed(struct llava_context * ctx_llava) {
int n_embd = llama_n_embd(llama_get_model(ctx_llava->ctx_llama));
int n_embd = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama));
int ne = n_embd * 4;
float vals[56 * 56 * 3];
// float embd[ne];
@ -575,7 +578,7 @@ int main(int argc, char ** argv) {
}
}
llama_free_model(model);
llama_model_free(model);
return 0;
}

13
examples/lookahead/lookahead.cpp
View file

@ -58,8 +58,10 @@ int main(int argc, char ** argv) {
// load the target model
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
const llama_vocab * vocab = llama_model_get_vocab(model);
// Tokenize the prompt
std::vector<llama_token> inp;
@ -147,7 +149,7 @@ int main(int argc, char ** argv) {
}
// here we keep adding new n-grams as we go
ngram_container ngrams_observed(llama_n_vocab(model), N, G);
ngram_container ngrams_observed(llama_vocab_n_tokens(vocab), N, G);
// debug
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
@ -297,7 +299,7 @@ int main(int argc, char ** argv) {
}
fflush(stdout);
if (llama_token_is_eog(model, id)) {
if (llama_vocab_is_eog(vocab, id)) {
has_eos = true;
}
@ -474,9 +476,6 @@ int main(int argc, char ** argv) {
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
LOG("\n\n");

13
examples/lookup/lookup-create.cpp
View file

@ -1,14 +1,9 @@
#include "arg.h"
#include "common.h"
#include "ngram-cache.h"
#include "ggml.h"
#include "llama.h"
#include <cstdint>
#include <fstream>
#include <iostream>
#include <string>
#include <unordered_map>
#include <vector>
int main(int argc, char ** argv){
@ -25,16 +20,16 @@ int main(int argc, char ** argv){
// load the model
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model_ptr & model = llama_init.model;
llama_context_ptr & ctx = llama_init.context;
GGML_ASSERT(model != nullptr);
// tokenize the prompt
std::vector<llama_token> inp;
inp = common_tokenize(ctx, params.prompt, true, true);
inp = common_tokenize(ctx.get(), params.prompt, true, true);
fprintf(stderr, "%s: tokenization done\n", __func__);
common_ngram_cache ngram_cache;
common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());

10
examples/lookup/lookup-stats.cpp
View file

@ -30,12 +30,11 @@ int main(int argc, char ** argv){
// load the model
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_context_ptr & ctx = llama_init.context;
// tokenize the prompt
std::vector<llama_token> inp;
inp = common_tokenize(ctx, params.prompt, true, true);
inp = common_tokenize(ctx.get(), params.prompt, true, true);
common_ngram_cache ngram_cache_context;
common_ngram_cache ngram_cache_dynamic;
@ -66,7 +65,7 @@ int main(int argc, char ** argv){
}
const int n_input = inp.size();
const int n_ctx = llama_n_ctx(ctx);
const int n_ctx = llama_n_ctx(ctx.get());
int n_drafted = 0;
int n_accept = 0;
@ -150,9 +149,6 @@ int main(int argc, char ** argv){
LOG_INF("n_accept = %d\n", n_accept);
LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
LOG("\n\n");

11
examples/lookup/lookup.cpp
View file

@ -33,8 +33,10 @@ int main(int argc, char ** argv){
// load the model
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
const llama_vocab * vocab = llama_model_get_vocab(model);
// tokenize the prompt
std::vector<llama_token> inp;
@ -136,7 +138,7 @@ int main(int argc, char ** argv){
LOG("%s", token_str.c_str());
}
if (llama_token_is_eog(model, id)) {
if (llama_vocab_is_eog(vocab, id)) {
has_eos = true;
}
@ -243,9 +245,6 @@ int main(int argc, char ** argv){
llama_batch_free(batch_tgt);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
LOG("\n\n");

32
examples/main-cmake-pkg/CMakeLists.txt
View file

@ -1,32 +0,0 @@
cmake_minimum_required(VERSION 3.12)
project("llama-cli-cmake-pkg" C CXX)
set(TARGET llama-cli-cmake-pkg)
find_package(Llama 0.0.1 REQUIRED)
# Bake common functionality in with target. Because applications
# using the relocatable Llama package should be outside of the
# source tree, llama-cli-cmake-pkg pretends the dependencies are built-in.
set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common")
add_library(common OBJECT)
file(GLOB _common_files
"${_common_path}/*.h"
"${_common_path}/*.cpp"
)
target_sources(common PRIVATE ${_common_files})
# If the common project was part of "llama-cli-cmake-pkg" the transient
# defines would automatically be attached. Because the common func-
# tionality is separate, but dependent upon the defines, it must be
# explicitly extracted from the "llama" target.
#
get_target_property(_llama_transient_defines llama
INTERFACE_COMPILE_DEFINITIONS)
target_compile_definitions(common PRIVATE "${_llama_transient_defines}")
add_executable(${TARGET} ${CMAKE_CURRENT_LIST_DIR}/../main/main.cpp)
target_include_directories(${TARGET} PRIVATE ${_common_path})
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

31
examples/main-cmake-pkg/README.md
View file

@ -1,31 +0,0 @@
# llama.cpp/example/main-cmake-pkg
This program builds [llama-cli](../main) using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree.
## Building
Because this example is "outside of the source tree", it is important to first build/install llama.cpp using CMake. An example is provided here, but please see the [llama.cpp build instructions](../..) for more detailed build instructions.
### Considerations
When hardware acceleration libraries are used (e.g. CUDA, Metal, etc.), CMake must be able to locate the associated CMake package.
### Build llama.cpp and install to C:\LlamaCPP directory
```cmd
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 17 2022" -A x64
cmake --build build --config Release
cmake --install build --prefix C:/LlamaCPP
```
### Build llama-cli-cmake-pkg
```cmd
cd ..\examples\main-cmake-pkg
cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
cmake --build build --config Release
cmake --install build --prefix C:/MyLlamaApp
```

4
examples/main/README.md
View file

@ -310,9 +310,9 @@ These options help improve the performance and memory usage of the LLaMA models.
### Batch Size
- `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
- `-ub N`, `--ubatch-size N`: Physical batch size. This is the maximum number of tokens that may be processed at a time. Increasing this value may improve performance during prompt processing, at the expense of higher memory usage. Default: `512`.
- `-ub N`, `--ubatch-size N`: physical maximum batch size. This is for pipeline parallelization. Default: `512`.
- `-b N`, `--batch-size N`: Logical batch size. Increasing this value above the value of the physical batch size may improve prompt processing performance when using multiple GPUs with pipeline parallelism. Default: `2048`.
### Prompt Caching

125
examples/main/main.cpp
View file

@ -4,8 +4,8 @@
#include "log.h"
#include "sampling.h"
#include "llama.h"
#include "chat-template.hpp"
#include <cassert>
#include <cstdio>
#include <cstring>
#include <ctime>
@ -31,6 +31,8 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static const char * DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant";
static llama_context ** g_ctx;
static llama_model ** g_model;
static common_sampler ** g_smpl;
@ -83,14 +85,6 @@ static void sigint_handler(int signo) {
}
#endif
static std::string chat_add_and_format(struct llama_model * model, std::vector<common_chat_msg> & chat_msgs, const std::string & role, const std::string & content) {
common_chat_msg new_msg{role, content};
auto formatted = common_chat_format_single(model, g_params->chat_template, chat_msgs, new_msg, role == "user");
chat_msgs.push_back({role, content});
LOG_DBG("formatted: '%s'\n", formatted.c_str());
return formatted;
}
int main(int argc, char ** argv) {
common_params params;
g_params = &params;
@ -145,24 +139,27 @@ int main(int argc, char ** argv) {
llama_context * ctx = nullptr;
common_sampler * smpl = nullptr;
std::vector<common_chat_msg> chat_msgs;
g_model = &model;
g_ctx = &ctx;
g_smpl = &smpl;
std::vector<common_chat_msg> chat_msgs;
// load the model and apply lora adapter, if any
LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
common_init_result llama_init = common_init_from_params(params);
model = llama_init.model;
ctx = llama_init.context;
model = llama_init.model.get();
ctx = llama_init.context.get();
if (model == NULL) {
LOG_ERR("%s: error: unable to load model\n", __func__);
return 1;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
auto chat_templates = common_chat_templates_from_model(model, params.chat_template);
LOG_INF("%s: llama threadpool init, n_threads = %d\n", __func__, (int) params.cpuparams.n_threads);
auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU));
@ -196,17 +193,33 @@ int main(int argc, char ** argv) {
llama_attach_threadpool(ctx, threadpool, threadpool_batch);
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx_train = llama_model_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
if (n_ctx > n_ctx_train) {
LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx);
}
// auto enable conversation mode if chat template is available
const bool has_chat_template = chat_templates.has_explicit_template && chat_templates.template_default;
if (params.conversation_mode == COMMON_CONVERSATION_MODE_AUTO) {
if (has_chat_template) {
LOG_INF("%s: chat template is available, enabling conversation mode (disable it with -no-cnv)\n", __func__);
params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
} else {
params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
}
}
// in case user force-activate conversation mode (via -cnv) without proper chat template, we show a warning
if (params.conversation_mode && !has_chat_template) {
LOG_WRN("%s: chat template is not available or is not supported. This may cause the model to output suboptimal responses\n", __func__);
}
// print chat template example in conversation mode
if (params.conversation) {
if (params.conversation_mode) {
if (params.enable_chat_template) {
LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(model, params.chat_template).c_str());
LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(*chat_templates.template_default, params.use_jinja).c_str());
} else {
LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
}
@ -241,18 +254,28 @@ int main(int argc, char ** argv) {
}
}
const bool add_bos = llama_add_bos_token(model);
const bool add_bos = llama_vocab_get_add_bos(vocab) && !params.use_jinja;
if (!llama_model_has_encoder(model)) {
GGML_ASSERT(!llama_add_eos_token(model));
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
}
LOG_DBG("n_ctx: %d, add_bos: %d\n", n_ctx, add_bos);
std::vector<llama_token> embd_inp;
auto chat_add_and_format = [&chat_msgs, &chat_templates](const std::string & role, const std::string & content) {
common_chat_msg new_msg{role, content, {}};
auto formatted = common_chat_format_single(*chat_templates.template_default, chat_msgs, new_msg, role == "user", g_params->use_jinja);
chat_msgs.push_back({role, content, {}});
LOG_DBG("formatted: '%s'\n", formatted.c_str());
return formatted;
};
{
auto prompt = (params.conversation && params.enable_chat_template && !params.prompt.empty())
? chat_add_and_format(model, chat_msgs, "system", params.prompt) // format the system prompt in conversation mode
auto prompt = (params.conversation_mode && params.enable_chat_template)
// format the system prompt in conversation mode (fallback to default if empty)
? chat_add_and_format("system", params.prompt.empty() ? DEFAULT_SYSTEM_MESSAGE : params.prompt)
// otherwise use the prompt as is
: params.prompt;
if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
LOG_DBG("tokenize the prompt\n");
@ -269,7 +292,7 @@ int main(int argc, char ** argv) {
// Should not run without any tokens
if (embd_inp.empty()) {
if (add_bos) {
embd_inp.push_back(llama_token_bos(model));
embd_inp.push_back(llama_vocab_bos(vocab));
LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str());
} else {
LOG_ERR("input is empty\n");
@ -326,7 +349,7 @@ int main(int argc, char ** argv) {
params.n_keep += add_bos; // always keep the BOS token
}
if (params.conversation) {
if (params.conversation_mode) {
params.interactive_first = true;
}
@ -450,7 +473,11 @@ int main(int argc, char ** argv) {
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
LOG_INF( " - Press Ctrl+C to interject at any time.\n");
#endif
LOG_INF( "%s\n", control_message);
LOG_INF( "%s", control_message);
if (params.conversation_mode && params.enable_chat_template && params.prompt.empty()) {
LOG_INF( " - Using default system message. To change it, set a different value via -p PROMPT or -f FILE argument.\n");
}
LOG_INF("\n");
is_interacting = params.interactive_first;
}
@ -476,12 +503,14 @@ int main(int argc, char ** argv) {
std::vector<llama_token> embd;
// tokenized antiprompts
std::vector<std::vector<llama_token>> antiprompt_ids;
// single-token antiprompts
std::vector<llama_token> antiprompt_token;
antiprompt_ids.reserve(params.antiprompt.size());
for (const std::string & antiprompt : params.antiprompt) {
antiprompt_ids.emplace_back(::common_tokenize(ctx, antiprompt, false, true));
auto ids = ::common_tokenize(ctx, antiprompt, false, true);
if (ids.size() == 1) {
antiprompt_token.push_back(ids[0]);
}
}
if (llama_model_has_encoder(model)) {
@ -494,8 +523,8 @@ int main(int argc, char ** argv) {
}
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
decoder_start_token_id = llama_token_bos(model);
if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
decoder_start_token_id = llama_vocab_bos(vocab);
}
embd_inp.clear();
@ -726,14 +755,11 @@ int main(int argc, char ** argv) {
// check for reverse prompt using special tokens
llama_token last_token = common_sampler_last(smpl);
for (std::vector<llama_token> ids : antiprompt_ids) {
if (ids.size() == 1 && last_token == ids[0]) {
if (params.interactive) {
is_interacting = true;
}
is_antiprompt = true;
break;
if (std::find(antiprompt_token.begin(), antiprompt_token.end(), last_token) != antiprompt_token.end()) {
if (params.interactive) {
is_interacting = true;
}
is_antiprompt = true;
}
if (is_antiprompt) {
@ -742,7 +768,7 @@ int main(int argc, char ** argv) {
}
// deal with end of generation tokens in interactive mode
if (llama_token_is_eog(model, common_sampler_last(smpl))) {
if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) {
LOG_DBG("found an EOG token\n");
if (params.interactive) {
@ -754,7 +780,7 @@ int main(int argc, char ** argv) {
}
if (params.enable_chat_template) {
chat_add_and_format(model, chat_msgs, "assistant", assistant_ss.str());
chat_add_and_format("assistant", assistant_ss.str());
}
is_interacting = true;
LOG("\n");
@ -762,7 +788,7 @@ int main(int argc, char ** argv) {
}
// if current token is not EOG, we add it to current assistant message
if (params.conversation) {
if (params.conversation_mode) {
const auto id = common_sampler_last(smpl);
assistant_ss << common_token_to_piece(ctx, id, false);
}
@ -770,17 +796,17 @@ int main(int argc, char ** argv) {
if (n_past > 0 && is_interacting) {
LOG_DBG("waiting for user input\n");
if (params.conversation) {
if (params.conversation_mode) {
LOG("\n> ");
}
if (params.input_prefix_bos) {
LOG_DBG("adding input prefix BOS token\n");
embd_inp.push_back(llama_token_bos(model));
embd_inp.push_back(llama_vocab_bos(vocab));
}
std::string buffer;
if (!params.input_prefix.empty() && !params.conversation) {
if (!params.input_prefix.empty() && !params.conversation_mode) {
LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str());
LOG("%s", params.input_prefix.c_str());
}
@ -804,7 +830,7 @@ int main(int argc, char ** argv) {
// Entering a empty line lets the user pass control back
if (buffer.length() > 1) {
// append input suffix if any
if (!params.input_suffix.empty() && !params.conversation) {
if (!params.input_suffix.empty() && !params.conversation_mode) {
LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str());
LOG("%s", params.input_suffix.c_str());
}
@ -817,9 +843,9 @@ int main(int argc, char ** argv) {
string_process_escapes(buffer);
}
bool format_chat = params.conversation && params.enable_chat_template;
bool format_chat = params.conversation_mode && params.enable_chat_template;
std::string user_inp = format_chat
? chat_add_and_format(model, chat_msgs, "user", std::move(buffer))
? chat_add_and_format("user", std::move(buffer))
: std::move(buffer);
// TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix)
const auto line_pfx = common_tokenize(ctx, params.input_prefix, false, true);
@ -830,8 +856,8 @@ int main(int argc, char ** argv) {
// if user stop generation mid-way, we must add EOT to finish model's last response
if (need_insert_eot && format_chat) {
llama_token eot = llama_token_eot(model);
embd_inp.push_back(eot == -1 ? llama_token_eos(model) : eot);
llama_token eot = llama_vocab_eot(vocab);
embd_inp.push_back(eot == LLAMA_TOKEN_NULL ? llama_vocab_eos(vocab) : eot);
need_insert_eot = false;
}
@ -866,7 +892,7 @@ int main(int argc, char ** argv) {
}
// end of generation
if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.interactive)) {
if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !(params.interactive)) {
LOG(" [end of text]\n");
break;
}
@ -889,9 +915,6 @@ int main(int argc, char ** argv) {
common_sampler_free(smpl);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
ggml_threadpool_free_fn(threadpool);

11
examples/parallel/parallel.cpp
View file

@ -132,8 +132,10 @@ int main(int argc, char ** argv) {
// load the target model
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
const llama_vocab * vocab = llama_model_get_vocab(model);
// load the prompts from an external file if there are any
if (params.prompt.empty()) {
@ -358,7 +360,7 @@ int main(int argc, char ** argv) {
// client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
if (client.n_decoded > 2 &&
(llama_token_is_eog(model, id) ||
(llama_vocab_is_eog(vocab, id) ||
(params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
client.response.find("User:") != std::string::npos ||
client.response.find('\n') != std::string::npos)) {
@ -416,9 +418,6 @@ int main(int argc, char ** argv) {
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
LOG("\n\n");

12
examples/passkey/passkey.cpp
View file

@ -63,22 +63,24 @@ int main(int argc, char ** argv) {
llama_model_params model_params = common_model_params_to_llama(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params);
if (model == NULL) {
LOG_ERR("%s: unable to load model\n" , __func__);
return 1;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
// initialize the context
llama_context_params ctx_params = common_context_params_to_llama(params);
ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep;
ctx_params.n_ctx = llama_model_n_ctx_train(model)*n_grp + n_keep;
GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp");
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
llama_context * ctx = llama_init_from_model(model, ctx_params);
if (ctx == NULL) {
LOG_ERR("%s: failed to create the llama_context\n" , __func__);
return 1;
@ -223,7 +225,7 @@ int main(int argc, char ** argv) {
const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1);
// is it an end of generation?
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) {
LOG("\n");
break;
@ -266,7 +268,7 @@ int main(int argc, char ** argv) {
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_model_free(model);
llama_backend_free();

60
examples/perplexity/perplexity.cpp
View file

@ -296,8 +296,11 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params
// Output: `perplexity: 13.5106 [114/114]`
// BOS tokens will be added for each chunk before eval
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const bool add_bos = llama_vocab_get_add_bos(vocab);
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
LOG_INF("%s: tokenizing the input ..\n", __func__);
@ -338,7 +341,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_vocab = llama_vocab_n_tokens(vocab);
int count = 0;
double nll = 0.0;
@ -382,7 +385,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params
// add BOS token for the first batch of each chunk
if (add_bos && j == 0) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
tokens[batch_start] = llama_vocab_bos(vocab);
}
const auto * batch_logits = llama_get_logits(ctx);
@ -444,8 +447,11 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
// Output: `perplexity: 13.5106 [114/114]`
// BOS tokens will be added for each chunk before eval
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const bool add_bos = llama_vocab_get_add_bos(vocab);
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
std::ofstream logits_stream;
if (!params.logits_file.empty()) {
@ -485,7 +491,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_vocab = llama_vocab_n_tokens(vocab);
int count = 0;
double nll = 0.0;
@ -557,7 +563,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
// add BOS token for the first batch of each chunk
if (add_bos && j == 0) {
tokens[seq_start] = llama_token_bos(llama_get_model(ctx));
tokens[seq_start] = llama_vocab_bos(vocab);
}
for (int k = 0; k < batch_size; ++k) {
@ -732,6 +738,9 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto
}
static void hellaswag_score(llama_context * ctx, const common_params & params) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
// Calculates hellaswag score (acc_norm) from prompt
//
// Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
@ -765,7 +774,7 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) {
size_t hs_task_count = prompt_lines.size()/6;
LOG_INF("%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;
const bool is_spm = llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_SPM;
LOG_INF("================================= is_spm = %d\n", is_spm);
// The tasks should be randomized so the score stabilizes quickly.
@ -848,7 +857,7 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) {
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_vocab = llama_vocab_n_tokens(vocab);
const int max_tasks_per_batch = 32;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@ -1072,6 +1081,8 @@ static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string
*
*/
static void winogrande_score(llama_context * ctx, const common_params & params) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
constexpr int k_min_trailing_ctx = 3;
@ -1130,7 +1141,7 @@ static void winogrande_score(llama_context * ctx, const common_params & params)
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_vocab = llama_vocab_n_tokens(vocab);
const int max_tasks_per_batch = 128;
const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@ -1374,6 +1385,8 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic
// https://huggingface.co/datasets/truthful_qa
//
static void multiple_choice_score(llama_context * ctx, const common_params & params) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
std::istringstream strstream(params.prompt);
uint32_t n_task;
@ -1482,7 +1495,7 @@ static void multiple_choice_score(llama_context * ctx, const common_params & par
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_vocab = llama_vocab_n_tokens(vocab);
const int max_tasks_per_batch = 32;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@ -1655,6 +1668,9 @@ static void multiple_choice_score(llama_context * ctx, const common_params & par
}
static void kl_divergence(llama_context * ctx, const common_params & params) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
if (params.logits_file.empty()) {
LOG_ERR("%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__);
return;
@ -1688,8 +1704,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
LOG_ERR("%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str());
return;
}
if (n_vocab != llama_n_vocab(llama_get_model(ctx))) {
LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_n_vocab(llama_get_model(ctx)));
if (n_vocab != llama_vocab_n_tokens(vocab)) {
LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_vocab_n_tokens(vocab));
}
std::vector<llama_token> tokens(size_t(n_ctx) * n_chunk);
@ -1701,8 +1717,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
const int n_batch = params.n_batch;
const int num_batches = (n_ctx + n_batch - 1)/n_batch;
const int nv = 2*((n_vocab + 1)/2) + 4;
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
const bool add_bos = llama_vocab_get_add_bos(vocab);
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
@ -1761,7 +1777,7 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
// add BOS token for the first batch of each chunk
if (add_bos && j == 0) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
tokens[batch_start] = llama_vocab_bos(vocab);
}
common_batch_clear(batch);
@ -1987,14 +2003,15 @@ int main(int argc, char ** argv) {
// load the model and apply lora adapter, if any
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
if (model == NULL) {
LOG_ERR("%s: unable to load model\n", __func__);
return 1;
}
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx_train = llama_model_n_ctx_train(model);
if (params.n_ctx > n_ctx_train) {
LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n",
@ -2023,9 +2040,6 @@ int main(int argc, char ** argv) {
LOG("\n");
llama_perf_context_print(ctx);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;

26
examples/quantize-stats/quantize-stats.cpp
View file

@ -1,7 +1,7 @@
#include "common.h"
#include "ggml.h"
#include "llama.h"
#include "llama-impl.h"
#include "llama-context.h"
#include "common.h"
#include <algorithm>
#include <cassert>
@ -9,11 +9,9 @@
#include <cmath>
#include <cstdio>
#include <cstring>
#include <map>
#include <numeric>
#include <regex>
#include <string>
#include <unordered_map>
#include <vector>
#include <thread>
#include <mutex>
@ -311,7 +309,7 @@ int main(int argc, char ** argv) {
auto mparams = llama_model_default_params();
mparams.use_mlock = false;
model = llama_load_model_from_file(params.model.c_str(), mparams);
model = llama_model_load_from_file(params.model.c_str(), mparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
@ -321,22 +319,22 @@ int main(int argc, char ** argv) {
auto cparams = llama_context_default_params();
cparams.n_ctx = 256;
ctx = llama_new_context_with_model(model, cparams);
ctx = llama_init_from_model(model, cparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
llama_model_free(model);
return 1;
}
}
const auto &tensors = llama_internal_get_tensor_map(ctx);
const auto & tensors = llama_internal_get_tensor_map(ctx);
// check layer tensors
int included_layers = 0;
int64_t max_nelements = 0;
bool is_f16 = false;
for (const auto& kv_tensor : tensors) {
for (const auto & kv_tensor : tensors) {
if (!layer_included(params, kv_tensor.first)) {
continue;
}
@ -349,7 +347,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: error: Quantization should be tested with a float model, "
"this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type);
llama_free(ctx);
llama_free_model(model);
llama_model_free(model);
return 1;
}
included_layers++;
@ -371,8 +369,8 @@ int main(int argc, char ** argv) {
if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
continue;
}
const auto * qfns = ggml_get_type_traits(type);
const auto * qfns_cpu = ggml_get_type_traits_cpu(type);
const auto * qfns = ggml_get_type_traits(type);
const auto * qfns_cpu = ggml_get_type_traits_cpu(type);
if (qfns_cpu->from_float && qfns->to_float) {
if (params.verbose) {
printf("testing %s ...\n", ggml_type_name(type));
@ -382,7 +380,7 @@ int main(int argc, char ** argv) {
error_stats global_stats {};
for (const auto& kv_tensor : tensors) {
for (const auto & kv_tensor : tensors) {
if (!layer_included(params, kv_tensor.first)) {
continue;
}
@ -411,7 +409,7 @@ int main(int argc, char ** argv) {
llama_free(ctx);
llama_free_model(model);
llama_model_free(model);
// report timing
{
const int64_t t_main_end_us = ggml_time_us();

4
examples/quantize/tests.sh
View file

@ -47,7 +47,7 @@ echo PASS
echo
# 3a. Test the requanted model is loading properly
$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32
$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32
echo PASS
echo
@ -57,7 +57,7 @@ echo PASS
echo
# 4b. Test the requanted model is loading properly
$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32
$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32
echo PASS
echo

16
examples/retrieval/retrieval.cpp
View file

@ -151,15 +151,17 @@ int main(int argc, char ** argv) {
// load the model
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
if (model == NULL) {
LOG_ERR("%s: unable to load model\n", __func__);
return 1;
}
const int n_ctx_train = llama_n_ctx_train(model);
const llama_vocab * vocab = llama_model_get_vocab(model);
const int n_ctx_train = llama_model_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
@ -192,8 +194,8 @@ int main(int argc, char ** argv) {
return 1;
}
// add eos if not present
if (llama_token_eos(model) >= 0 && (inp.empty() || inp.back() != llama_token_eos(model))) {
inp.push_back(llama_token_eos(model));
if (llama_vocab_eos(vocab) >= 0 && (inp.empty() || inp.back() != llama_vocab_eos(vocab))) {
inp.push_back(llama_vocab_eos(vocab));
}
chunk.tokens = inp;
}
@ -215,7 +217,7 @@ int main(int argc, char ** argv) {
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
// allocate output
const int n_embd = llama_n_embd(model);
const int n_embd = llama_model_n_embd(model);
std::vector<float> embeddings(n_chunks * n_embd, 0);
float * emb = embeddings.data();
@ -298,7 +300,5 @@ int main(int argc, char ** argv) {
// clean up
llama_batch_free(query_batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
}

2
examples/run/CMakeLists.txt
View file

@ -1,5 +1,5 @@
set(TARGET llama-run)
add_executable(${TARGET} run.cpp)
add_executable(${TARGET} run.cpp linenoise.cpp/linenoise.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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